From 7c66af33b9c59f37f1e90339bded0c1a04be170e Mon Sep 17 00:00:00 2001
From: bbauvin <baptiste.bauvin@centrale-marseille.fr>
Date: Tue, 6 Sep 2016 10:42:29 -0400
Subject: [PATCH] Resolved randomized search for multicore issue
---
Code/MonoMutliViewClassifiers/ExecClassif.py | 8 +-
.../Monoview/ExecClassifMonoView.py | 4 +-
.../Monoview/analyzeResult.py | 9 +-
.../Multiview/ExecMultiview.py | 18 +-
.../Multiview/Fusion/Fusion.py | 1 +
.../LateFusionPackage/BayesianInference.py | 1 -
.../Multiview/Fusion/analyzeResults.py | 29 +-
.../Multiview/Mumbo/analyzeResults.py | 36 +-
.../ResultAnalysis.py | 35 +-
...k-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log | 8 +
...k-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log | 15 +
...k-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log | 1310 +++++++++++++
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...0Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...0Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...1Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...1Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...2Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...2Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...k-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log | 1376 ++++++++++++++
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...3Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...4Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...4Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...5Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...5Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...6Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...6Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...7Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...k-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log | 1370 ++++++++++++++
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...9Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...9Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...0Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...0Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...1Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...1Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...2Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...2Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...k-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log | 1363 ++++++++++++++
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...8Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...8Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...9Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...9Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...0Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...0Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...1Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...k-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log | 1375 ++++++++++++++
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...5Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...6Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...6Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...7Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...7Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...8Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...k-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log | 1381 ++++++++++++++
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...8Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...9Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...9Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...0Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...0Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...1Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...1Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...2Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...k-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log | 1369 ++++++++++++++
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...5Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...6Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...6Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...7Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...7Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...8Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...9Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...9Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...k-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log | 1623 ++++++++++++++++
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...8Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...9Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...9Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...0Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...0Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...1Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...1Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...2Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...-learnRate0.7-Fake-accuracyByIteration.png | Bin 0 -> 53629 bytes
...RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt | 235 +++
...k-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log | 1624 +++++++++++++++++
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...1Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...1Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...2Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...2Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...3Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...3Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...4Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...5Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...-learnRate0.7-Fake-accuracyByIteration.png | Bin 0 -> 54649 bytes
...RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt | 235 +++
...k-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log | 1580 ++++++++++++++++
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...4Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...4Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...5Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...5Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...6Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...6Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...7Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...-learnRate0.7-Fake-accuracyByIteration.png | Bin 0 -> 53433 bytes
...RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt | 215 +++
...k-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log | 1570 ++++++++++++++++
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...7Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...7Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...8Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...8Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...9Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...9Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...0Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...-learnRate0.7-Fake-accuracyByIteration.png | Bin 0 -> 52514 bytes
...RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt | 209 +++
...k-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log | 1250 +++++++++++++
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...5Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...5Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...6Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...6Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...7Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...7Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...8Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...k-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log | 1250 +++++++++++++
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...0Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...1Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...1Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...2Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...3Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...3Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...lts-Adaboost-Non-Oui-learnRate0.7-Fake.txt | 27 +
...DecisionTree-Non-Oui-learnRate0.7-Fake.txt | 24 +
...4Results-KNN-Non-Oui-learnRate0.7-Fake.txt | 24 +
...RandomForest-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ults-SVMPoly-Non-Oui-learnRate0.7-Fake.txt | 24 +
...sults-SVMRBF-Non-Oui-learnRate0.7-Fake.txt | 24 +
...5Results-SGD-Non-Oui-learnRate0.7-Fake.txt | 24 +
...ts-SVMLinear-Non-Oui-learnRate0.7-Fake.txt | 24 +
...hyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log | 664 +++++++
...ionTree-Non-Oui-learnRate0.7-MultiOmic.txt | 24 +
...daboost-Non-Oui-learnRate0.7-MultiOmic.txt | 27 +
...mForest-Non-Oui-learnRate0.7-MultiOmic.txt | 24 +
...lts-KNN-Non-Oui-learnRate0.7-MultiOmic.txt | 24 +
...lts-SGD-Non-Oui-learnRate0.7-MultiOmic.txt | 24 +
...MLinear-Non-Oui-learnRate0.7-MultiOmic.txt | 24 +
...SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt | 24 +
...daboost-Non-Oui-learnRate0.7-MultiOmic.txt | 27 +
...ionTree-Non-Oui-learnRate0.7-MultiOmic.txt | 24 +
...-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt | 24 +
...lts-KNN-Non-Oui-learnRate0.7-MultiOmic.txt | 24 +
...mForest-Non-Oui-learnRate0.7-MultiOmic.txt | 24 +
...lts-SGD-Non-Oui-learnRate0.7-MultiOmic.txt | 24 +
...MLinear-Non-Oui-learnRate0.7-MultiOmic.txt | 24 +
...SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt | 24 +
...-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt | 24 +
410 files changed, 29320 insertions(+), 38 deletions(-)
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-092557-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100622-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100729-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100730Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100730Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100730Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100730Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100731Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100731Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100731Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100731Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100732Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100732Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100732Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100732Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100738-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100743Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100743Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100743Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100744Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100744Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100744Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100744Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100745Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100745Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100745Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100745Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100746Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100746Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100746Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100746Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100747Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100747Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100747Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100834-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100839Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100839Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100839Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100839Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100839Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100839Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100840Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100840Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100840Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100840Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100841Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100841Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100841Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100841Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100842Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100842Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100842Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100842Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100842Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100842Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100853-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100858Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100858Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100858Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100858Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100859Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100859Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100859Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100859Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100900Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100900Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100900Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100900Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100901Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-100901Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-101330-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-101335Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-101335Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-101335Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-101335Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-101336Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-101336Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-101336Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-101336Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-101337Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-101337Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-101337Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-101337Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-101338Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-101338Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102343-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102348Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102348Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102348Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102348Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102349Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102349Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102349Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102349Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102350Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102350Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102350Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102350Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102351Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102351Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102351Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102351Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102352Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102352Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102440-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102445Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102445Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102445Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102445Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102446Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102446Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102446Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102447Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102447Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102447Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102447Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102448Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102449Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102449Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102449Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102449Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102449Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102449Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102543-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102548Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102548Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102548Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102549Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102549Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102549Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102549Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102550Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102550Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102550Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102550Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102551Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102551Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102551Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102551Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102552Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102552Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102552Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102602Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-102602Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103115-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103121Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103121Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103121Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103121Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103122Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103122Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103122Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103122Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103122Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103122Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103123Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103123Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103123Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103123Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103124Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103124Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103124Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103124Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103124Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103125Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103125Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103125Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103135Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103135Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103409-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103414Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103414Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103414Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103414Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103415Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103415Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103415Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103415Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103416Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103416Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103416Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103416Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103417Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103417Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103424Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103424Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103742-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103747Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103747Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103747Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103747Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103748Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103748Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103748Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103748Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103749Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103749Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103749Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103749Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103750Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103750Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103757Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103757Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103929-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103934Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103934Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103935Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103935Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103935Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103935Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103936Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103936Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103936Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103936Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103937Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103937Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103937Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103937Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103938Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103938Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-103938Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104015-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104020Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104020Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104020Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104021Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104021Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104021Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104021Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104022Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104023Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104023Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104023Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104023Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104023Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104023Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104024Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104024Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104024Results-KNN-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104024Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104024Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104024Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104025Results-SGD-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104025Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104051-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104114Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104115Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104116Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104123Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104126Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104138Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104154Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104157Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104157Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104157Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104158Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104158Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104158Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104158Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104158Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160906-104159Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
diff --git a/Code/MonoMutliViewClassifiers/ExecClassif.py b/Code/MonoMutliViewClassifiers/ExecClassif.py
index 5a97f9a5..490c4a27 100644
--- a/Code/MonoMutliViewClassifiers/ExecClassif.py
+++ b/Code/MonoMutliViewClassifiers/ExecClassif.py
@@ -160,6 +160,7 @@ groupFusion.add_argument('--FU_cl_config', metavar='STRING', action='store', nar
args = parser.parse_args()
os.nice(args.nice)
nbCores = args.CL_cores
+start = time.time()
if args.name not in ["MultiOmic", "ModifiedMultiOmic", "Caltech", "Fake"]:
getDatabase = getattr(DB, "getClassicDB" + args.type[1:])
else:
@@ -265,8 +266,9 @@ SGDKWARGS = {"2": map(float, args.CL_SGD_alpha.split(":"))[0], "1": args.CL_SGD_
KNNKWARGS = {"0": map(float, args.CL_KNN_neigh.split(":"))[0]}
AdaboostKWARGS = {"0": args.CL_Ada_n_est.split(":")[0], "1": args.CL_Ada_b_est.split(":")[0]}
-
+dataBaseTime = time.time()-start
argumentDictionaries = {"Monoview": {}, "Multiview": []}
+print benchmark
try:
if benchmark["Monoview"]:
argumentDictionaries["Monoview"] = []
@@ -308,6 +310,7 @@ else:
for viewIndex, view in enumerate(views):
bestClassifiers.append(classifiersNames[viewIndex][np.argmax(np.array(accuracies[viewIndex]))])
bestClassifiersConfigs.append(classifiersConfigs[viewIndex][np.argmax(np.array(accuracies[viewIndex]))])
+monoviewTime = time.time()-dataBaseTime
try:
if benchmark["Multiview"]:
try:
@@ -376,12 +379,13 @@ else:
resultsMultiview = [ExecMultiview(DATASET, args.name, args.CL_split, args.CL_nbFolds, 1, args.type, args.pathF,
LABELS_DICTIONARY, gridSearch=gridSearch,
metrics=metrics, **arguments) for arguments in argumentDictionaries["Multiview"]]
-
+multiviewTime = time.time()-monoviewTime
if nbCores>1:
logging.debug("Start:\t Deleting "+str(nbCores)+" temporary datasets for multiprocessing")
datasetFiles = DB.deleteHDF5(args.pathF, args.name, nbCores)
logging.debug("Start:\t Deleting datasets for multiprocessing")
+times = [dataBaseTime, monoviewTime, multiviewTime]
results = (resultsMonoview, resultsMultiview)
resultAnalysis(benchmark, results, args.name)
diff --git a/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py b/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
index cda01eec..a3024b98 100644
--- a/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
+++ b/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
@@ -105,7 +105,7 @@ def ExecMonoview(X, Y, name, learningRate, nbFolds, nbCores, databaseType, path,
logging.debug("Start:\t Getting Results")
#Accuracy classification score
- stringAnalysis, imagesAnalysis, train, ham, test = execute(name, learningRate, nbFolds, nbCores, gridSearch, metrics, nIter, feat, CL_type,
+ stringAnalysis, imagesAnalysis, metricsScores = execute(name, learningRate, nbFolds, nbCores, gridSearch, metrics, nIter, feat, CL_type,
clKWARGS, classLabelsNames, X.shape,
y_train, y_train_pred, y_test, y_test_pred, t_end)
cl_desc = [value for key, value in sorted(clKWARGS.iteritems())]
@@ -134,7 +134,7 @@ def ExecMonoview(X, Y, name, learningRate, nbFolds, nbCores, databaseType, path,
logging.info("Done:\t Result Analysis")
viewIndex = args["viewIndex"]
- return viewIndex, [CL_type, test, cl_desc, feat]
+ return viewIndex, [CL_type, cl_desc.append(feat), metricsScores]
# # Classification Report with Precision, Recall, F1 , Support
# logging.debug("Info:\t Classification report:")
# filename = datetime.datetime.now().strftime("%Y_%m_%d") + "-CMV-" + name + "-" + feat + "-Report"
diff --git a/Code/MonoMutliViewClassifiers/Monoview/analyzeResult.py b/Code/MonoMutliViewClassifiers/Monoview/analyzeResult.py
index b4586af6..091021f1 100644
--- a/Code/MonoMutliViewClassifiers/Monoview/analyzeResult.py
+++ b/Code/MonoMutliViewClassifiers/Monoview/analyzeResult.py
@@ -38,6 +38,7 @@ def getMetricScore(metric, y_train, y_train_pred, y_test, y_test_pred):
def execute(name, learningRate, nbFolds, nbCores, gridSearch, metrics, nIter, feat, CL_type, clKWARGS, classLabelsNames,
shape, y_train, y_train_pred, y_test, y_test_pred, time):
+ metricsScores = {}
metricModule = getattr(Metrics, metrics[0][0])
train = metricModule.score(y_train, y_train_pred)
val = metricModule.score(y_test, y_test_pred)
@@ -47,7 +48,13 @@ def execute(name, learningRate, nbFolds, nbCores, gridSearch, metrics, nIter, fe
stringAnalysis += getClassifierConfigString(CL_type, gridSearch, nbCores, nIter, clKWARGS)
for metric in metrics:
stringAnalysis+=getMetricScore(metric, y_train, y_train_pred, y_test, y_test_pred)
+ if metric[1]!=None:
+ metricKWARGS = dict((index, metricConfig) for index, metricConfig in enumerate(metric[1]))
+ else:
+ metricKWARGS = {}
+ metricsScores[metric[0]] = [getattr(Metrics, metric[0]).score(y_train, y_train_pred, **metricKWARGS), "",
+ getattr(Metrics, metric[0]).score(y_test, y_test_pred, **metricKWARGS)]
stringAnalysis += "\n\n Classification took "+ str(hms(seconds=int(time)))
imageAnalysis = {}
- return stringAnalysis, imageAnalysis, train, "", val
\ No newline at end of file
+ return stringAnalysis, imageAnalysis, metricsScores
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py b/Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py
index b932d33b..95125ee8 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py
@@ -26,7 +26,7 @@ def ExecMultiview_multicore(coreIndex, name, learningRate, nbFolds, databaseType
gridSearch=False, nbCores=1, metrics=None, nIter=30, **arguments):
DATASET = h5py.File(path+name+str(coreIndex)+".hdf5", "r")
return ExecMultiview(DATASET, name, learningRate, nbFolds, 1, databaseType, path, LABELS_DICTIONARY,
- gridSearch=False, metrics=None, nIter=30, **arguments)
+ gridSearch=gridSearch, metrics=None, nIter=30, **arguments)
def ExecMultiview(DATASET, name, learningRate, nbFolds, nbCores, databaseType, path, LABELS_DICTIONARY,
@@ -59,15 +59,15 @@ def ExecMultiview(DATASET, name, learningRate, nbFolds, nbCores, databaseType, p
logging.info("Start:\t Determine validation split for ratio " + str(learningRate))
validationIndices = DB.splitDataset(DATASET, learningRate, datasetLength)
learningIndices = [index for index in range(datasetLength) if index not in validationIndices]
- datasetLength = len(learningIndices)
+ classificationSetLength = len(learningIndices)
logging.info("Done:\t Determine validation split")
logging.info("Start:\t Determine "+str(nbFolds)+" folds")
if nbFolds != 1:
kFolds = DB.getKFoldIndices(nbFolds, DATASET.get("labels")[...], NB_CLASS, learningIndices)
else:
- kFolds = [[], range(datasetLength)]
- logging.info("Info:\t Length of Learning Sets: " + str(datasetLength - len(kFolds[0])))
+ kFolds = [[], range(classificationSetLength)]
+ logging.info("Info:\t Length of Learning Sets: " + str(classificationSetLength - len(kFolds[0])))
logging.info("Info:\t Length of Testing Sets: " + str(len(kFolds[0])))
logging.info("Info:\t Length of Validation Set: " + str(len(validationIndices)))
logging.info("Done:\t Determine folds")
@@ -89,7 +89,7 @@ def ExecMultiview(DATASET, name, learningRate, nbFolds, nbCores, databaseType, p
kFoldPredictionTime = []
kFoldClassifier = []
-
+ gridSearch=True
if gridSearch:
logging.info("Start:\t Randomsearching best settings for monoview classifiers")
bestSettings, fusionConfig = classifierGridSearch(DATASET, classificationKWARGS, learningIndices
@@ -104,10 +104,10 @@ def ExecMultiview(DATASET, name, learningRate, nbFolds, nbCores, databaseType, p
logging.info("Start:\t Classification")
# Begin Classification
for foldIdx, fold in enumerate(kFolds):
- if fold != range(datasetLength):
+ if fold != range(classificationSetLength):
fold.sort()
logging.info("\tStart:\t Fold number " + str(foldIdx + 1))
- trainIndices = [index for index in range(datasetLength) if index not in fold]
+ trainIndices = [index for index in range(datasetLength) if (index not in fold) and (index not in validationIndices)]
DATASET_LENGTH = len(trainIndices)
classifier = classifierClass(NB_VIEW, DATASET_LENGTH, DATASET.get("labels").value, NB_CORES=nbCores, **classificationKWARGS)
@@ -132,7 +132,7 @@ def ExecMultiview(DATASET, name, learningRate, nbFolds, nbCores, databaseType, p
times = (extractionTime, kFoldLearningTime, kFoldPredictionTime, classificationTime)
- stringAnalysis, imagesAnalysis, train, test, val = analysisModule.execute(kFoldClassifier, kFoldPredictedTrainLabels,
+ stringAnalysis, imagesAnalysis, metricsScores = analysisModule.execute(kFoldClassifier, kFoldPredictedTrainLabels,
kFoldPredictedTestLabels, kFoldPredictedValidationLabels,
DATASET, classificationKWARGS, learningRate, LABELS_DICTIONARY,
views, nbCores, times, kFolds, name, nbFolds,
@@ -166,7 +166,7 @@ def ExecMultiview(DATASET, name, learningRate, nbFolds, nbCores, databaseType, p
imagesAnalysis[imageName].savefig(outputFileName + imageName + '.png')
logging.info("Done:\t Result Analysis")
- return CL_type, classificationKWARGS, train, test, val
+ return CL_type, classificationKWARGS, metricsScores
if __name__=='__main__':
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Fusion.py b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Fusion.py
index dc4360cc..4f1e43a4 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Fusion.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Fusion.py
@@ -44,6 +44,7 @@ def gridSearch_hdf5(DATASET, classificationKWARGS, learningIndices, metric=None,
nIter=nIter))
logging.debug("\tDone:\t Random search for "+classifierName)
classificationKWARGS["classifiersConfigs"] = bestSettings
+ print bestSettings
fusionMethodConfig = fusionMethodModule.gridSearch(DATASET, classificationKWARGS, learningIndices, nIter=nIter)
return bestSettings, fusionMethodConfig
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusionPackage/BayesianInference.py b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusionPackage/BayesianInference.py
index 1b6ffd42..5abae3d1 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusionPackage/BayesianInference.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusionPackage/BayesianInference.py
@@ -51,6 +51,5 @@ class BayesianInference(LateFusionClassifier):
"\n\t-With monoview classifiers : "
for monoviewClassifierConfig, monoviewClassifierName in zip(monoviewClassifiersConfigs, monoviewClassifiersNames):
monoviewClassifierModule = getattr(MonoviewClassifiers, monoviewClassifierName)
- print monoviewClassifierConfig
configString += monoviewClassifierModule.getConfig(monoviewClassifierConfig)
return configString
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Fusion/analyzeResults.py b/Code/MonoMutliViewClassifiers/Multiview/Fusion/analyzeResults.py
index ea37f744..79d9f9b3 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/Fusion/analyzeResults.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Fusion/analyzeResults.py
@@ -32,6 +32,31 @@ def getMetricScore(metric, y_train, y_train_pred, y_test, y_test_pred):
metricScoreString += "\n"
return metricScoreString
+def getTotalMetricScores(metric, kFoldPredictedTrainLabels, kFoldPredictedTestLabels,
+ kFoldPredictedValidationLabels, DATASET, validationIndices, kFolds):
+ labels = DATASET.get("labels").value
+ metricModule = getattr(Metrics, metric[0])
+ if metric[1]!=None:
+ metricKWARGS = dict((index, metricConfig) for index, metricConfig in enumerate(metric[1]))
+ else:
+ metricKWARGS = {}
+ trainScore = np.mean(np.array([metricModule.score([label for index, label in enumerate(labels) if index not in fold+validationIndices], predictedLabels, **metricKWARGS) for fold, predictedLabels in zip(kFolds, kFoldPredictedTrainLabels)]))
+ testScore = np.mean(np.array([metricModule.score(labels[fold], predictedLabels, **metricKWARGS) for fold, predictedLabels in zip(kFolds, kFoldPredictedTestLabels)]))
+ validationScore = np.mean(np.array([metricModule.score(labels[validationIndices], predictedLabels, **metricKWARGS) for predictedLabels in kFoldPredictedValidationLabels]))
+ return [trainScore, testScore, validationScore]
+
+
+def getMetricsScores(metrics, kFoldPredictedTrainLabels, kFoldPredictedTestLabels,
+ kFoldPredictedValidationLabels, DATASET, validationIndices, kFolds):
+ metricsScores = {}
+ for metric in metrics:
+ metricsScores[metric[0]] = getTotalMetricScores(metric, kFoldPredictedTrainLabels, kFoldPredictedTestLabels,
+ kFoldPredictedValidationLabels, DATASET, validationIndices, kFolds)
+ return metricsScores
+
+
+
+
def execute(kFoldClassifier, kFoldPredictedTrainLabels,
kFoldPredictedTestLabels, kFoldPredictedValidationLabels,
@@ -95,4 +120,6 @@ def execute(kFoldClassifier, kFoldPredictedTrainLabels,
str(hms(seconds=int(sum(kFoldPredictionTime))))])
stringAnalysis += "\n\tSo a total classification time of " + str(hms(seconds=int(classificationTime))) + ".\n\n"
imagesAnalysis = {}
- return stringAnalysis, imagesAnalysis, totalAccuracyOnTrain, totalAccuracyOnTest, totalAccuracyOnValidation
+ metricsScores = getMetricsScores(metrics, kFoldPredictedTrainLabels, kFoldPredictedTestLabels,
+ kFoldPredictedValidationLabels, DATASET, validationIndices, kFolds)
+ return stringAnalysis, imagesAnalysis, metricsScores
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Mumbo/analyzeResults.py b/Code/MonoMutliViewClassifiers/Multiview/Mumbo/analyzeResults.py
index 07502247..6230c983 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/Mumbo/analyzeResults.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Mumbo/analyzeResults.py
@@ -131,7 +131,7 @@ def getAlgoConfig(initKWARGS, NB_CORES, viewNames, gridSearch, nIter, times):
def getClassificationReport(kFolds, kFoldClassifier, CLASS_LABELS, validationIndices, DATASET,
kFoldPredictedTrainLabels, kFoldPredictedTestLabels, kFoldPredictedValidationLabels):
- DATASET_LENGTH = DATASET.get("Metadata").attrs["datasetLength"]-len(validationIndices)
+ DATASET_LENGTH = DATASET.get("Metadata").attrs["datasetLength"]
nbView = DATASET.get("Metadata").attrs["nbView"]
NB_CLASS = DATASET.get("Metadata").attrs["nbClass"]
kFoldPredictedTrainLabelsByIter = []
@@ -152,7 +152,7 @@ def getClassificationReport(kFolds, kFoldClassifier, CLASS_LABELS, validationInd
mumboClassifier = kFoldClassifier[foldIdx]
meanAverageAccuracies = np.mean(mumboClassifier.averageAccuracies, axis=0)
kFoldMeanAverageAccuracies.append(meanAverageAccuracies)
- trainIndices = [index for index in range(DATASET_LENGTH) if index not in fold]
+ trainIndices = [index for index in range(DATASET_LENGTH) if (index not in fold) and (index not in validationIndices)]
testLabels = CLASS_LABELS[fold]
trainLabels = CLASS_LABELS[trainIndices]
validationLabels = CLASS_LABELS[validationIndices]
@@ -232,6 +232,31 @@ def getMetricScore(metric, y_train, y_train_pred, y_test, y_test_pred):
metricScoreString += "\n"
return metricScoreString
+
+def getTotalMetricScores(metric, kFoldPredictedTrainLabels, kFoldPredictedTestLabels,
+ kFoldPredictedValidationLabels, DATASET, validationIndices, kFolds):
+ labels = DATASET.get("labels").value
+ metricModule = getattr(Metrics, metric[0])
+ if metric[1]!=None:
+ metricKWARGS = dict((index, metricConfig) for index, metricConfig in enumerate(metric[1]))
+ else:
+ metricKWARGS = {}
+ trainScore = np.mean(np.array([metricModule.score([label for index, label in enumerate(labels) if (index not in fold) and (index not in validationIndices)], predictedLabels, **metricKWARGS) for fold, predictedLabels in zip(kFolds, kFoldPredictedTrainLabels)]))
+ testScore = np.mean(np.array([metricModule.score(labels[fold], predictedLabels, **metricKWARGS) for fold, predictedLabels in zip(kFolds, kFoldPredictedTestLabels)]))
+ validationScore = np.mean(np.array([metricModule.score(labels[validationIndices], predictedLabels, **metricKWARGS) for predictedLabels in kFoldPredictedValidationLabels]))
+ return [trainScore, testScore, validationScore]
+
+
+def getMetricsScores(metrics, kFoldPredictedTrainLabels, kFoldPredictedTestLabels,
+ kFoldPredictedValidationLabels, DATASET, validationIndices, kFolds,):
+ metricsScores = {}
+ for metric in metrics:
+ metricsScores[metric[0]] = getTotalMetricScores(metric, kFoldPredictedTrainLabels, kFoldPredictedTestLabels,
+ kFoldPredictedValidationLabels, DATASET, validationIndices, kFolds)
+ return metricsScores
+
+
+
def execute(kFoldClassifier, kFoldPredictedTrainLabels, kFoldPredictedTestLabels, kFoldPredictedValidationLabels,
DATASET, initKWARGS, LEARNING_RATE, LABELS_DICTIONARY, views, NB_CORES, times, kFolds, databaseName,
nbFolds, validationIndices, gridSearch, nIter, metrics):
@@ -280,7 +305,6 @@ def execute(kFoldClassifier, kFoldPredictedTrainLabels, kFoldPredictedTestLabels
" : \n\t\t\t- Mean average Accuracy : "+str(meanAverageAccuracy)+\
"\n\t\t\t- Percentage of time chosen : "+str(bestViewStat)
stringAnalysis += "\n\n For each iteration : "
- print iterRelevant(0, kFoldClassifier)
for iterIndex in range(maxIter):
if iterRelevant(iterIndex, kFoldClassifier).any():
stringAnalysis += "\n\t- Iteration " + str(iterIndex + 1)
@@ -294,9 +318,9 @@ def execute(kFoldClassifier, kFoldPredictedTrainLabels, kFoldPredictedTestLabels
trainAccuracyByIter = list(formatedAccuracies["Train"].mean(axis=0))+modifiedMean(surplusAccuracies["Train"])
testAccuracyByIter = list(formatedAccuracies["Test"].mean(axis=0))+modifiedMean(surplusAccuracies["Test"])
validationAccuracyByIter = list(formatedAccuracies["Validation"].mean(axis=0))+modifiedMean(surplusAccuracies["Validation"])
- print nbMaxIter
name, image = plotAccuracyByIter(trainAccuracyByIter, testAccuracyByIter, validationAccuracyByIter, nbMaxIter,
bestViews, views, classifierAnalysis)
imagesAnalysis = {name: image}
-
- return stringAnalysis, imagesAnalysis, totalAccuracyOnTrain, totalAccuracyOnTest, totalAccuracyOnValidation
+ metricsScores = getMetricsScores(metrics, kFoldPredictedTrainLabels, kFoldPredictedTestLabels,
+ kFoldPredictedValidationLabels, DATASET, validationIndices, kFolds)
+ return stringAnalysis, imagesAnalysis, metricsScores
diff --git a/Code/MonoMutliViewClassifiers/ResultAnalysis.py b/Code/MonoMutliViewClassifiers/ResultAnalysis.py
index 2a5fb0a5..032eee4f 100644
--- a/Code/MonoMutliViewClassifiers/ResultAnalysis.py
+++ b/Code/MonoMutliViewClassifiers/ResultAnalysis.py
@@ -1,6 +1,7 @@
# Import built-in modules
import time
import pylab
+import logging
# Import third party modules
import matplotlib
@@ -12,22 +13,24 @@ __author__ = "Baptiste Bauvin"
__status__ = "Prototype" # Production, Development, Prototype
-def resultAnalysis(benchmark, results, name):
- mono, multi = results
- names = [res[1][0]+res[1][3] for res in mono]
- names+=[type_ if type_ != "Fusion" else a["fusionType"]+a["fusionMethod"] for type_, a, b, c, d in multi]
- nbResults = len(mono)+len(multi)
- accuracies = [100*float(res[1][1]) for res in mono]
- accuracies += [float(accuracy) for a, b, c, d, accuracy in multi]
- f = pylab.figure(figsize=(40, 30))
- fig = plt.gcf()
- fig.subplots_adjust(bottom=105.0, top=105.01)
- ax = f.add_axes([0.1, 0.1, 0.8, 0.8])
- ax.set_title("Accuracies on validation set for each classifier")
- ax.bar(range(nbResults), accuracies, align='center')
- ax.set_xticks(range(nbResults))
- ax.set_xticklabels(names, rotation="vertical")
+def resultAnalysis(benchmark, results, name, times, metrics):
+ for metric in metrics:
+ mono, multi = results
+ names = [res[1][0]+"-"+res[1][1][-1] for res in mono]
+ names+=[type_ if type_ != "Fusion" else a["fusionType"]+"-"+a["fusionMethod"] for type_, a, b in multi]
+ nbResults = len(mono)+len(multi)
+ validationScores = [float(res[1][2][metric[0]][2]) for res in mono]
+ validationScores += [float(scores[metric[0]][2]) for a, b, scores in multi]
+ f = pylab.figure(figsize=(40, 30))
+ fig = plt.gcf()
+ fig.subplots_adjust(bottom=105.0, top=105.01)
+ ax = f.add_axes([0.1, 0.1, 0.8, 0.8])
+ ax.set_title(metric[0]+" on validation set for each classifier")
+ ax.bar(range(nbResults), validationScores, align='center')
+ ax.set_xticks(range(nbResults))
+ ax.set_xticklabels(names, rotation="vertical")
- f.savefig("Results/"+name+time.strftime("%Y%m%d-%H%M%S")+".png")
+ f.savefig("Results/"+name+"-"+metric[0]+"-"+time.strftime("%Y%m%d-%H%M%S")+".png")
+ logging.info("Extraction time : "+str(times[0])+"s, Monoview time : "+str(times[1])+"s, Multiview Time : "+str(times[2])+"s")
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-092557-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-092557-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 00000000..c45e025b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-092557-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,8 @@
+2016-09-06 09:25:57,714 INFO: Start: Finding all available mono- & multiview algorithms
+2016-09-06 09:25:57,717 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 09:25:57,717 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 09:25:57,717 DEBUG: Start: Determine Train/Test split
+2016-09-06 09:25:57,717 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 09:25:57,717 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 09:25:57,717 DEBUG: Done: Determine Train/Test split
+2016-09-06 09:25:57,717 DEBUG: Start: RandomSearch best settings with 30 iterations
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100622-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-100622-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 00000000..8bd52115
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100622-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,15 @@
+2016-09-06 10:06:22,879 INFO: Start: Finding all available mono- & multiview algorithms
+2016-09-06 10:06:22,881 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:06:22,881 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:06:22,881 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:06:22,882 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 10:06:22,882 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 10:06:22,882 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:06:22,882 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:06:22,965 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:06:22,965 DEBUG: Start: Training
+2016-09-06 10:06:22,970 DEBUG: Info: Time for Training: 0.0897569656372[s]
+2016-09-06 10:06:22,971 DEBUG: Done: Training
+2016-09-06 10:06:22,971 DEBUG: Start: Predicting
+2016-09-06 10:06:22,984 DEBUG: Done: Predicting
+2016-09-06 10:06:22,984 DEBUG: Start: Getting Results
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100729-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-100729-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 00000000..b3758f07
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100729-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,1310 @@
+2016-09-06 10:07:29,943 INFO: Start: Finding all available mono- & multiview algorithms
+2016-09-06 10:07:29,945 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:29,945 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:07:29,945 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:29,946 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
+2016-09-06 10:07:29,946 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
+2016-09-06 10:07:29,946 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:29,946 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:30,025 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:30,025 DEBUG: Start: Training
+2016-09-06 10:07:30,029 DEBUG: Info: Time for Training: 0.0845639705658[s]
+2016-09-06 10:07:30,029 DEBUG: Done: Training
+2016-09-06 10:07:30,029 DEBUG: Start: Predicting
+2016-09-06 10:07:30,032 DEBUG: Done: Predicting
+2016-09-06 10:07:30,032 DEBUG: Start: Getting Results
+2016-09-06 10:07:30,033 DEBUG: Done: Getting Results
+2016-09-06 10:07:30,034 INFO: Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 11)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:30,034 INFO: Done: Result Analysis
+2016-09-06 10:07:30,035 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:30,035 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:07:30,035 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:30,035 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
+2016-09-06 10:07:30,035 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
+2016-09-06 10:07:30,035 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:30,035 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:30,065 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:30,065 DEBUG: Start: Training
+2016-09-06 10:07:30,066 DEBUG: Info: Time for Training: 0.0320420265198[s]
+2016-09-06 10:07:30,067 DEBUG: Done: Training
+2016-09-06 10:07:30,067 DEBUG: Start: Predicting
+2016-09-06 10:07:30,068 DEBUG: Done: Predicting
+2016-09-06 10:07:30,068 DEBUG: Start: Getting Results
+2016-09-06 10:07:30,069 DEBUG: Done: Getting Results
+2016-09-06 10:07:30,069 INFO: Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 0.866666666667
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 11)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 7
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.866666666667
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:30,070 INFO: Done: Result Analysis
+2016-09-06 10:07:30,071 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:30,071 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:07:30,071 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:30,071 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
+2016-09-06 10:07:30,071 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
+2016-09-06 10:07:30,071 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:30,071 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:30,113 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:30,113 DEBUG: Start: Training
+2016-09-06 10:07:30,113 DEBUG: Info: Time for Training: 0.0430898666382[s]
+2016-09-06 10:07:30,113 DEBUG: Done: Training
+2016-09-06 10:07:30,114 DEBUG: Start: Predicting
+2016-09-06 10:07:30,119 DEBUG: Done: Predicting
+2016-09-06 10:07:30,120 DEBUG: Start: Getting Results
+2016-09-06 10:07:30,121 DEBUG: Done: Getting Results
+2016-09-06 10:07:30,121 INFO: Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.571428571429
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 11)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 44
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.571428571429
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:30,121 INFO: Done: Result Analysis
+2016-09-06 10:07:30,122 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:30,122 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:07:30,122 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:30,122 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
+2016-09-06 10:07:30,123 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
+2016-09-06 10:07:30,123 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:30,123 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:30,425 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:30,425 DEBUG: Start: Training
+2016-09-06 10:07:30,470 DEBUG: Info: Time for Training: 0.348073959351[s]
+2016-09-06 10:07:30,470 DEBUG: Done: Training
+2016-09-06 10:07:30,470 DEBUG: Start: Predicting
+2016-09-06 10:07:30,475 DEBUG: Done: Predicting
+2016-09-06 10:07:30,475 DEBUG: Start: Getting Results
+2016-09-06 10:07:30,476 DEBUG: Done: Getting Results
+2016-09-06 10:07:30,476 INFO: Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.766666666667
+accuracy_score on test : 0.433333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 11)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 18, max_depth : 3
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.766666666667
+ - Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:30,477 INFO: Done: Result Analysis
+2016-09-06 10:07:30,478 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:30,478 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:07:30,478 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:30,478 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
+2016-09-06 10:07:30,478 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
+2016-09-06 10:07:30,478 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:30,478 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:30,540 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:30,540 DEBUG: Start: Training
+2016-09-06 10:07:30,541 DEBUG: Info: Time for Training: 0.0633749961853[s]
+2016-09-06 10:07:30,541 DEBUG: Done: Training
+2016-09-06 10:07:30,541 DEBUG: Start: Predicting
+2016-09-06 10:07:30,587 DEBUG: Done: Predicting
+2016-09-06 10:07:30,587 DEBUG: Start: Getting Results
+2016-09-06 10:07:30,589 DEBUG: Done: Getting Results
+2016-09-06 10:07:30,590 INFO: Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.62380952381
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 11)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.62380952381
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:30,590 INFO: Done: Result Analysis
+2016-09-06 10:07:30,592 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:30,592 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:07:30,592 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:30,593 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
+2016-09-06 10:07:30,593 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
+2016-09-06 10:07:30,593 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:30,593 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:30,640 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:30,640 DEBUG: Start: Training
+2016-09-06 10:07:30,658 DEBUG: Info: Time for Training: 0.0669250488281[s]
+2016-09-06 10:07:30,658 DEBUG: Done: Training
+2016-09-06 10:07:30,658 DEBUG: Start: Predicting
+2016-09-06 10:07:30,661 DEBUG: Done: Predicting
+2016-09-06 10:07:30,661 DEBUG: Start: Getting Results
+2016-09-06 10:07:30,662 DEBUG: Done: Getting Results
+2016-09-06 10:07:30,662 INFO: Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.52380952381
+accuracy_score on test : 0.444444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 11)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 491
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.52380952381
+ - Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:30,662 INFO: Done: Result Analysis
+2016-09-06 10:07:30,663 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:30,664 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:07:30,664 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:30,664 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
+2016-09-06 10:07:30,664 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
+2016-09-06 10:07:30,664 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:30,664 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:30,709 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:30,710 DEBUG: Start: Training
+2016-09-06 10:07:30,726 DEBUG: Info: Time for Training: 0.0627450942993[s]
+2016-09-06 10:07:30,726 DEBUG: Done: Training
+2016-09-06 10:07:30,726 DEBUG: Start: Predicting
+2016-09-06 10:07:30,729 DEBUG: Done: Predicting
+2016-09-06 10:07:30,729 DEBUG: Start: Getting Results
+2016-09-06 10:07:30,730 DEBUG: Done: Getting Results
+2016-09-06 10:07:30,730 INFO: Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 11)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2405
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:30,730 INFO: Done: Result Analysis
+2016-09-06 10:07:30,731 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:30,731 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:07:30,731 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:30,732 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
+2016-09-06 10:07:30,732 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
+2016-09-06 10:07:30,732 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:30,732 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:30,773 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:30,773 DEBUG: Start: Training
+2016-09-06 10:07:30,790 DEBUG: Info: Time for Training: 0.0591881275177[s]
+2016-09-06 10:07:30,790 DEBUG: Done: Training
+2016-09-06 10:07:30,790 DEBUG: Start: Predicting
+2016-09-06 10:07:30,794 DEBUG: Done: Predicting
+2016-09-06 10:07:30,795 DEBUG: Start: Getting Results
+2016-09-06 10:07:30,796 DEBUG: Done: Getting Results
+2016-09-06 10:07:30,796 INFO: Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.411111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 11)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 9676
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:30,796 INFO: Done: Result Analysis
+2016-09-06 10:07:30,797 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:30,797 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:07:30,797 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:30,797 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:07:30,798 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:07:30,798 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:30,798 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:30,840 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:30,840 DEBUG: Start: Training
+2016-09-06 10:07:30,844 DEBUG: Info: Time for Training: 0.0470898151398[s]
+2016-09-06 10:07:30,844 DEBUG: Done: Training
+2016-09-06 10:07:30,844 DEBUG: Start: Predicting
+2016-09-06 10:07:30,846 DEBUG: Done: Predicting
+2016-09-06 10:07:30,846 DEBUG: Start: Getting Results
+2016-09-06 10:07:30,848 DEBUG: Done: Getting Results
+2016-09-06 10:07:30,848 INFO: Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 13, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:30,848 INFO: Done: Result Analysis
+2016-09-06 10:07:30,849 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:30,849 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:07:30,849 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:30,849 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:07:30,849 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:07:30,850 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:30,850 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:30,877 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:30,877 DEBUG: Start: Training
+2016-09-06 10:07:30,879 DEBUG: Info: Time for Training: 0.0301620960236[s]
+2016-09-06 10:07:30,879 DEBUG: Done: Training
+2016-09-06 10:07:30,879 DEBUG: Start: Predicting
+2016-09-06 10:07:30,880 DEBUG: Done: Predicting
+2016-09-06 10:07:30,880 DEBUG: Start: Getting Results
+2016-09-06 10:07:30,882 DEBUG: Done: Getting Results
+2016-09-06 10:07:30,882 INFO: Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.544444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 21
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:30,882 INFO: Done: Result Analysis
+2016-09-06 10:07:30,883 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:30,883 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:07:30,883 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:30,883 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:07:30,883 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:07:30,883 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:30,884 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:30,909 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:30,909 DEBUG: Start: Training
+2016-09-06 10:07:30,910 DEBUG: Info: Time for Training: 0.0272629261017[s]
+2016-09-06 10:07:30,910 DEBUG: Done: Training
+2016-09-06 10:07:30,910 DEBUG: Start: Predicting
+2016-09-06 10:07:30,914 DEBUG: Done: Predicting
+2016-09-06 10:07:30,914 DEBUG: Start: Getting Results
+2016-09-06 10:07:30,916 DEBUG: Done: Getting Results
+2016-09-06 10:07:30,916 INFO: Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.619047619048
+accuracy_score on test : 0.588888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 26
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.619047619048
+ - Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:30,916 INFO: Done: Result Analysis
+2016-09-06 10:07:30,917 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:30,917 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:07:30,917 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:30,917 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:07:30,918 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:07:30,918 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:30,918 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:31,132 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:31,133 DEBUG: Start: Training
+2016-09-06 10:07:31,164 DEBUG: Info: Time for Training: 0.247138977051[s]
+2016-09-06 10:07:31,164 DEBUG: Done: Training
+2016-09-06 10:07:31,164 DEBUG: Start: Predicting
+2016-09-06 10:07:31,168 DEBUG: Done: Predicting
+2016-09-06 10:07:31,168 DEBUG: Start: Getting Results
+2016-09-06 10:07:31,169 DEBUG: Done: Getting Results
+2016-09-06 10:07:31,169 INFO: Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.980952380952
+accuracy_score on test : 0.455555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 12, max_depth : 17
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.980952380952
+ - Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:31,170 INFO: Done: Result Analysis
+2016-09-06 10:07:31,171 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:31,171 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:07:31,171 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:31,171 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:07:31,171 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:07:31,171 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:31,171 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:31,209 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:31,209 DEBUG: Start: Training
+2016-09-06 10:07:31,210 DEBUG: Info: Time for Training: 0.0394690036774[s]
+2016-09-06 10:07:31,210 DEBUG: Done: Training
+2016-09-06 10:07:31,210 DEBUG: Start: Predicting
+2016-09-06 10:07:31,211 DEBUG: Done: Predicting
+2016-09-06 10:07:31,212 DEBUG: Start: Getting Results
+2016-09-06 10:07:31,213 DEBUG: Done: Getting Results
+2016-09-06 10:07:31,213 INFO: Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.538095238095
+accuracy_score on test : 0.6
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.538095238095
+ - Score on test : 0.6
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:31,213 INFO: Done: Result Analysis
+2016-09-06 10:07:31,214 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:31,214 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:07:31,214 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:31,214 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:07:31,215 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:07:31,215 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:31,215 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:31,257 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:31,257 DEBUG: Start: Training
+2016-09-06 10:07:31,275 DEBUG: Info: Time for Training: 0.061126947403[s]
+2016-09-06 10:07:31,275 DEBUG: Done: Training
+2016-09-06 10:07:31,275 DEBUG: Start: Predicting
+2016-09-06 10:07:31,277 DEBUG: Done: Predicting
+2016-09-06 10:07:31,277 DEBUG: Start: Getting Results
+2016-09-06 10:07:31,279 DEBUG: Done: Getting Results
+2016-09-06 10:07:31,279 INFO: Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.466666666667
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 1250
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.466666666667
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:31,279 INFO: Done: Result Analysis
+2016-09-06 10:07:31,280 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:31,280 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:07:31,280 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:31,280 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:07:31,280 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:07:31,281 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:31,281 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:31,329 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:31,329 DEBUG: Start: Training
+2016-09-06 10:07:31,347 DEBUG: Info: Time for Training: 0.0671949386597[s]
+2016-09-06 10:07:31,347 DEBUG: Done: Training
+2016-09-06 10:07:31,347 DEBUG: Start: Predicting
+2016-09-06 10:07:31,349 DEBUG: Done: Predicting
+2016-09-06 10:07:31,349 DEBUG: Start: Getting Results
+2016-09-06 10:07:31,351 DEBUG: Done: Getting Results
+2016-09-06 10:07:31,351 INFO: Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 0.97619047619
+accuracy_score on test : 0.633333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 432
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.97619047619
+ - Score on test : 0.633333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:31,351 INFO: Done: Result Analysis
+2016-09-06 10:07:31,352 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:31,352 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:07:31,352 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:31,352 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:07:31,352 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:07:31,352 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:31,353 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:31,393 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:31,393 DEBUG: Start: Training
+2016-09-06 10:07:31,409 DEBUG: Info: Time for Training: 0.0578751564026[s]
+2016-09-06 10:07:31,410 DEBUG: Done: Training
+2016-09-06 10:07:31,410 DEBUG: Start: Predicting
+2016-09-06 10:07:31,414 DEBUG: Done: Predicting
+2016-09-06 10:07:31,414 DEBUG: Start: Getting Results
+2016-09-06 10:07:31,415 DEBUG: Done: Getting Results
+2016-09-06 10:07:31,415 INFO: Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 6005
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:31,415 INFO: Done: Result Analysis
+2016-09-06 10:07:31,416 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:31,416 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:07:31,417 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:31,417 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:07:31,417 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:07:31,417 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:31,417 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:31,461 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:31,461 DEBUG: Start: Training
+2016-09-06 10:07:31,465 DEBUG: Info: Time for Training: 0.0488121509552[s]
+2016-09-06 10:07:31,465 DEBUG: Done: Training
+2016-09-06 10:07:31,465 DEBUG: Start: Predicting
+2016-09-06 10:07:31,467 DEBUG: Done: Predicting
+2016-09-06 10:07:31,467 DEBUG: Start: Getting Results
+2016-09-06 10:07:31,469 DEBUG: Done: Getting Results
+2016-09-06 10:07:31,469 INFO: Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.577777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:31,469 INFO: Done: Result Analysis
+2016-09-06 10:07:31,470 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:31,470 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:07:31,470 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:31,471 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:07:31,471 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:07:31,471 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:31,471 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:31,502 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:31,502 DEBUG: Start: Training
+2016-09-06 10:07:31,505 DEBUG: Info: Time for Training: 0.0348341464996[s]
+2016-09-06 10:07:31,505 DEBUG: Done: Training
+2016-09-06 10:07:31,505 DEBUG: Start: Predicting
+2016-09-06 10:07:31,506 DEBUG: Done: Predicting
+2016-09-06 10:07:31,506 DEBUG: Start: Getting Results
+2016-09-06 10:07:31,507 DEBUG: Done: Getting Results
+2016-09-06 10:07:31,507 INFO: Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.588888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 23
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:31,508 INFO: Done: Result Analysis
+2016-09-06 10:07:31,509 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:31,509 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:07:31,509 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:31,509 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:07:31,509 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:07:31,509 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:31,509 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:31,535 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:31,535 DEBUG: Start: Training
+2016-09-06 10:07:31,535 DEBUG: Info: Time for Training: 0.0270109176636[s]
+2016-09-06 10:07:31,535 DEBUG: Done: Training
+2016-09-06 10:07:31,535 DEBUG: Start: Predicting
+2016-09-06 10:07:31,540 DEBUG: Done: Predicting
+2016-09-06 10:07:31,540 DEBUG: Start: Getting Results
+2016-09-06 10:07:31,541 DEBUG: Done: Getting Results
+2016-09-06 10:07:31,541 INFO: Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.628571428571
+accuracy_score on test : 0.588888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.628571428571
+ - Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:31,541 INFO: Done: Result Analysis
+2016-09-06 10:07:31,542 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:31,542 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:07:31,542 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:31,543 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:07:31,543 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:07:31,543 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:31,543 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:31,808 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:31,808 DEBUG: Start: Training
+2016-09-06 10:07:31,847 DEBUG: Info: Time for Training: 0.305594921112[s]
+2016-09-06 10:07:31,848 DEBUG: Done: Training
+2016-09-06 10:07:31,848 DEBUG: Start: Predicting
+2016-09-06 10:07:31,852 DEBUG: Done: Predicting
+2016-09-06 10:07:31,852 DEBUG: Start: Getting Results
+2016-09-06 10:07:31,854 DEBUG: Done: Getting Results
+2016-09-06 10:07:31,854 INFO: Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 15, max_depth : 13
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.995238095238
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:31,854 INFO: Done: Result Analysis
+2016-09-06 10:07:31,855 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:31,855 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:07:31,855 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:31,855 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:07:31,855 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:07:31,856 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:31,856 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:31,893 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:31,894 DEBUG: Start: Training
+2016-09-06 10:07:31,894 DEBUG: Info: Time for Training: 0.0398690700531[s]
+2016-09-06 10:07:31,895 DEBUG: Done: Training
+2016-09-06 10:07:31,895 DEBUG: Start: Predicting
+2016-09-06 10:07:31,896 DEBUG: Done: Predicting
+2016-09-06 10:07:31,896 DEBUG: Start: Getting Results
+2016-09-06 10:07:31,897 DEBUG: Done: Getting Results
+2016-09-06 10:07:31,897 INFO: Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.571428571429
+accuracy_score on test : 0.6
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : elasticnet
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.571428571429
+ - Score on test : 0.6
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:31,898 INFO: Done: Result Analysis
+2016-09-06 10:07:31,899 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:31,899 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:07:31,899 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:31,899 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:07:31,899 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:07:31,899 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:31,899 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:31,943 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:31,943 DEBUG: Start: Training
+2016-09-06 10:07:31,963 DEBUG: Info: Time for Training: 0.0644710063934[s]
+2016-09-06 10:07:31,963 DEBUG: Done: Training
+2016-09-06 10:07:31,963 DEBUG: Start: Predicting
+2016-09-06 10:07:31,965 DEBUG: Done: Predicting
+2016-09-06 10:07:31,965 DEBUG: Start: Getting Results
+2016-09-06 10:07:31,967 DEBUG: Done: Getting Results
+2016-09-06 10:07:31,967 INFO: Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.5
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 6060
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.5
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:31,967 INFO: Done: Result Analysis
+2016-09-06 10:07:31,968 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:31,968 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:07:31,968 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:31,968 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:07:31,968 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:07:31,969 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:31,969 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:32,017 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:32,017 DEBUG: Start: Training
+2016-09-06 10:07:32,034 DEBUG: Info: Time for Training: 0.0663030147552[s]
+2016-09-06 10:07:32,034 DEBUG: Done: Training
+2016-09-06 10:07:32,034 DEBUG: Start: Predicting
+2016-09-06 10:07:32,037 DEBUG: Done: Predicting
+2016-09-06 10:07:32,037 DEBUG: Start: Getting Results
+2016-09-06 10:07:32,038 DEBUG: Done: Getting Results
+2016-09-06 10:07:32,039 INFO: Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 569
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:32,039 INFO: Done: Result Analysis
+2016-09-06 10:07:32,040 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:32,040 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:07:32,040 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:32,040 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:07:32,040 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:07:32,040 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:32,041 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:32,082 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:32,082 DEBUG: Start: Training
+2016-09-06 10:07:32,098 DEBUG: Info: Time for Training: 0.0586409568787[s]
+2016-09-06 10:07:32,098 DEBUG: Done: Training
+2016-09-06 10:07:32,098 DEBUG: Start: Predicting
+2016-09-06 10:07:32,103 DEBUG: Done: Predicting
+2016-09-06 10:07:32,103 DEBUG: Start: Getting Results
+2016-09-06 10:07:32,104 DEBUG: Done: Getting Results
+2016-09-06 10:07:32,104 INFO: Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 1510
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:32,104 INFO: Done: Result Analysis
+2016-09-06 10:07:32,105 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:32,105 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:07:32,106 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:32,106 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:07:32,106 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:07:32,106 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:32,106 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:32,152 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:32,152 DEBUG: Start: Training
+2016-09-06 10:07:32,156 DEBUG: Info: Time for Training: 0.0510900020599[s]
+2016-09-06 10:07:32,156 DEBUG: Done: Training
+2016-09-06 10:07:32,156 DEBUG: Start: Predicting
+2016-09-06 10:07:32,158 DEBUG: Done: Predicting
+2016-09-06 10:07:32,158 DEBUG: Start: Getting Results
+2016-09-06 10:07:32,160 DEBUG: Done: Getting Results
+2016-09-06 10:07:32,160 INFO: Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:32,160 INFO: Done: Result Analysis
+2016-09-06 10:07:32,161 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:32,161 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:07:32,161 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:32,162 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:07:32,162 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:07:32,162 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:32,162 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:32,195 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:32,195 DEBUG: Start: Training
+2016-09-06 10:07:32,198 DEBUG: Info: Time for Training: 0.0367488861084[s]
+2016-09-06 10:07:32,198 DEBUG: Done: Training
+2016-09-06 10:07:32,198 DEBUG: Start: Predicting
+2016-09-06 10:07:32,199 DEBUG: Done: Predicting
+2016-09-06 10:07:32,199 DEBUG: Start: Getting Results
+2016-09-06 10:07:32,201 DEBUG: Done: Getting Results
+2016-09-06 10:07:32,201 INFO: Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 27
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:32,201 INFO: Done: Result Analysis
+2016-09-06 10:07:32,202 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:32,202 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:07:32,202 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:32,202 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:07:32,202 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:07:32,202 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:32,203 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:32,228 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:32,228 DEBUG: Start: Training
+2016-09-06 10:07:32,229 DEBUG: Info: Time for Training: 0.0273449420929[s]
+2016-09-06 10:07:32,229 DEBUG: Done: Training
+2016-09-06 10:07:32,229 DEBUG: Start: Predicting
+2016-09-06 10:07:32,234 DEBUG: Done: Predicting
+2016-09-06 10:07:32,234 DEBUG: Start: Getting Results
+2016-09-06 10:07:32,235 DEBUG: Done: Getting Results
+2016-09-06 10:07:32,235 INFO: Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.566666666667
+accuracy_score on test : 0.444444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 13
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.566666666667
+ - Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:32,235 INFO: Done: Result Analysis
+2016-09-06 10:07:32,236 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:32,236 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:07:32,236 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:32,237 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:07:32,237 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:07:32,237 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:32,237 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:32,394 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:32,395 DEBUG: Start: Training
+2016-09-06 10:07:32,416 DEBUG: Info: Time for Training: 0.180504083633[s]
+2016-09-06 10:07:32,417 DEBUG: Done: Training
+2016-09-06 10:07:32,417 DEBUG: Start: Predicting
+2016-09-06 10:07:32,420 DEBUG: Done: Predicting
+2016-09-06 10:07:32,420 DEBUG: Start: Getting Results
+2016-09-06 10:07:32,421 DEBUG: Done: Getting Results
+2016-09-06 10:07:32,421 INFO: Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.966666666667
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 8, max_depth : 18
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.966666666667
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:32,421 INFO: Done: Result Analysis
+2016-09-06 10:07:32,423 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:32,423 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:07:32,423 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:32,423 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:07:32,423 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:07:32,423 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:32,423 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:32,461 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:32,461 DEBUG: Start: Training
+2016-09-06 10:07:32,462 DEBUG: Info: Time for Training: 0.0399260520935[s]
+2016-09-06 10:07:32,462 DEBUG: Done: Training
+2016-09-06 10:07:32,462 DEBUG: Start: Predicting
+2016-09-06 10:07:32,464 DEBUG: Done: Predicting
+2016-09-06 10:07:32,464 DEBUG: Start: Getting Results
+2016-09-06 10:07:32,465 DEBUG: Done: Getting Results
+2016-09-06 10:07:32,465 INFO: Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.6
+accuracy_score on test : 0.566666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.6
+ - Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:32,465 INFO: Done: Result Analysis
+2016-09-06 10:07:32,466 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:32,466 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:07:32,466 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:32,467 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:07:32,467 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:07:32,467 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:32,467 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:32,512 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:32,512 DEBUG: Start: Training
+2016-09-06 10:07:32,532 DEBUG: Info: Time for Training: 0.0659711360931[s]
+2016-09-06 10:07:32,532 DEBUG: Done: Training
+2016-09-06 10:07:32,532 DEBUG: Start: Predicting
+2016-09-06 10:07:32,535 DEBUG: Done: Predicting
+2016-09-06 10:07:32,535 DEBUG: Start: Getting Results
+2016-09-06 10:07:32,536 DEBUG: Done: Getting Results
+2016-09-06 10:07:32,536 INFO: Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.542857142857
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 3802
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.542857142857
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:32,536 INFO: Done: Result Analysis
+2016-09-06 10:07:32,537 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:32,537 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:07:32,537 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:32,538 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:07:32,538 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:07:32,538 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:32,538 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:32,587 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:32,587 DEBUG: Start: Training
+2016-09-06 10:07:32,607 DEBUG: Info: Time for Training: 0.0699560642242[s]
+2016-09-06 10:07:32,607 DEBUG: Done: Training
+2016-09-06 10:07:32,607 DEBUG: Start: Predicting
+2016-09-06 10:07:32,610 DEBUG: Done: Predicting
+2016-09-06 10:07:32,610 DEBUG: Start: Getting Results
+2016-09-06 10:07:32,612 DEBUG: Done: Getting Results
+2016-09-06 10:07:32,612 INFO: Classification on Fake database for View3 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 6378
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:32,612 INFO: Done: Result Analysis
+2016-09-06 10:07:32,613 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:32,613 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:07:32,613 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:32,613 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:07:32,614 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:07:32,614 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:32,614 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:32,656 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:32,656 DEBUG: Start: Training
+2016-09-06 10:07:32,673 DEBUG: Info: Time for Training: 0.0603799819946[s]
+2016-09-06 10:07:32,673 DEBUG: Done: Training
+2016-09-06 10:07:32,673 DEBUG: Start: Predicting
+2016-09-06 10:07:32,678 DEBUG: Done: Predicting
+2016-09-06 10:07:32,678 DEBUG: Start: Getting Results
+2016-09-06 10:07:32,679 DEBUG: Done: Getting Results
+2016-09-06 10:07:32,679 INFO: Classification on Fake database for View3 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 6783
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:32,680 INFO: Done: Result Analysis
+2016-09-06 10:07:32,681 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:07:32,681 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-09-06 10:07:32,682 INFO: Info: Shape of View0 :(300, 11)
+2016-09-06 10:07:32,682 INFO: Info: Shape of View1 :(300, 8)
+2016-09-06 10:07:32,683 INFO: Info: Shape of View2 :(300, 14)
+2016-09-06 10:07:32,683 INFO: Info: Shape of View3 :(300, 17)
+2016-09-06 10:07:32,683 INFO: Done: Read Database Files
+2016-09-06 10:07:32,683 INFO: Start: Determine validation split for ratio 0.7
+2016-09-06 10:07:32,687 INFO: Done: Determine validation split
+2016-09-06 10:07:32,687 INFO: Start: Determine 5 folds
+2016-09-06 10:07:32,693 INFO: Info: Length of Learning Sets: 169
+2016-09-06 10:07:32,693 INFO: Info: Length of Testing Sets: 42
+2016-09-06 10:07:32,693 INFO: Info: Length of Validation Set: 89
+2016-09-06 10:07:32,693 INFO: Done: Determine folds
+2016-09-06 10:07:32,693 INFO: Start: Learning with Mumbo and 5 folds
+2016-09-06 10:07:32,693 INFO: Start: Randomsearching best settings for monoview classifiers
+2016-09-06 10:07:32,694 DEBUG: Start: Gridsearch for DecisionTree on View0
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..eb326c0f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 13, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..ae733e19
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.544444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 21
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..3db0decf
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.619047619048
+accuracy_score on test : 0.588888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 26
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.619047619048
+ - Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..67f77df1
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.766666666667
+accuracy_score on test : 0.433333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 11)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 18, max_depth : 3
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.766666666667
+ - Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..20a567ad
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.62380952381
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 11)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.62380952381
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..22e8c074
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.52380952381
+accuracy_score on test : 0.444444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 11)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 491
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.52380952381
+ - Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..8c9cc330
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 11)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2405
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..41068448
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.411111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 11)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 9676
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..7029410e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.577777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..13ea4b2b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.588888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 23
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..f008d4bd
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.628571428571
+accuracy_score on test : 0.588888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.628571428571
+ - Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..e9ecf6a0
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 15, max_depth : 13
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.995238095238
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..5522a9de
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.571428571429
+accuracy_score on test : 0.6
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : elasticnet
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.571428571429
+ - Score on test : 0.6
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..da23f07f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.5
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 6060
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.5
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..112d345d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 0.97619047619
+accuracy_score on test : 0.633333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 432
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.97619047619
+ - Score on test : 0.633333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..271f35f0
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 6005
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..dded33ad
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..13882acd
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 27
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..6dd96027
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.566666666667
+accuracy_score on test : 0.444444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 13
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.566666666667
+ - Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..218c51f1
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.966666666667
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 8, max_depth : 18
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.966666666667
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..e4f1a860
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.6
+accuracy_score on test : 0.566666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.6
+ - Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..96c36fc1
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.542857142857
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 3802
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.542857142857
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..37117611
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 6378
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..824dde85
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 6783
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100738-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-100738-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 00000000..d22ae631
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100738-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,1376 @@
+2016-09-06 10:07:38,682 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-06 10:07:38,682 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.00015915625 Gbytes /!\
+2016-09-06 10:07:43,694 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-06 10:07:43,696 INFO: Start: Finding all available mono- & multiview algorithms
+2016-09-06 10:07:43,766 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:43,766 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:07:43,767 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:43,767 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:07:43,768 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:07:43,768 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:43,768 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:43,768 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:43,768 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:07:43,768 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:43,769 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:07:43,769 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:07:43,769 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:43,769 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:43,808 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:43,808 DEBUG: Start: Training
+2016-09-06 10:07:43,810 DEBUG: Info: Time for Training: 0.0434219837189[s]
+2016-09-06 10:07:43,810 DEBUG: Done: Training
+2016-09-06 10:07:43,810 DEBUG: Start: Predicting
+2016-09-06 10:07:43,813 DEBUG: Done: Predicting
+2016-09-06 10:07:43,813 DEBUG: Start: Getting Results
+2016-09-06 10:07:43,815 DEBUG: Done: Getting Results
+2016-09-06 10:07:43,815 INFO: Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 25
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:43,816 INFO: Done: Result Analysis
+2016-09-06 10:07:43,825 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:43,826 DEBUG: Start: Training
+2016-09-06 10:07:43,831 DEBUG: Info: Time for Training: 0.0656042098999[s]
+2016-09-06 10:07:43,831 DEBUG: Done: Training
+2016-09-06 10:07:43,831 DEBUG: Start: Predicting
+2016-09-06 10:07:43,834 DEBUG: Done: Predicting
+2016-09-06 10:07:43,834 DEBUG: Start: Getting Results
+2016-09-06 10:07:43,836 DEBUG: Done: Getting Results
+2016-09-06 10:07:43,836 INFO: Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:43,836 INFO: Done: Result Analysis
+2016-09-06 10:07:43,916 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:43,916 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:43,916 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:07:43,916 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:07:43,916 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:43,916 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:43,917 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:07:43,917 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:07:43,917 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:07:43,917 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:07:43,917 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:43,917 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:43,918 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:43,918 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:43,951 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:43,951 DEBUG: Start: Training
+2016-09-06 10:07:43,952 DEBUG: Info: Time for Training: 0.0365099906921[s]
+2016-09-06 10:07:43,952 DEBUG: Done: Training
+2016-09-06 10:07:43,952 DEBUG: Start: Predicting
+2016-09-06 10:07:43,958 DEBUG: Done: Predicting
+2016-09-06 10:07:43,959 DEBUG: Start: Getting Results
+2016-09-06 10:07:43,960 DEBUG: Done: Getting Results
+2016-09-06 10:07:43,960 INFO: Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.619047619048
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 32
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.619047619048
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:43,960 INFO: Done: Result Analysis
+2016-09-06 10:07:44,207 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:44,207 DEBUG: Start: Training
+2016-09-06 10:07:44,249 DEBUG: Info: Time for Training: 0.333906173706[s]
+2016-09-06 10:07:44,249 DEBUG: Done: Training
+2016-09-06 10:07:44,249 DEBUG: Start: Predicting
+2016-09-06 10:07:44,255 DEBUG: Done: Predicting
+2016-09-06 10:07:44,255 DEBUG: Start: Getting Results
+2016-09-06 10:07:44,257 DEBUG: Done: Getting Results
+2016-09-06 10:07:44,257 INFO: Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.985714285714
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 16, max_depth : 25
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.985714285714
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:44,257 INFO: Done: Result Analysis
+2016-09-06 10:07:44,364 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:44,364 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:44,364 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:07:44,364 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:07:44,365 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:44,365 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:44,365 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:07:44,365 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:07:44,366 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:07:44,366 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:07:44,366 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:44,366 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:44,366 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:44,366 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:44,435 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:44,436 DEBUG: Start: Training
+2016-09-06 10:07:44,437 DEBUG: Info: Time for Training: 0.0736479759216[s]
+2016-09-06 10:07:44,437 DEBUG: Done: Training
+2016-09-06 10:07:44,437 DEBUG: Start: Predicting
+2016-09-06 10:07:44,441 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:44,441 DEBUG: Start: Training
+2016-09-06 10:07:44,450 DEBUG: Done: Predicting
+2016-09-06 10:07:44,450 DEBUG: Start: Getting Results
+2016-09-06 10:07:44,453 DEBUG: Done: Getting Results
+2016-09-06 10:07:44,453 INFO: Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.577777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.533333333333
+ - Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:44,454 INFO: Done: Result Analysis
+2016-09-06 10:07:44,468 DEBUG: Info: Time for Training: 0.104951143265[s]
+2016-09-06 10:07:44,468 DEBUG: Done: Training
+2016-09-06 10:07:44,468 DEBUG: Start: Predicting
+2016-09-06 10:07:44,472 DEBUG: Done: Predicting
+2016-09-06 10:07:44,472 DEBUG: Start: Getting Results
+2016-09-06 10:07:44,473 DEBUG: Done: Getting Results
+2016-09-06 10:07:44,473 INFO: Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.514285714286
+accuracy_score on test : 0.544444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 8318
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.514285714286
+ - Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:44,473 INFO: Done: Result Analysis
+2016-09-06 10:07:44,609 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:44,609 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:44,609 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:07:44,609 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:44,609 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:07:44,609 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:44,610 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:07:44,610 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:07:44,610 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:07:44,610 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:44,610 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:07:44,610 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:44,610 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:44,610 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:44,657 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:44,657 DEBUG: Start: Training
+2016-09-06 10:07:44,663 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:44,663 DEBUG: Start: Training
+2016-09-06 10:07:44,675 DEBUG: Info: Time for Training: 0.066269159317[s]
+2016-09-06 10:07:44,675 DEBUG: Done: Training
+2016-09-06 10:07:44,675 DEBUG: Start: Predicting
+2016-09-06 10:07:44,680 DEBUG: Done: Predicting
+2016-09-06 10:07:44,681 DEBUG: Start: Getting Results
+2016-09-06 10:07:44,682 DEBUG: Info: Time for Training: 0.0731539726257[s]
+2016-09-06 10:07:44,682 DEBUG: Done: Training
+2016-09-06 10:07:44,682 DEBUG: Start: Predicting
+2016-09-06 10:07:44,682 DEBUG: Done: Getting Results
+2016-09-06 10:07:44,682 INFO: Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 8318
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:44,682 INFO: Done: Result Analysis
+2016-09-06 10:07:44,686 DEBUG: Done: Predicting
+2016-09-06 10:07:44,686 DEBUG: Start: Getting Results
+2016-09-06 10:07:44,687 DEBUG: Done: Getting Results
+2016-09-06 10:07:44,687 INFO: Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 8318
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:44,688 INFO: Done: Result Analysis
+2016-09-06 10:07:44,764 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:44,764 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:44,764 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:07:44,764 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:07:44,764 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:44,764 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:44,766 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:07:44,766 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:07:44,766 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:07:44,766 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:07:44,766 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:44,766 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:44,766 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:44,766 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:44,823 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:44,823 DEBUG: Start: Training
+2016-09-06 10:07:44,826 DEBUG: Info: Time for Training: 0.0639500617981[s]
+2016-09-06 10:07:44,827 DEBUG: Done: Training
+2016-09-06 10:07:44,827 DEBUG: Start: Predicting
+2016-09-06 10:07:44,830 DEBUG: Done: Predicting
+2016-09-06 10:07:44,831 DEBUG: Start: Getting Results
+2016-09-06 10:07:44,832 DEBUG: Done: Getting Results
+2016-09-06 10:07:44,833 INFO: Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 25
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:44,833 INFO: Done: Result Analysis
+2016-09-06 10:07:44,841 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:44,841 DEBUG: Start: Training
+2016-09-06 10:07:44,845 DEBUG: Info: Time for Training: 0.0828351974487[s]
+2016-09-06 10:07:44,845 DEBUG: Done: Training
+2016-09-06 10:07:44,846 DEBUG: Start: Predicting
+2016-09-06 10:07:44,848 DEBUG: Done: Predicting
+2016-09-06 10:07:44,848 DEBUG: Start: Getting Results
+2016-09-06 10:07:44,850 DEBUG: Done: Getting Results
+2016-09-06 10:07:44,850 INFO: Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:44,851 INFO: Done: Result Analysis
+2016-09-06 10:07:44,909 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:44,910 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:07:44,910 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:44,910 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:44,910 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:07:44,910 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:44,911 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:07:44,911 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:07:44,911 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:07:44,912 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:44,912 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:07:44,912 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:44,912 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:44,912 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:44,945 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:44,946 DEBUG: Start: Training
+2016-09-06 10:07:44,946 DEBUG: Info: Time for Training: 0.037682056427[s]
+2016-09-06 10:07:44,946 DEBUG: Done: Training
+2016-09-06 10:07:44,946 DEBUG: Start: Predicting
+2016-09-06 10:07:44,953 DEBUG: Done: Predicting
+2016-09-06 10:07:44,954 DEBUG: Start: Getting Results
+2016-09-06 10:07:44,955 DEBUG: Done: Getting Results
+2016-09-06 10:07:44,955 INFO: Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.566666666667
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 32
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.566666666667
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:44,955 INFO: Done: Result Analysis
+2016-09-06 10:07:45,240 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:45,240 DEBUG: Start: Training
+2016-09-06 10:07:45,287 DEBUG: Info: Time for Training: 0.378414154053[s]
+2016-09-06 10:07:45,287 DEBUG: Done: Training
+2016-09-06 10:07:45,288 DEBUG: Start: Predicting
+2016-09-06 10:07:45,295 DEBUG: Done: Predicting
+2016-09-06 10:07:45,295 DEBUG: Start: Getting Results
+2016-09-06 10:07:45,296 DEBUG: Done: Getting Results
+2016-09-06 10:07:45,296 INFO: Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 16, max_depth : 25
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.995238095238
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:45,296 INFO: Done: Result Analysis
+2016-09-06 10:07:45,357 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:45,358 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:07:45,358 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:45,359 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:45,359 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:07:45,359 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:07:45,359 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:07:45,359 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:45,359 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:45,359 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:45,360 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:07:45,360 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:07:45,360 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:45,360 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:45,411 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:45,411 DEBUG: Start: Training
+2016-09-06 10:07:45,412 DEBUG: Info: Time for Training: 0.0539691448212[s]
+2016-09-06 10:07:45,412 DEBUG: Done: Training
+2016-09-06 10:07:45,412 DEBUG: Start: Predicting
+2016-09-06 10:07:45,417 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:45,417 DEBUG: Start: Training
+2016-09-06 10:07:45,423 DEBUG: Done: Predicting
+2016-09-06 10:07:45,423 DEBUG: Start: Getting Results
+2016-09-06 10:07:45,426 DEBUG: Done: Getting Results
+2016-09-06 10:07:45,426 INFO: Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.619047619048
+accuracy_score on test : 0.633333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.619047619048
+ - Score on test : 0.633333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:45,426 INFO: Done: Result Analysis
+2016-09-06 10:07:45,441 DEBUG: Info: Time for Training: 0.0843439102173[s]
+2016-09-06 10:07:45,441 DEBUG: Done: Training
+2016-09-06 10:07:45,441 DEBUG: Start: Predicting
+2016-09-06 10:07:45,445 DEBUG: Done: Predicting
+2016-09-06 10:07:45,445 DEBUG: Start: Getting Results
+2016-09-06 10:07:45,446 DEBUG: Done: Getting Results
+2016-09-06 10:07:45,446 INFO: Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.528571428571
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 8318
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.528571428571
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:45,447 INFO: Done: Result Analysis
+2016-09-06 10:07:45,509 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:45,509 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:45,509 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:07:45,509 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:07:45,510 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:45,510 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:45,510 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:07:45,510 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:07:45,511 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:07:45,511 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:07:45,511 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:45,511 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:45,511 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:45,511 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:45,559 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:45,559 DEBUG: Start: Training
+2016-09-06 10:07:45,562 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:45,562 DEBUG: Start: Training
+2016-09-06 10:07:45,577 DEBUG: Info: Time for Training: 0.0689029693604[s]
+2016-09-06 10:07:45,577 DEBUG: Done: Training
+2016-09-06 10:07:45,577 DEBUG: Start: Predicting
+2016-09-06 10:07:45,583 DEBUG: Info: Time for Training: 0.0745868682861[s]
+2016-09-06 10:07:45,583 DEBUG: Done: Training
+2016-09-06 10:07:45,583 DEBUG: Start: Predicting
+2016-09-06 10:07:45,583 DEBUG: Done: Predicting
+2016-09-06 10:07:45,583 DEBUG: Start: Getting Results
+2016-09-06 10:07:45,585 DEBUG: Done: Getting Results
+2016-09-06 10:07:45,585 INFO: Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 8318
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:45,585 INFO: Done: Result Analysis
+2016-09-06 10:07:45,587 DEBUG: Done: Predicting
+2016-09-06 10:07:45,587 DEBUG: Start: Getting Results
+2016-09-06 10:07:45,588 DEBUG: Done: Getting Results
+2016-09-06 10:07:45,588 INFO: Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 8318
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:45,589 INFO: Done: Result Analysis
+2016-09-06 10:07:45,663 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:45,663 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:45,664 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:07:45,664 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:07:45,664 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:45,664 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:45,665 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:07:45,665 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:07:45,665 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:07:45,665 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:07:45,666 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:45,666 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:45,666 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:45,666 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:45,725 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:45,725 DEBUG: Start: Training
+2016-09-06 10:07:45,729 DEBUG: Info: Time for Training: 0.0669369697571[s]
+2016-09-06 10:07:45,729 DEBUG: Done: Training
+2016-09-06 10:07:45,729 DEBUG: Start: Predicting
+2016-09-06 10:07:45,733 DEBUG: Done: Predicting
+2016-09-06 10:07:45,733 DEBUG: Start: Getting Results
+2016-09-06 10:07:45,735 DEBUG: Done: Getting Results
+2016-09-06 10:07:45,735 INFO: Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 25
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:45,735 INFO: Done: Result Analysis
+2016-09-06 10:07:45,743 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:45,744 DEBUG: Start: Training
+2016-09-06 10:07:45,748 DEBUG: Info: Time for Training: 0.086000919342[s]
+2016-09-06 10:07:45,748 DEBUG: Done: Training
+2016-09-06 10:07:45,748 DEBUG: Start: Predicting
+2016-09-06 10:07:45,751 DEBUG: Done: Predicting
+2016-09-06 10:07:45,751 DEBUG: Start: Getting Results
+2016-09-06 10:07:45,753 DEBUG: Done: Getting Results
+2016-09-06 10:07:45,753 INFO: Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:45,754 INFO: Done: Result Analysis
+2016-09-06 10:07:45,915 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:45,915 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:07:45,915 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:45,916 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:45,916 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:07:45,916 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:45,917 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:07:45,917 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:07:45,917 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:07:45,917 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:45,917 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:07:45,918 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:45,918 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:45,918 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:45,967 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:45,967 DEBUG: Start: Training
+2016-09-06 10:07:45,968 DEBUG: Info: Time for Training: 0.0544729232788[s]
+2016-09-06 10:07:45,968 DEBUG: Done: Training
+2016-09-06 10:07:45,968 DEBUG: Start: Predicting
+2016-09-06 10:07:45,979 DEBUG: Done: Predicting
+2016-09-06 10:07:45,979 DEBUG: Start: Getting Results
+2016-09-06 10:07:45,982 DEBUG: Done: Getting Results
+2016-09-06 10:07:45,982 INFO: Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.62380952381
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 32
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.62380952381
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:45,982 INFO: Done: Result Analysis
+2016-09-06 10:07:46,233 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:46,233 DEBUG: Start: Training
+2016-09-06 10:07:46,276 DEBUG: Info: Time for Training: 0.361407995224[s]
+2016-09-06 10:07:46,276 DEBUG: Done: Training
+2016-09-06 10:07:46,276 DEBUG: Start: Predicting
+2016-09-06 10:07:46,281 DEBUG: Done: Predicting
+2016-09-06 10:07:46,281 DEBUG: Start: Getting Results
+2016-09-06 10:07:46,283 DEBUG: Done: Getting Results
+2016-09-06 10:07:46,283 INFO: Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 16, max_depth : 25
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.995238095238
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:46,283 INFO: Done: Result Analysis
+2016-09-06 10:07:46,361 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:46,361 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:07:46,361 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:46,362 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:46,362 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:07:46,362 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:46,362 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:07:46,362 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:07:46,362 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:46,362 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:46,362 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:07:46,363 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:07:46,363 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:46,363 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:46,413 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:46,414 DEBUG: Start: Training
+2016-09-06 10:07:46,415 DEBUG: Info: Time for Training: 0.0534558296204[s]
+2016-09-06 10:07:46,415 DEBUG: Done: Training
+2016-09-06 10:07:46,415 DEBUG: Start: Predicting
+2016-09-06 10:07:46,416 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:46,416 DEBUG: Start: Training
+2016-09-06 10:07:46,437 DEBUG: Done: Predicting
+2016-09-06 10:07:46,437 DEBUG: Start: Getting Results
+2016-09-06 10:07:46,437 DEBUG: Info: Time for Training: 0.077164888382[s]
+2016-09-06 10:07:46,437 DEBUG: Done: Training
+2016-09-06 10:07:46,438 DEBUG: Start: Predicting
+2016-09-06 10:07:46,438 DEBUG: Done: Getting Results
+2016-09-06 10:07:46,439 INFO: Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.585714285714
+accuracy_score on test : 0.455555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.585714285714
+ - Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:46,439 INFO: Done: Result Analysis
+2016-09-06 10:07:46,442 DEBUG: Done: Predicting
+2016-09-06 10:07:46,442 DEBUG: Start: Getting Results
+2016-09-06 10:07:46,443 DEBUG: Done: Getting Results
+2016-09-06 10:07:46,443 INFO: Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.433333333333
+accuracy_score on test : 0.377777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 8318
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.433333333333
+ - Score on test : 0.377777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:46,443 INFO: Done: Result Analysis
+2016-09-06 10:07:46,505 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:46,505 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:07:46,505 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:46,505 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:46,506 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:07:46,506 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:46,506 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:07:46,506 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:07:46,507 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:46,507 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:07:46,507 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:46,507 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:07:46,507 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:46,507 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:46,556 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:46,557 DEBUG: Start: Training
+2016-09-06 10:07:46,561 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:46,562 DEBUG: Start: Training
+2016-09-06 10:07:46,575 DEBUG: Info: Time for Training: 0.0704419612885[s]
+2016-09-06 10:07:46,575 DEBUG: Done: Training
+2016-09-06 10:07:46,575 DEBUG: Start: Predicting
+2016-09-06 10:07:46,581 DEBUG: Done: Predicting
+2016-09-06 10:07:46,581 DEBUG: Start: Getting Results
+2016-09-06 10:07:46,583 DEBUG: Info: Time for Training: 0.0788052082062[s]
+2016-09-06 10:07:46,583 DEBUG: Done: Getting Results
+2016-09-06 10:07:46,583 DEBUG: Done: Training
+2016-09-06 10:07:46,583 INFO: Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 8318
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:46,583 DEBUG: Start: Predicting
+2016-09-06 10:07:46,583 INFO: Done: Result Analysis
+2016-09-06 10:07:46,588 DEBUG: Done: Predicting
+2016-09-06 10:07:46,588 DEBUG: Start: Getting Results
+2016-09-06 10:07:46,589 DEBUG: Done: Getting Results
+2016-09-06 10:07:46,589 INFO: Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 8318
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:46,589 INFO: Done: Result Analysis
+2016-09-06 10:07:46,653 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:46,653 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:46,654 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:07:46,654 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:07:46,654 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:46,654 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:46,655 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:07:46,655 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:07:46,655 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:07:46,655 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:07:46,655 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:46,655 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:46,655 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:46,655 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:46,709 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:46,709 DEBUG: Start: Training
+2016-09-06 10:07:46,712 DEBUG: Info: Time for Training: 0.0588240623474[s]
+2016-09-06 10:07:46,712 DEBUG: Done: Training
+2016-09-06 10:07:46,712 DEBUG: Start: Predicting
+2016-09-06 10:07:46,715 DEBUG: Done: Predicting
+2016-09-06 10:07:46,716 DEBUG: Start: Getting Results
+2016-09-06 10:07:46,718 DEBUG: Done: Getting Results
+2016-09-06 10:07:46,718 INFO: Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 25
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:46,718 INFO: Done: Result Analysis
+2016-09-06 10:07:46,727 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:46,727 DEBUG: Start: Training
+2016-09-06 10:07:46,731 DEBUG: Info: Time for Training: 0.0781581401825[s]
+2016-09-06 10:07:46,731 DEBUG: Done: Training
+2016-09-06 10:07:46,731 DEBUG: Start: Predicting
+2016-09-06 10:07:46,734 DEBUG: Done: Predicting
+2016-09-06 10:07:46,734 DEBUG: Start: Getting Results
+2016-09-06 10:07:46,736 DEBUG: Done: Getting Results
+2016-09-06 10:07:46,736 INFO: Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:46,736 INFO: Done: Result Analysis
+2016-09-06 10:07:46,802 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:46,803 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:07:46,802 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:46,803 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:46,803 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:07:46,803 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:46,804 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:07:46,804 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:07:46,804 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:07:46,804 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:07:46,804 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:46,804 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:46,804 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:46,804 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:46,837 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:46,837 DEBUG: Start: Training
+2016-09-06 10:07:46,838 DEBUG: Info: Time for Training: 0.0363059043884[s]
+2016-09-06 10:07:46,838 DEBUG: Done: Training
+2016-09-06 10:07:46,838 DEBUG: Start: Predicting
+2016-09-06 10:07:46,844 DEBUG: Done: Predicting
+2016-09-06 10:07:46,845 DEBUG: Start: Getting Results
+2016-09-06 10:07:46,846 DEBUG: Done: Getting Results
+2016-09-06 10:07:46,846 INFO: Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.580952380952
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 32
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.580952380952
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:46,846 INFO: Done: Result Analysis
+2016-09-06 10:07:47,123 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:47,123 DEBUG: Start: Training
+2016-09-06 10:07:47,170 DEBUG: Info: Time for Training: 0.368059158325[s]
+2016-09-06 10:07:47,170 DEBUG: Done: Training
+2016-09-06 10:07:47,170 DEBUG: Start: Predicting
+2016-09-06 10:07:47,175 DEBUG: Done: Predicting
+2016-09-06 10:07:47,176 DEBUG: Start: Getting Results
+2016-09-06 10:07:47,177 DEBUG: Done: Getting Results
+2016-09-06 10:07:47,177 INFO: Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 16, max_depth : 25
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.995238095238
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:47,177 INFO: Done: Result Analysis
+2016-09-06 10:07:47,249 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:47,249 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:47,249 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:07:47,249 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:07:47,249 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:47,249 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:07:47,250 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:07:47,250 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:07:47,250 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:07:47,250 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:07:47,250 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:47,250 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:07:47,250 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:47,251 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:07:47,297 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:47,297 DEBUG: Start: Training
+2016-09-06 10:07:47,298 DEBUG: Info: Time for Training: 0.049859046936[s]
+2016-09-06 10:07:47,298 DEBUG: Done: Training
+2016-09-06 10:07:47,298 DEBUG: Start: Predicting
+2016-09-06 10:07:47,305 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:07:47,305 DEBUG: Start: Training
+2016-09-06 10:07:47,324 DEBUG: Info: Time for Training: 0.0752458572388[s]
+2016-09-06 10:07:47,324 DEBUG: Done: Training
+2016-09-06 10:07:47,324 DEBUG: Start: Predicting
+2016-09-06 10:07:47,325 DEBUG: Done: Predicting
+2016-09-06 10:07:47,325 DEBUG: Start: Getting Results
+2016-09-06 10:07:47,327 DEBUG: Done: Getting Results
+2016-09-06 10:07:47,327 INFO: Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.542857142857
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.542857142857
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:47,327 INFO: Done: Result Analysis
+2016-09-06 10:07:47,327 DEBUG: Done: Predicting
+2016-09-06 10:07:47,327 DEBUG: Start: Getting Results
+2016-09-06 10:07:47,329 DEBUG: Done: Getting Results
+2016-09-06 10:07:47,329 INFO: Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.490476190476
+accuracy_score on test : 0.566666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 8318
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.490476190476
+ - Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:47,329 INFO: Done: Result Analysis
+2016-09-06 10:07:47,543 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:07:47,544 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-09-06 10:07:47,544 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:07:47,545 INFO: Info: Shape of View0 :(300, 13)
+2016-09-06 10:07:47,545 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-06 10:07:47,546 INFO: Info: Shape of View1 :(300, 17)
+2016-09-06 10:07:47,546 INFO: Info: Shape of View0 :(300, 13)
+2016-09-06 10:07:47,547 INFO: Info: Shape of View2 :(300, 20)
+2016-09-06 10:07:47,547 INFO: Info: Shape of View1 :(300, 17)
+2016-09-06 10:07:47,547 INFO: Info: Shape of View2 :(300, 20)
+2016-09-06 10:07:47,547 INFO: Info: Shape of View3 :(300, 10)
+2016-09-06 10:07:47,548 INFO: Done: Read Database Files
+2016-09-06 10:07:47,548 INFO: Start: Determine validation split for ratio 0.7
+2016-09-06 10:07:47,548 INFO: Info: Shape of View3 :(300, 10)
+2016-09-06 10:07:47,548 INFO: Done: Read Database Files
+2016-09-06 10:07:47,549 INFO: Start: Determine validation split for ratio 0.7
+2016-09-06 10:07:47,553 INFO: Done: Determine validation split
+2016-09-06 10:07:47,553 INFO: Start: Determine 5 folds
+2016-09-06 10:07:47,553 INFO: Done: Determine validation split
+2016-09-06 10:07:47,553 INFO: Start: Determine 5 folds
+2016-09-06 10:07:47,560 INFO: Info: Length of Learning Sets: 170
+2016-09-06 10:07:47,560 INFO: Info: Length of Testing Sets: 41
+2016-09-06 10:07:47,560 INFO: Info: Length of Validation Set: 89
+2016-09-06 10:07:47,560 INFO: Done: Determine folds
+2016-09-06 10:07:47,560 INFO: Start: Learning with Mumbo and 5 folds
+2016-09-06 10:07:47,561 INFO: Start: Classification
+2016-09-06 10:07:47,561 INFO: Info: Length of Learning Sets: 170
+2016-09-06 10:07:47,561 INFO: Start: Fold number 1
+2016-09-06 10:07:47,561 INFO: Info: Length of Testing Sets: 41
+2016-09-06 10:07:47,561 INFO: Info: Length of Validation Set: 89
+2016-09-06 10:07:47,561 INFO: Done: Determine folds
+2016-09-06 10:07:47,561 INFO: Start: Learning with Fusion and 5 folds
+2016-09-06 10:07:47,561 INFO: Start: Classification
+2016-09-06 10:07:47,561 INFO: Start: Fold number 1
+2016-09-06 10:07:47,598 DEBUG: Start: Iteration 1
+2016-09-06 10:07:47,609 DEBUG: View 0 : 0.497175141243
+2016-09-06 10:07:47,616 DEBUG: View 1 : 0.480225988701
+2016-09-06 10:07:47,625 DEBUG: View 2 : 0.570621468927
+2016-09-06 10:07:47,632 DEBUG: View 3 : 0.570621468927
+2016-09-06 10:07:47,672 DEBUG: Best view : View2
+2016-09-06 10:07:47,759 DEBUG: Start: Iteration 2
+2016-09-06 10:07:47,767 DEBUG: View 0 : 0.71186440678
+2016-09-06 10:07:47,778 DEBUG: View 1 : 0.717514124294
+2016-09-06 10:07:47,788 DEBUG: View 2 : 0.706214689266
+2016-09-06 10:07:47,798 DEBUG: View 3 : 0.745762711864
+2016-09-06 10:07:47,844 DEBUG: Best view : View3
+2016-09-06 10:07:48,124 DEBUG: Start: Iteration 3
+2016-09-06 10:07:48,132 DEBUG: View 0 : 0.71186440678
+2016-09-06 10:07:48,140 DEBUG: View 1 : 0.717514124294
+2016-09-06 10:07:48,148 DEBUG: View 2 : 0.706214689266
+2016-09-06 10:07:48,155 DEBUG: View 3 : 0.745762711864
+2016-09-06 10:07:48,196 DEBUG: Best view : View3
+2016-09-06 10:07:48,423 DEBUG: Start: Iteration 4
+2016-09-06 10:07:48,430 DEBUG: View 0 : 0.683615819209
+2016-09-06 10:07:48,438 DEBUG: View 1 : 0.677966101695
+2016-09-06 10:07:48,446 DEBUG: View 2 : 0.666666666667
+2016-09-06 10:07:48,453 DEBUG: View 3 : 0.706214689266
+2016-09-06 10:07:48,497 DEBUG: Best view : View3
+2016-09-06 10:07:48,779 INFO: Start: Classification
+2016-09-06 10:07:49,244 INFO: Done: Fold number 1
+2016-09-06 10:07:49,244 INFO: Start: Fold number 2
+2016-09-06 10:07:49,274 DEBUG: Start: Iteration 1
+2016-09-06 10:07:49,285 DEBUG: View 0 : 0.508287292818
+2016-09-06 10:07:49,301 DEBUG: View 1 : 0.563535911602
+2016-09-06 10:07:49,309 DEBUG: View 2 : 0.497237569061
+2016-09-06 10:07:49,316 DEBUG: View 3 : 0.519337016575
+2016-09-06 10:07:49,348 DEBUG: Best view : View0
+2016-09-06 10:07:49,429 DEBUG: Start: Iteration 2
+2016-09-06 10:07:49,436 DEBUG: View 0 : 0.707182320442
+2016-09-06 10:07:49,444 DEBUG: View 1 : 0.71270718232
+2016-09-06 10:07:49,451 DEBUG: View 2 : 0.723756906077
+2016-09-06 10:07:49,458 DEBUG: View 3 : 0.657458563536
+2016-09-06 10:07:49,497 DEBUG: Best view : View2
+2016-09-06 10:07:49,646 DEBUG: Start: Iteration 3
+2016-09-06 10:07:49,654 DEBUG: View 0 : 0.707182320442
+2016-09-06 10:07:49,661 DEBUG: View 1 : 0.71270718232
+2016-09-06 10:07:49,669 DEBUG: View 2 : 0.723756906077
+2016-09-06 10:07:49,675 DEBUG: View 3 : 0.657458563536
+2016-09-06 10:07:49,717 DEBUG: Best view : View2
+2016-09-06 10:07:49,935 INFO: Start: Classification
+2016-09-06 10:07:50,288 INFO: Done: Fold number 2
+2016-09-06 10:07:50,289 INFO: Start: Fold number 3
+2016-09-06 10:07:50,319 DEBUG: Start: Iteration 1
+2016-09-06 10:07:50,327 DEBUG: View 0 : 0.481081081081
+2016-09-06 10:07:50,334 DEBUG: View 1 : 0.491891891892
+2016-09-06 10:07:50,342 DEBUG: View 2 : 0.47027027027
+2016-09-06 10:07:50,349 DEBUG: View 3 : 0.508108108108
+2016-09-06 10:07:50,382 DEBUG: Best view : View3
+2016-09-06 10:07:50,463 DEBUG: Start: Iteration 2
+2016-09-06 10:07:50,471 DEBUG: View 0 : 0.713513513514
+2016-09-06 10:07:50,478 DEBUG: View 1 : 0.702702702703
+2016-09-06 10:07:50,486 DEBUG: View 2 : 0.675675675676
+2016-09-06 10:07:50,493 DEBUG: View 3 : 0.654054054054
+2016-09-06 10:07:50,533 DEBUG: Best view : View0
+2016-09-06 10:07:50,686 DEBUG: Start: Iteration 3
+2016-09-06 10:07:50,694 DEBUG: View 0 : 0.713513513514
+2016-09-06 10:07:50,701 DEBUG: View 1 : 0.702702702703
+2016-09-06 10:07:50,709 DEBUG: View 2 : 0.675675675676
+2016-09-06 10:07:50,716 DEBUG: View 3 : 0.654054054054
+2016-09-06 10:07:50,759 DEBUG: Best view : View0
+2016-09-06 10:07:50,981 INFO: Start: Classification
+2016-09-06 10:07:51,339 INFO: Done: Fold number 3
+2016-09-06 10:07:51,340 INFO: Start: Fold number 4
+2016-09-06 10:07:51,369 DEBUG: Start: Iteration 1
+2016-09-06 10:07:51,376 DEBUG: View 0 : 0.483333333333
+2016-09-06 10:07:51,382 DEBUG: View 1 : 0.483333333333
+2016-09-06 10:07:51,389 DEBUG: View 2 : 0.483333333333
+2016-09-06 10:07:51,395 DEBUG: View 3 : 0.483333333333
+2016-09-06 10:07:51,396 WARNING: WARNING: All bad for iteration 0
+2016-09-06 10:07:51,428 DEBUG: Best view : View0
+2016-09-06 10:07:51,507 DEBUG: Start: Iteration 2
+2016-09-06 10:07:51,515 DEBUG: View 0 : 0.738888888889
+2016-09-06 10:07:51,522 DEBUG: View 1 : 0.744444444444
+2016-09-06 10:07:51,530 DEBUG: View 2 : 0.761111111111
+2016-09-06 10:07:51,537 DEBUG: View 3 : 0.711111111111
+2016-09-06 10:07:51,575 DEBUG: Best view : View2
+2016-09-06 10:07:51,724 DEBUG: Start: Iteration 3
+2016-09-06 10:07:51,732 DEBUG: View 0 : 0.738888888889
+2016-09-06 10:07:51,739 DEBUG: View 1 : 0.744444444444
+2016-09-06 10:07:51,746 DEBUG: View 2 : 0.761111111111
+2016-09-06 10:07:51,754 DEBUG: View 3 : 0.711111111111
+2016-09-06 10:07:51,795 DEBUG: Best view : View2
+2016-09-06 10:07:52,011 DEBUG: Start: Iteration 4
+2016-09-06 10:07:52,018 DEBUG: View 0 : 0.677777777778
+2016-09-06 10:07:52,026 DEBUG: View 1 : 0.711111111111
+2016-09-06 10:07:52,033 DEBUG: View 2 : 0.694444444444
+2016-09-06 10:07:52,040 DEBUG: View 3 : 0.661111111111
+2016-09-06 10:07:52,084 DEBUG: Best view : View0
+2016-09-06 10:07:52,368 INFO: Start: Classification
+2016-09-06 10:07:52,836 INFO: Done: Fold number 4
+2016-09-06 10:07:52,837 INFO: Start: Fold number 5
+2016-09-06 10:07:52,867 DEBUG: Start: Iteration 1
+2016-09-06 10:07:52,874 DEBUG: View 0 : 0.53591160221
+2016-09-06 10:07:52,881 DEBUG: View 1 : 0.563535911602
+2016-09-06 10:07:52,888 DEBUG: View 2 : 0.552486187845
+2016-09-06 10:07:52,895 DEBUG: View 3 : 0.513812154696
+2016-09-06 10:07:52,929 DEBUG: Best view : View3
+2016-09-06 10:07:53,013 DEBUG: Start: Iteration 2
+2016-09-06 10:07:53,021 DEBUG: View 0 : 0.690607734807
+2016-09-06 10:07:53,029 DEBUG: View 1 : 0.767955801105
+2016-09-06 10:07:53,036 DEBUG: View 2 : 0.696132596685
+2016-09-06 10:07:53,044 DEBUG: View 3 : 0.662983425414
+2016-09-06 10:07:53,084 DEBUG: Best view : View1
+2016-09-06 10:07:53,237 DEBUG: Start: Iteration 3
+2016-09-06 10:07:53,244 DEBUG: View 0 : 0.690607734807
+2016-09-06 10:07:53,252 DEBUG: View 1 : 0.767955801105
+2016-09-06 10:07:53,259 DEBUG: View 2 : 0.696132596685
+2016-09-06 10:07:53,267 DEBUG: View 3 : 0.662983425414
+2016-09-06 10:07:53,311 DEBUG: Best view : View1
+2016-09-06 10:07:53,532 DEBUG: Start: Iteration 4
+2016-09-06 10:07:53,539 DEBUG: View 0 : 0.624309392265
+2016-09-06 10:07:53,547 DEBUG: View 1 : 0.596685082873
+2016-09-06 10:07:53,554 DEBUG: View 2 : 0.723756906077
+2016-09-06 10:07:53,562 DEBUG: View 3 : 0.640883977901
+2016-09-06 10:07:53,606 DEBUG: Best view : View2
+2016-09-06 10:07:53,901 INFO: Start: Classification
+2016-09-06 10:07:54,384 INFO: Done: Fold number 5
+2016-09-06 10:07:54,385 INFO: Done: Classification
+2016-09-06 10:07:54,385 INFO: Info: Time for Classification: 6[s]
+2016-09-06 10:07:54,385 INFO: Start: Result Analysis for Mumbo
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..6e2ae222
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..5fc348df
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 25
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..10de1910
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.619047619048
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 32
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.619047619048
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..ecebfbe8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..3476de3b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 25
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..03ccc8e3
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.566666666667
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 32
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.566666666667
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..e69ac2b1
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.985714285714
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 16, max_depth : 25
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.985714285714
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..6743e8cc
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.577777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.533333333333
+ - Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..9d769aa7
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.514285714286
+accuracy_score on test : 0.544444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 8318
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.514285714286
+ - Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..6b7bdbac
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 8318
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..97799939
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 8318
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..b51560b4
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..94513afc
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 25
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..a44df1d6
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.62380952381
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 32
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.62380952381
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..6133fdbf
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 16, max_depth : 25
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.995238095238
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..268dc6e3
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.619047619048
+accuracy_score on test : 0.633333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.619047619048
+ - Score on test : 0.633333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..d2acb180
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.528571428571
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 8318
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.528571428571
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..57b1b4cd
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 8318
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..622583f8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 8318
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..922eef96
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..4c6b0f49
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 25
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..a9615039
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.580952380952
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 32
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.580952380952
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..6e0fa905
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 16, max_depth : 25
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.995238095238
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..6352e1f5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.585714285714
+accuracy_score on test : 0.455555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.585714285714
+ - Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..f9f92f1b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.433333333333
+accuracy_score on test : 0.377777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 8318
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.433333333333
+ - Score on test : 0.377777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..cd43ab94
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 8318
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..253cd1cb
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 8318
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..34a64102
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 16, max_depth : 25
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.995238095238
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..54bf6a98
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.542857142857
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.542857142857
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..fb0c6265
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.490476190476
+accuracy_score on test : 0.566666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 8318
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.490476190476
+ - Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100834-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-100834-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 00000000..ccabbd2f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100834-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,1370 @@
+2016-09-06 10:08:34,156 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-06 10:08:34,156 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.00014978125 Gbytes /!\
+2016-09-06 10:08:39,164 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-06 10:08:39,166 INFO: Start: Finding all available mono- & multiview algorithms
+2016-09-06 10:08:39,222 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:39,222 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:39,222 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:08:39,222 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:08:39,222 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:39,222 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:39,223 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:08:39,223 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:08:39,223 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:08:39,223 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:39,223 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:08:39,223 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:39,223 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:39,223 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:39,265 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:39,265 DEBUG: Start: Training
+2016-09-06 10:08:39,268 DEBUG: Info: Time for Training: 0.0466451644897[s]
+2016-09-06 10:08:39,268 DEBUG: Done: Training
+2016-09-06 10:08:39,268 DEBUG: Start: Predicting
+2016-09-06 10:08:39,271 DEBUG: Done: Predicting
+2016-09-06 10:08:39,271 DEBUG: Start: Getting Results
+2016-09-06 10:08:39,272 DEBUG: Done: Getting Results
+2016-09-06 10:08:39,272 INFO: Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 28
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:39,273 INFO: Done: Result Analysis
+2016-09-06 10:08:39,287 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:39,287 DEBUG: Start: Training
+2016-09-06 10:08:39,292 DEBUG: Info: Time for Training: 0.0710310935974[s]
+2016-09-06 10:08:39,292 DEBUG: Done: Training
+2016-09-06 10:08:39,292 DEBUG: Start: Predicting
+2016-09-06 10:08:39,295 DEBUG: Done: Predicting
+2016-09-06 10:08:39,296 DEBUG: Start: Getting Results
+2016-09-06 10:08:39,298 DEBUG: Done: Getting Results
+2016-09-06 10:08:39,298 INFO: Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:39,298 INFO: Done: Result Analysis
+2016-09-06 10:08:39,371 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:39,372 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:08:39,372 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:39,372 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:39,372 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:08:39,372 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:39,372 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:08:39,372 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:08:39,372 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:08:39,373 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:39,373 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:08:39,373 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:39,373 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:39,373 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:39,410 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:39,410 DEBUG: Start: Training
+2016-09-06 10:08:39,411 DEBUG: Info: Time for Training: 0.039687871933[s]
+2016-09-06 10:08:39,411 DEBUG: Done: Training
+2016-09-06 10:08:39,411 DEBUG: Start: Predicting
+2016-09-06 10:08:39,418 DEBUG: Done: Predicting
+2016-09-06 10:08:39,418 DEBUG: Start: Getting Results
+2016-09-06 10:08:39,420 DEBUG: Done: Getting Results
+2016-09-06 10:08:39,420 INFO: Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.5
+accuracy_score on test : 0.444444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 28
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.5
+ - Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:39,420 INFO: Done: Result Analysis
+2016-09-06 10:08:39,655 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:39,655 DEBUG: Start: Training
+2016-09-06 10:08:39,694 DEBUG: Info: Time for Training: 0.322620153427[s]
+2016-09-06 10:08:39,694 DEBUG: Done: Training
+2016-09-06 10:08:39,694 DEBUG: Start: Predicting
+2016-09-06 10:08:39,700 DEBUG: Done: Predicting
+2016-09-06 10:08:39,700 DEBUG: Start: Getting Results
+2016-09-06 10:08:39,701 DEBUG: Done: Getting Results
+2016-09-06 10:08:39,701 INFO: Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.97619047619
+accuracy_score on test : 0.455555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 14, max_depth : 28
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.97619047619
+ - Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:39,702 INFO: Done: Result Analysis
+2016-09-06 10:08:39,821 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:39,822 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:08:39,822 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:39,822 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:39,822 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:08:39,822 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:39,823 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:08:39,823 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:08:39,823 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:39,823 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:08:39,823 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:39,823 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:08:39,823 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:39,823 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:39,869 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:39,869 DEBUG: Start: Training
+2016-09-06 10:08:39,870 DEBUG: Info: Time for Training: 0.0494568347931[s]
+2016-09-06 10:08:39,870 DEBUG: Done: Training
+2016-09-06 10:08:39,870 DEBUG: Start: Predicting
+2016-09-06 10:08:39,881 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:39,881 DEBUG: Start: Training
+2016-09-06 10:08:39,897 DEBUG: Done: Predicting
+2016-09-06 10:08:39,897 DEBUG: Start: Getting Results
+2016-09-06 10:08:39,899 DEBUG: Done: Getting Results
+2016-09-06 10:08:39,899 INFO: Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.533333333333
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:39,899 INFO: Done: Result Analysis
+2016-09-06 10:08:39,903 DEBUG: Info: Time for Training: 0.0822620391846[s]
+2016-09-06 10:08:39,904 DEBUG: Done: Training
+2016-09-06 10:08:39,904 DEBUG: Start: Predicting
+2016-09-06 10:08:39,907 DEBUG: Done: Predicting
+2016-09-06 10:08:39,907 DEBUG: Start: Getting Results
+2016-09-06 10:08:39,908 DEBUG: Done: Getting Results
+2016-09-06 10:08:39,909 INFO: Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.52380952381
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7580
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.52380952381
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:39,909 INFO: Done: Result Analysis
+2016-09-06 10:08:39,972 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:39,972 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:39,972 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:08:39,972 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:08:39,973 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:39,973 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:39,973 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:08:39,973 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:08:39,974 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:08:39,974 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:08:39,974 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:39,974 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:39,974 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:39,974 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:40,022 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:40,022 DEBUG: Start: Training
+2016-09-06 10:08:40,026 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:40,026 DEBUG: Start: Training
+2016-09-06 10:08:40,039 DEBUG: Info: Time for Training: 0.0676848888397[s]
+2016-09-06 10:08:40,039 DEBUG: Done: Training
+2016-09-06 10:08:40,039 DEBUG: Start: Predicting
+2016-09-06 10:08:40,043 DEBUG: Info: Time for Training: 0.0717689990997[s]
+2016-09-06 10:08:40,043 DEBUG: Done: Training
+2016-09-06 10:08:40,043 DEBUG: Start: Predicting
+2016-09-06 10:08:40,045 DEBUG: Done: Predicting
+2016-09-06 10:08:40,045 DEBUG: Start: Getting Results
+2016-09-06 10:08:40,047 DEBUG: Done: Getting Results
+2016-09-06 10:08:40,047 INFO: Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.411111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7580
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:40,047 INFO: Done: Result Analysis
+2016-09-06 10:08:40,047 DEBUG: Done: Predicting
+2016-09-06 10:08:40,047 DEBUG: Start: Getting Results
+2016-09-06 10:08:40,049 DEBUG: Done: Getting Results
+2016-09-06 10:08:40,049 INFO: Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7580
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:40,049 INFO: Done: Result Analysis
+2016-09-06 10:08:40,117 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:40,117 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:40,117 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:08:40,117 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:08:40,117 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:40,117 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:40,118 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:08:40,118 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:08:40,118 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:08:40,118 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:08:40,118 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:40,118 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:40,118 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:40,118 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:40,164 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:40,164 DEBUG: Start: Training
+2016-09-06 10:08:40,167 DEBUG: Info: Time for Training: 0.0504739284515[s]
+2016-09-06 10:08:40,167 DEBUG: Done: Training
+2016-09-06 10:08:40,167 DEBUG: Start: Predicting
+2016-09-06 10:08:40,170 DEBUG: Done: Predicting
+2016-09-06 10:08:40,170 DEBUG: Start: Getting Results
+2016-09-06 10:08:40,171 DEBUG: Done: Getting Results
+2016-09-06 10:08:40,171 INFO: Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.444444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 28
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:40,171 INFO: Done: Result Analysis
+2016-09-06 10:08:40,175 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:40,175 DEBUG: Start: Training
+2016-09-06 10:08:40,180 DEBUG: Info: Time for Training: 0.0639250278473[s]
+2016-09-06 10:08:40,181 DEBUG: Done: Training
+2016-09-06 10:08:40,181 DEBUG: Start: Predicting
+2016-09-06 10:08:40,184 DEBUG: Done: Predicting
+2016-09-06 10:08:40,184 DEBUG: Start: Getting Results
+2016-09-06 10:08:40,186 DEBUG: Done: Getting Results
+2016-09-06 10:08:40,186 INFO: Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:40,186 INFO: Done: Result Analysis
+2016-09-06 10:08:40,268 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:40,268 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:40,268 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:08:40,268 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:08:40,268 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:40,268 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:40,269 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:08:40,269 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:08:40,269 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:08:40,269 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:08:40,269 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:40,269 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:40,270 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:40,270 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:40,303 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:40,303 DEBUG: Start: Training
+2016-09-06 10:08:40,303 DEBUG: Info: Time for Training: 0.0363190174103[s]
+2016-09-06 10:08:40,303 DEBUG: Done: Training
+2016-09-06 10:08:40,304 DEBUG: Start: Predicting
+2016-09-06 10:08:40,312 DEBUG: Done: Predicting
+2016-09-06 10:08:40,312 DEBUG: Start: Getting Results
+2016-09-06 10:08:40,313 DEBUG: Done: Getting Results
+2016-09-06 10:08:40,313 INFO: Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.571428571429
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 28
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.571428571429
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:40,314 INFO: Done: Result Analysis
+2016-09-06 10:08:40,540 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:40,540 DEBUG: Start: Training
+2016-09-06 10:08:40,580 DEBUG: Info: Time for Training: 0.312906980515[s]
+2016-09-06 10:08:40,580 DEBUG: Done: Training
+2016-09-06 10:08:40,580 DEBUG: Start: Predicting
+2016-09-06 10:08:40,586 DEBUG: Done: Predicting
+2016-09-06 10:08:40,586 DEBUG: Start: Getting Results
+2016-09-06 10:08:40,587 DEBUG: Done: Getting Results
+2016-09-06 10:08:40,587 INFO: Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 14, max_depth : 28
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.995238095238
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:40,587 INFO: Done: Result Analysis
+2016-09-06 10:08:40,724 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:40,724 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:40,725 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:08:40,725 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:08:40,725 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:40,725 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:40,726 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:08:40,726 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:08:40,726 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:08:40,726 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:08:40,726 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:40,726 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:40,726 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:40,726 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:40,774 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:40,774 DEBUG: Start: Training
+2016-09-06 10:08:40,775 DEBUG: Info: Time for Training: 0.0510659217834[s]
+2016-09-06 10:08:40,775 DEBUG: Done: Training
+2016-09-06 10:08:40,775 DEBUG: Start: Predicting
+2016-09-06 10:08:40,779 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:40,779 DEBUG: Start: Training
+2016-09-06 10:08:40,801 DEBUG: Done: Predicting
+2016-09-06 10:08:40,801 DEBUG: Start: Getting Results
+2016-09-06 10:08:40,803 DEBUG: Done: Getting Results
+2016-09-06 10:08:40,804 INFO: Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.533333333333
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:40,804 INFO: Done: Result Analysis
+2016-09-06 10:08:40,810 DEBUG: Info: Time for Training: 0.0859551429749[s]
+2016-09-06 10:08:40,810 DEBUG: Done: Training
+2016-09-06 10:08:40,810 DEBUG: Start: Predicting
+2016-09-06 10:08:40,816 DEBUG: Done: Predicting
+2016-09-06 10:08:40,816 DEBUG: Start: Getting Results
+2016-09-06 10:08:40,818 DEBUG: Done: Getting Results
+2016-09-06 10:08:40,818 INFO: Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7580
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.533333333333
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:40,818 INFO: Done: Result Analysis
+2016-09-06 10:08:40,981 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:40,981 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:40,981 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:08:40,981 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:08:40,981 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:40,981 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:40,982 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:08:40,982 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:08:40,982 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:08:40,982 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:08:40,982 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:40,982 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:40,982 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:40,982 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:41,039 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:41,039 DEBUG: Start: Training
+2016-09-06 10:08:41,041 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:41,041 DEBUG: Start: Training
+2016-09-06 10:08:41,060 DEBUG: Info: Time for Training: 0.0792870521545[s]
+2016-09-06 10:08:41,060 DEBUG: Done: Training
+2016-09-06 10:08:41,060 DEBUG: Start: Predicting
+2016-09-06 10:08:41,062 DEBUG: Info: Time for Training: 0.0816400051117[s]
+2016-09-06 10:08:41,062 DEBUG: Done: Training
+2016-09-06 10:08:41,062 DEBUG: Start: Predicting
+2016-09-06 10:08:41,066 DEBUG: Done: Predicting
+2016-09-06 10:08:41,067 DEBUG: Start: Getting Results
+2016-09-06 10:08:41,067 DEBUG: Done: Predicting
+2016-09-06 10:08:41,067 DEBUG: Start: Getting Results
+2016-09-06 10:08:41,068 DEBUG: Done: Getting Results
+2016-09-06 10:08:41,068 INFO: Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7580
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:41,068 INFO: Done: Result Analysis
+2016-09-06 10:08:41,069 DEBUG: Done: Getting Results
+2016-09-06 10:08:41,069 INFO: Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.433333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7580
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:41,069 INFO: Done: Result Analysis
+2016-09-06 10:08:41,127 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:41,128 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:08:41,128 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:41,128 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
+2016-09-06 10:08:41,129 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
+2016-09-06 10:08:41,129 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:41,129 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:41,129 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:41,129 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:08:41,129 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:41,130 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
+2016-09-06 10:08:41,130 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
+2016-09-06 10:08:41,130 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:41,130 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:41,179 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:41,179 DEBUG: Start: Training
+2016-09-06 10:08:41,181 DEBUG: Info: Time for Training: 0.0534510612488[s]
+2016-09-06 10:08:41,182 DEBUG: Done: Training
+2016-09-06 10:08:41,182 DEBUG: Start: Predicting
+2016-09-06 10:08:41,186 DEBUG: Done: Predicting
+2016-09-06 10:08:41,186 DEBUG: Start: Getting Results
+2016-09-06 10:08:41,188 DEBUG: Done: Getting Results
+2016-09-06 10:08:41,188 INFO: Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 15)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 28
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:41,188 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:41,188 DEBUG: Start: Training
+2016-09-06 10:08:41,188 INFO: Done: Result Analysis
+2016-09-06 10:08:41,193 DEBUG: Info: Time for Training: 0.0664410591125[s]
+2016-09-06 10:08:41,193 DEBUG: Done: Training
+2016-09-06 10:08:41,194 DEBUG: Start: Predicting
+2016-09-06 10:08:41,197 DEBUG: Done: Predicting
+2016-09-06 10:08:41,197 DEBUG: Start: Getting Results
+2016-09-06 10:08:41,199 DEBUG: Done: Getting Results
+2016-09-06 10:08:41,199 INFO: Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 15)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:41,199 INFO: Done: Result Analysis
+2016-09-06 10:08:41,275 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:41,275 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:41,276 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:08:41,276 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:08:41,276 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:41,276 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:41,276 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
+2016-09-06 10:08:41,276 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
+2016-09-06 10:08:41,276 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
+2016-09-06 10:08:41,276 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
+2016-09-06 10:08:41,277 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:41,277 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:41,277 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:41,277 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:41,312 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:41,312 DEBUG: Start: Training
+2016-09-06 10:08:41,313 DEBUG: Info: Time for Training: 0.0382509231567[s]
+2016-09-06 10:08:41,313 DEBUG: Done: Training
+2016-09-06 10:08:41,313 DEBUG: Start: Predicting
+2016-09-06 10:08:41,320 DEBUG: Done: Predicting
+2016-09-06 10:08:41,321 DEBUG: Start: Getting Results
+2016-09-06 10:08:41,322 DEBUG: Done: Getting Results
+2016-09-06 10:08:41,322 INFO: Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.566666666667
+accuracy_score on test : 0.411111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 15)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 28
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.566666666667
+ - Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:41,322 INFO: Done: Result Analysis
+2016-09-06 10:08:41,539 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:41,539 DEBUG: Start: Training
+2016-09-06 10:08:41,581 DEBUG: Info: Time for Training: 0.306406974792[s]
+2016-09-06 10:08:41,581 DEBUG: Done: Training
+2016-09-06 10:08:41,581 DEBUG: Start: Predicting
+2016-09-06 10:08:41,587 DEBUG: Done: Predicting
+2016-09-06 10:08:41,587 DEBUG: Start: Getting Results
+2016-09-06 10:08:41,589 DEBUG: Done: Getting Results
+2016-09-06 10:08:41,589 INFO: Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.985714285714
+accuracy_score on test : 0.411111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 15)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 14, max_depth : 28
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.985714285714
+ - Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:41,589 INFO: Done: Result Analysis
+2016-09-06 10:08:41,723 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:41,723 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:41,723 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:08:41,723 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:08:41,723 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:41,723 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:41,724 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
+2016-09-06 10:08:41,724 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
+2016-09-06 10:08:41,724 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
+2016-09-06 10:08:41,724 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
+2016-09-06 10:08:41,725 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:41,725 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:41,725 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:41,725 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:41,775 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:41,775 DEBUG: Start: Training
+2016-09-06 10:08:41,776 DEBUG: Info: Time for Training: 0.0535860061646[s]
+2016-09-06 10:08:41,776 DEBUG: Done: Training
+2016-09-06 10:08:41,776 DEBUG: Start: Predicting
+2016-09-06 10:08:41,780 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:41,780 DEBUG: Start: Training
+2016-09-06 10:08:41,790 DEBUG: Done: Predicting
+2016-09-06 10:08:41,790 DEBUG: Start: Getting Results
+2016-09-06 10:08:41,793 DEBUG: Done: Getting Results
+2016-09-06 10:08:41,793 INFO: Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 15)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.533333333333
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:41,794 INFO: Done: Result Analysis
+2016-09-06 10:08:41,807 DEBUG: Info: Time for Training: 0.0841271877289[s]
+2016-09-06 10:08:41,807 DEBUG: Done: Training
+2016-09-06 10:08:41,807 DEBUG: Start: Predicting
+2016-09-06 10:08:41,810 DEBUG: Done: Predicting
+2016-09-06 10:08:41,810 DEBUG: Start: Getting Results
+2016-09-06 10:08:41,812 DEBUG: Done: Getting Results
+2016-09-06 10:08:41,812 INFO: Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.585714285714
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 15)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7580
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.585714285714
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:41,812 INFO: Done: Result Analysis
+2016-09-06 10:08:41,874 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:41,874 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:41,874 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:08:41,874 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:08:41,874 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:41,874 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:41,875 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
+2016-09-06 10:08:41,875 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
+2016-09-06 10:08:41,875 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
+2016-09-06 10:08:41,875 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
+2016-09-06 10:08:41,875 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:41,875 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:41,876 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:41,876 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:41,927 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:41,927 DEBUG: Start: Training
+2016-09-06 10:08:41,935 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:41,935 DEBUG: Start: Training
+2016-09-06 10:08:41,945 DEBUG: Info: Time for Training: 0.0712940692902[s]
+2016-09-06 10:08:41,945 DEBUG: Done: Training
+2016-09-06 10:08:41,945 DEBUG: Start: Predicting
+2016-09-06 10:08:41,951 DEBUG: Done: Predicting
+2016-09-06 10:08:41,951 DEBUG: Start: Getting Results
+2016-09-06 10:08:41,952 DEBUG: Done: Getting Results
+2016-09-06 10:08:41,952 INFO: Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 15)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7580
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:41,953 INFO: Done: Result Analysis
+2016-09-06 10:08:41,953 DEBUG: Info: Time for Training: 0.079491853714[s]
+2016-09-06 10:08:41,953 DEBUG: Done: Training
+2016-09-06 10:08:41,953 DEBUG: Start: Predicting
+2016-09-06 10:08:41,957 DEBUG: Done: Predicting
+2016-09-06 10:08:41,957 DEBUG: Start: Getting Results
+2016-09-06 10:08:41,959 DEBUG: Done: Getting Results
+2016-09-06 10:08:41,959 INFO: Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 15)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7580
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:41,959 INFO: Done: Result Analysis
+2016-09-06 10:08:42,026 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:42,026 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:42,026 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:08:42,026 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:08:42,027 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:42,027 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:42,027 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:08:42,028 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:08:42,028 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:08:42,028 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:08:42,028 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:42,028 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:42,028 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:42,028 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:42,066 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:42,066 DEBUG: Start: Training
+2016-09-06 10:08:42,067 DEBUG: Info: Time for Training: 0.0417790412903[s]
+2016-09-06 10:08:42,068 DEBUG: Done: Training
+2016-09-06 10:08:42,068 DEBUG: Start: Predicting
+2016-09-06 10:08:42,071 DEBUG: Done: Predicting
+2016-09-06 10:08:42,071 DEBUG: Start: Getting Results
+2016-09-06 10:08:42,072 DEBUG: Done: Getting Results
+2016-09-06 10:08:42,072 INFO: Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 28
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:42,073 INFO: Done: Result Analysis
+2016-09-06 10:08:42,084 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:42,084 DEBUG: Start: Training
+2016-09-06 10:08:42,088 DEBUG: Info: Time for Training: 0.0621321201324[s]
+2016-09-06 10:08:42,088 DEBUG: Done: Training
+2016-09-06 10:08:42,088 DEBUG: Start: Predicting
+2016-09-06 10:08:42,091 DEBUG: Done: Predicting
+2016-09-06 10:08:42,091 DEBUG: Start: Getting Results
+2016-09-06 10:08:42,093 DEBUG: Done: Getting Results
+2016-09-06 10:08:42,093 INFO: Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:42,093 INFO: Done: Result Analysis
+2016-09-06 10:08:42,174 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:42,174 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:42,174 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:08:42,174 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:42,174 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:08:42,174 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:42,175 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:08:42,175 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:08:42,175 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:08:42,175 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:08:42,175 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:42,175 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:42,175 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:42,175 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:42,208 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:42,208 DEBUG: Start: Training
+2016-09-06 10:08:42,209 DEBUG: Info: Time for Training: 0.0357630252838[s]
+2016-09-06 10:08:42,209 DEBUG: Done: Training
+2016-09-06 10:08:42,209 DEBUG: Start: Predicting
+2016-09-06 10:08:42,215 DEBUG: Done: Predicting
+2016-09-06 10:08:42,215 DEBUG: Start: Getting Results
+2016-09-06 10:08:42,216 DEBUG: Done: Getting Results
+2016-09-06 10:08:42,216 INFO: Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.509523809524
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 28
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.509523809524
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:42,217 INFO: Done: Result Analysis
+2016-09-06 10:08:42,450 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:42,451 DEBUG: Start: Training
+2016-09-06 10:08:42,492 DEBUG: Info: Time for Training: 0.318718194962[s]
+2016-09-06 10:08:42,492 DEBUG: Done: Training
+2016-09-06 10:08:42,492 DEBUG: Start: Predicting
+2016-09-06 10:08:42,498 DEBUG: Done: Predicting
+2016-09-06 10:08:42,498 DEBUG: Start: Getting Results
+2016-09-06 10:08:42,500 DEBUG: Done: Getting Results
+2016-09-06 10:08:42,500 INFO: Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.980952380952
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 14, max_depth : 28
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.980952380952
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:42,500 INFO: Done: Result Analysis
+2016-09-06 10:08:42,623 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:42,624 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:08:42,624 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:42,625 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:08:42,625 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:08:42,625 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:42,625 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:42,625 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:42,626 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:08:42,626 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:42,627 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:08:42,627 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:08:42,627 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:42,627 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:42,673 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:42,674 DEBUG: Start: Training
+2016-09-06 10:08:42,674 DEBUG: Info: Time for Training: 0.0515320301056[s]
+2016-09-06 10:08:42,675 DEBUG: Done: Training
+2016-09-06 10:08:42,675 DEBUG: Start: Predicting
+2016-09-06 10:08:42,682 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:42,683 DEBUG: Start: Training
+2016-09-06 10:08:42,690 DEBUG: Done: Predicting
+2016-09-06 10:08:42,690 DEBUG: Start: Getting Results
+2016-09-06 10:08:42,692 DEBUG: Done: Getting Results
+2016-09-06 10:08:42,692 INFO: Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.533333333333
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:42,693 INFO: Done: Result Analysis
+2016-09-06 10:08:42,707 DEBUG: Info: Time for Training: 0.0825428962708[s]
+2016-09-06 10:08:42,707 DEBUG: Done: Training
+2016-09-06 10:08:42,707 DEBUG: Start: Predicting
+2016-09-06 10:08:42,710 DEBUG: Done: Predicting
+2016-09-06 10:08:42,710 DEBUG: Start: Getting Results
+2016-09-06 10:08:42,711 DEBUG: Done: Getting Results
+2016-09-06 10:08:42,712 INFO: Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.509523809524
+accuracy_score on test : 0.566666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7580
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.509523809524
+ - Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:42,712 INFO: Done: Result Analysis
+2016-09-06 10:08:42,917 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:08:42,918 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-09-06 10:08:42,919 INFO: Info: Shape of View0 :(300, 13)
+2016-09-06 10:08:42,919 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:08:42,920 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-06 10:08:42,920 INFO: Info: Shape of View1 :(300, 19)
+2016-09-06 10:08:42,921 INFO: Info: Shape of View0 :(300, 13)
+2016-09-06 10:08:42,921 INFO: Info: Shape of View2 :(300, 15)
+2016-09-06 10:08:42,921 INFO: Info: Shape of View1 :(300, 19)
+2016-09-06 10:08:42,922 INFO: Info: Shape of View3 :(300, 9)
+2016-09-06 10:08:42,922 INFO: Done: Read Database Files
+2016-09-06 10:08:42,922 INFO: Start: Determine validation split for ratio 0.7
+2016-09-06 10:08:42,922 INFO: Info: Shape of View2 :(300, 15)
+2016-09-06 10:08:42,923 INFO: Info: Shape of View3 :(300, 9)
+2016-09-06 10:08:42,923 INFO: Done: Read Database Files
+2016-09-06 10:08:42,924 INFO: Start: Determine validation split for ratio 0.7
+2016-09-06 10:08:42,931 INFO: Done: Determine validation split
+2016-09-06 10:08:42,931 INFO: Start: Determine 5 folds
+2016-09-06 10:08:42,932 INFO: Done: Determine validation split
+2016-09-06 10:08:42,932 INFO: Start: Determine 5 folds
+2016-09-06 10:08:42,944 INFO: Info: Length of Learning Sets: 168
+2016-09-06 10:08:42,945 INFO: Info: Length of Testing Sets: 42
+2016-09-06 10:08:42,945 INFO: Info: Length of Validation Set: 90
+2016-09-06 10:08:42,945 INFO: Done: Determine folds
+2016-09-06 10:08:42,945 INFO: Info: Length of Learning Sets: 168
+2016-09-06 10:08:42,945 INFO: Start: Learning with Fusion and 5 folds
+2016-09-06 10:08:42,945 INFO: Info: Length of Testing Sets: 42
+2016-09-06 10:08:42,945 INFO: Info: Length of Validation Set: 90
+2016-09-06 10:08:42,945 INFO: Start: Classification
+2016-09-06 10:08:42,945 INFO: Done: Determine folds
+2016-09-06 10:08:42,945 INFO: Start: Fold number 1
+2016-09-06 10:08:42,946 INFO: Start: Learning with Mumbo and 5 folds
+2016-09-06 10:08:42,946 INFO: Start: Classification
+2016-09-06 10:08:42,946 INFO: Start: Fold number 1
+2016-09-06 10:08:43,005 DEBUG: Start: Iteration 1
+2016-09-06 10:08:43,014 DEBUG: View 0 : 0.463687150838
+2016-09-06 10:08:43,022 DEBUG: View 1 : 0.530726256983
+2016-09-06 10:08:43,029 DEBUG: View 2 : 0.541899441341
+2016-09-06 10:08:43,037 DEBUG: View 3 : 0.513966480447
+2016-09-06 10:08:43,071 DEBUG: Best view : View3
+2016-09-06 10:08:43,154 DEBUG: Start: Iteration 2
+2016-09-06 10:08:43,161 DEBUG: View 0 : 0.653631284916
+2016-09-06 10:08:43,170 DEBUG: View 1 : 0.720670391061
+2016-09-06 10:08:43,178 DEBUG: View 2 : 0.720670391061
+2016-09-06 10:08:43,186 DEBUG: View 3 : 0.698324022346
+2016-09-06 10:08:43,226 DEBUG: Best view : View1
+2016-09-06 10:08:43,378 DEBUG: Start: Iteration 3
+2016-09-06 10:08:43,394 DEBUG: View 0 : 0.653631284916
+2016-09-06 10:08:43,405 DEBUG: View 1 : 0.720670391061
+2016-09-06 10:08:43,413 DEBUG: View 2 : 0.720670391061
+2016-09-06 10:08:43,420 DEBUG: View 3 : 0.698324022346
+2016-09-06 10:08:43,462 DEBUG: Best view : View1
+2016-09-06 10:08:43,721 INFO: Start: Classification
+2016-09-06 10:08:44,142 INFO: Done: Fold number 1
+2016-09-06 10:08:44,143 INFO: Start: Fold number 2
+2016-09-06 10:08:44,175 DEBUG: Start: Iteration 1
+2016-09-06 10:08:44,183 DEBUG: View 0 : 0.530386740331
+2016-09-06 10:08:44,191 DEBUG: View 1 : 0.591160220994
+2016-09-06 10:08:44,199 DEBUG: View 2 : 0.53591160221
+2016-09-06 10:08:44,206 DEBUG: View 3 : 0.491712707182
+2016-09-06 10:08:44,241 DEBUG: Best view : View1
+2016-09-06 10:08:44,326 DEBUG: Start: Iteration 2
+2016-09-06 10:08:44,334 DEBUG: View 0 : 0.718232044199
+2016-09-06 10:08:44,342 DEBUG: View 1 : 0.696132596685
+2016-09-06 10:08:44,350 DEBUG: View 2 : 0.674033149171
+2016-09-06 10:08:44,358 DEBUG: View 3 : 0.635359116022
+2016-09-06 10:08:44,410 DEBUG: Best view : View0
+2016-09-06 10:08:44,581 DEBUG: Start: Iteration 3
+2016-09-06 10:08:44,589 DEBUG: View 0 : 0.718232044199
+2016-09-06 10:08:44,596 DEBUG: View 1 : 0.696132596685
+2016-09-06 10:08:44,604 DEBUG: View 2 : 0.674033149171
+2016-09-06 10:08:44,611 DEBUG: View 3 : 0.635359116022
+2016-09-06 10:08:44,652 DEBUG: Best view : View0
+2016-09-06 10:08:44,871 INFO: Start: Classification
+2016-09-06 10:08:45,230 INFO: Done: Fold number 2
+2016-09-06 10:08:45,230 INFO: Start: Fold number 3
+2016-09-06 10:08:45,259 DEBUG: Start: Iteration 1
+2016-09-06 10:08:45,267 DEBUG: View 0 : 0.505617977528
+2016-09-06 10:08:45,274 DEBUG: View 1 : 0.522471910112
+2016-09-06 10:08:45,281 DEBUG: View 2 : 0.516853932584
+2016-09-06 10:08:45,288 DEBUG: View 3 : 0.52808988764
+2016-09-06 10:08:45,320 DEBUG: Best view : View1
+2016-09-06 10:08:45,399 DEBUG: Start: Iteration 2
+2016-09-06 10:08:45,406 DEBUG: View 0 : 0.629213483146
+2016-09-06 10:08:45,414 DEBUG: View 1 : 0.724719101124
+2016-09-06 10:08:45,421 DEBUG: View 2 : 0.691011235955
+2016-09-06 10:08:45,429 DEBUG: View 3 : 0.629213483146
+2016-09-06 10:08:45,467 DEBUG: Best view : View1
+2016-09-06 10:08:45,616 DEBUG: Start: Iteration 3
+2016-09-06 10:08:45,624 DEBUG: View 0 : 0.629213483146
+2016-09-06 10:08:45,631 DEBUG: View 1 : 0.724719101124
+2016-09-06 10:08:45,639 DEBUG: View 2 : 0.691011235955
+2016-09-06 10:08:45,646 DEBUG: View 3 : 0.629213483146
+2016-09-06 10:08:45,686 DEBUG: Best view : View1
+2016-09-06 10:08:45,910 DEBUG: Start: Iteration 4
+2016-09-06 10:08:45,918 DEBUG: View 0 : 0.623595505618
+2016-09-06 10:08:45,926 DEBUG: View 1 : 0.662921348315
+2016-09-06 10:08:45,934 DEBUG: View 2 : 0.651685393258
+2016-09-06 10:08:45,942 DEBUG: View 3 : 0.623595505618
+2016-09-06 10:08:45,986 DEBUG: Best view : View0
+2016-09-06 10:08:46,283 INFO: Start: Classification
+2016-09-06 10:08:46,944 INFO: Done: Fold number 3
+2016-09-06 10:08:46,944 INFO: Start: Fold number 4
+2016-09-06 10:08:46,983 DEBUG: Start: Iteration 1
+2016-09-06 10:08:46,993 DEBUG: View 0 : 0.56043956044
+2016-09-06 10:08:47,001 DEBUG: View 1 : 0.521978021978
+2016-09-06 10:08:47,010 DEBUG: View 2 : 0.554945054945
+2016-09-06 10:08:47,018 DEBUG: View 3 : 0.521978021978
+2016-09-06 10:08:47,055 DEBUG: Best view : View1
+2016-09-06 10:08:47,142 DEBUG: Start: Iteration 2
+2016-09-06 10:08:47,150 DEBUG: View 0 : 0.67032967033
+2016-09-06 10:08:47,158 DEBUG: View 1 : 0.736263736264
+2016-09-06 10:08:47,167 DEBUG: View 2 : 0.681318681319
+2016-09-06 10:08:47,175 DEBUG: View 3 : 0.714285714286
+2016-09-06 10:08:47,218 DEBUG: Best view : View1
+2016-09-06 10:08:47,372 DEBUG: Start: Iteration 3
+2016-09-06 10:08:47,380 DEBUG: View 0 : 0.67032967033
+2016-09-06 10:08:47,388 DEBUG: View 1 : 0.736263736264
+2016-09-06 10:08:47,395 DEBUG: View 2 : 0.681318681319
+2016-09-06 10:08:47,402 DEBUG: View 3 : 0.714285714286
+2016-09-06 10:08:47,445 DEBUG: Best view : View1
+2016-09-06 10:08:47,666 DEBUG: Start: Iteration 4
+2016-09-06 10:08:47,674 DEBUG: View 0 : 0.549450549451
+2016-09-06 10:08:47,682 DEBUG: View 1 : 0.763736263736
+2016-09-06 10:08:47,690 DEBUG: View 2 : 0.598901098901
+2016-09-06 10:08:47,697 DEBUG: View 3 : 0.637362637363
+2016-09-06 10:08:47,741 DEBUG: Best view : View1
+2016-09-06 10:08:48,048 INFO: Start: Classification
+2016-09-06 10:08:48,536 INFO: Done: Fold number 4
+2016-09-06 10:08:48,536 INFO: Start: Fold number 5
+2016-09-06 10:08:48,569 DEBUG: Start: Iteration 1
+2016-09-06 10:08:48,577 DEBUG: View 0 : 0.491712707182
+2016-09-06 10:08:48,584 DEBUG: View 1 : 0.491712707182
+2016-09-06 10:08:48,594 DEBUG: View 2 : 0.491712707182
+2016-09-06 10:08:48,606 DEBUG: View 3 : 0.491712707182
+2016-09-06 10:08:48,606 WARNING: WARNING: All bad for iteration 0
+2016-09-06 10:08:48,657 DEBUG: Best view : View0
+2016-09-06 10:08:48,759 DEBUG: Start: Iteration 2
+2016-09-06 10:08:48,767 DEBUG: View 0 : 0.651933701657
+2016-09-06 10:08:48,777 DEBUG: View 1 : 0.646408839779
+2016-09-06 10:08:48,789 DEBUG: View 2 : 0.662983425414
+2016-09-06 10:08:48,803 DEBUG: View 3 : 0.591160220994
+2016-09-06 10:08:48,867 DEBUG: Best view : View2
+2016-09-06 10:08:49,051 DEBUG: Start: Iteration 3
+2016-09-06 10:08:49,059 DEBUG: View 0 : 0.651933701657
+2016-09-06 10:08:49,068 DEBUG: View 1 : 0.646408839779
+2016-09-06 10:08:49,077 DEBUG: View 2 : 0.662983425414
+2016-09-06 10:08:49,085 DEBUG: View 3 : 0.591160220994
+2016-09-06 10:08:49,135 DEBUG: Best view : View2
+2016-09-06 10:08:49,386 INFO: Start: Classification
+2016-09-06 10:08:49,796 INFO: Done: Fold number 5
+2016-09-06 10:08:49,796 INFO: Done: Classification
+2016-09-06 10:08:49,796 INFO: Info: Time for Classification: 6[s]
+2016-09-06 10:08:49,796 INFO: Start: Result Analysis for Mumbo
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..ddea2b4f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..e1d14880
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 28
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..87eef941
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.5
+accuracy_score on test : 0.444444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 28
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.5
+ - Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..57e7471b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.97619047619
+accuracy_score on test : 0.455555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 14, max_depth : 28
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.97619047619
+ - Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..cbd7b4f1
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.533333333333
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..b91d1651
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.52380952381
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7580
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.52380952381
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..d413cf87
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..c7edd45a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.444444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 28
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..d0be9dd2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.571428571429
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 28
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.571428571429
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..20848fbc
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 14, max_depth : 28
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.995238095238
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..0da52710
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.533333333333
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..d1c231bd
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7580
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.533333333333
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..1d1835ab
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7580
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..3fd73c43
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.411111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7580
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..45bc125e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 15)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..e933492b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 15)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 28
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..30c528bc
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.566666666667
+accuracy_score on test : 0.411111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 15)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 28
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.566666666667
+ - Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..b47c31af
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.985714285714
+accuracy_score on test : 0.411111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 15)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 14, max_depth : 28
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.985714285714
+ - Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..7e8ec934
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 15)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.533333333333
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..5ea49c27
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.585714285714
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 15)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7580
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.585714285714
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..ac93c75f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 15)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7580
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..47e6566c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 15)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7580
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..77221a3d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..e0f6b680
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 28
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..917a2d1c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.509523809524
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 28
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.509523809524
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..84b2e632
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.980952380952
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 14, max_depth : 28
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.980952380952
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..0fabbd85
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.533333333333
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..ba1f9386
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.509523809524
+accuracy_score on test : 0.566666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7580
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.509523809524
+ - Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100853-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-100853-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 00000000..47a28cd4
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100853-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,1363 @@
+2016-09-06 10:08:53,165 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-06 10:08:53,165 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.0001193125 Gbytes /!\
+2016-09-06 10:08:58,177 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-06 10:08:58,179 INFO: Start: Finding all available mono- & multiview algorithms
+2016-09-06 10:08:58,234 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:58,234 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:58,234 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:08:58,234 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:08:58,234 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:58,234 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:58,235 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:08:58,235 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:08:58,235 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:08:58,235 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:08:58,235 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:58,235 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:58,235 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:58,235 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:58,274 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:58,274 DEBUG: Start: Training
+2016-09-06 10:08:58,276 DEBUG: Info: Time for Training: 0.0428988933563[s]
+2016-09-06 10:08:58,276 DEBUG: Done: Training
+2016-09-06 10:08:58,276 DEBUG: Start: Predicting
+2016-09-06 10:08:58,279 DEBUG: Done: Predicting
+2016-09-06 10:08:58,279 DEBUG: Start: Getting Results
+2016-09-06 10:08:58,280 DEBUG: Done: Getting Results
+2016-09-06 10:08:58,280 INFO: Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 27
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:58,281 INFO: Done: Result Analysis
+2016-09-06 10:08:58,288 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:58,288 DEBUG: Start: Training
+2016-09-06 10:08:58,293 DEBUG: Info: Time for Training: 0.0592339038849[s]
+2016-09-06 10:08:58,293 DEBUG: Done: Training
+2016-09-06 10:08:58,293 DEBUG: Start: Predicting
+2016-09-06 10:08:58,295 DEBUG: Done: Predicting
+2016-09-06 10:08:58,296 DEBUG: Start: Getting Results
+2016-09-06 10:08:58,298 DEBUG: Done: Getting Results
+2016-09-06 10:08:58,298 INFO: Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:58,298 INFO: Done: Result Analysis
+2016-09-06 10:08:58,378 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:58,378 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:58,379 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:08:58,379 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:08:58,379 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:58,379 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:58,380 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:08:58,380 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:08:58,380 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:08:58,380 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:08:58,380 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:58,380 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:58,380 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:58,380 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:58,418 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:58,418 DEBUG: Start: Training
+2016-09-06 10:08:58,419 DEBUG: Info: Time for Training: 0.0408041477203[s]
+2016-09-06 10:08:58,419 DEBUG: Done: Training
+2016-09-06 10:08:58,419 DEBUG: Start: Predicting
+2016-09-06 10:08:58,426 DEBUG: Done: Predicting
+2016-09-06 10:08:58,426 DEBUG: Start: Getting Results
+2016-09-06 10:08:58,428 DEBUG: Done: Getting Results
+2016-09-06 10:08:58,428 INFO: Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.585714285714
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 35
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.585714285714
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:58,428 INFO: Done: Result Analysis
+2016-09-06 10:08:58,469 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:58,469 DEBUG: Start: Training
+2016-09-06 10:08:58,478 DEBUG: Info: Time for Training: 0.0997009277344[s]
+2016-09-06 10:08:58,478 DEBUG: Done: Training
+2016-09-06 10:08:58,478 DEBUG: Start: Predicting
+2016-09-06 10:08:58,481 DEBUG: Done: Predicting
+2016-09-06 10:08:58,481 DEBUG: Start: Getting Results
+2016-09-06 10:08:58,483 DEBUG: Done: Getting Results
+2016-09-06 10:08:58,483 INFO: Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.890476190476
+accuracy_score on test : 0.433333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 3, max_depth : 27
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.890476190476
+ - Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:58,483 INFO: Done: Result Analysis
+2016-09-06 10:08:58,633 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:58,633 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:58,634 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:08:58,634 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:08:58,634 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:58,634 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:58,635 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:08:58,635 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:08:58,635 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:08:58,635 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:08:58,635 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:58,635 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:58,635 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:58,635 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:58,686 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:58,686 DEBUG: Start: Training
+2016-09-06 10:08:58,687 DEBUG: Info: Time for Training: 0.0547561645508[s]
+2016-09-06 10:08:58,687 DEBUG: Done: Training
+2016-09-06 10:08:58,687 DEBUG: Start: Predicting
+2016-09-06 10:08:58,692 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:58,692 DEBUG: Start: Training
+2016-09-06 10:08:58,713 DEBUG: Info: Time for Training: 0.0805191993713[s]
+2016-09-06 10:08:58,713 DEBUG: Done: Training
+2016-09-06 10:08:58,713 DEBUG: Done: Predicting
+2016-09-06 10:08:58,713 DEBUG: Start: Predicting
+2016-09-06 10:08:58,713 DEBUG: Start: Getting Results
+2016-09-06 10:08:58,714 DEBUG: Done: Getting Results
+2016-09-06 10:08:58,715 INFO: Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.661904761905
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.661904761905
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:58,715 INFO: Done: Result Analysis
+2016-09-06 10:08:58,717 DEBUG: Done: Predicting
+2016-09-06 10:08:58,717 DEBUG: Start: Getting Results
+2016-09-06 10:08:58,718 DEBUG: Done: Getting Results
+2016-09-06 10:08:58,719 INFO: Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.547619047619
+accuracy_score on test : 0.577777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 830
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.547619047619
+ - Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:58,719 INFO: Done: Result Analysis
+2016-09-06 10:08:58,777 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:58,777 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:58,777 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:08:58,777 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:58,777 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:08:58,777 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:58,778 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:08:58,778 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:08:58,778 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:08:58,778 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:08:58,778 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:58,778 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:58,778 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:58,778 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:58,829 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:58,829 DEBUG: Start: Training
+2016-09-06 10:08:58,838 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:58,838 DEBUG: Start: Training
+2016-09-06 10:08:58,847 DEBUG: Info: Time for Training: 0.0710601806641[s]
+2016-09-06 10:08:58,847 DEBUG: Done: Training
+2016-09-06 10:08:58,847 DEBUG: Start: Predicting
+2016-09-06 10:08:58,854 DEBUG: Done: Predicting
+2016-09-06 10:08:58,854 DEBUG: Start: Getting Results
+2016-09-06 10:08:58,855 DEBUG: Done: Getting Results
+2016-09-06 10:08:58,855 INFO: Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 830
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:58,856 INFO: Done: Result Analysis
+2016-09-06 10:08:58,861 DEBUG: Info: Time for Training: 0.0853440761566[s]
+2016-09-06 10:08:58,861 DEBUG: Done: Training
+2016-09-06 10:08:58,862 DEBUG: Start: Predicting
+2016-09-06 10:08:58,866 DEBUG: Done: Predicting
+2016-09-06 10:08:58,866 DEBUG: Start: Getting Results
+2016-09-06 10:08:58,867 DEBUG: Done: Getting Results
+2016-09-06 10:08:58,867 INFO: Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.566666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 830
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:58,867 INFO: Done: Result Analysis
+2016-09-06 10:08:58,923 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:58,923 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:08:58,923 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:58,924 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:08:58,923 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:58,924 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:08:58,924 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:58,924 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:08:58,924 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:58,924 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:58,925 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:08:58,925 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:08:58,925 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:58,925 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:58,960 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:58,961 DEBUG: Start: Training
+2016-09-06 10:08:58,963 DEBUG: Info: Time for Training: 0.0409200191498[s]
+2016-09-06 10:08:58,963 DEBUG: Done: Training
+2016-09-06 10:08:58,963 DEBUG: Start: Predicting
+2016-09-06 10:08:58,966 DEBUG: Done: Predicting
+2016-09-06 10:08:58,966 DEBUG: Start: Getting Results
+2016-09-06 10:08:58,967 DEBUG: Done: Getting Results
+2016-09-06 10:08:58,967 INFO: Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 27
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:58,968 INFO: Done: Result Analysis
+2016-09-06 10:08:58,981 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:58,981 DEBUG: Start: Training
+2016-09-06 10:08:58,986 DEBUG: Info: Time for Training: 0.0627229213715[s]
+2016-09-06 10:08:58,986 DEBUG: Done: Training
+2016-09-06 10:08:58,986 DEBUG: Start: Predicting
+2016-09-06 10:08:58,989 DEBUG: Done: Predicting
+2016-09-06 10:08:58,989 DEBUG: Start: Getting Results
+2016-09-06 10:08:58,992 DEBUG: Done: Getting Results
+2016-09-06 10:08:58,992 INFO: Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:58,992 INFO: Done: Result Analysis
+2016-09-06 10:08:59,074 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:59,074 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:59,075 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:08:59,075 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:08:59,075 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:59,075 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:59,075 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:08:59,075 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:08:59,076 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:08:59,076 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:08:59,076 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:59,076 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:59,076 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:59,076 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:59,108 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:59,108 DEBUG: Start: Training
+2016-09-06 10:08:59,109 DEBUG: Info: Time for Training: 0.0350739955902[s]
+2016-09-06 10:08:59,109 DEBUG: Done: Training
+2016-09-06 10:08:59,109 DEBUG: Start: Predicting
+2016-09-06 10:08:59,116 DEBUG: Done: Predicting
+2016-09-06 10:08:59,116 DEBUG: Start: Getting Results
+2016-09-06 10:08:59,117 DEBUG: Done: Getting Results
+2016-09-06 10:08:59,117 INFO: Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.561904761905
+accuracy_score on test : 0.433333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 35
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.561904761905
+ - Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:59,117 INFO: Done: Result Analysis
+2016-09-06 10:08:59,170 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:59,170 DEBUG: Start: Training
+2016-09-06 10:08:59,178 DEBUG: Info: Time for Training: 0.10413312912[s]
+2016-09-06 10:08:59,178 DEBUG: Done: Training
+2016-09-06 10:08:59,178 DEBUG: Start: Predicting
+2016-09-06 10:08:59,182 DEBUG: Done: Predicting
+2016-09-06 10:08:59,182 DEBUG: Start: Getting Results
+2016-09-06 10:08:59,184 DEBUG: Done: Getting Results
+2016-09-06 10:08:59,184 INFO: Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.9
+accuracy_score on test : 0.455555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 3, max_depth : 27
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.9
+ - Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:59,184 INFO: Done: Result Analysis
+2016-09-06 10:08:59,324 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:59,324 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:59,324 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:08:59,324 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:08:59,325 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:59,325 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:59,326 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:08:59,326 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:08:59,326 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:08:59,326 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:08:59,326 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:59,326 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:59,326 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:59,326 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:59,396 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:59,396 DEBUG: Start: Training
+2016-09-06 10:08:59,397 DEBUG: Info: Time for Training: 0.074168920517[s]
+2016-09-06 10:08:59,397 DEBUG: Done: Training
+2016-09-06 10:08:59,398 DEBUG: Start: Predicting
+2016-09-06 10:08:59,403 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:59,403 DEBUG: Start: Training
+2016-09-06 10:08:59,421 DEBUG: Done: Predicting
+2016-09-06 10:08:59,421 DEBUG: Start: Getting Results
+2016-09-06 10:08:59,424 DEBUG: Done: Getting Results
+2016-09-06 10:08:59,424 INFO: Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.590476190476
+accuracy_score on test : 0.544444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.590476190476
+ - Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:59,424 INFO: Done: Result Analysis
+2016-09-06 10:08:59,431 DEBUG: Info: Time for Training: 0.107607841492[s]
+2016-09-06 10:08:59,431 DEBUG: Done: Training
+2016-09-06 10:08:59,431 DEBUG: Start: Predicting
+2016-09-06 10:08:59,434 DEBUG: Done: Predicting
+2016-09-06 10:08:59,435 DEBUG: Start: Getting Results
+2016-09-06 10:08:59,436 DEBUG: Done: Getting Results
+2016-09-06 10:08:59,436 INFO: Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.557142857143
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 830
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.557142857143
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:59,436 INFO: Done: Result Analysis
+2016-09-06 10:08:59,571 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:59,571 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:59,571 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:08:59,571 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:08:59,571 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:59,571 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:59,571 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:08:59,571 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:08:59,572 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:08:59,572 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:08:59,572 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:59,572 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:59,572 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:59,572 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:59,620 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:59,620 DEBUG: Start: Training
+2016-09-06 10:08:59,624 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:59,624 DEBUG: Start: Training
+2016-09-06 10:08:59,637 DEBUG: Info: Time for Training: 0.0667371749878[s]
+2016-09-06 10:08:59,637 DEBUG: Done: Training
+2016-09-06 10:08:59,637 DEBUG: Start: Predicting
+2016-09-06 10:08:59,643 DEBUG: Done: Predicting
+2016-09-06 10:08:59,643 DEBUG: Start: Getting Results
+2016-09-06 10:08:59,643 DEBUG: Info: Time for Training: 0.0726878643036[s]
+2016-09-06 10:08:59,643 DEBUG: Done: Training
+2016-09-06 10:08:59,643 DEBUG: Start: Predicting
+2016-09-06 10:08:59,644 DEBUG: Done: Getting Results
+2016-09-06 10:08:59,644 INFO: Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 830
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:59,645 INFO: Done: Result Analysis
+2016-09-06 10:08:59,647 DEBUG: Done: Predicting
+2016-09-06 10:08:59,647 DEBUG: Start: Getting Results
+2016-09-06 10:08:59,649 DEBUG: Done: Getting Results
+2016-09-06 10:08:59,649 INFO: Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 830
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:59,649 INFO: Done: Result Analysis
+2016-09-06 10:08:59,723 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:59,723 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:08:59,723 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:59,724 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:59,724 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:08:59,724 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:59,724 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:08:59,724 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:08:59,724 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:59,725 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:08:59,725 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:59,725 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:08:59,725 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:59,725 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:59,756 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:59,756 DEBUG: Start: Training
+2016-09-06 10:08:59,757 DEBUG: Info: Time for Training: 0.0341780185699[s]
+2016-09-06 10:08:59,757 DEBUG: Done: Training
+2016-09-06 10:08:59,757 DEBUG: Start: Predicting
+2016-09-06 10:08:59,760 DEBUG: Done: Predicting
+2016-09-06 10:08:59,760 DEBUG: Start: Getting Results
+2016-09-06 10:08:59,761 DEBUG: Done: Getting Results
+2016-09-06 10:08:59,761 INFO: Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.344444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 27
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.344444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:59,762 INFO: Done: Result Analysis
+2016-09-06 10:08:59,771 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:59,771 DEBUG: Start: Training
+2016-09-06 10:08:59,774 DEBUG: Info: Time for Training: 0.0514109134674[s]
+2016-09-06 10:08:59,774 DEBUG: Done: Training
+2016-09-06 10:08:59,774 DEBUG: Start: Predicting
+2016-09-06 10:08:59,777 DEBUG: Done: Predicting
+2016-09-06 10:08:59,777 DEBUG: Start: Getting Results
+2016-09-06 10:08:59,779 DEBUG: Done: Getting Results
+2016-09-06 10:08:59,779 INFO: Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.366666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.366666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:59,779 INFO: Done: Result Analysis
+2016-09-06 10:08:59,875 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:59,875 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:59,876 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:08:59,876 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:08:59,877 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:59,877 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:08:59,878 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:08:59,878 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:08:59,878 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:08:59,878 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:08:59,878 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:59,878 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:08:59,878 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:59,878 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:08:59,909 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:59,910 DEBUG: Start: Training
+2016-09-06 10:08:59,910 DEBUG: Info: Time for Training: 0.0356111526489[s]
+2016-09-06 10:08:59,910 DEBUG: Done: Training
+2016-09-06 10:08:59,910 DEBUG: Start: Predicting
+2016-09-06 10:08:59,916 DEBUG: Done: Predicting
+2016-09-06 10:08:59,916 DEBUG: Start: Getting Results
+2016-09-06 10:08:59,918 DEBUG: Done: Getting Results
+2016-09-06 10:08:59,918 INFO: Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.566666666667
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 35
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.566666666667
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:59,918 INFO: Done: Result Analysis
+2016-09-06 10:08:59,962 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:08:59,962 DEBUG: Start: Training
+2016-09-06 10:08:59,971 DEBUG: Info: Time for Training: 0.0960199832916[s]
+2016-09-06 10:08:59,971 DEBUG: Done: Training
+2016-09-06 10:08:59,971 DEBUG: Start: Predicting
+2016-09-06 10:08:59,974 DEBUG: Done: Predicting
+2016-09-06 10:08:59,974 DEBUG: Start: Getting Results
+2016-09-06 10:08:59,975 DEBUG: Done: Getting Results
+2016-09-06 10:08:59,976 INFO: Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.895238095238
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 3, max_depth : 27
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.895238095238
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:59,976 INFO: Done: Result Analysis
+2016-09-06 10:09:00,126 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:09:00,126 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:09:00,127 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:09:00,127 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:09:00,127 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:09:00,127 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:09:00,128 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:09:00,128 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:09:00,128 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:09:00,128 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:09:00,128 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:09:00,128 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:09:00,128 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:09:00,128 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:09:00,198 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:09:00,198 DEBUG: Start: Training
+2016-09-06 10:09:00,199 DEBUG: Info: Time for Training: 0.073979139328[s]
+2016-09-06 10:09:00,199 DEBUG: Done: Training
+2016-09-06 10:09:00,199 DEBUG: Start: Predicting
+2016-09-06 10:09:00,201 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:09:00,201 DEBUG: Start: Training
+2016-09-06 10:09:00,217 DEBUG: Done: Predicting
+2016-09-06 10:09:00,217 DEBUG: Start: Getting Results
+2016-09-06 10:09:00,217 DEBUG: Info: Time for Training: 0.09197306633[s]
+2016-09-06 10:09:00,217 DEBUG: Done: Training
+2016-09-06 10:09:00,217 DEBUG: Start: Predicting
+2016-09-06 10:09:00,219 DEBUG: Done: Getting Results
+2016-09-06 10:09:00,219 INFO: Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.590476190476
+accuracy_score on test : 0.544444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.590476190476
+ - Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:09:00,219 INFO: Done: Result Analysis
+2016-09-06 10:09:00,220 DEBUG: Done: Predicting
+2016-09-06 10:09:00,220 DEBUG: Start: Getting Results
+2016-09-06 10:09:00,221 DEBUG: Done: Getting Results
+2016-09-06 10:09:00,222 INFO: Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 830
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.533333333333
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:09:00,222 INFO: Done: Result Analysis
+2016-09-06 10:09:00,375 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:09:00,375 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:09:00,376 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:09:00,376 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:09:00,376 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:09:00,376 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:09:00,377 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:09:00,377 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:09:00,377 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:09:00,377 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:09:00,378 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:09:00,378 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:09:00,378 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:09:00,378 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:09:00,452 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:09:00,452 DEBUG: Start: Training
+2016-09-06 10:09:00,454 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:09:00,454 DEBUG: Start: Training
+2016-09-06 10:09:00,472 DEBUG: Info: Time for Training: 0.0980281829834[s]
+2016-09-06 10:09:00,472 DEBUG: Done: Training
+2016-09-06 10:09:00,472 DEBUG: Start: Predicting
+2016-09-06 10:09:00,474 DEBUG: Info: Time for Training: 0.100123167038[s]
+2016-09-06 10:09:00,474 DEBUG: Done: Training
+2016-09-06 10:09:00,474 DEBUG: Start: Predicting
+2016-09-06 10:09:00,475 DEBUG: Done: Predicting
+2016-09-06 10:09:00,475 DEBUG: Start: Getting Results
+2016-09-06 10:09:00,477 DEBUG: Done: Getting Results
+2016-09-06 10:09:00,477 INFO: Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 0.519047619048
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 830
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.519047619048
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:09:00,477 INFO: Done: Result Analysis
+2016-09-06 10:09:00,479 DEBUG: Done: Predicting
+2016-09-06 10:09:00,479 DEBUG: Start: Getting Results
+2016-09-06 10:09:00,481 DEBUG: Done: Getting Results
+2016-09-06 10:09:00,481 INFO: Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 830
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:09:00,481 INFO: Done: Result Analysis
+2016-09-06 10:09:00,627 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:09:00,627 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:09:00,627 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:09:00,627 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:09:00,627 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:09:00,627 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:09:00,628 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:09:00,628 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:09:00,629 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:09:00,629 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:09:00,629 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:09:00,629 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:09:00,629 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:09:00,629 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:09:00,678 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:09:00,679 DEBUG: Start: Training
+2016-09-06 10:09:00,680 DEBUG: Info: Time for Training: 0.054713010788[s]
+2016-09-06 10:09:00,681 DEBUG: Done: Training
+2016-09-06 10:09:00,681 DEBUG: Start: Predicting
+2016-09-06 10:09:00,684 DEBUG: Done: Predicting
+2016-09-06 10:09:00,684 DEBUG: Start: Getting Results
+2016-09-06 10:09:00,687 DEBUG: Done: Getting Results
+2016-09-06 10:09:00,687 INFO: Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 27
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:09:00,687 INFO: Done: Result Analysis
+2016-09-06 10:09:00,696 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:09:00,696 DEBUG: Start: Training
+2016-09-06 10:09:00,700 DEBUG: Info: Time for Training: 0.0740790367126[s]
+2016-09-06 10:09:00,700 DEBUG: Done: Training
+2016-09-06 10:09:00,700 DEBUG: Start: Predicting
+2016-09-06 10:09:00,703 DEBUG: Done: Predicting
+2016-09-06 10:09:00,703 DEBUG: Start: Getting Results
+2016-09-06 10:09:00,705 DEBUG: Done: Getting Results
+2016-09-06 10:09:00,705 INFO: Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:09:00,705 INFO: Done: Result Analysis
+2016-09-06 10:09:00,775 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:09:00,775 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:09:00,775 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:09:00,775 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:09:00,775 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:09:00,775 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:09:00,776 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:09:00,776 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:09:00,776 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:09:00,776 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:09:00,776 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:09:00,776 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:09:00,777 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:09:00,777 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:09:00,824 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:09:00,824 DEBUG: Start: Training
+2016-09-06 10:09:00,825 DEBUG: Info: Time for Training: 0.0511150360107[s]
+2016-09-06 10:09:00,825 DEBUG: Done: Training
+2016-09-06 10:09:00,825 DEBUG: Start: Predicting
+2016-09-06 10:09:00,834 DEBUG: Done: Predicting
+2016-09-06 10:09:00,834 DEBUG: Start: Getting Results
+2016-09-06 10:09:00,836 DEBUG: Done: Getting Results
+2016-09-06 10:09:00,836 INFO: Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.561904761905
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 35
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.561904761905
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:09:00,836 INFO: Done: Result Analysis
+2016-09-06 10:09:00,882 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:09:00,882 DEBUG: Start: Training
+2016-09-06 10:09:00,891 DEBUG: Info: Time for Training: 0.116482019424[s]
+2016-09-06 10:09:00,891 DEBUG: Done: Training
+2016-09-06 10:09:00,891 DEBUG: Start: Predicting
+2016-09-06 10:09:00,894 DEBUG: Done: Predicting
+2016-09-06 10:09:00,894 DEBUG: Start: Getting Results
+2016-09-06 10:09:00,895 DEBUG: Done: Getting Results
+2016-09-06 10:09:00,895 INFO: Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.890476190476
+accuracy_score on test : 0.355555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 3, max_depth : 27
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.890476190476
+ - Score on test : 0.355555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:09:00,896 INFO: Done: Result Analysis
+2016-09-06 10:09:01,024 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:09:01,024 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:09:01,025 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:09:01,025 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:09:01,025 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:09:01,025 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:09:01,026 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:09:01,026 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:09:01,026 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:09:01,026 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:09:01,026 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:09:01,026 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:09:01,026 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:09:01,026 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:09:01,095 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:09:01,095 DEBUG: Start: Training
+2016-09-06 10:09:01,096 DEBUG: Info: Time for Training: 0.072772026062[s]
+2016-09-06 10:09:01,096 DEBUG: Done: Training
+2016-09-06 10:09:01,096 DEBUG: Start: Predicting
+2016-09-06 10:09:01,098 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:09:01,098 DEBUG: Start: Training
+2016-09-06 10:09:01,109 DEBUG: Done: Predicting
+2016-09-06 10:09:01,110 DEBUG: Start: Getting Results
+2016-09-06 10:09:01,112 DEBUG: Done: Getting Results
+2016-09-06 10:09:01,112 INFO: Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.552380952381
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.552380952381
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:09:01,112 INFO: Done: Result Analysis
+2016-09-06 10:09:01,118 DEBUG: Info: Time for Training: 0.0949590206146[s]
+2016-09-06 10:09:01,119 DEBUG: Done: Training
+2016-09-06 10:09:01,119 DEBUG: Start: Predicting
+2016-09-06 10:09:01,123 DEBUG: Done: Predicting
+2016-09-06 10:09:01,123 DEBUG: Start: Getting Results
+2016-09-06 10:09:01,124 DEBUG: Done: Getting Results
+2016-09-06 10:09:01,125 INFO: Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.485714285714
+accuracy_score on test : 0.444444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 830
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.485714285714
+ - Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:09:01,125 INFO: Done: Result Analysis
+2016-09-06 10:09:01,423 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:09:01,424 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-09-06 10:09:01,425 INFO: Info: Shape of View0 :(300, 19)
+2016-09-06 10:09:01,425 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:09:01,425 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-06 10:09:01,426 INFO: Info: Shape of View1 :(300, 14)
+2016-09-06 10:09:01,426 INFO: Info: Shape of View0 :(300, 19)
+2016-09-06 10:09:01,426 INFO: Info: Shape of View2 :(300, 5)
+2016-09-06 10:09:01,427 INFO: Info: Shape of View1 :(300, 14)
+2016-09-06 10:09:01,427 INFO: Info: Shape of View3 :(300, 5)
+2016-09-06 10:09:01,427 INFO: Done: Read Database Files
+2016-09-06 10:09:01,428 INFO: Start: Determine validation split for ratio 0.7
+2016-09-06 10:09:01,428 INFO: Info: Shape of View2 :(300, 5)
+2016-09-06 10:09:01,429 INFO: Info: Shape of View3 :(300, 5)
+2016-09-06 10:09:01,429 INFO: Done: Read Database Files
+2016-09-06 10:09:01,429 INFO: Start: Determine validation split for ratio 0.7
+2016-09-06 10:09:01,436 INFO: Done: Determine validation split
+2016-09-06 10:09:01,436 INFO: Start: Determine 5 folds
+2016-09-06 10:09:01,436 INFO: Done: Determine validation split
+2016-09-06 10:09:01,437 INFO: Start: Determine 5 folds
+2016-09-06 10:09:01,450 INFO: Info: Length of Learning Sets: 169
+2016-09-06 10:09:01,450 INFO: Info: Length of Testing Sets: 42
+2016-09-06 10:09:01,450 INFO: Info: Length of Validation Set: 89
+2016-09-06 10:09:01,450 INFO: Info: Length of Learning Sets: 169
+2016-09-06 10:09:01,450 INFO: Done: Determine folds
+2016-09-06 10:09:01,450 INFO: Info: Length of Testing Sets: 42
+2016-09-06 10:09:01,450 INFO: Start: Learning with Fusion and 5 folds
+2016-09-06 10:09:01,450 INFO: Info: Length of Validation Set: 89
+2016-09-06 10:09:01,450 INFO: Start: Classification
+2016-09-06 10:09:01,450 INFO: Done: Determine folds
+2016-09-06 10:09:01,451 INFO: Start: Fold number 1
+2016-09-06 10:09:01,451 INFO: Start: Learning with Mumbo and 5 folds
+2016-09-06 10:09:01,451 INFO: Start: Classification
+2016-09-06 10:09:01,451 INFO: Start: Fold number 1
+2016-09-06 10:09:01,514 DEBUG: Start: Iteration 1
+2016-09-06 10:09:01,528 DEBUG: View 0 : 0.433333333333
+2016-09-06 10:09:01,541 DEBUG: View 1 : 0.577777777778
+2016-09-06 10:09:01,554 DEBUG: View 2 : 0.444444444444
+2016-09-06 10:09:01,566 DEBUG: View 3 : 0.433333333333
+2016-09-06 10:09:01,621 DEBUG: Best view : View0
+2016-09-06 10:09:01,751 DEBUG: Start: Iteration 2
+2016-09-06 10:09:01,765 DEBUG: View 0 : 0.75
+2016-09-06 10:09:01,779 DEBUG: View 1 : 0.622222222222
+2016-09-06 10:09:01,792 DEBUG: View 2 : 0.711111111111
+2016-09-06 10:09:01,805 DEBUG: View 3 : 0.683333333333
+2016-09-06 10:09:01,851 DEBUG: Best view : View0
+2016-09-06 10:09:02,004 DEBUG: Start: Iteration 3
+2016-09-06 10:09:02,013 DEBUG: View 0 : 0.75
+2016-09-06 10:09:02,022 DEBUG: View 1 : 0.622222222222
+2016-09-06 10:09:02,030 DEBUG: View 2 : 0.711111111111
+2016-09-06 10:09:02,037 DEBUG: View 3 : 0.683333333333
+2016-09-06 10:09:02,077 DEBUG: Best view : View0
+2016-09-06 10:09:02,295 DEBUG: Start: Iteration 4
+2016-09-06 10:09:02,303 DEBUG: View 0 : 0.738888888889
+2016-09-06 10:09:02,310 DEBUG: View 1 : 0.666666666667
+2016-09-06 10:09:02,317 DEBUG: View 2 : 0.677777777778
+2016-09-06 10:09:02,324 DEBUG: View 3 : 0.666666666667
+2016-09-06 10:09:02,367 DEBUG: Best view : View0
+2016-09-06 10:09:02,653 INFO: Start: Classification
+2016-09-06 10:09:03,124 INFO: Done: Fold number 1
+2016-09-06 10:09:03,124 INFO: Start: Fold number 2
+2016-09-06 10:09:03,155 DEBUG: Start: Iteration 1
+2016-09-06 10:09:03,162 DEBUG: View 0 : 0.472826086957
+2016-09-06 10:09:03,169 DEBUG: View 1 : 0.559782608696
+2016-09-06 10:09:03,176 DEBUG: View 2 : 0.527173913043
+2016-09-06 10:09:03,183 DEBUG: View 3 : 0.570652173913
+2016-09-06 10:09:03,216 DEBUG: Best view : View2
+2016-09-06 10:09:03,297 DEBUG: Start: Iteration 2
+2016-09-06 10:09:03,305 DEBUG: View 0 : 0.695652173913
+2016-09-06 10:09:03,313 DEBUG: View 1 : 0.625
+2016-09-06 10:09:03,320 DEBUG: View 2 : 0.657608695652
+2016-09-06 10:09:03,327 DEBUG: View 3 : 0.657608695652
+2016-09-06 10:09:03,366 DEBUG: Best view : View0
+2016-09-06 10:09:03,518 DEBUG: Start: Iteration 3
+2016-09-06 10:09:03,526 DEBUG: View 0 : 0.695652173913
+2016-09-06 10:09:03,533 DEBUG: View 1 : 0.625
+2016-09-06 10:09:03,540 DEBUG: View 2 : 0.657608695652
+2016-09-06 10:09:03,548 DEBUG: View 3 : 0.657608695652
+2016-09-06 10:09:03,589 DEBUG: Best view : View0
+2016-09-06 10:09:03,812 INFO: Start: Classification
+2016-09-06 10:09:04,173 INFO: Done: Fold number 2
+2016-09-06 10:09:04,173 INFO: Start: Fold number 3
+2016-09-06 10:09:04,204 DEBUG: Start: Iteration 1
+2016-09-06 10:09:04,211 DEBUG: View 0 : 0.513812154696
+2016-09-06 10:09:04,218 DEBUG: View 1 : 0.480662983425
+2016-09-06 10:09:04,225 DEBUG: View 2 : 0.441988950276
+2016-09-06 10:09:04,232 DEBUG: View 3 : 0.453038674033
+2016-09-06 10:09:04,264 DEBUG: Best view : View0
+2016-09-06 10:09:04,345 DEBUG: Start: Iteration 2
+2016-09-06 10:09:04,353 DEBUG: View 0 : 0.685082872928
+2016-09-06 10:09:04,360 DEBUG: View 1 : 0.662983425414
+2016-09-06 10:09:04,367 DEBUG: View 2 : 0.685082872928
+2016-09-06 10:09:04,374 DEBUG: View 3 : 0.707182320442
+2016-09-06 10:09:04,413 DEBUG: Best view : View3
+2016-09-06 10:09:04,562 DEBUG: Start: Iteration 3
+2016-09-06 10:09:04,569 DEBUG: View 0 : 0.685082872928
+2016-09-06 10:09:04,577 DEBUG: View 1 : 0.662983425414
+2016-09-06 10:09:04,584 DEBUG: View 2 : 0.685082872928
+2016-09-06 10:09:04,591 DEBUG: View 3 : 0.707182320442
+2016-09-06 10:09:04,631 DEBUG: Best view : View3
+2016-09-06 10:09:04,849 INFO: Start: Classification
+2016-09-06 10:09:05,203 INFO: Done: Fold number 3
+2016-09-06 10:09:05,203 INFO: Start: Fold number 4
+2016-09-06 10:09:05,235 DEBUG: Start: Iteration 1
+2016-09-06 10:09:05,242 DEBUG: View 0 : 0.491891891892
+2016-09-06 10:09:05,249 DEBUG: View 1 : 0.47027027027
+2016-09-06 10:09:05,256 DEBUG: View 2 : 0.491891891892
+2016-09-06 10:09:05,263 DEBUG: View 3 : 0.513513513514
+2016-09-06 10:09:05,296 DEBUG: Best view : View1
+2016-09-06 10:09:05,378 DEBUG: Start: Iteration 2
+2016-09-06 10:09:05,385 DEBUG: View 0 : 0.675675675676
+2016-09-06 10:09:05,393 DEBUG: View 1 : 0.686486486486
+2016-09-06 10:09:05,400 DEBUG: View 2 : 0.681081081081
+2016-09-06 10:09:05,407 DEBUG: View 3 : 0.686486486486
+2016-09-06 10:09:05,447 DEBUG: Best view : View1
+2016-09-06 10:09:05,600 DEBUG: Start: Iteration 3
+2016-09-06 10:09:05,608 DEBUG: View 0 : 0.675675675676
+2016-09-06 10:09:05,615 DEBUG: View 1 : 0.686486486486
+2016-09-06 10:09:05,622 DEBUG: View 2 : 0.681081081081
+2016-09-06 10:09:05,629 DEBUG: View 3 : 0.686486486486
+2016-09-06 10:09:05,671 DEBUG: Best view : View1
+2016-09-06 10:09:05,894 INFO: Start: Classification
+2016-09-06 10:09:06,257 INFO: Done: Fold number 4
+2016-09-06 10:09:06,257 INFO: Start: Fold number 5
+2016-09-06 10:09:06,287 DEBUG: Start: Iteration 1
+2016-09-06 10:09:06,295 DEBUG: View 0 : 0.538888888889
+2016-09-06 10:09:06,302 DEBUG: View 1 : 0.461111111111
+2016-09-06 10:09:06,309 DEBUG: View 2 : 0.511111111111
+2016-09-06 10:09:06,316 DEBUG: View 3 : 0.561111111111
+2016-09-06 10:09:06,348 DEBUG: Best view : View0
+2016-09-06 10:09:06,428 DEBUG: Start: Iteration 2
+2016-09-06 10:09:06,435 DEBUG: View 0 : 0.722222222222
+2016-09-06 10:09:06,443 DEBUG: View 1 : 0.638888888889
+2016-09-06 10:09:06,450 DEBUG: View 2 : 0.644444444444
+2016-09-06 10:09:06,457 DEBUG: View 3 : 0.611111111111
+2016-09-06 10:09:06,496 DEBUG: Best view : View0
+2016-09-06 10:09:06,646 DEBUG: Start: Iteration 3
+2016-09-06 10:09:06,654 DEBUG: View 0 : 0.722222222222
+2016-09-06 10:09:06,662 DEBUG: View 1 : 0.638888888889
+2016-09-06 10:09:06,669 DEBUG: View 2 : 0.644444444444
+2016-09-06 10:09:06,676 DEBUG: View 3 : 0.572222222222
+2016-09-06 10:09:06,717 DEBUG: Best view : View0
+2016-09-06 10:09:06,935 INFO: Start: Classification
+2016-09-06 10:09:07,291 INFO: Done: Fold number 5
+2016-09-06 10:09:07,291 INFO: Done: Classification
+2016-09-06 10:09:07,291 INFO: Info: Time for Classification: 5[s]
+2016-09-06 10:09:07,291 INFO: Start: Result Analysis for Mumbo
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..5340a4ef
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..401659ee
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 27
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..2b148b8a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.585714285714
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 35
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.585714285714
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..4370daf7
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.890476190476
+accuracy_score on test : 0.433333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 3, max_depth : 27
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.890476190476
+ - Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..0f779050
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.661904761905
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.661904761905
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..bbd4f050
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.547619047619
+accuracy_score on test : 0.577777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 830
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.547619047619
+ - Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..ea7ee5a0
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.566666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 830
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..238e7f55
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 830
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..a919948f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.366666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.366666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..53b3252b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.344444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 27
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.344444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..71631804
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.566666666667
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 35
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.566666666667
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..5ffc954f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.895238095238
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 3, max_depth : 27
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.895238095238
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..4a7d3a5e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.590476190476
+accuracy_score on test : 0.544444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.590476190476
+ - Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..848051bc
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.557142857143
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 830
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.557142857143
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..f77cf58d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 830
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..2282cfc9
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 830
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..7e19a21f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..0cc72c43
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 27
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..ad6d2c4f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.561904761905
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 35
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.561904761905
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..02ce1d84
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.890476190476
+accuracy_score on test : 0.355555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 3, max_depth : 27
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.890476190476
+ - Score on test : 0.355555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..013f2790
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.590476190476
+accuracy_score on test : 0.544444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.590476190476
+ - Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..2c1b8cde
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 830
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.533333333333
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..52bd8c30
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 0.519047619048
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 830
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.519047619048
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..291885b0
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 830
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100901Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100901Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..722f6d1c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100901Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.552380952381
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.552380952381
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100901Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100901Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..36d5aa61
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100901Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.485714285714
+accuracy_score on test : 0.444444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 830
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.485714285714
+ - Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101330-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-101330-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 00000000..17ec1f47
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101330-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,1375 @@
+2016-09-06 10:13:30,568 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-06 10:13:30,568 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.000133375 Gbytes /!\
+2016-09-06 10:13:35,578 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-06 10:13:35,581 INFO: Start: Finding all available mono- & multiview algorithms
+2016-09-06 10:13:35,639 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:35,640 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:35,640 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:13:35,640 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:13:35,640 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:13:35,640 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:13:35,641 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:13:35,641 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:13:35,641 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:13:35,641 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:13:35,641 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:13:35,641 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:13:35,641 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:13:35,641 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:13:35,678 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:13:35,678 DEBUG: Start: Training
+2016-09-06 10:13:35,680 DEBUG: Info: Time for Training: 0.0411851406097[s]
+2016-09-06 10:13:35,680 DEBUG: Done: Training
+2016-09-06 10:13:35,680 DEBUG: Start: Predicting
+2016-09-06 10:13:35,683 DEBUG: Done: Predicting
+2016-09-06 10:13:35,683 DEBUG: Start: Getting Results
+2016-09-06 10:13:35,684 DEBUG: Done: Getting Results
+2016-09-06 10:13:35,684 INFO: Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 16
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:35,685 INFO: Done: Result Analysis
+2016-09-06 10:13:35,692 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:13:35,692 DEBUG: Start: Training
+2016-09-06 10:13:35,696 DEBUG: Info: Time for Training: 0.0574531555176[s]
+2016-09-06 10:13:35,696 DEBUG: Done: Training
+2016-09-06 10:13:35,696 DEBUG: Start: Predicting
+2016-09-06 10:13:35,699 DEBUG: Done: Predicting
+2016-09-06 10:13:35,700 DEBUG: Start: Getting Results
+2016-09-06 10:13:35,701 DEBUG: Done: Getting Results
+2016-09-06 10:13:35,701 INFO: Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:35,702 INFO: Done: Result Analysis
+2016-09-06 10:13:35,786 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:35,786 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:35,786 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:13:35,786 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:13:35,786 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:13:35,786 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:13:35,787 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:13:35,787 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:13:35,787 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:13:35,787 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:13:35,787 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:13:35,788 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:13:35,788 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:13:35,788 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:13:35,820 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:13:35,820 DEBUG: Start: Training
+2016-09-06 10:13:35,821 DEBUG: Info: Time for Training: 0.0358920097351[s]
+2016-09-06 10:13:35,821 DEBUG: Done: Training
+2016-09-06 10:13:35,821 DEBUG: Start: Predicting
+2016-09-06 10:13:35,828 DEBUG: Done: Predicting
+2016-09-06 10:13:35,829 DEBUG: Start: Getting Results
+2016-09-06 10:13:35,831 DEBUG: Done: Getting Results
+2016-09-06 10:13:35,831 INFO: Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.557142857143
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 48
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.557142857143
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:35,831 INFO: Done: Result Analysis
+2016-09-06 10:13:35,871 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:13:35,871 DEBUG: Start: Training
+2016-09-06 10:13:35,880 DEBUG: Info: Time for Training: 0.094918012619[s]
+2016-09-06 10:13:35,880 DEBUG: Done: Training
+2016-09-06 10:13:35,880 DEBUG: Start: Predicting
+2016-09-06 10:13:35,883 DEBUG: Done: Predicting
+2016-09-06 10:13:35,883 DEBUG: Start: Getting Results
+2016-09-06 10:13:35,885 DEBUG: Done: Getting Results
+2016-09-06 10:13:35,885 INFO: Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.9
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 3, max_depth : 16
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.9
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:35,885 INFO: Done: Result Analysis
+2016-09-06 10:13:36,032 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:36,032 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:36,032 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:13:36,032 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:13:36,033 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:13:36,033 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:13:36,033 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:13:36,033 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:13:36,033 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:13:36,033 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:13:36,033 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:13:36,033 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:13:36,034 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:13:36,034 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:13:36,079 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:13:36,079 DEBUG: Start: Training
+2016-09-06 10:13:36,080 DEBUG: Info: Time for Training: 0.0481998920441[s]
+2016-09-06 10:13:36,080 DEBUG: Done: Training
+2016-09-06 10:13:36,080 DEBUG: Start: Predicting
+2016-09-06 10:13:36,084 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:13:36,084 DEBUG: Start: Training
+2016-09-06 10:13:36,102 DEBUG: Info: Time for Training: 0.070839881897[s]
+2016-09-06 10:13:36,103 DEBUG: Done: Training
+2016-09-06 10:13:36,103 DEBUG: Start: Predicting
+2016-09-06 10:13:36,106 DEBUG: Done: Predicting
+2016-09-06 10:13:36,106 DEBUG: Start: Getting Results
+2016-09-06 10:13:36,108 DEBUG: Done: Getting Results
+2016-09-06 10:13:36,108 INFO: Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.490476190476
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 9584
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.490476190476
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:36,108 INFO: Done: Result Analysis
+2016-09-06 10:13:36,110 DEBUG: Done: Predicting
+2016-09-06 10:13:36,111 DEBUG: Start: Getting Results
+2016-09-06 10:13:36,114 DEBUG: Done: Getting Results
+2016-09-06 10:13:36,114 INFO: Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.547619047619
+accuracy_score on test : 0.566666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.547619047619
+ - Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:36,115 INFO: Done: Result Analysis
+2016-09-06 10:13:36,177 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:36,177 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:36,178 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:13:36,178 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:13:36,178 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:13:36,178 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:13:36,178 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:13:36,178 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:13:36,179 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:13:36,179 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:13:36,179 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:13:36,179 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:13:36,179 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:13:36,179 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:13:36,233 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:13:36,233 DEBUG: Start: Training
+2016-09-06 10:13:36,235 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:13:36,236 DEBUG: Start: Training
+2016-09-06 10:13:36,250 DEBUG: Info: Time for Training: 0.0732500553131[s]
+2016-09-06 10:13:36,250 DEBUG: Done: Training
+2016-09-06 10:13:36,250 DEBUG: Start: Predicting
+2016-09-06 10:13:36,254 DEBUG: Info: Time for Training: 0.0775668621063[s]
+2016-09-06 10:13:36,254 DEBUG: Done: Training
+2016-09-06 10:13:36,255 DEBUG: Start: Predicting
+2016-09-06 10:13:36,256 DEBUG: Done: Predicting
+2016-09-06 10:13:36,257 DEBUG: Start: Getting Results
+2016-09-06 10:13:36,258 DEBUG: Done: Predicting
+2016-09-06 10:13:36,258 DEBUG: Start: Getting Results
+2016-09-06 10:13:36,259 DEBUG: Done: Getting Results
+2016-09-06 10:13:36,259 INFO: Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 9584
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:36,259 INFO: Done: Result Analysis
+2016-09-06 10:13:36,260 DEBUG: Done: Getting Results
+2016-09-06 10:13:36,260 INFO: Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 0.990476190476
+accuracy_score on test : 0.444444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 9584
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.990476190476
+ - Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:36,261 INFO: Done: Result Analysis
+2016-09-06 10:13:36,328 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:36,328 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:13:36,329 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:13:36,330 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:13:36,330 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:13:36,330 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:13:36,330 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:13:36,330 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:36,331 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:13:36,331 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:13:36,331 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:13:36,332 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:13:36,332 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:13:36,332 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:13:36,372 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:13:36,372 DEBUG: Start: Training
+2016-09-06 10:13:36,374 DEBUG: Info: Time for Training: 0.0452740192413[s]
+2016-09-06 10:13:36,375 DEBUG: Done: Training
+2016-09-06 10:13:36,375 DEBUG: Start: Predicting
+2016-09-06 10:13:36,378 DEBUG: Done: Predicting
+2016-09-06 10:13:36,378 DEBUG: Start: Getting Results
+2016-09-06 10:13:36,380 DEBUG: Done: Getting Results
+2016-09-06 10:13:36,380 INFO: Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 16
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:36,381 INFO: Done: Result Analysis
+2016-09-06 10:13:36,383 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:13:36,383 DEBUG: Start: Training
+2016-09-06 10:13:36,387 DEBUG: Info: Time for Training: 0.0596179962158[s]
+2016-09-06 10:13:36,387 DEBUG: Done: Training
+2016-09-06 10:13:36,387 DEBUG: Start: Predicting
+2016-09-06 10:13:36,390 DEBUG: Done: Predicting
+2016-09-06 10:13:36,390 DEBUG: Start: Getting Results
+2016-09-06 10:13:36,392 DEBUG: Done: Getting Results
+2016-09-06 10:13:36,392 INFO: Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:36,392 INFO: Done: Result Analysis
+2016-09-06 10:13:36,471 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:36,471 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:36,471 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:13:36,471 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:13:36,471 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:13:36,471 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:13:36,472 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:13:36,472 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:13:36,472 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:13:36,472 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:13:36,472 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:13:36,472 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:13:36,472 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:13:36,472 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:13:36,505 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:13:36,505 DEBUG: Start: Training
+2016-09-06 10:13:36,506 DEBUG: Info: Time for Training: 0.0356941223145[s]
+2016-09-06 10:13:36,506 DEBUG: Done: Training
+2016-09-06 10:13:36,506 DEBUG: Start: Predicting
+2016-09-06 10:13:36,513 DEBUG: Done: Predicting
+2016-09-06 10:13:36,513 DEBUG: Start: Getting Results
+2016-09-06 10:13:36,515 DEBUG: Done: Getting Results
+2016-09-06 10:13:36,515 INFO: Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.571428571429
+accuracy_score on test : 0.566666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 48
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.571428571429
+ - Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:36,515 INFO: Done: Result Analysis
+2016-09-06 10:13:36,557 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:13:36,557 DEBUG: Start: Training
+2016-09-06 10:13:36,565 DEBUG: Info: Time for Training: 0.0949969291687[s]
+2016-09-06 10:13:36,565 DEBUG: Done: Training
+2016-09-06 10:13:36,566 DEBUG: Start: Predicting
+2016-09-06 10:13:36,569 DEBUG: Done: Predicting
+2016-09-06 10:13:36,569 DEBUG: Start: Getting Results
+2016-09-06 10:13:36,570 DEBUG: Done: Getting Results
+2016-09-06 10:13:36,571 INFO: Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.904761904762
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 3, max_depth : 16
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.904761904762
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:36,571 INFO: Done: Result Analysis
+2016-09-06 10:13:36,718 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:36,718 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:36,718 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:13:36,718 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:13:36,718 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:13:36,718 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:13:36,719 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:13:36,719 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:13:36,719 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:13:36,719 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:13:36,719 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:13:36,719 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:13:36,719 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:13:36,719 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:13:36,771 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:13:36,771 DEBUG: Start: Training
+2016-09-06 10:13:36,779 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:13:36,779 DEBUG: Start: Training
+2016-09-06 10:13:36,780 DEBUG: Info: Time for Training: 0.063157081604[s]
+2016-09-06 10:13:36,780 DEBUG: Done: Training
+2016-09-06 10:13:36,781 DEBUG: Start: Predicting
+2016-09-06 10:13:36,792 DEBUG: Info: Time for Training: 0.0748438835144[s]
+2016-09-06 10:13:36,792 DEBUG: Done: Training
+2016-09-06 10:13:36,792 DEBUG: Start: Predicting
+2016-09-06 10:13:36,796 DEBUG: Done: Predicting
+2016-09-06 10:13:36,796 DEBUG: Start: Getting Results
+2016-09-06 10:13:36,797 DEBUG: Done: Getting Results
+2016-09-06 10:13:36,797 INFO: Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.466666666667
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 9584
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.466666666667
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:36,797 INFO: Done: Result Analysis
+2016-09-06 10:13:36,801 DEBUG: Done: Predicting
+2016-09-06 10:13:36,801 DEBUG: Start: Getting Results
+2016-09-06 10:13:36,804 DEBUG: Done: Getting Results
+2016-09-06 10:13:36,804 INFO: Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.552380952381
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.552380952381
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:36,804 INFO: Done: Result Analysis
+2016-09-06 10:13:36,869 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:36,870 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:13:36,870 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:13:36,870 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:36,870 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:13:36,870 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:13:36,871 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:13:36,871 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:13:36,871 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:13:36,871 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:13:36,872 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:13:36,872 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:13:36,872 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:13:36,872 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:13:36,918 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:13:36,919 DEBUG: Start: Training
+2016-09-06 10:13:36,930 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:13:36,930 DEBUG: Start: Training
+2016-09-06 10:13:36,935 DEBUG: Info: Time for Training: 0.0670058727264[s]
+2016-09-06 10:13:36,936 DEBUG: Done: Training
+2016-09-06 10:13:36,936 DEBUG: Start: Predicting
+2016-09-06 10:13:36,941 DEBUG: Done: Predicting
+2016-09-06 10:13:36,941 DEBUG: Start: Getting Results
+2016-09-06 10:13:36,943 DEBUG: Done: Getting Results
+2016-09-06 10:13:36,943 INFO: Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 9584
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:36,943 INFO: Done: Result Analysis
+2016-09-06 10:13:36,947 DEBUG: Info: Time for Training: 0.0786368846893[s]
+2016-09-06 10:13:36,947 DEBUG: Done: Training
+2016-09-06 10:13:36,947 DEBUG: Start: Predicting
+2016-09-06 10:13:36,952 DEBUG: Done: Predicting
+2016-09-06 10:13:36,952 DEBUG: Start: Getting Results
+2016-09-06 10:13:36,954 DEBUG: Done: Getting Results
+2016-09-06 10:13:36,954 INFO: Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 9584
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:36,954 INFO: Done: Result Analysis
+2016-09-06 10:13:37,012 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:37,012 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:37,013 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:13:37,013 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:13:37,013 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:13:37,013 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:13:37,013 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:13:37,013 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:13:37,014 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:13:37,014 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:13:37,014 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:13:37,014 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:13:37,014 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:13:37,014 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:13:37,046 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:13:37,046 DEBUG: Start: Training
+2016-09-06 10:13:37,048 DEBUG: Info: Time for Training: 0.035749912262[s]
+2016-09-06 10:13:37,048 DEBUG: Done: Training
+2016-09-06 10:13:37,048 DEBUG: Start: Predicting
+2016-09-06 10:13:37,051 DEBUG: Done: Predicting
+2016-09-06 10:13:37,051 DEBUG: Start: Getting Results
+2016-09-06 10:13:37,052 DEBUG: Done: Getting Results
+2016-09-06 10:13:37,052 INFO: Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 16
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:37,052 INFO: Done: Result Analysis
+2016-09-06 10:13:37,061 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:13:37,061 DEBUG: Start: Training
+2016-09-06 10:13:37,064 DEBUG: Info: Time for Training: 0.0523209571838[s]
+2016-09-06 10:13:37,064 DEBUG: Done: Training
+2016-09-06 10:13:37,065 DEBUG: Start: Predicting
+2016-09-06 10:13:37,067 DEBUG: Done: Predicting
+2016-09-06 10:13:37,068 DEBUG: Start: Getting Results
+2016-09-06 10:13:37,069 DEBUG: Done: Getting Results
+2016-09-06 10:13:37,070 INFO: Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.455555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:37,078 INFO: Done: Result Analysis
+2016-09-06 10:13:37,160 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:37,160 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:37,161 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:13:37,161 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:13:37,162 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:13:37,162 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:13:37,163 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:13:37,163 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:13:37,163 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:13:37,163 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:13:37,163 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:13:37,163 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:13:37,163 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:13:37,163 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:13:37,195 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:13:37,195 DEBUG: Start: Training
+2016-09-06 10:13:37,196 DEBUG: Info: Time for Training: 0.0357730388641[s]
+2016-09-06 10:13:37,196 DEBUG: Done: Training
+2016-09-06 10:13:37,196 DEBUG: Start: Predicting
+2016-09-06 10:13:37,202 DEBUG: Done: Predicting
+2016-09-06 10:13:37,202 DEBUG: Start: Getting Results
+2016-09-06 10:13:37,204 DEBUG: Done: Getting Results
+2016-09-06 10:13:37,204 INFO: Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.547619047619
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 48
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.547619047619
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:37,204 INFO: Done: Result Analysis
+2016-09-06 10:13:37,251 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:13:37,252 DEBUG: Start: Training
+2016-09-06 10:13:37,260 DEBUG: Info: Time for Training: 0.0996959209442[s]
+2016-09-06 10:13:37,260 DEBUG: Done: Training
+2016-09-06 10:13:37,260 DEBUG: Start: Predicting
+2016-09-06 10:13:37,263 DEBUG: Done: Predicting
+2016-09-06 10:13:37,263 DEBUG: Start: Getting Results
+2016-09-06 10:13:37,265 DEBUG: Done: Getting Results
+2016-09-06 10:13:37,265 INFO: Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.895238095238
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 3, max_depth : 16
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.895238095238
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:37,265 INFO: Done: Result Analysis
+2016-09-06 10:13:37,416 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:37,416 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:37,417 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:13:37,417 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:13:37,417 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:13:37,417 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:13:37,417 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:13:37,417 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:13:37,418 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:13:37,418 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:13:37,418 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:13:37,418 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:13:37,418 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:13:37,418 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:13:37,462 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:13:37,463 DEBUG: Start: Training
+2016-09-06 10:13:37,463 DEBUG: Info: Time for Training: 0.0475831031799[s]
+2016-09-06 10:13:37,463 DEBUG: Done: Training
+2016-09-06 10:13:37,463 DEBUG: Start: Predicting
+2016-09-06 10:13:37,467 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:13:37,468 DEBUG: Start: Training
+2016-09-06 10:13:37,476 DEBUG: Done: Predicting
+2016-09-06 10:13:37,477 DEBUG: Start: Getting Results
+2016-09-06 10:13:37,478 DEBUG: Done: Getting Results
+2016-09-06 10:13:37,478 INFO: Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.552380952381
+accuracy_score on test : 0.433333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.552380952381
+ - Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:37,479 INFO: Done: Result Analysis
+2016-09-06 10:13:37,492 DEBUG: Info: Time for Training: 0.0758819580078[s]
+2016-09-06 10:13:37,492 DEBUG: Done: Training
+2016-09-06 10:13:37,492 DEBUG: Start: Predicting
+2016-09-06 10:13:37,495 DEBUG: Done: Predicting
+2016-09-06 10:13:37,495 DEBUG: Start: Getting Results
+2016-09-06 10:13:37,496 DEBUG: Done: Getting Results
+2016-09-06 10:13:37,496 INFO: Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.519047619048
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 9584
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.519047619048
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:37,497 INFO: Done: Result Analysis
+2016-09-06 10:13:37,558 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:37,558 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:37,559 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:13:37,559 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:13:37,559 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:13:37,559 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:13:37,559 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:13:37,559 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:13:37,559 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:13:37,560 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:13:37,560 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:13:37,560 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:13:37,560 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:13:37,560 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:13:37,610 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:13:37,610 DEBUG: Start: Training
+2016-09-06 10:13:37,614 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:13:37,614 DEBUG: Start: Training
+2016-09-06 10:13:37,627 DEBUG: Info: Time for Training: 0.0695948600769[s]
+2016-09-06 10:13:37,627 DEBUG: Done: Training
+2016-09-06 10:13:37,628 DEBUG: Start: Predicting
+2016-09-06 10:13:37,632 DEBUG: Info: Time for Training: 0.0741398334503[s]
+2016-09-06 10:13:37,632 DEBUG: Done: Training
+2016-09-06 10:13:37,632 DEBUG: Start: Predicting
+2016-09-06 10:13:37,633 DEBUG: Done: Predicting
+2016-09-06 10:13:37,633 DEBUG: Start: Getting Results
+2016-09-06 10:13:37,634 DEBUG: Done: Getting Results
+2016-09-06 10:13:37,634 INFO: Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 9584
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:37,634 INFO: Done: Result Analysis
+2016-09-06 10:13:37,635 DEBUG: Done: Predicting
+2016-09-06 10:13:37,635 DEBUG: Start: Getting Results
+2016-09-06 10:13:37,637 DEBUG: Done: Getting Results
+2016-09-06 10:13:37,637 INFO: Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 0.628571428571
+accuracy_score on test : 0.455555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 9584
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.628571428571
+ - Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:37,637 INFO: Done: Result Analysis
+2016-09-06 10:13:37,708 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:37,708 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:13:37,708 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:13:37,708 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:37,709 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:13:37,709 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:13:37,709 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:13:37,709 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:13:37,709 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:13:37,709 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:13:37,710 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:13:37,710 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:13:37,710 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:13:37,710 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:13:37,749 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:13:37,749 DEBUG: Start: Training
+2016-09-06 10:13:37,751 DEBUG: Info: Time for Training: 0.0440349578857[s]
+2016-09-06 10:13:37,751 DEBUG: Done: Training
+2016-09-06 10:13:37,752 DEBUG: Start: Predicting
+2016-09-06 10:13:37,754 DEBUG: Done: Predicting
+2016-09-06 10:13:37,754 DEBUG: Start: Getting Results
+2016-09-06 10:13:37,755 DEBUG: Done: Getting Results
+2016-09-06 10:13:37,756 INFO: Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 16
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:37,756 INFO: Done: Result Analysis
+2016-09-06 10:13:37,768 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:13:37,768 DEBUG: Start: Training
+2016-09-06 10:13:37,773 DEBUG: Info: Time for Training: 0.0648808479309[s]
+2016-09-06 10:13:37,773 DEBUG: Done: Training
+2016-09-06 10:13:37,773 DEBUG: Start: Predicting
+2016-09-06 10:13:37,776 DEBUG: Done: Predicting
+2016-09-06 10:13:37,776 DEBUG: Start: Getting Results
+2016-09-06 10:13:37,778 DEBUG: Done: Getting Results
+2016-09-06 10:13:37,778 INFO: Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.544444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:37,778 INFO: Done: Result Analysis
+2016-09-06 10:13:37,859 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:37,859 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:37,860 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:13:37,860 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:13:37,860 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:13:37,860 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:13:37,861 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:13:37,861 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:13:37,862 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:13:37,862 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:13:37,862 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:13:37,862 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:13:37,862 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:13:37,862 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:13:37,914 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:13:37,915 DEBUG: Start: Training
+2016-09-06 10:13:37,915 DEBUG: Info: Time for Training: 0.0569260120392[s]
+2016-09-06 10:13:37,915 DEBUG: Done: Training
+2016-09-06 10:13:37,916 DEBUG: Start: Predicting
+2016-09-06 10:13:37,927 DEBUG: Done: Predicting
+2016-09-06 10:13:37,927 DEBUG: Start: Getting Results
+2016-09-06 10:13:37,929 DEBUG: Done: Getting Results
+2016-09-06 10:13:37,929 INFO: Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.542857142857
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 48
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.542857142857
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:37,930 INFO: Done: Result Analysis
+2016-09-06 10:13:37,973 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:13:37,974 DEBUG: Start: Training
+2016-09-06 10:13:37,982 DEBUG: Info: Time for Training: 0.123476028442[s]
+2016-09-06 10:13:37,982 DEBUG: Done: Training
+2016-09-06 10:13:37,982 DEBUG: Start: Predicting
+2016-09-06 10:13:37,985 DEBUG: Done: Predicting
+2016-09-06 10:13:37,985 DEBUG: Start: Getting Results
+2016-09-06 10:13:37,987 DEBUG: Done: Getting Results
+2016-09-06 10:13:37,987 INFO: Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.890476190476
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 3, max_depth : 16
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.890476190476
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:37,987 INFO: Done: Result Analysis
+2016-09-06 10:13:38,103 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:38,103 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:38,104 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:13:38,104 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:13:38,104 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:13:38,104 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:13:38,105 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:13:38,105 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:13:38,105 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:13:38,105 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:13:38,105 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:13:38,105 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:13:38,105 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:13:38,105 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:13:38,152 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:13:38,152 DEBUG: Start: Training
+2016-09-06 10:13:38,153 DEBUG: Info: Time for Training: 0.0500018596649[s]
+2016-09-06 10:13:38,153 DEBUG: Done: Training
+2016-09-06 10:13:38,153 DEBUG: Start: Predicting
+2016-09-06 10:13:38,160 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:13:38,160 DEBUG: Start: Training
+2016-09-06 10:13:38,170 DEBUG: Done: Predicting
+2016-09-06 10:13:38,170 DEBUG: Start: Getting Results
+2016-09-06 10:13:38,172 DEBUG: Done: Getting Results
+2016-09-06 10:13:38,172 INFO: Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.547619047619
+accuracy_score on test : 0.433333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.547619047619
+ - Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:38,172 INFO: Done: Result Analysis
+2016-09-06 10:13:38,182 DEBUG: Info: Time for Training: 0.0787019729614[s]
+2016-09-06 10:13:38,182 DEBUG: Done: Training
+2016-09-06 10:13:38,182 DEBUG: Start: Predicting
+2016-09-06 10:13:38,185 DEBUG: Done: Predicting
+2016-09-06 10:13:38,186 DEBUG: Start: Getting Results
+2016-09-06 10:13:38,187 DEBUG: Done: Getting Results
+2016-09-06 10:13:38,187 INFO: Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.52380952381
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 9584
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.52380952381
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:38,187 INFO: Done: Result Analysis
+2016-09-06 10:13:38,404 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:13:38,404 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-09-06 10:13:38,405 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:13:38,405 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-06 10:13:38,405 INFO: Info: Shape of View0 :(300, 10)
+2016-09-06 10:13:38,406 INFO: Info: Shape of View0 :(300, 10)
+2016-09-06 10:13:38,406 INFO: Info: Shape of View1 :(300, 13)
+2016-09-06 10:13:38,407 INFO: Info: Shape of View1 :(300, 13)
+2016-09-06 10:13:38,407 INFO: Info: Shape of View2 :(300, 7)
+2016-09-06 10:13:38,407 INFO: Info: Shape of View2 :(300, 7)
+2016-09-06 10:13:38,407 INFO: Info: Shape of View3 :(300, 19)
+2016-09-06 10:13:38,407 INFO: Done: Read Database Files
+2016-09-06 10:13:38,408 INFO: Start: Determine validation split for ratio 0.7
+2016-09-06 10:13:38,408 INFO: Info: Shape of View3 :(300, 19)
+2016-09-06 10:13:38,408 INFO: Done: Read Database Files
+2016-09-06 10:13:38,408 INFO: Start: Determine validation split for ratio 0.7
+2016-09-06 10:13:38,412 INFO: Done: Determine validation split
+2016-09-06 10:13:38,412 INFO: Start: Determine 5 folds
+2016-09-06 10:13:38,412 INFO: Done: Determine validation split
+2016-09-06 10:13:38,413 INFO: Start: Determine 5 folds
+2016-09-06 10:13:38,422 INFO: Info: Length of Learning Sets: 169
+2016-09-06 10:13:38,422 INFO: Info: Length of Testing Sets: 42
+2016-09-06 10:13:38,422 INFO: Info: Length of Validation Set: 89
+2016-09-06 10:13:38,422 INFO: Done: Determine folds
+2016-09-06 10:13:38,423 INFO: Start: Learning with Fusion and 5 folds
+2016-09-06 10:13:38,423 INFO: Start: Classification
+2016-09-06 10:13:38,423 INFO: Start: Fold number 1
+2016-09-06 10:13:38,425 INFO: Info: Length of Learning Sets: 169
+2016-09-06 10:13:38,425 INFO: Info: Length of Testing Sets: 42
+2016-09-06 10:13:38,425 INFO: Info: Length of Validation Set: 89
+2016-09-06 10:13:38,425 INFO: Done: Determine folds
+2016-09-06 10:13:38,425 INFO: Start: Learning with Mumbo and 5 folds
+2016-09-06 10:13:38,425 INFO: Start: Classification
+2016-09-06 10:13:38,426 INFO: Start: Fold number 1
+2016-09-06 10:13:38,463 DEBUG: Start: Iteration 1
+2016-09-06 10:13:38,473 DEBUG: View 0 : 0.535135135135
+2016-09-06 10:13:38,481 DEBUG: View 1 : 0.524324324324
+2016-09-06 10:13:38,491 DEBUG: View 2 : 0.437837837838
+2016-09-06 10:13:38,500 DEBUG: View 3 : 0.535135135135
+2016-09-06 10:13:38,541 DEBUG: Best view : View1
+2016-09-06 10:13:38,635 DEBUG: Start: Iteration 2
+2016-09-06 10:13:38,645 DEBUG: View 0 : 0.756756756757
+2016-09-06 10:13:38,652 DEBUG: View 1 : 0.735135135135
+2016-09-06 10:13:38,662 DEBUG: View 2 : 0.697297297297
+2016-09-06 10:13:38,669 DEBUG: View 3 : 0.697297297297
+2016-09-06 10:13:38,719 DEBUG: Best view : View0
+2016-09-06 10:13:38,877 DEBUG: Start: Iteration 3
+2016-09-06 10:13:38,885 DEBUG: View 0 : 0.756756756757
+2016-09-06 10:13:38,892 DEBUG: View 1 : 0.735135135135
+2016-09-06 10:13:38,899 DEBUG: View 2 : 0.697297297297
+2016-09-06 10:13:38,907 DEBUG: View 3 : 0.697297297297
+2016-09-06 10:13:38,949 DEBUG: Best view : View0
+2016-09-06 10:13:39,172 DEBUG: Start: Iteration 4
+2016-09-06 10:13:39,179 DEBUG: View 0 : 0.702702702703
+2016-09-06 10:13:39,187 DEBUG: View 1 : 0.67027027027
+2016-09-06 10:13:39,194 DEBUG: View 2 : 0.708108108108
+2016-09-06 10:13:39,202 DEBUG: View 3 : 0.67027027027
+2016-09-06 10:13:39,246 DEBUG: Best view : View0
+2016-09-06 10:13:39,538 INFO: Start: Classification
+2016-09-06 10:13:40,019 INFO: Done: Fold number 1
+2016-09-06 10:13:40,019 INFO: Start: Fold number 2
+2016-09-06 10:13:40,049 DEBUG: Start: Iteration 1
+2016-09-06 10:13:40,056 DEBUG: View 0 : 0.522222222222
+2016-09-06 10:13:40,063 DEBUG: View 1 : 0.516666666667
+2016-09-06 10:13:40,069 DEBUG: View 2 : 0.477777777778
+2016-09-06 10:13:40,077 DEBUG: View 3 : 0.516666666667
+2016-09-06 10:13:40,109 DEBUG: Best view : View0
+2016-09-06 10:13:40,187 DEBUG: Start: Iteration 2
+2016-09-06 10:13:40,195 DEBUG: View 0 : 0.622222222222
+2016-09-06 10:13:40,202 DEBUG: View 1 : 0.672222222222
+2016-09-06 10:13:40,209 DEBUG: View 2 : 0.677777777778
+2016-09-06 10:13:40,217 DEBUG: View 3 : 0.672222222222
+2016-09-06 10:13:40,256 DEBUG: Best view : View2
+2016-09-06 10:13:40,404 DEBUG: Start: Iteration 3
+2016-09-06 10:13:40,411 DEBUG: View 0 : 0.622222222222
+2016-09-06 10:13:40,419 DEBUG: View 1 : 0.672222222222
+2016-09-06 10:13:40,426 DEBUG: View 2 : 0.677777777778
+2016-09-06 10:13:40,433 DEBUG: View 3 : 0.672222222222
+2016-09-06 10:13:40,475 DEBUG: Best view : View2
+2016-09-06 10:13:40,691 INFO: Start: Classification
+2016-09-06 10:13:41,043 INFO: Done: Fold number 2
+2016-09-06 10:13:41,043 INFO: Start: Fold number 3
+2016-09-06 10:13:41,073 DEBUG: Start: Iteration 1
+2016-09-06 10:13:41,079 DEBUG: View 0 : 0.544444444444
+2016-09-06 10:13:41,087 DEBUG: View 1 : 0.522222222222
+2016-09-06 10:13:41,093 DEBUG: View 2 : 0.466666666667
+2016-09-06 10:13:41,101 DEBUG: View 3 : 0.511111111111
+2016-09-06 10:13:41,133 DEBUG: Best view : View1
+2016-09-06 10:13:41,212 DEBUG: Start: Iteration 2
+2016-09-06 10:13:41,220 DEBUG: View 0 : 0.688888888889
+2016-09-06 10:13:41,227 DEBUG: View 1 : 0.627777777778
+2016-09-06 10:13:41,234 DEBUG: View 2 : 0.716666666667
+2016-09-06 10:13:41,242 DEBUG: View 3 : 0.733333333333
+2016-09-06 10:13:41,280 DEBUG: Best view : View3
+2016-09-06 10:13:41,428 DEBUG: Start: Iteration 3
+2016-09-06 10:13:41,435 DEBUG: View 0 : 0.688888888889
+2016-09-06 10:13:41,443 DEBUG: View 1 : 0.627777777778
+2016-09-06 10:13:41,450 DEBUG: View 2 : 0.716666666667
+2016-09-06 10:13:41,457 DEBUG: View 3 : 0.733333333333
+2016-09-06 10:13:41,498 DEBUG: Best view : View3
+2016-09-06 10:13:41,716 DEBUG: Start: Iteration 4
+2016-09-06 10:13:41,723 DEBUG: View 0 : 0.666666666667
+2016-09-06 10:13:41,730 DEBUG: View 1 : 0.611111111111
+2016-09-06 10:13:41,737 DEBUG: View 2 : 0.705555555556
+2016-09-06 10:13:41,745 DEBUG: View 3 : 0.688888888889
+2016-09-06 10:13:41,789 DEBUG: Best view : View2
+2016-09-06 10:13:42,070 INFO: Start: Classification
+2016-09-06 10:13:42,538 INFO: Done: Fold number 3
+2016-09-06 10:13:42,538 INFO: Start: Fold number 4
+2016-09-06 10:13:42,568 DEBUG: Start: Iteration 1
+2016-09-06 10:13:42,575 DEBUG: View 0 : 0.527472527473
+2016-09-06 10:13:42,582 DEBUG: View 1 : 0.582417582418
+2016-09-06 10:13:42,588 DEBUG: View 2 : 0.516483516484
+2016-09-06 10:13:42,596 DEBUG: View 3 : 0.516483516484
+2016-09-06 10:13:42,628 DEBUG: Best view : View0
+2016-09-06 10:13:42,708 DEBUG: Start: Iteration 2
+2016-09-06 10:13:42,716 DEBUG: View 0 : 0.697802197802
+2016-09-06 10:13:42,723 DEBUG: View 1 : 0.675824175824
+2016-09-06 10:13:42,730 DEBUG: View 2 : 0.675824175824
+2016-09-06 10:13:42,738 DEBUG: View 3 : 0.686813186813
+2016-09-06 10:13:42,776 DEBUG: Best view : View0
+2016-09-06 10:13:42,926 DEBUG: Start: Iteration 3
+2016-09-06 10:13:42,933 DEBUG: View 0 : 0.697802197802
+2016-09-06 10:13:42,940 DEBUG: View 1 : 0.675824175824
+2016-09-06 10:13:42,947 DEBUG: View 2 : 0.675824175824
+2016-09-06 10:13:42,955 DEBUG: View 3 : 0.686813186813
+2016-09-06 10:13:42,996 DEBUG: Best view : View0
+2016-09-06 10:13:43,215 INFO: Start: Classification
+2016-09-06 10:13:43,569 INFO: Done: Fold number 4
+2016-09-06 10:13:43,570 INFO: Start: Fold number 5
+2016-09-06 10:13:43,600 DEBUG: Start: Iteration 1
+2016-09-06 10:13:43,607 DEBUG: View 0 : 0.497297297297
+2016-09-06 10:13:43,615 DEBUG: View 1 : 0.486486486486
+2016-09-06 10:13:43,621 DEBUG: View 2 : 0.491891891892
+2016-09-06 10:13:43,629 DEBUG: View 3 : 0.502702702703
+2016-09-06 10:13:43,662 DEBUG: Best view : View3
+2016-09-06 10:13:43,743 DEBUG: Start: Iteration 2
+2016-09-06 10:13:43,751 DEBUG: View 0 : 0.702702702703
+2016-09-06 10:13:43,758 DEBUG: View 1 : 0.702702702703
+2016-09-06 10:13:43,765 DEBUG: View 2 : 0.605405405405
+2016-09-06 10:13:43,773 DEBUG: View 3 : 0.718918918919
+2016-09-06 10:13:43,812 DEBUG: Best view : View3
+2016-09-06 10:13:43,965 DEBUG: Start: Iteration 3
+2016-09-06 10:13:43,972 DEBUG: View 0 : 0.702702702703
+2016-09-06 10:13:43,980 DEBUG: View 1 : 0.702702702703
+2016-09-06 10:13:43,987 DEBUG: View 2 : 0.605405405405
+2016-09-06 10:13:43,995 DEBUG: View 3 : 0.718918918919
+2016-09-06 10:13:44,037 DEBUG: Best view : View3
+2016-09-06 10:13:44,260 DEBUG: Start: Iteration 4
+2016-09-06 10:13:44,268 DEBUG: View 0 : 0.724324324324
+2016-09-06 10:13:44,275 DEBUG: View 1 : 0.627027027027
+2016-09-06 10:13:44,283 DEBUG: View 2 : 0.681081081081
+2016-09-06 10:13:44,290 DEBUG: View 3 : 0.648648648649
+2016-09-06 10:13:44,335 DEBUG: Best view : View0
+2016-09-06 10:13:44,628 INFO: Start: Classification
+2016-09-06 10:13:45,105 INFO: Done: Fold number 5
+2016-09-06 10:13:45,106 INFO: Done: Classification
+2016-09-06 10:13:45,106 INFO: Info: Time for Classification: 6[s]
+2016-09-06 10:13:45,106 INFO: Start: Result Analysis for Mumbo
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..d5944341
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..41319f9a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 16
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..ffd9cc63
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.557142857143
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 48
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.557142857143
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..61f7d864
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.9
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 3, max_depth : 16
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.9
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..5e68fb88
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..73356fd5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 16
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..1a9bd291
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.571428571429
+accuracy_score on test : 0.566666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 48
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.571428571429
+ - Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..7ece7302
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.904761904762
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 3, max_depth : 16
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.904761904762
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..23e4c20c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.552380952381
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.552380952381
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..a6ee9073
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.466666666667
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 9584
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.466666666667
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..736ada89
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 9584
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..749dc0af
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 9584
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..05b31d53
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.544444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..0e7c951d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 16
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..58caffdd
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.542857142857
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 48
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.542857142857
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..fffde9a8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.890476190476
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 3, max_depth : 16
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.890476190476
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..35e2c637
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.552380952381
+accuracy_score on test : 0.433333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.552380952381
+ - Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..fc1d9bb0
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.519047619048
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 9584
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.519047619048
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..3b5c3c54
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 0.628571428571
+accuracy_score on test : 0.455555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 9584
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.628571428571
+ - Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..785b3511
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 9584
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101338Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101338Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..5be94406
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101338Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.547619047619
+accuracy_score on test : 0.433333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.547619047619
+ - Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101338Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101338Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..7668376b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101338Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.52380952381
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 9584
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.52380952381
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102343-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-102343-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 00000000..5867a8bf
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102343-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,1381 @@
+2016-09-06 10:23:43,264 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-06 10:23:43,264 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 9.821875e-05 Gbytes /!\
+2016-09-06 10:23:48,275 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-06 10:23:48,277 INFO: Start: Finding all available mono- & multiview algorithms
+2016-09-06 10:23:48,332 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:48,332 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:48,332 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:23:48,332 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:23:48,332 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:23:48,332 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:23:48,333 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:23:48,333 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:23:48,333 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:23:48,333 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:23:48,333 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:23:48,333 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:23:48,333 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:23:48,333 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:23:48,366 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:23:48,367 DEBUG: Start: Training
+2016-09-06 10:23:48,368 DEBUG: Info: Time for Training: 0.0366399288177[s]
+2016-09-06 10:23:48,368 DEBUG: Done: Training
+2016-09-06 10:23:48,368 DEBUG: Start: Predicting
+2016-09-06 10:23:48,371 DEBUG: Done: Predicting
+2016-09-06 10:23:48,371 DEBUG: Start: Getting Results
+2016-09-06 10:23:48,372 DEBUG: Done: Getting Results
+2016-09-06 10:23:48,372 INFO: Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 0.919047619048
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 7
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.919047619048
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:48,372 INFO: Done: Result Analysis
+2016-09-06 10:23:48,382 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:23:48,382 DEBUG: Start: Training
+2016-09-06 10:23:48,385 DEBUG: Info: Time for Training: 0.0539109706879[s]
+2016-09-06 10:23:48,385 DEBUG: Done: Training
+2016-09-06 10:23:48,385 DEBUG: Start: Predicting
+2016-09-06 10:23:48,388 DEBUG: Done: Predicting
+2016-09-06 10:23:48,388 DEBUG: Start: Getting Results
+2016-09-06 10:23:48,390 DEBUG: Done: Getting Results
+2016-09-06 10:23:48,390 INFO: Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.566666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:48,390 INFO: Done: Result Analysis
+2016-09-06 10:23:48,477 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:48,477 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:48,477 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:23:48,477 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:23:48,477 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:23:48,477 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:23:48,478 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:23:48,478 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:23:48,478 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:23:48,478 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:23:48,478 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:23:48,478 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:23:48,478 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:23:48,478 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:23:48,509 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:23:48,509 DEBUG: Start: Training
+2016-09-06 10:23:48,510 DEBUG: Info: Time for Training: 0.0335669517517[s]
+2016-09-06 10:23:48,510 DEBUG: Done: Training
+2016-09-06 10:23:48,510 DEBUG: Start: Predicting
+2016-09-06 10:23:48,514 DEBUG: Done: Predicting
+2016-09-06 10:23:48,514 DEBUG: Start: Getting Results
+2016-09-06 10:23:48,516 DEBUG: Done: Getting Results
+2016-09-06 10:23:48,516 INFO: Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.67619047619
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 7
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.67619047619
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:48,516 INFO: Done: Result Analysis
+2016-09-06 10:23:48,820 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:23:48,820 DEBUG: Start: Training
+2016-09-06 10:23:48,870 DEBUG: Info: Time for Training: 0.393655061722[s]
+2016-09-06 10:23:48,870 DEBUG: Done: Training
+2016-09-06 10:23:48,870 DEBUG: Start: Predicting
+2016-09-06 10:23:48,876 DEBUG: Done: Predicting
+2016-09-06 10:23:48,876 DEBUG: Start: Getting Results
+2016-09-06 10:23:48,878 DEBUG: Done: Getting Results
+2016-09-06 10:23:48,878 INFO: Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.966666666667
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 20, max_depth : 7
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.966666666667
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:48,878 INFO: Done: Result Analysis
+2016-09-06 10:23:49,036 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:49,036 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:49,037 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:23:49,037 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:23:49,037 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:23:49,037 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:23:49,038 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:23:49,038 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:23:49,038 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:23:49,038 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:23:49,038 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:23:49,038 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:23:49,038 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:23:49,038 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:23:49,100 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:23:49,100 DEBUG: Start: Training
+2016-09-06 10:23:49,122 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:23:49,122 DEBUG: Start: Training
+2016-09-06 10:23:49,123 DEBUG: Info: Time for Training: 0.0881431102753[s]
+2016-09-06 10:23:49,124 DEBUG: Done: Training
+2016-09-06 10:23:49,124 DEBUG: Start: Predicting
+2016-09-06 10:23:49,125 DEBUG: Info: Time for Training: 0.0898480415344[s]
+2016-09-06 10:23:49,125 DEBUG: Done: Training
+2016-09-06 10:23:49,125 DEBUG: Start: Predicting
+2016-09-06 10:23:49,129 DEBUG: Done: Predicting
+2016-09-06 10:23:49,129 DEBUG: Start: Getting Results
+2016-09-06 10:23:49,130 DEBUG: Done: Getting Results
+2016-09-06 10:23:49,130 INFO: Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.480952380952
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 3508
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.480952380952
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:49,130 INFO: Done: Result Analysis
+2016-09-06 10:23:49,138 DEBUG: Done: Predicting
+2016-09-06 10:23:49,138 DEBUG: Start: Getting Results
+2016-09-06 10:23:49,140 DEBUG: Done: Getting Results
+2016-09-06 10:23:49,140 INFO: Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.604761904762
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.604761904762
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:49,140 INFO: Done: Result Analysis
+2016-09-06 10:23:49,278 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:49,279 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:23:49,279 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:23:49,280 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:23:49,280 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:49,280 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:23:49,280 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:23:49,280 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:23:49,280 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:23:49,280 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:23:49,281 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:23:49,281 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:23:49,282 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:23:49,282 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:23:49,329 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:23:49,329 DEBUG: Start: Training
+2016-09-06 10:23:49,340 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:23:49,340 DEBUG: Start: Training
+2016-09-06 10:23:49,345 DEBUG: Info: Time for Training: 0.0676798820496[s]
+2016-09-06 10:23:49,346 DEBUG: Done: Training
+2016-09-06 10:23:49,346 DEBUG: Start: Predicting
+2016-09-06 10:23:49,351 DEBUG: Done: Predicting
+2016-09-06 10:23:49,351 DEBUG: Start: Getting Results
+2016-09-06 10:23:49,352 DEBUG: Done: Getting Results
+2016-09-06 10:23:49,353 INFO: Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 3508
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:49,353 INFO: Done: Result Analysis
+2016-09-06 10:23:49,358 DEBUG: Info: Time for Training: 0.0790169239044[s]
+2016-09-06 10:23:49,358 DEBUG: Done: Training
+2016-09-06 10:23:49,358 DEBUG: Start: Predicting
+2016-09-06 10:23:49,361 DEBUG: Done: Predicting
+2016-09-06 10:23:49,361 DEBUG: Start: Getting Results
+2016-09-06 10:23:49,363 DEBUG: Done: Getting Results
+2016-09-06 10:23:49,364 INFO: Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 0.752380952381
+accuracy_score on test : 0.433333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 3508
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.752380952381
+ - Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:49,364 INFO: Done: Result Analysis
+2016-09-06 10:23:49,427 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:49,427 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:49,427 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:23:49,427 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:23:49,427 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:23:49,427 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:23:49,428 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:23:49,428 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:23:49,428 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:23:49,428 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:23:49,428 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:23:49,428 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:23:49,428 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:23:49,428 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:23:49,470 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:23:49,470 DEBUG: Start: Training
+2016-09-06 10:23:49,472 DEBUG: Info: Time for Training: 0.0461599826813[s]
+2016-09-06 10:23:49,472 DEBUG: Done: Training
+2016-09-06 10:23:49,473 DEBUG: Start: Predicting
+2016-09-06 10:23:49,475 DEBUG: Done: Predicting
+2016-09-06 10:23:49,475 DEBUG: Start: Getting Results
+2016-09-06 10:23:49,476 DEBUG: Done: Getting Results
+2016-09-06 10:23:49,477 INFO: Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 0.780952380952
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 7
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.780952380952
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:49,477 INFO: Done: Result Analysis
+2016-09-06 10:23:49,479 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:23:49,479 DEBUG: Start: Training
+2016-09-06 10:23:49,483 DEBUG: Info: Time for Training: 0.057247877121[s]
+2016-09-06 10:23:49,484 DEBUG: Done: Training
+2016-09-06 10:23:49,484 DEBUG: Start: Predicting
+2016-09-06 10:23:49,487 DEBUG: Done: Predicting
+2016-09-06 10:23:49,487 DEBUG: Start: Getting Results
+2016-09-06 10:23:49,489 DEBUG: Done: Getting Results
+2016-09-06 10:23:49,489 INFO: Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:49,489 INFO: Done: Result Analysis
+2016-09-06 10:23:49,575 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:49,575 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:49,575 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:23:49,575 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:23:49,575 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:23:49,575 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:23:49,576 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:23:49,576 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:23:49,576 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:23:49,576 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:23:49,576 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:23:49,576 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:23:49,576 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:23:49,576 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:23:49,606 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:23:49,607 DEBUG: Start: Training
+2016-09-06 10:23:49,607 DEBUG: Info: Time for Training: 0.0329051017761[s]
+2016-09-06 10:23:49,607 DEBUG: Done: Training
+2016-09-06 10:23:49,607 DEBUG: Start: Predicting
+2016-09-06 10:23:49,612 DEBUG: Done: Predicting
+2016-09-06 10:23:49,613 DEBUG: Start: Getting Results
+2016-09-06 10:23:49,614 DEBUG: Done: Getting Results
+2016-09-06 10:23:49,614 INFO: Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.657142857143
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 7
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.657142857143
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:49,614 INFO: Done: Result Analysis
+2016-09-06 10:23:49,922 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:23:49,922 DEBUG: Start: Training
+2016-09-06 10:23:49,974 DEBUG: Info: Time for Training: 0.399342060089[s]
+2016-09-06 10:23:49,974 DEBUG: Done: Training
+2016-09-06 10:23:49,974 DEBUG: Start: Predicting
+2016-09-06 10:23:49,981 DEBUG: Done: Predicting
+2016-09-06 10:23:49,981 DEBUG: Start: Getting Results
+2016-09-06 10:23:49,982 DEBUG: Done: Getting Results
+2016-09-06 10:23:49,982 INFO: Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.966666666667
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 20, max_depth : 7
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.966666666667
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:49,982 INFO: Done: Result Analysis
+2016-09-06 10:23:50,127 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:50,127 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:50,128 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:23:50,128 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:23:50,128 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:23:50,128 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:23:50,129 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:23:50,129 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:23:50,129 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:23:50,129 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:23:50,129 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:23:50,129 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:23:50,129 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:23:50,129 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:23:50,173 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:23:50,173 DEBUG: Start: Training
+2016-09-06 10:23:50,174 DEBUG: Info: Time for Training: 0.0477719306946[s]
+2016-09-06 10:23:50,174 DEBUG: Done: Training
+2016-09-06 10:23:50,174 DEBUG: Start: Predicting
+2016-09-06 10:23:50,179 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:23:50,179 DEBUG: Start: Training
+2016-09-06 10:23:50,187 DEBUG: Done: Predicting
+2016-09-06 10:23:50,188 DEBUG: Start: Getting Results
+2016-09-06 10:23:50,190 DEBUG: Done: Getting Results
+2016-09-06 10:23:50,190 INFO: Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.590476190476
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.590476190476
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:50,190 INFO: Done: Result Analysis
+2016-09-06 10:23:50,203 DEBUG: Info: Time for Training: 0.0768611431122[s]
+2016-09-06 10:23:50,204 DEBUG: Done: Training
+2016-09-06 10:23:50,204 DEBUG: Start: Predicting
+2016-09-06 10:23:50,207 DEBUG: Done: Predicting
+2016-09-06 10:23:50,207 DEBUG: Start: Getting Results
+2016-09-06 10:23:50,208 DEBUG: Done: Getting Results
+2016-09-06 10:23:50,208 INFO: Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.5
+accuracy_score on test : 0.455555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 3508
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.5
+ - Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:50,209 INFO: Done: Result Analysis
+2016-09-06 10:23:50,271 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:50,271 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:23:50,271 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:50,271 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:23:50,272 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:23:50,272 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:23:50,272 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:23:50,273 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:23:50,273 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:23:50,273 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:23:50,273 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:23:50,273 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:23:50,274 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:23:50,274 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:23:50,323 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:23:50,323 DEBUG: Start: Training
+2016-09-06 10:23:50,325 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:23:50,325 DEBUG: Start: Training
+2016-09-06 10:23:50,341 DEBUG: Info: Time for Training: 0.0708639621735[s]
+2016-09-06 10:23:50,341 DEBUG: Done: Training
+2016-09-06 10:23:50,341 DEBUG: Start: Predicting
+2016-09-06 10:23:50,344 DEBUG: Info: Time for Training: 0.0738418102264[s]
+2016-09-06 10:23:50,344 DEBUG: Done: Training
+2016-09-06 10:23:50,344 DEBUG: Start: Predicting
+2016-09-06 10:23:50,347 DEBUG: Done: Predicting
+2016-09-06 10:23:50,347 DEBUG: Start: Getting Results
+2016-09-06 10:23:50,347 DEBUG: Done: Predicting
+2016-09-06 10:23:50,348 DEBUG: Start: Getting Results
+2016-09-06 10:23:50,348 DEBUG: Done: Getting Results
+2016-09-06 10:23:50,348 INFO: Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.411111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 3508
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:50,348 INFO: Done: Result Analysis
+2016-09-06 10:23:50,349 DEBUG: Done: Getting Results
+2016-09-06 10:23:50,349 INFO: Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 3508
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:50,349 INFO: Done: Result Analysis
+2016-09-06 10:23:50,420 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:50,420 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:50,421 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:23:50,421 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:23:50,421 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:23:50,421 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:23:50,422 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:23:50,422 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:23:50,422 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:23:50,422 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:23:50,423 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:23:50,423 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:23:50,423 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:23:50,423 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:23:50,474 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:23:50,474 DEBUG: Start: Training
+2016-09-06 10:23:50,476 DEBUG: Info: Time for Training: 0.0563578605652[s]
+2016-09-06 10:23:50,476 DEBUG: Done: Training
+2016-09-06 10:23:50,476 DEBUG: Start: Predicting
+2016-09-06 10:23:50,480 DEBUG: Done: Predicting
+2016-09-06 10:23:50,480 DEBUG: Start: Getting Results
+2016-09-06 10:23:50,482 DEBUG: Done: Getting Results
+2016-09-06 10:23:50,482 INFO: Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 0.904761904762
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 7
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.904761904762
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:50,483 INFO: Done: Result Analysis
+2016-09-06 10:23:50,493 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:23:50,493 DEBUG: Start: Training
+2016-09-06 10:23:50,496 DEBUG: Info: Time for Training: 0.0766098499298[s]
+2016-09-06 10:23:50,496 DEBUG: Done: Training
+2016-09-06 10:23:50,496 DEBUG: Start: Predicting
+2016-09-06 10:23:50,499 DEBUG: Done: Predicting
+2016-09-06 10:23:50,499 DEBUG: Start: Getting Results
+2016-09-06 10:23:50,501 DEBUG: Done: Getting Results
+2016-09-06 10:23:50,501 INFO: Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:50,501 INFO: Done: Result Analysis
+2016-09-06 10:23:50,564 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:50,564 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:50,564 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:23:50,564 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:23:50,564 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:23:50,565 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:23:50,565 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:23:50,565 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:23:50,565 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:23:50,565 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:23:50,565 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:23:50,565 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:23:50,565 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:23:50,566 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:23:50,596 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:23:50,596 DEBUG: Start: Training
+2016-09-06 10:23:50,597 DEBUG: Info: Time for Training: 0.0331511497498[s]
+2016-09-06 10:23:50,597 DEBUG: Done: Training
+2016-09-06 10:23:50,597 DEBUG: Start: Predicting
+2016-09-06 10:23:50,602 DEBUG: Done: Predicting
+2016-09-06 10:23:50,602 DEBUG: Start: Getting Results
+2016-09-06 10:23:50,603 DEBUG: Done: Getting Results
+2016-09-06 10:23:50,603 INFO: Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.62380952381
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 7
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.62380952381
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:50,603 INFO: Done: Result Analysis
+2016-09-06 10:23:50,908 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:23:50,908 DEBUG: Start: Training
+2016-09-06 10:23:50,959 DEBUG: Info: Time for Training: 0.395528078079[s]
+2016-09-06 10:23:50,959 DEBUG: Done: Training
+2016-09-06 10:23:50,959 DEBUG: Start: Predicting
+2016-09-06 10:23:50,966 DEBUG: Done: Predicting
+2016-09-06 10:23:50,966 DEBUG: Start: Getting Results
+2016-09-06 10:23:50,967 DEBUG: Done: Getting Results
+2016-09-06 10:23:50,968 INFO: Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.971428571429
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 20, max_depth : 7
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.971428571429
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:50,968 INFO: Done: Result Analysis
+2016-09-06 10:23:51,112 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:51,112 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:51,113 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:23:51,113 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:23:51,113 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:23:51,113 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:23:51,113 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:23:51,113 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:23:51,113 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:23:51,113 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:23:51,113 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:23:51,113 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:23:51,114 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:23:51,114 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:23:51,158 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:23:51,159 DEBUG: Start: Training
+2016-09-06 10:23:51,159 DEBUG: Info: Time for Training: 0.0475599765778[s]
+2016-09-06 10:23:51,160 DEBUG: Done: Training
+2016-09-06 10:23:51,160 DEBUG: Start: Predicting
+2016-09-06 10:23:51,163 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:23:51,163 DEBUG: Start: Training
+2016-09-06 10:23:51,180 DEBUG: Info: Time for Training: 0.0679569244385[s]
+2016-09-06 10:23:51,180 DEBUG: Done: Training
+2016-09-06 10:23:51,180 DEBUG: Start: Predicting
+2016-09-06 10:23:51,184 DEBUG: Done: Predicting
+2016-09-06 10:23:51,184 DEBUG: Done: Predicting
+2016-09-06 10:23:51,184 DEBUG: Start: Getting Results
+2016-09-06 10:23:51,184 DEBUG: Start: Getting Results
+2016-09-06 10:23:51,186 DEBUG: Done: Getting Results
+2016-09-06 10:23:51,187 INFO: Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.490476190476
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 3508
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.490476190476
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:51,187 DEBUG: Done: Getting Results
+2016-09-06 10:23:51,187 INFO: Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.542857142857
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.542857142857
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:51,187 INFO: Done: Result Analysis
+2016-09-06 10:23:51,187 INFO: Done: Result Analysis
+2016-09-06 10:23:51,265 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:51,265 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:51,265 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:23:51,265 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:23:51,265 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:23:51,265 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:23:51,266 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:23:51,266 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:23:51,266 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:23:51,266 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:23:51,266 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:23:51,266 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:23:51,266 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:23:51,266 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:23:51,315 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:23:51,315 DEBUG: Start: Training
+2016-09-06 10:23:51,321 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:23:51,321 DEBUG: Start: Training
+2016-09-06 10:23:51,333 DEBUG: Info: Time for Training: 0.0680320262909[s]
+2016-09-06 10:23:51,333 DEBUG: Done: Training
+2016-09-06 10:23:51,333 DEBUG: Start: Predicting
+2016-09-06 10:23:51,338 DEBUG: Done: Predicting
+2016-09-06 10:23:51,338 DEBUG: Start: Getting Results
+2016-09-06 10:23:51,339 DEBUG: Done: Getting Results
+2016-09-06 10:23:51,340 INFO: Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 3508
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:51,340 INFO: Done: Result Analysis
+2016-09-06 10:23:51,340 DEBUG: Info: Time for Training: 0.075443983078[s]
+2016-09-06 10:23:51,340 DEBUG: Done: Training
+2016-09-06 10:23:51,340 DEBUG: Start: Predicting
+2016-09-06 10:23:51,343 DEBUG: Done: Predicting
+2016-09-06 10:23:51,343 DEBUG: Start: Getting Results
+2016-09-06 10:23:51,345 DEBUG: Done: Getting Results
+2016-09-06 10:23:51,345 INFO: Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 0.609523809524
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 3508
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.609523809524
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:51,345 INFO: Done: Result Analysis
+2016-09-06 10:23:51,420 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:51,420 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:51,420 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:23:51,420 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:23:51,420 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:23:51,420 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:23:51,421 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
+2016-09-06 10:23:51,421 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
+2016-09-06 10:23:51,421 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
+2016-09-06 10:23:51,421 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
+2016-09-06 10:23:51,421 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:23:51,422 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:23:51,422 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:23:51,422 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:23:51,462 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:23:51,462 DEBUG: Start: Training
+2016-09-06 10:23:51,463 DEBUG: Info: Time for Training: 0.0440928936005[s]
+2016-09-06 10:23:51,463 DEBUG: Done: Training
+2016-09-06 10:23:51,463 DEBUG: Start: Predicting
+2016-09-06 10:23:51,466 DEBUG: Done: Predicting
+2016-09-06 10:23:51,466 DEBUG: Start: Getting Results
+2016-09-06 10:23:51,467 DEBUG: Done: Getting Results
+2016-09-06 10:23:51,467 INFO: Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 0.880952380952
+accuracy_score on test : 0.611111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 6)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 7
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.880952380952
+ - Score on test : 0.611111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:51,467 INFO: Done: Result Analysis
+2016-09-06 10:23:51,470 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:23:51,470 DEBUG: Start: Training
+2016-09-06 10:23:51,473 DEBUG: Info: Time for Training: 0.0543620586395[s]
+2016-09-06 10:23:51,473 DEBUG: Done: Training
+2016-09-06 10:23:51,473 DEBUG: Start: Predicting
+2016-09-06 10:23:51,476 DEBUG: Done: Predicting
+2016-09-06 10:23:51,476 DEBUG: Start: Getting Results
+2016-09-06 10:23:51,478 DEBUG: Done: Getting Results
+2016-09-06 10:23:51,479 INFO: Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.6
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 6)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.6
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:51,479 INFO: Done: Result Analysis
+2016-09-06 10:23:51,574 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:51,574 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:51,574 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:23:51,574 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:23:51,574 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:23:51,574 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:23:51,575 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
+2016-09-06 10:23:51,575 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
+2016-09-06 10:23:51,575 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
+2016-09-06 10:23:51,575 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
+2016-09-06 10:23:51,576 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:23:51,576 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:23:51,576 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:23:51,576 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:23:51,623 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:23:51,623 DEBUG: Start: Training
+2016-09-06 10:23:51,624 DEBUG: Info: Time for Training: 0.0511050224304[s]
+2016-09-06 10:23:51,624 DEBUG: Done: Training
+2016-09-06 10:23:51,624 DEBUG: Start: Predicting
+2016-09-06 10:23:51,631 DEBUG: Done: Predicting
+2016-09-06 10:23:51,631 DEBUG: Start: Getting Results
+2016-09-06 10:23:51,633 DEBUG: Done: Getting Results
+2016-09-06 10:23:51,633 INFO: Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.695238095238
+accuracy_score on test : 0.611111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 6)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 7
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.695238095238
+ - Score on test : 0.611111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:51,634 INFO: Done: Result Analysis
+2016-09-06 10:23:51,937 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:23:51,937 DEBUG: Start: Training
+2016-09-06 10:23:51,987 DEBUG: Info: Time for Training: 0.414555072784[s]
+2016-09-06 10:23:51,988 DEBUG: Done: Training
+2016-09-06 10:23:51,988 DEBUG: Start: Predicting
+2016-09-06 10:23:51,994 DEBUG: Done: Predicting
+2016-09-06 10:23:51,994 DEBUG: Start: Getting Results
+2016-09-06 10:23:51,995 DEBUG: Done: Getting Results
+2016-09-06 10:23:51,996 INFO: Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.952380952381
+accuracy_score on test : 0.544444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 6)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 20, max_depth : 7
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.952380952381
+ - Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:51,996 INFO: Done: Result Analysis
+2016-09-06 10:23:52,119 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:52,119 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:52,120 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:23:52,120 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:23:52,120 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:23:52,120 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:23:52,120 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
+2016-09-06 10:23:52,120 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
+2016-09-06 10:23:52,121 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
+2016-09-06 10:23:52,121 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
+2016-09-06 10:23:52,121 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:23:52,121 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:23:52,121 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:23:52,121 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:23:52,167 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:23:52,167 DEBUG: Start: Training
+2016-09-06 10:23:52,167 DEBUG: Info: Time for Training: 0.0486381053925[s]
+2016-09-06 10:23:52,168 DEBUG: Done: Training
+2016-09-06 10:23:52,168 DEBUG: Start: Predicting
+2016-09-06 10:23:52,170 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:23:52,170 DEBUG: Start: Training
+2016-09-06 10:23:52,186 DEBUG: Done: Predicting
+2016-09-06 10:23:52,186 DEBUG: Start: Getting Results
+2016-09-06 10:23:52,187 DEBUG: Info: Time for Training: 0.0685579776764[s]
+2016-09-06 10:23:52,188 DEBUG: Done: Training
+2016-09-06 10:23:52,188 DEBUG: Done: Getting Results
+2016-09-06 10:23:52,188 DEBUG: Start: Predicting
+2016-09-06 10:23:52,188 INFO: Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.519047619048
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 6)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.519047619048
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:52,188 INFO: Done: Result Analysis
+2016-09-06 10:23:52,191 DEBUG: Done: Predicting
+2016-09-06 10:23:52,191 DEBUG: Start: Getting Results
+2016-09-06 10:23:52,192 DEBUG: Done: Getting Results
+2016-09-06 10:23:52,192 INFO: Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.514285714286
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 6)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 3508
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.514285714286
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:52,193 INFO: Done: Result Analysis
+2016-09-06 10:23:52,415 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:23:52,415 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-09-06 10:23:52,416 INFO: Info: Shape of View0 :(300, 8)
+2016-09-06 10:23:52,417 INFO: Info: Shape of View1 :(300, 13)
+2016-09-06 10:23:52,417 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:23:52,417 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-06 10:23:52,417 INFO: Info: Shape of View2 :(300, 7)
+2016-09-06 10:23:52,418 INFO: Info: Shape of View0 :(300, 8)
+2016-09-06 10:23:52,418 INFO: Info: Shape of View3 :(300, 6)
+2016-09-06 10:23:52,419 INFO: Done: Read Database Files
+2016-09-06 10:23:52,419 INFO: Start: Determine validation split for ratio 0.7
+2016-09-06 10:23:52,419 INFO: Info: Shape of View1 :(300, 13)
+2016-09-06 10:23:52,420 INFO: Info: Shape of View2 :(300, 7)
+2016-09-06 10:23:52,421 INFO: Info: Shape of View3 :(300, 6)
+2016-09-06 10:23:52,421 INFO: Done: Read Database Files
+2016-09-06 10:23:52,421 INFO: Start: Determine validation split for ratio 0.7
+2016-09-06 10:23:52,426 INFO: Done: Determine validation split
+2016-09-06 10:23:52,426 INFO: Start: Determine 5 folds
+2016-09-06 10:23:52,427 INFO: Done: Determine validation split
+2016-09-06 10:23:52,427 INFO: Start: Determine 5 folds
+2016-09-06 10:23:52,435 INFO: Info: Length of Learning Sets: 169
+2016-09-06 10:23:52,435 INFO: Info: Length of Testing Sets: 42
+2016-09-06 10:23:52,435 INFO: Info: Length of Validation Set: 89
+2016-09-06 10:23:52,435 INFO: Done: Determine folds
+2016-09-06 10:23:52,435 INFO: Start: Learning with Mumbo and 5 folds
+2016-09-06 10:23:52,435 INFO: Start: Classification
+2016-09-06 10:23:52,435 INFO: Start: Fold number 1
+2016-09-06 10:23:52,436 INFO: Info: Length of Learning Sets: 169
+2016-09-06 10:23:52,436 INFO: Info: Length of Testing Sets: 42
+2016-09-06 10:23:52,436 INFO: Info: Length of Validation Set: 89
+2016-09-06 10:23:52,436 INFO: Done: Determine folds
+2016-09-06 10:23:52,436 INFO: Start: Learning with Fusion and 5 folds
+2016-09-06 10:23:52,436 INFO: Start: Classification
+2016-09-06 10:23:52,437 INFO: Start: Fold number 1
+2016-09-06 10:23:52,457 DEBUG: Start: Iteration 1
+2016-09-06 10:23:52,463 DEBUG: View 0 : 0.516666666667
+2016-09-06 10:23:52,469 DEBUG: View 1 : 0.475
+2016-09-06 10:23:52,475 DEBUG: View 2 : 0.525
+2016-09-06 10:23:52,480 DEBUG: View 3 : 0.525
+2016-09-06 10:23:52,503 DEBUG: Best view : View2
+2016-09-06 10:23:52,559 DEBUG: Start: Iteration 2
+2016-09-06 10:23:52,565 DEBUG: View 0 : 0.733333333333
+2016-09-06 10:23:52,572 DEBUG: View 1 : 0.741666666667
+2016-09-06 10:23:52,577 DEBUG: View 2 : 0.741666666667
+2016-09-06 10:23:52,583 DEBUG: View 3 : 0.658333333333
+2016-09-06 10:23:52,610 DEBUG: Best view : View1
+2016-09-06 10:23:52,727 DEBUG: Start: Iteration 3
+2016-09-06 10:23:52,737 DEBUG: View 0 : 0.733333333333
+2016-09-06 10:23:52,748 DEBUG: View 1 : 0.741666666667
+2016-09-06 10:23:52,758 DEBUG: View 2 : 0.741666666667
+2016-09-06 10:23:52,768 DEBUG: View 3 : 0.658333333333
+2016-09-06 10:23:52,803 DEBUG: Best view : View1
+2016-09-06 10:23:52,947 DEBUG: Start: Iteration 4
+2016-09-06 10:23:52,953 DEBUG: View 0 : 0.675
+2016-09-06 10:23:52,958 DEBUG: View 1 : 0.725
+2016-09-06 10:23:52,964 DEBUG: View 2 : 0.691666666667
+2016-09-06 10:23:52,969 DEBUG: View 3 : 0.641666666667
+2016-09-06 10:23:52,998 DEBUG: Best view : View1
+2016-09-06 10:23:53,188 INFO: Start: Classification
+2016-09-06 10:23:53,569 INFO: Done: Fold number 1
+2016-09-06 10:23:53,569 INFO: Start: Fold number 2
+2016-09-06 10:23:53,589 DEBUG: Start: Iteration 1
+2016-09-06 10:23:53,594 DEBUG: View 0 : 0.603448275862
+2016-09-06 10:23:53,600 DEBUG: View 1 : 0.568965517241
+2016-09-06 10:23:53,605 DEBUG: View 2 : 0.413793103448
+2016-09-06 10:23:53,609 DEBUG: View 3 : 0.48275862069
+2016-09-06 10:23:53,631 DEBUG: Best view : View1
+2016-09-06 10:23:53,682 DEBUG: Start: Iteration 2
+2016-09-06 10:23:53,687 DEBUG: View 0 : 0.741379310345
+2016-09-06 10:23:53,693 DEBUG: View 1 : 0.724137931034
+2016-09-06 10:23:53,698 DEBUG: View 2 : 0.741379310345
+2016-09-06 10:23:53,703 DEBUG: View 3 : 0.741379310345
+2016-09-06 10:23:53,728 DEBUG: Best view : View0
+2016-09-06 10:23:53,823 DEBUG: Start: Iteration 3
+2016-09-06 10:23:53,828 DEBUG: View 0 : 0.741379310345
+2016-09-06 10:23:53,833 DEBUG: View 1 : 0.724137931034
+2016-09-06 10:23:53,839 DEBUG: View 2 : 0.741379310345
+2016-09-06 10:23:53,844 DEBUG: View 3 : 0.741379310345
+2016-09-06 10:23:53,870 DEBUG: Best view : View0
+2016-09-06 10:23:54,011 INFO: Start: Classification
+2016-09-06 10:23:54,292 INFO: Done: Fold number 2
+2016-09-06 10:23:54,292 INFO: Start: Fold number 3
+2016-09-06 10:23:54,313 DEBUG: Start: Iteration 1
+2016-09-06 10:23:54,319 DEBUG: View 0 : 0.471074380165
+2016-09-06 10:23:54,324 DEBUG: View 1 : 0.504132231405
+2016-09-06 10:23:54,329 DEBUG: View 2 : 0.504132231405
+2016-09-06 10:23:54,334 DEBUG: View 3 : 0.528925619835
+2016-09-06 10:23:54,356 DEBUG: Best view : View0
+2016-09-06 10:23:54,410 DEBUG: Start: Iteration 2
+2016-09-06 10:23:54,415 DEBUG: View 0 : 0.685950413223
+2016-09-06 10:23:54,421 DEBUG: View 1 : 0.619834710744
+2016-09-06 10:23:54,426 DEBUG: View 2 : 0.685950413223
+2016-09-06 10:23:54,431 DEBUG: View 3 : 0.760330578512
+2016-09-06 10:23:54,457 DEBUG: Best view : View3
+2016-09-06 10:23:54,556 DEBUG: Start: Iteration 3
+2016-09-06 10:23:54,562 DEBUG: View 0 : 0.685950413223
+2016-09-06 10:23:54,567 DEBUG: View 1 : 0.619834710744
+2016-09-06 10:23:54,573 DEBUG: View 2 : 0.702479338843
+2016-09-06 10:23:54,578 DEBUG: View 3 : 0.760330578512
+2016-09-06 10:23:54,605 DEBUG: Best view : View3
+2016-09-06 10:23:54,750 DEBUG: Start: Iteration 4
+2016-09-06 10:23:54,756 DEBUG: View 0 : 0.702479338843
+2016-09-06 10:23:54,761 DEBUG: View 1 : 0.669421487603
+2016-09-06 10:23:54,766 DEBUG: View 2 : 0.735537190083
+2016-09-06 10:23:54,772 DEBUG: View 3 : 0.669421487603
+2016-09-06 10:23:54,801 DEBUG: Best view : View2
+2016-09-06 10:23:54,990 INFO: Start: Classification
+2016-09-06 10:23:55,369 INFO: Done: Fold number 3
+2016-09-06 10:23:55,369 INFO: Start: Fold number 4
+2016-09-06 10:23:55,389 DEBUG: Start: Iteration 1
+2016-09-06 10:23:55,394 DEBUG: View 0 : 0.44347826087
+2016-09-06 10:23:55,399 DEBUG: View 1 : 0.547826086957
+2016-09-06 10:23:55,404 DEBUG: View 2 : 0.565217391304
+2016-09-06 10:23:55,409 DEBUG: View 3 : 0.582608695652
+2016-09-06 10:23:55,430 DEBUG: Best view : View0
+2016-09-06 10:23:55,481 DEBUG: Start: Iteration 2
+2016-09-06 10:23:55,487 DEBUG: View 0 : 0.713043478261
+2016-09-06 10:23:55,492 DEBUG: View 1 : 0.75652173913
+2016-09-06 10:23:55,497 DEBUG: View 2 : 0.747826086957
+2016-09-06 10:23:55,502 DEBUG: View 3 : 0.704347826087
+2016-09-06 10:23:55,527 DEBUG: Best view : View1
+2016-09-06 10:23:55,624 DEBUG: Start: Iteration 3
+2016-09-06 10:23:55,629 DEBUG: View 0 : 0.713043478261
+2016-09-06 10:23:55,634 DEBUG: View 1 : 0.75652173913
+2016-09-06 10:23:55,639 DEBUG: View 2 : 0.747826086957
+2016-09-06 10:23:55,644 DEBUG: View 3 : 0.704347826087
+2016-09-06 10:23:55,671 DEBUG: Best view : View1
+2016-09-06 10:23:55,811 DEBUG: Start: Iteration 4
+2016-09-06 10:23:55,817 DEBUG: View 0 : 0.730434782609
+2016-09-06 10:23:55,822 DEBUG: View 1 : 0.704347826087
+2016-09-06 10:23:55,827 DEBUG: View 2 : 0.747826086957
+2016-09-06 10:23:55,832 DEBUG: View 3 : 0.704347826087
+2016-09-06 10:23:55,860 DEBUG: Best view : View0
+2016-09-06 10:23:56,042 INFO: Start: Classification
+2016-09-06 10:23:56,413 INFO: Done: Fold number 4
+2016-09-06 10:23:56,413 INFO: Start: Fold number 5
+2016-09-06 10:23:56,433 DEBUG: Start: Iteration 1
+2016-09-06 10:23:56,438 DEBUG: View 0 : 0.529914529915
+2016-09-06 10:23:56,444 DEBUG: View 1 : 0.581196581197
+2016-09-06 10:23:56,449 DEBUG: View 2 : 0.512820512821
+2016-09-06 10:23:56,454 DEBUG: View 3 : 0.470085470085
+2016-09-06 10:23:56,475 DEBUG: Best view : View1
+2016-09-06 10:23:56,527 DEBUG: Start: Iteration 2
+2016-09-06 10:23:56,533 DEBUG: View 0 : 0.717948717949
+2016-09-06 10:23:56,538 DEBUG: View 1 : 0.752136752137
+2016-09-06 10:23:56,543 DEBUG: View 2 : 0.735042735043
+2016-09-06 10:23:56,548 DEBUG: View 3 : 0.769230769231
+2016-09-06 10:23:56,573 DEBUG: Best view : View3
+2016-09-06 10:23:56,669 DEBUG: Start: Iteration 3
+2016-09-06 10:23:56,675 DEBUG: View 0 : 0.717948717949
+2016-09-06 10:23:56,680 DEBUG: View 1 : 0.752136752137
+2016-09-06 10:23:56,686 DEBUG: View 2 : 0.735042735043
+2016-09-06 10:23:56,691 DEBUG: View 3 : 0.769230769231
+2016-09-06 10:23:56,718 DEBUG: Best view : View3
+2016-09-06 10:23:56,859 DEBUG: Start: Iteration 4
+2016-09-06 10:23:56,864 DEBUG: View 0 : 0.683760683761
+2016-09-06 10:23:56,869 DEBUG: View 1 : 0.709401709402
+2016-09-06 10:23:56,875 DEBUG: View 2 : 0.683760683761
+2016-09-06 10:23:56,879 DEBUG: View 3 : 0.735042735043
+2016-09-06 10:23:56,908 DEBUG: Best view : View0
+2016-09-06 10:23:57,092 INFO: Start: Classification
+2016-09-06 10:23:57,464 INFO: Done: Fold number 5
+2016-09-06 10:23:57,464 INFO: Done: Classification
+2016-09-06 10:23:57,464 INFO: Info: Time for Classification: 5[s]
+2016-09-06 10:23:57,464 INFO: Start: Result Analysis for Mumbo
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..2b8d6b9f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.566666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..7eddcdd0
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 0.919047619048
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 7
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.919047619048
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..a458111b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.67619047619
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 7
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.67619047619
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..64b9eb5d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.966666666667
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 20, max_depth : 7
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.966666666667
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..3cfc7072
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..1253f669
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 0.780952380952
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 7
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.780952380952
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..8df47bba
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.657142857143
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 7
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.657142857143
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..87a46146
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.966666666667
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 20, max_depth : 7
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.966666666667
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..87eb9ae8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.604761904762
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.604761904762
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..464f736b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.480952380952
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 3508
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.480952380952
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..353cb868
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 0.752380952381
+accuracy_score on test : 0.433333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 3508
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.752380952381
+ - Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..e442b3e4
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 3508
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..f728e303
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..50877a2d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 0.904761904762
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 7
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.904761904762
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..b10d7427
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.62380952381
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 7
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.62380952381
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..49d16072
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.971428571429
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 20, max_depth : 7
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.971428571429
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..19aa2ae1
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.590476190476
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.590476190476
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..ff73da41
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.5
+accuracy_score on test : 0.455555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 3508
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.5
+ - Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..c05ffbde
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 3508
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..a611a167
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.411111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 3508
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..f3a2e850
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.6
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 6)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.6
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..d2adb74a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 0.880952380952
+accuracy_score on test : 0.611111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 6)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 7
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.880952380952
+ - Score on test : 0.611111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..f4bdfa1d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.695238095238
+accuracy_score on test : 0.611111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 6)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 7
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.695238095238
+ - Score on test : 0.611111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..463c74e7
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.952380952381
+accuracy_score on test : 0.544444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 6)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 20, max_depth : 7
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.952380952381
+ - Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..d2530167
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.542857142857
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.542857142857
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..6564b15f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.490476190476
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 3508
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.490476190476
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..ffe6f80e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 0.609523809524
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 3508
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.609523809524
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..dbdf7554
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 3508
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102352Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102352Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..624e0007
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102352Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.519047619048
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 6)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.519047619048
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102352Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102352Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..56a9a0dc
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102352Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.514285714286
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 6)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 3508
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.514285714286
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102440-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-102440-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 00000000..aa39260a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102440-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,1369 @@
+2016-09-06 10:24:40,231 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-06 10:24:40,231 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.00012634375 Gbytes /!\
+2016-09-06 10:24:45,246 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-06 10:24:45,248 INFO: Start: Finding all available mono- & multiview algorithms
+2016-09-06 10:24:45,303 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:45,303 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:45,303 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:24:45,303 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:24:45,303 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:24:45,303 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:24:45,304 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:24:45,304 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:24:45,305 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:24:45,305 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:24:45,305 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:24:45,305 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:24:45,305 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:24:45,305 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:24:45,348 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:24:45,348 DEBUG: Start: Training
+2016-09-06 10:24:45,350 DEBUG: Info: Time for Training: 0.0480980873108[s]
+2016-09-06 10:24:45,350 DEBUG: Done: Training
+2016-09-06 10:24:45,350 DEBUG: Start: Predicting
+2016-09-06 10:24:45,352 DEBUG: Done: Predicting
+2016-09-06 10:24:45,353 DEBUG: Start: Getting Results
+2016-09-06 10:24:45,354 DEBUG: Done: Getting Results
+2016-09-06 10:24:45,354 INFO: Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 18
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:45,354 INFO: Done: Result Analysis
+2016-09-06 10:24:45,362 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:24:45,362 DEBUG: Start: Training
+2016-09-06 10:24:45,366 DEBUG: Info: Time for Training: 0.0647637844086[s]
+2016-09-06 10:24:45,366 DEBUG: Done: Training
+2016-09-06 10:24:45,366 DEBUG: Start: Predicting
+2016-09-06 10:24:45,369 DEBUG: Done: Predicting
+2016-09-06 10:24:45,369 DEBUG: Start: Getting Results
+2016-09-06 10:24:45,371 DEBUG: Done: Getting Results
+2016-09-06 10:24:45,371 INFO: Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:45,372 INFO: Done: Result Analysis
+2016-09-06 10:24:45,442 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:45,442 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:45,443 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:24:45,443 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:24:45,443 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:24:45,443 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:24:45,444 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:24:45,444 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:24:45,444 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:24:45,444 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:24:45,444 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:24:45,444 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:24:45,444 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:24:45,444 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:24:45,476 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:24:45,476 DEBUG: Start: Training
+2016-09-06 10:24:45,476 DEBUG: Info: Time for Training: 0.0347051620483[s]
+2016-09-06 10:24:45,476 DEBUG: Done: Training
+2016-09-06 10:24:45,476 DEBUG: Start: Predicting
+2016-09-06 10:24:45,482 DEBUG: Done: Predicting
+2016-09-06 10:24:45,482 DEBUG: Start: Getting Results
+2016-09-06 10:24:45,484 DEBUG: Done: Getting Results
+2016-09-06 10:24:45,484 INFO: Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.642857142857
+accuracy_score on test : 0.433333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 26
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.642857142857
+ - Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:45,484 INFO: Done: Result Analysis
+2016-09-06 10:24:45,890 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:24:45,890 DEBUG: Start: Training
+2016-09-06 10:24:45,958 DEBUG: Info: Time for Training: 0.516037940979[s]
+2016-09-06 10:24:45,958 DEBUG: Done: Training
+2016-09-06 10:24:45,958 DEBUG: Start: Predicting
+2016-09-06 10:24:45,965 DEBUG: Done: Predicting
+2016-09-06 10:24:45,966 DEBUG: Start: Getting Results
+2016-09-06 10:24:45,967 DEBUG: Done: Getting Results
+2016-09-06 10:24:45,967 INFO: Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 26, max_depth : 18
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:45,967 INFO: Done: Result Analysis
+2016-09-06 10:24:46,099 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:46,099 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:46,099 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:24:46,099 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:24:46,099 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:24:46,099 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:24:46,100 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:24:46,100 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:24:46,100 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:24:46,100 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:24:46,100 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:24:46,100 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:24:46,100 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:24:46,100 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:24:46,144 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:24:46,144 DEBUG: Start: Training
+2016-09-06 10:24:46,144 DEBUG: Info: Time for Training: 0.0459389686584[s]
+2016-09-06 10:24:46,144 DEBUG: Done: Training
+2016-09-06 10:24:46,145 DEBUG: Start: Predicting
+2016-09-06 10:24:46,149 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:24:46,149 DEBUG: Start: Training
+2016-09-06 10:24:46,162 DEBUG: Done: Predicting
+2016-09-06 10:24:46,162 DEBUG: Start: Getting Results
+2016-09-06 10:24:46,164 DEBUG: Done: Getting Results
+2016-09-06 10:24:46,164 INFO: Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.595238095238
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.595238095238
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:46,164 INFO: Done: Result Analysis
+2016-09-06 10:24:46,168 DEBUG: Info: Time for Training: 0.0690729618073[s]
+2016-09-06 10:24:46,168 DEBUG: Done: Training
+2016-09-06 10:24:46,168 DEBUG: Start: Predicting
+2016-09-06 10:24:46,171 DEBUG: Done: Predicting
+2016-09-06 10:24:46,171 DEBUG: Start: Getting Results
+2016-09-06 10:24:46,172 DEBUG: Done: Getting Results
+2016-09-06 10:24:46,172 INFO: Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.4
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5274
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.533333333333
+ - Score on test : 0.4
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:46,173 INFO: Done: Result Analysis
+2016-09-06 10:24:46,250 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:46,250 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:46,251 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:24:46,251 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:24:46,251 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:24:46,251 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:24:46,252 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:24:46,252 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:24:46,252 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:24:46,252 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:24:46,253 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:24:46,253 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:24:46,253 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:24:46,253 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:24:46,325 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:24:46,325 DEBUG: Start: Training
+2016-09-06 10:24:46,329 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:24:46,330 DEBUG: Start: Training
+2016-09-06 10:24:46,348 DEBUG: Info: Time for Training: 0.0991640090942[s]
+2016-09-06 10:24:46,349 DEBUG: Done: Training
+2016-09-06 10:24:46,349 DEBUG: Start: Predicting
+2016-09-06 10:24:46,354 DEBUG: Info: Time for Training: 0.104753017426[s]
+2016-09-06 10:24:46,354 DEBUG: Done: Training
+2016-09-06 10:24:46,354 DEBUG: Start: Predicting
+2016-09-06 10:24:46,356 DEBUG: Done: Predicting
+2016-09-06 10:24:46,357 DEBUG: Start: Getting Results
+2016-09-06 10:24:46,359 DEBUG: Done: Getting Results
+2016-09-06 10:24:46,359 INFO: Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5274
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:46,359 DEBUG: Done: Predicting
+2016-09-06 10:24:46,359 INFO: Done: Result Analysis
+2016-09-06 10:24:46,359 DEBUG: Start: Getting Results
+2016-09-06 10:24:46,361 DEBUG: Done: Getting Results
+2016-09-06 10:24:46,361 INFO: Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5274
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:46,361 INFO: Done: Result Analysis
+2016-09-06 10:24:46,499 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:46,499 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:46,500 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:24:46,500 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:24:46,500 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:24:46,500 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:24:46,500 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
+2016-09-06 10:24:46,500 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
+2016-09-06 10:24:46,501 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
+2016-09-06 10:24:46,501 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
+2016-09-06 10:24:46,501 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:24:46,501 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:24:46,501 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:24:46,501 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:24:46,540 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:24:46,541 DEBUG: Start: Training
+2016-09-06 10:24:46,543 DEBUG: Info: Time for Training: 0.0447499752045[s]
+2016-09-06 10:24:46,543 DEBUG: Done: Training
+2016-09-06 10:24:46,544 DEBUG: Start: Predicting
+2016-09-06 10:24:46,546 DEBUG: Done: Predicting
+2016-09-06 10:24:46,546 DEBUG: Start: Getting Results
+2016-09-06 10:24:46,548 DEBUG: Done: Getting Results
+2016-09-06 10:24:46,548 INFO: Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 16)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 18
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:46,548 INFO: Done: Result Analysis
+2016-09-06 10:24:46,555 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:24:46,555 DEBUG: Start: Training
+2016-09-06 10:24:46,560 DEBUG: Info: Time for Training: 0.0609669685364[s]
+2016-09-06 10:24:46,560 DEBUG: Done: Training
+2016-09-06 10:24:46,560 DEBUG: Start: Predicting
+2016-09-06 10:24:46,563 DEBUG: Done: Predicting
+2016-09-06 10:24:46,563 DEBUG: Start: Getting Results
+2016-09-06 10:24:46,565 DEBUG: Done: Getting Results
+2016-09-06 10:24:46,565 INFO: Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 16)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:46,565 INFO: Done: Result Analysis
+2016-09-06 10:24:46,647 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:46,647 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:46,647 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:24:46,647 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:24:46,647 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:24:46,647 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:24:46,648 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
+2016-09-06 10:24:46,648 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
+2016-09-06 10:24:46,648 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
+2016-09-06 10:24:46,648 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
+2016-09-06 10:24:46,649 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:24:46,649 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:24:46,649 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:24:46,649 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:24:46,681 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:24:46,681 DEBUG: Start: Training
+2016-09-06 10:24:46,681 DEBUG: Info: Time for Training: 0.0352909564972[s]
+2016-09-06 10:24:46,681 DEBUG: Done: Training
+2016-09-06 10:24:46,682 DEBUG: Start: Predicting
+2016-09-06 10:24:46,688 DEBUG: Done: Predicting
+2016-09-06 10:24:46,688 DEBUG: Start: Getting Results
+2016-09-06 10:24:46,689 DEBUG: Done: Getting Results
+2016-09-06 10:24:46,689 INFO: Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.557142857143
+accuracy_score on test : 0.544444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 16)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 26
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.557142857143
+ - Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:46,690 INFO: Done: Result Analysis
+2016-09-06 10:24:47,105 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:24:47,106 DEBUG: Start: Training
+2016-09-06 10:24:47,176 DEBUG: Info: Time for Training: 0.529899120331[s]
+2016-09-06 10:24:47,176 DEBUG: Done: Training
+2016-09-06 10:24:47,176 DEBUG: Start: Predicting
+2016-09-06 10:24:47,184 DEBUG: Done: Predicting
+2016-09-06 10:24:47,184 DEBUG: Start: Getting Results
+2016-09-06 10:24:47,185 DEBUG: Done: Getting Results
+2016-09-06 10:24:47,185 INFO: Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.411111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 16)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 26, max_depth : 18
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:47,186 INFO: Done: Result Analysis
+2016-09-06 10:24:47,297 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:47,297 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:47,297 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:24:47,297 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:24:47,297 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:24:47,297 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:24:47,298 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
+2016-09-06 10:24:47,298 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
+2016-09-06 10:24:47,298 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
+2016-09-06 10:24:47,298 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
+2016-09-06 10:24:47,299 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:24:47,299 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:24:47,299 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:24:47,299 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:24:47,369 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:24:47,369 DEBUG: Start: Training
+2016-09-06 10:24:47,370 DEBUG: Info: Time for Training: 0.0735681056976[s]
+2016-09-06 10:24:47,370 DEBUG: Done: Training
+2016-09-06 10:24:47,370 DEBUG: Start: Predicting
+2016-09-06 10:24:47,376 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:24:47,376 DEBUG: Start: Training
+2016-09-06 10:24:47,387 DEBUG: Done: Predicting
+2016-09-06 10:24:47,388 DEBUG: Start: Getting Results
+2016-09-06 10:24:47,390 DEBUG: Done: Getting Results
+2016-09-06 10:24:47,390 INFO: Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.552380952381
+accuracy_score on test : 0.377777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 16)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.552380952381
+ - Score on test : 0.377777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:47,390 INFO: Done: Result Analysis
+2016-09-06 10:24:47,402 DEBUG: Info: Time for Training: 0.106291055679[s]
+2016-09-06 10:24:47,403 DEBUG: Done: Training
+2016-09-06 10:24:47,403 DEBUG: Start: Predicting
+2016-09-06 10:24:47,406 DEBUG: Done: Predicting
+2016-09-06 10:24:47,406 DEBUG: Start: Getting Results
+2016-09-06 10:24:47,408 DEBUG: Done: Getting Results
+2016-09-06 10:24:47,408 INFO: Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.485714285714
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 16)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5274
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.485714285714
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:47,408 INFO: Done: Result Analysis
+2016-09-06 10:24:47,547 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:47,547 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:47,547 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:24:47,547 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:24:47,548 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:24:47,548 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:24:47,549 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
+2016-09-06 10:24:47,549 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
+2016-09-06 10:24:47,549 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
+2016-09-06 10:24:47,549 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
+2016-09-06 10:24:47,549 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:24:47,549 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:24:47,549 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:24:47,549 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:24:47,623 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:24:47,623 DEBUG: Start: Training
+2016-09-06 10:24:47,631 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:24:47,631 DEBUG: Start: Training
+2016-09-06 10:24:47,648 DEBUG: Info: Time for Training: 0.102329015732[s]
+2016-09-06 10:24:47,648 DEBUG: Done: Training
+2016-09-06 10:24:47,649 DEBUG: Start: Predicting
+2016-09-06 10:24:47,657 DEBUG: Done: Predicting
+2016-09-06 10:24:47,657 DEBUG: Start: Getting Results
+2016-09-06 10:24:47,658 DEBUG: Info: Time for Training: 0.112071990967[s]
+2016-09-06 10:24:47,658 DEBUG: Done: Training
+2016-09-06 10:24:47,658 DEBUG: Start: Predicting
+2016-09-06 10:24:47,659 DEBUG: Done: Getting Results
+2016-09-06 10:24:47,659 INFO: Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.444444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 16)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5274
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:47,660 INFO: Done: Result Analysis
+2016-09-06 10:24:47,663 DEBUG: Done: Predicting
+2016-09-06 10:24:47,663 DEBUG: Start: Getting Results
+2016-09-06 10:24:47,664 DEBUG: Done: Getting Results
+2016-09-06 10:24:47,665 INFO: Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.577777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 16)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5274
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:47,665 INFO: Done: Result Analysis
+2016-09-06 10:24:47,798 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:47,798 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:47,798 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:24:47,798 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:24:47,799 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:24:47,799 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:24:47,799 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
+2016-09-06 10:24:47,799 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
+2016-09-06 10:24:47,800 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
+2016-09-06 10:24:47,800 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
+2016-09-06 10:24:47,800 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:24:47,800 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:24:47,800 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:24:47,800 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:24:47,837 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:24:47,837 DEBUG: Start: Training
+2016-09-06 10:24:47,839 DEBUG: Info: Time for Training: 0.0416760444641[s]
+2016-09-06 10:24:47,839 DEBUG: Done: Training
+2016-09-06 10:24:47,839 DEBUG: Start: Predicting
+2016-09-06 10:24:47,842 DEBUG: Done: Predicting
+2016-09-06 10:24:47,842 DEBUG: Start: Getting Results
+2016-09-06 10:24:47,843 DEBUG: Done: Getting Results
+2016-09-06 10:24:47,843 INFO: Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 11)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 18
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:47,844 INFO: Done: Result Analysis
+2016-09-06 10:24:47,852 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:24:47,852 DEBUG: Start: Training
+2016-09-06 10:24:47,856 DEBUG: Info: Time for Training: 0.0591020584106[s]
+2016-09-06 10:24:47,856 DEBUG: Done: Training
+2016-09-06 10:24:47,857 DEBUG: Start: Predicting
+2016-09-06 10:24:47,860 DEBUG: Done: Predicting
+2016-09-06 10:24:47,860 DEBUG: Start: Getting Results
+2016-09-06 10:24:47,862 DEBUG: Done: Getting Results
+2016-09-06 10:24:47,862 INFO: Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 11)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:47,862 INFO: Done: Result Analysis
+2016-09-06 10:24:47,951 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:47,951 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:47,952 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:24:47,952 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:24:47,952 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:24:47,952 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:24:47,953 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
+2016-09-06 10:24:47,953 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
+2016-09-06 10:24:47,953 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
+2016-09-06 10:24:47,953 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
+2016-09-06 10:24:47,953 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:24:47,953 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:24:47,953 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:24:47,953 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:24:47,985 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:24:47,985 DEBUG: Start: Training
+2016-09-06 10:24:47,986 DEBUG: Info: Time for Training: 0.0345377922058[s]
+2016-09-06 10:24:47,986 DEBUG: Done: Training
+2016-09-06 10:24:47,986 DEBUG: Start: Predicting
+2016-09-06 10:24:47,992 DEBUG: Done: Predicting
+2016-09-06 10:24:47,992 DEBUG: Start: Getting Results
+2016-09-06 10:24:47,994 DEBUG: Done: Getting Results
+2016-09-06 10:24:47,994 INFO: Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.590476190476
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 11)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 26
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.590476190476
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:47,994 INFO: Done: Result Analysis
+2016-09-06 10:24:48,411 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:24:48,411 DEBUG: Start: Training
+2016-09-06 10:24:48,481 DEBUG: Info: Time for Training: 0.529783964157[s]
+2016-09-06 10:24:48,481 DEBUG: Done: Training
+2016-09-06 10:24:48,481 DEBUG: Start: Predicting
+2016-09-06 10:24:48,489 DEBUG: Done: Predicting
+2016-09-06 10:24:48,489 DEBUG: Start: Getting Results
+2016-09-06 10:24:48,490 DEBUG: Done: Getting Results
+2016-09-06 10:24:48,490 INFO: Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 11)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 26, max_depth : 18
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:48,491 INFO: Done: Result Analysis
+2016-09-06 10:24:48,607 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:48,607 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:48,608 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:24:48,608 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:24:48,608 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:24:48,608 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:24:48,609 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
+2016-09-06 10:24:48,609 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
+2016-09-06 10:24:48,609 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
+2016-09-06 10:24:48,609 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
+2016-09-06 10:24:48,609 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:24:48,609 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:24:48,609 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:24:48,609 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:24:48,677 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:24:48,677 DEBUG: Start: Training
+2016-09-06 10:24:48,678 DEBUG: Info: Time for Training: 0.0721070766449[s]
+2016-09-06 10:24:48,679 DEBUG: Done: Training
+2016-09-06 10:24:48,679 DEBUG: Start: Predicting
+2016-09-06 10:24:48,684 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:24:48,684 DEBUG: Start: Training
+2016-09-06 10:24:48,693 DEBUG: Done: Predicting
+2016-09-06 10:24:48,693 DEBUG: Start: Getting Results
+2016-09-06 10:24:48,694 DEBUG: Done: Getting Results
+2016-09-06 10:24:48,694 INFO: Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.628571428571
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 11)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.628571428571
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:48,695 INFO: Done: Result Analysis
+2016-09-06 10:24:48,703 DEBUG: Info: Time for Training: 0.0963509082794[s]
+2016-09-06 10:24:48,703 DEBUG: Done: Training
+2016-09-06 10:24:48,703 DEBUG: Start: Predicting
+2016-09-06 10:24:48,706 DEBUG: Done: Predicting
+2016-09-06 10:24:48,707 DEBUG: Start: Getting Results
+2016-09-06 10:24:48,708 DEBUG: Done: Getting Results
+2016-09-06 10:24:48,708 INFO: Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.42380952381
+accuracy_score on test : 0.444444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 11)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5274
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.42380952381
+ - Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:48,708 INFO: Done: Result Analysis
+2016-09-06 10:24:48,851 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:48,851 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:48,851 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:24:48,851 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:24:48,852 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:24:48,852 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:24:48,852 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
+2016-09-06 10:24:48,852 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
+2016-09-06 10:24:48,852 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
+2016-09-06 10:24:48,852 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:24:48,853 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
+2016-09-06 10:24:48,853 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:24:48,853 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:24:48,853 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:24:48,902 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:24:48,902 DEBUG: Start: Training
+2016-09-06 10:24:48,903 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:24:48,903 DEBUG: Start: Training
+2016-09-06 10:24:48,918 DEBUG: Info: Time for Training: 0.066841840744[s]
+2016-09-06 10:24:48,918 DEBUG: Done: Training
+2016-09-06 10:24:48,918 DEBUG: Start: Predicting
+2016-09-06 10:24:48,921 DEBUG: Done: Predicting
+2016-09-06 10:24:48,922 DEBUG: Start: Getting Results
+2016-09-06 10:24:48,922 DEBUG: Info: Time for Training: 0.0710518360138[s]
+2016-09-06 10:24:48,922 DEBUG: Done: Training
+2016-09-06 10:24:48,922 DEBUG: Start: Predicting
+2016-09-06 10:24:48,923 DEBUG: Done: Getting Results
+2016-09-06 10:24:48,923 INFO: Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.6
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 11)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5274
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.6
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:48,923 INFO: Done: Result Analysis
+2016-09-06 10:24:48,928 DEBUG: Done: Predicting
+2016-09-06 10:24:48,928 DEBUG: Start: Getting Results
+2016-09-06 10:24:48,929 DEBUG: Done: Getting Results
+2016-09-06 10:24:48,929 INFO: Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.444444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 11)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5274
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:48,929 INFO: Done: Result Analysis
+2016-09-06 10:24:48,999 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:49,000 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:24:49,000 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:24:49,000 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:49,000 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:24:49,001 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:24:49,001 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:24:49,001 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:24:49,001 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:24:49,001 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:24:49,002 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:24:49,002 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:24:49,002 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:24:49,002 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:24:49,040 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:24:49,040 DEBUG: Start: Training
+2016-09-06 10:24:49,042 DEBUG: Info: Time for Training: 0.0424060821533[s]
+2016-09-06 10:24:49,042 DEBUG: Done: Training
+2016-09-06 10:24:49,042 DEBUG: Start: Predicting
+2016-09-06 10:24:49,045 DEBUG: Done: Predicting
+2016-09-06 10:24:49,046 DEBUG: Start: Getting Results
+2016-09-06 10:24:49,047 DEBUG: Done: Getting Results
+2016-09-06 10:24:49,047 INFO: Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.577777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 18
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:49,048 INFO: Done: Result Analysis
+2016-09-06 10:24:49,051 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:24:49,051 DEBUG: Start: Training
+2016-09-06 10:24:49,054 DEBUG: Info: Time for Training: 0.0557050704956[s]
+2016-09-06 10:24:49,055 DEBUG: Done: Training
+2016-09-06 10:24:49,055 DEBUG: Start: Predicting
+2016-09-06 10:24:49,057 DEBUG: Done: Predicting
+2016-09-06 10:24:49,058 DEBUG: Start: Getting Results
+2016-09-06 10:24:49,060 DEBUG: Done: Getting Results
+2016-09-06 10:24:49,060 INFO: Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.588888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:49,060 INFO: Done: Result Analysis
+2016-09-06 10:24:49,150 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:49,151 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:24:49,151 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:24:49,151 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:24:49,152 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:24:49,152 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:24:49,152 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:24:49,152 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:49,152 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:24:49,152 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:24:49,153 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:24:49,153 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:24:49,154 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:24:49,154 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:24:49,185 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:24:49,185 DEBUG: Start: Training
+2016-09-06 10:24:49,185 DEBUG: Info: Time for Training: 0.0358011722565[s]
+2016-09-06 10:24:49,185 DEBUG: Done: Training
+2016-09-06 10:24:49,186 DEBUG: Start: Predicting
+2016-09-06 10:24:49,192 DEBUG: Done: Predicting
+2016-09-06 10:24:49,192 DEBUG: Start: Getting Results
+2016-09-06 10:24:49,193 DEBUG: Done: Getting Results
+2016-09-06 10:24:49,193 INFO: Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.57619047619
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 26
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.57619047619
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:49,194 INFO: Done: Result Analysis
+2016-09-06 10:24:49,647 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:24:49,647 DEBUG: Start: Training
+2016-09-06 10:24:49,717 DEBUG: Info: Time for Training: 0.565485954285[s]
+2016-09-06 10:24:49,717 DEBUG: Done: Training
+2016-09-06 10:24:49,717 DEBUG: Start: Predicting
+2016-09-06 10:24:49,725 DEBUG: Done: Predicting
+2016-09-06 10:24:49,725 DEBUG: Start: Getting Results
+2016-09-06 10:24:49,726 DEBUG: Done: Getting Results
+2016-09-06 10:24:49,726 INFO: Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.544444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 26, max_depth : 18
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:49,727 INFO: Done: Result Analysis
+2016-09-06 10:24:49,800 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:49,801 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:24:49,801 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:49,801 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:24:49,801 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:24:49,801 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:24:49,802 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:24:49,802 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:24:49,802 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:24:49,802 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:24:49,802 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:24:49,802 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:24:49,802 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:24:49,802 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:24:49,847 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:24:49,847 DEBUG: Start: Training
+2016-09-06 10:24:49,848 DEBUG: Info: Time for Training: 0.0476620197296[s]
+2016-09-06 10:24:49,848 DEBUG: Done: Training
+2016-09-06 10:24:49,848 DEBUG: Start: Predicting
+2016-09-06 10:24:49,854 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:24:49,854 DEBUG: Start: Training
+2016-09-06 10:24:49,869 DEBUG: Done: Predicting
+2016-09-06 10:24:49,870 DEBUG: Start: Getting Results
+2016-09-06 10:24:49,872 DEBUG: Done: Getting Results
+2016-09-06 10:24:49,872 INFO: Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.557142857143
+accuracy_score on test : 0.455555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.557142857143
+ - Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:49,872 INFO: Done: Result Analysis
+2016-09-06 10:24:49,879 DEBUG: Info: Time for Training: 0.0796000957489[s]
+2016-09-06 10:24:49,879 DEBUG: Done: Training
+2016-09-06 10:24:49,879 DEBUG: Start: Predicting
+2016-09-06 10:24:49,883 DEBUG: Done: Predicting
+2016-09-06 10:24:49,883 DEBUG: Start: Getting Results
+2016-09-06 10:24:49,884 DEBUG: Done: Getting Results
+2016-09-06 10:24:49,884 INFO: Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.547619047619
+accuracy_score on test : 0.544444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5274
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.547619047619
+ - Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:49,884 INFO: Done: Result Analysis
+2016-09-06 10:24:50,099 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:24:50,100 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-09-06 10:24:50,100 INFO: Info: Shape of View0 :(300, 9)
+2016-09-06 10:24:50,101 INFO: Info: Shape of View1 :(300, 16)
+2016-09-06 10:24:50,101 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:24:50,101 INFO: Info: Shape of View2 :(300, 11)
+2016-09-06 10:24:50,101 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-06 10:24:50,102 INFO: Info: Shape of View3 :(300, 10)
+2016-09-06 10:24:50,102 INFO: Info: Shape of View0 :(300, 9)
+2016-09-06 10:24:50,102 INFO: Done: Read Database Files
+2016-09-06 10:24:50,102 INFO: Start: Determine validation split for ratio 0.7
+2016-09-06 10:24:50,102 INFO: Info: Shape of View1 :(300, 16)
+2016-09-06 10:24:50,103 INFO: Info: Shape of View2 :(300, 11)
+2016-09-06 10:24:50,104 INFO: Info: Shape of View3 :(300, 10)
+2016-09-06 10:24:50,104 INFO: Done: Read Database Files
+2016-09-06 10:24:50,104 INFO: Start: Determine validation split for ratio 0.7
+2016-09-06 10:24:50,106 INFO: Done: Determine validation split
+2016-09-06 10:24:50,106 INFO: Start: Determine 5 folds
+2016-09-06 10:24:50,108 INFO: Done: Determine validation split
+2016-09-06 10:24:50,108 INFO: Start: Determine 5 folds
+2016-09-06 10:24:50,116 INFO: Info: Length of Learning Sets: 169
+2016-09-06 10:24:50,116 INFO: Info: Length of Testing Sets: 42
+2016-09-06 10:24:50,116 INFO: Info: Length of Validation Set: 89
+2016-09-06 10:24:50,117 INFO: Done: Determine folds
+2016-09-06 10:24:50,117 INFO: Start: Learning with Mumbo and 5 folds
+2016-09-06 10:24:50,117 INFO: Start: Classification
+2016-09-06 10:24:50,117 INFO: Start: Fold number 1
+2016-09-06 10:24:50,118 INFO: Info: Length of Learning Sets: 169
+2016-09-06 10:24:50,118 INFO: Info: Length of Testing Sets: 42
+2016-09-06 10:24:50,118 INFO: Info: Length of Validation Set: 89
+2016-09-06 10:24:50,118 INFO: Done: Determine folds
+2016-09-06 10:24:50,119 INFO: Start: Learning with Fusion and 5 folds
+2016-09-06 10:24:50,119 INFO: Start: Classification
+2016-09-06 10:24:50,119 INFO: Start: Fold number 1
+2016-09-06 10:24:50,149 DEBUG: Start: Iteration 1
+2016-09-06 10:24:50,158 DEBUG: View 0 : 0.479289940828
+2016-09-06 10:24:50,167 DEBUG: View 1 : 0.526627218935
+2016-09-06 10:24:50,175 DEBUG: View 2 : 0.538461538462
+2016-09-06 10:24:50,184 DEBUG: View 3 : 0.532544378698
+2016-09-06 10:24:50,217 DEBUG: Best view : View2
+2016-09-06 10:24:50,296 DEBUG: Start: Iteration 2
+2016-09-06 10:24:50,304 DEBUG: View 0 : 0.704142011834
+2016-09-06 10:24:50,312 DEBUG: View 1 : 0.644970414201
+2016-09-06 10:24:50,320 DEBUG: View 2 : 0.668639053254
+2016-09-06 10:24:50,328 DEBUG: View 3 : 0.739644970414
+2016-09-06 10:24:50,366 DEBUG: Best view : View3
+2016-09-06 10:24:50,510 DEBUG: Start: Iteration 3
+2016-09-06 10:24:50,521 DEBUG: View 0 : 0.704142011834
+2016-09-06 10:24:50,529 DEBUG: View 1 : 0.644970414201
+2016-09-06 10:24:50,537 DEBUG: View 2 : 0.668639053254
+2016-09-06 10:24:50,544 DEBUG: View 3 : 0.739644970414
+2016-09-06 10:24:50,582 DEBUG: Best view : View3
+2016-09-06 10:24:50,783 INFO: Start: Classification
+2016-09-06 10:24:51,128 INFO: Done: Fold number 1
+2016-09-06 10:24:51,128 INFO: Start: Fold number 2
+2016-09-06 10:24:51,157 DEBUG: Start: Iteration 1
+2016-09-06 10:24:51,164 DEBUG: View 0 : 0.502958579882
+2016-09-06 10:24:51,172 DEBUG: View 1 : 0.526627218935
+2016-09-06 10:24:51,179 DEBUG: View 2 : 0.479289940828
+2016-09-06 10:24:51,186 DEBUG: View 3 : 0.562130177515
+2016-09-06 10:24:51,216 DEBUG: Best view : View0
+2016-09-06 10:24:51,291 DEBUG: Start: Iteration 2
+2016-09-06 10:24:51,298 DEBUG: View 0 : 0.656804733728
+2016-09-06 10:24:51,306 DEBUG: View 1 : 0.710059171598
+2016-09-06 10:24:51,313 DEBUG: View 2 : 0.745562130178
+2016-09-06 10:24:51,321 DEBUG: View 3 : 0.757396449704
+2016-09-06 10:24:51,357 DEBUG: Best view : View3
+2016-09-06 10:24:51,497 DEBUG: Start: Iteration 3
+2016-09-06 10:24:51,505 DEBUG: View 0 : 0.656804733728
+2016-09-06 10:24:51,512 DEBUG: View 1 : 0.710059171598
+2016-09-06 10:24:51,520 DEBUG: View 2 : 0.745562130178
+2016-09-06 10:24:51,527 DEBUG: View 3 : 0.757396449704
+2016-09-06 10:24:51,566 DEBUG: Best view : View3
+2016-09-06 10:24:51,768 DEBUG: Start: Iteration 4
+2016-09-06 10:24:51,776 DEBUG: View 0 : 0.639053254438
+2016-09-06 10:24:51,783 DEBUG: View 1 : 0.627218934911
+2016-09-06 10:24:51,791 DEBUG: View 2 : 0.721893491124
+2016-09-06 10:24:51,798 DEBUG: View 3 : 0.692307692308
+2016-09-06 10:24:51,839 DEBUG: Best view : View2
+2016-09-06 10:24:52,104 INFO: Start: Classification
+2016-09-06 10:24:52,553 INFO: Done: Fold number 2
+2016-09-06 10:24:52,553 INFO: Start: Fold number 3
+2016-09-06 10:24:52,582 DEBUG: Start: Iteration 1
+2016-09-06 10:24:52,589 DEBUG: View 0 : 0.467455621302
+2016-09-06 10:24:52,596 DEBUG: View 1 : 0.550295857988
+2016-09-06 10:24:52,603 DEBUG: View 2 : 0.520710059172
+2016-09-06 10:24:52,609 DEBUG: View 3 : 0.479289940828
+2016-09-06 10:24:52,640 DEBUG: Best view : View0
+2016-09-06 10:24:52,714 DEBUG: Start: Iteration 2
+2016-09-06 10:24:52,722 DEBUG: View 0 : 0.721893491124
+2016-09-06 10:24:52,729 DEBUG: View 1 : 0.674556213018
+2016-09-06 10:24:52,737 DEBUG: View 2 : 0.656804733728
+2016-09-06 10:24:52,744 DEBUG: View 3 : 0.674556213018
+2016-09-06 10:24:52,780 DEBUG: Best view : View0
+2016-09-06 10:24:52,919 DEBUG: Start: Iteration 3
+2016-09-06 10:24:52,926 DEBUG: View 0 : 0.721893491124
+2016-09-06 10:24:52,933 DEBUG: View 1 : 0.674556213018
+2016-09-06 10:24:52,941 DEBUG: View 2 : 0.656804733728
+2016-09-06 10:24:52,948 DEBUG: View 3 : 0.674556213018
+2016-09-06 10:24:52,987 DEBUG: Best view : View0
+2016-09-06 10:24:53,188 INFO: Start: Classification
+2016-09-06 10:24:53,527 INFO: Done: Fold number 3
+2016-09-06 10:24:53,527 INFO: Start: Fold number 4
+2016-09-06 10:24:53,556 DEBUG: Start: Iteration 1
+2016-09-06 10:24:53,563 DEBUG: View 0 : 0.526627218935
+2016-09-06 10:24:53,570 DEBUG: View 1 : 0.485207100592
+2016-09-06 10:24:53,577 DEBUG: View 2 : 0.514792899408
+2016-09-06 10:24:53,584 DEBUG: View 3 : 0.497041420118
+2016-09-06 10:24:53,614 DEBUG: Best view : View1
+2016-09-06 10:24:53,689 DEBUG: Start: Iteration 2
+2016-09-06 10:24:53,696 DEBUG: View 0 : 0.692307692308
+2016-09-06 10:24:53,704 DEBUG: View 1 : 0.680473372781
+2016-09-06 10:24:53,711 DEBUG: View 2 : 0.739644970414
+2016-09-06 10:24:53,719 DEBUG: View 3 : 0.615384615385
+2016-09-06 10:24:53,755 DEBUG: Best view : View2
+2016-09-06 10:24:53,893 DEBUG: Start: Iteration 3
+2016-09-06 10:24:53,900 DEBUG: View 0 : 0.692307692308
+2016-09-06 10:24:53,908 DEBUG: View 1 : 0.615384615385
+2016-09-06 10:24:53,915 DEBUG: View 2 : 0.739644970414
+2016-09-06 10:24:53,923 DEBUG: View 3 : 0.615384615385
+2016-09-06 10:24:53,960 DEBUG: Best view : View2
+2016-09-06 10:24:54,163 INFO: Start: Classification
+2016-09-06 10:24:54,501 INFO: Done: Fold number 4
+2016-09-06 10:24:54,501 INFO: Start: Fold number 5
+2016-09-06 10:24:54,530 DEBUG: Start: Iteration 1
+2016-09-06 10:24:54,538 DEBUG: View 0 : 0.538461538462
+2016-09-06 10:24:54,545 DEBUG: View 1 : 0.556213017751
+2016-09-06 10:24:54,552 DEBUG: View 2 : 0.479289940828
+2016-09-06 10:24:54,560 DEBUG: View 3 : 0.550295857988
+2016-09-06 10:24:54,590 DEBUG: Best view : View2
+2016-09-06 10:24:54,665 DEBUG: Start: Iteration 2
+2016-09-06 10:24:54,672 DEBUG: View 0 : 0.686390532544
+2016-09-06 10:24:54,680 DEBUG: View 1 : 0.710059171598
+2016-09-06 10:24:54,687 DEBUG: View 2 : 0.745562130178
+2016-09-06 10:24:54,695 DEBUG: View 3 : 0.763313609467
+2016-09-06 10:24:54,731 DEBUG: Best view : View3
+2016-09-06 10:24:54,871 DEBUG: Start: Iteration 3
+2016-09-06 10:24:54,878 DEBUG: View 0 : 0.710059171598
+2016-09-06 10:24:54,886 DEBUG: View 1 : 0.710059171598
+2016-09-06 10:24:54,893 DEBUG: View 2 : 0.745562130178
+2016-09-06 10:24:54,900 DEBUG: View 3 : 0.763313609467
+2016-09-06 10:24:54,939 DEBUG: Best view : View3
+2016-09-06 10:24:55,142 DEBUG: Start: Iteration 4
+2016-09-06 10:24:55,149 DEBUG: View 0 : 0.680473372781
+2016-09-06 10:24:55,157 DEBUG: View 1 : 0.668639053254
+2016-09-06 10:24:55,164 DEBUG: View 2 : 0.775147928994
+2016-09-06 10:24:55,172 DEBUG: View 3 : 0.698224852071
+2016-09-06 10:24:55,213 DEBUG: Best view : View2
+2016-09-06 10:24:55,477 INFO: Start: Classification
+2016-09-06 10:24:55,929 INFO: Done: Fold number 5
+2016-09-06 10:24:55,930 INFO: Done: Classification
+2016-09-06 10:24:55,930 INFO: Info: Time for Classification: 5[s]
+2016-09-06 10:24:55,930 INFO: Start: Result Analysis for Mumbo
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..0a29c62f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..50d1ffc4
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 18
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..ec4b6b24
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.642857142857
+accuracy_score on test : 0.433333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 26
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.642857142857
+ - Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..f6c91352
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 26, max_depth : 18
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..99221590
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 16)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..ff3894ab
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 16)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 18
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..d1109d9f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.557142857143
+accuracy_score on test : 0.544444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 16)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 26
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.557142857143
+ - Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..68f5fddd
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.595238095238
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.595238095238
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..2ddf8a81
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.4
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5274
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.533333333333
+ - Score on test : 0.4
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..8f033747
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5274
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..417cdc11
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5274
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..803d6342
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 11)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..1bfba82f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 11)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 18
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..7fbf2011
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.590476190476
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 11)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 26
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.590476190476
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..4792d66c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.411111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 16)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 26, max_depth : 18
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..886be201
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.552380952381
+accuracy_score on test : 0.377777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 16)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.552380952381
+ - Score on test : 0.377777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..8612a084
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.485714285714
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 16)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5274
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.485714285714
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..2421faf6
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.577777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 16)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5274
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..bfb5e334
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.444444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 16)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5274
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..90bc0163
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 11)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 26, max_depth : 18
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..5cf265c5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.628571428571
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 11)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.628571428571
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..d333a548
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.42380952381
+accuracy_score on test : 0.444444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 11)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5274
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.42380952381
+ - Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..9bb3ab95
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.6
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 11)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5274
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.6
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..3985361b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.444444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 11)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5274
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..81641b03
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.588888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..b6a08cb0
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.577777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 18
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..774d9b32
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.57619047619
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 26
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.57619047619
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..ca851391
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.544444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 26, max_depth : 18
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..4672960d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.557142857143
+accuracy_score on test : 0.455555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.557142857143
+ - Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..7f7f53ac
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.547619047619
+accuracy_score on test : 0.544444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5274
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.547619047619
+ - Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102543-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-102543-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 00000000..5e79d406
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102543-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,1623 @@
+2016-09-06 10:25:43,492 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-06 10:25:43,492 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.00020134375 Gbytes /!\
+2016-09-06 10:25:48,507 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-06 10:25:48,510 INFO: Start: Finding all available mono- & multiview algorithms
+2016-09-06 10:25:48,559 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:48,559 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:48,560 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:25:48,560 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:25:48,560 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:25:48,560 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:25:48,560 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:48,560 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:48,561 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:48,561 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:48,561 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:25:48,561 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:25:48,561 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:25:48,561 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:25:48,604 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:25:48,604 DEBUG: Start: Training
+2016-09-06 10:25:48,607 DEBUG: Info: Time for Training: 0.0481219291687[s]
+2016-09-06 10:25:48,607 DEBUG: Done: Training
+2016-09-06 10:25:48,607 DEBUG: Start: Predicting
+2016-09-06 10:25:48,609 DEBUG: Done: Predicting
+2016-09-06 10:25:48,610 DEBUG: Start: Getting Results
+2016-09-06 10:25:48,611 DEBUG: Done: Getting Results
+2016-09-06 10:25:48,611 INFO: Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 0.890476190476
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.890476190476
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:48,611 INFO: Done: Result Analysis
+2016-09-06 10:25:48,619 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:25:48,619 DEBUG: Start: Training
+2016-09-06 10:25:48,624 DEBUG: Info: Time for Training: 0.0653121471405[s]
+2016-09-06 10:25:48,624 DEBUG: Done: Training
+2016-09-06 10:25:48,624 DEBUG: Start: Predicting
+2016-09-06 10:25:48,627 DEBUG: Done: Predicting
+2016-09-06 10:25:48,627 DEBUG: Start: Getting Results
+2016-09-06 10:25:48,629 DEBUG: Done: Getting Results
+2016-09-06 10:25:48,629 INFO: Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.577777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:48,629 INFO: Done: Result Analysis
+2016-09-06 10:25:48,709 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:48,709 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:48,710 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:25:48,710 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:25:48,710 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:25:48,710 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:25:48,710 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:48,710 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:48,711 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:48,711 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:48,711 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:25:48,711 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:25:48,711 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:25:48,711 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:25:48,767 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:25:48,767 DEBUG: Start: Training
+2016-09-06 10:25:48,768 DEBUG: Info: Time for Training: 0.0595369338989[s]
+2016-09-06 10:25:48,768 DEBUG: Done: Training
+2016-09-06 10:25:48,768 DEBUG: Start: Predicting
+2016-09-06 10:25:48,780 DEBUG: Done: Predicting
+2016-09-06 10:25:48,780 DEBUG: Start: Getting Results
+2016-09-06 10:25:48,782 DEBUG: Done: Getting Results
+2016-09-06 10:25:48,782 INFO: Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.547619047619
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 41
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.547619047619
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:48,783 INFO: Done: Result Analysis
+2016-09-06 10:25:49,068 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:25:49,068 DEBUG: Start: Training
+2016-09-06 10:25:49,115 DEBUG: Info: Time for Training: 0.406928062439[s]
+2016-09-06 10:25:49,116 DEBUG: Done: Training
+2016-09-06 10:25:49,116 DEBUG: Start: Predicting
+2016-09-06 10:25:49,122 DEBUG: Done: Predicting
+2016-09-06 10:25:49,122 DEBUG: Start: Getting Results
+2016-09-06 10:25:49,123 DEBUG: Done: Getting Results
+2016-09-06 10:25:49,123 INFO: Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 18, max_depth : 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.995238095238
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:49,123 INFO: Done: Result Analysis
+2016-09-06 10:25:49,263 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:49,263 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:49,264 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:25:49,264 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:25:49,264 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:25:49,264 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:25:49,265 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:49,265 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:49,265 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:49,265 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:49,265 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:25:49,265 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:25:49,265 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:25:49,265 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:25:49,313 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:25:49,313 DEBUG: Start: Training
+2016-09-06 10:25:49,314 DEBUG: Info: Time for Training: 0.0512311458588[s]
+2016-09-06 10:25:49,314 DEBUG: Done: Training
+2016-09-06 10:25:49,314 DEBUG: Start: Predicting
+2016-09-06 10:25:49,320 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:25:49,320 DEBUG: Start: Training
+2016-09-06 10:25:49,330 DEBUG: Done: Predicting
+2016-09-06 10:25:49,330 DEBUG: Start: Getting Results
+2016-09-06 10:25:49,332 DEBUG: Done: Getting Results
+2016-09-06 10:25:49,332 INFO: Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.566666666667
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.566666666667
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:49,332 INFO: Done: Result Analysis
+2016-09-06 10:25:49,344 DEBUG: Info: Time for Training: 0.0809261798859[s]
+2016-09-06 10:25:49,344 DEBUG: Done: Training
+2016-09-06 10:25:49,344 DEBUG: Start: Predicting
+2016-09-06 10:25:49,348 DEBUG: Done: Predicting
+2016-09-06 10:25:49,348 DEBUG: Start: Getting Results
+2016-09-06 10:25:49,349 DEBUG: Done: Getting Results
+2016-09-06 10:25:49,349 INFO: Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.466666666667
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5608
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.466666666667
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:49,349 INFO: Done: Result Analysis
+2016-09-06 10:25:49,411 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:49,411 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:49,412 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:25:49,412 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:25:49,412 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:25:49,412 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:25:49,413 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:49,413 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:49,413 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:49,413 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:25:49,413 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:49,413 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:25:49,413 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:25:49,413 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:25:49,462 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:25:49,462 DEBUG: Start: Training
+2016-09-06 10:25:49,472 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:25:49,472 DEBUG: Start: Training
+2016-09-06 10:25:49,480 DEBUG: Info: Time for Training: 0.0694200992584[s]
+2016-09-06 10:25:49,480 DEBUG: Done: Training
+2016-09-06 10:25:49,480 DEBUG: Start: Predicting
+2016-09-06 10:25:49,486 DEBUG: Done: Predicting
+2016-09-06 10:25:49,486 DEBUG: Start: Getting Results
+2016-09-06 10:25:49,487 DEBUG: Done: Getting Results
+2016-09-06 10:25:49,488 INFO: Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5608
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:49,488 INFO: Done: Result Analysis
+2016-09-06 10:25:49,494 DEBUG: Info: Time for Training: 0.0838282108307[s]
+2016-09-06 10:25:49,495 DEBUG: Done: Training
+2016-09-06 10:25:49,495 DEBUG: Start: Predicting
+2016-09-06 10:25:49,499 DEBUG: Done: Predicting
+2016-09-06 10:25:49,499 DEBUG: Start: Getting Results
+2016-09-06 10:25:49,500 DEBUG: Done: Getting Results
+2016-09-06 10:25:49,501 INFO: Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5608
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:49,501 INFO: Done: Result Analysis
+2016-09-06 10:25:49,565 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:49,565 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:49,566 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:25:49,566 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:25:49,566 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:25:49,566 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:25:49,567 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:49,567 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:49,567 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:49,567 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:49,567 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:25:49,567 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:25:49,568 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:25:49,568 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:25:49,617 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:25:49,617 DEBUG: Start: Training
+2016-09-06 10:25:49,620 DEBUG: Info: Time for Training: 0.0563011169434[s]
+2016-09-06 10:25:49,620 DEBUG: Done: Training
+2016-09-06 10:25:49,620 DEBUG: Start: Predicting
+2016-09-06 10:25:49,623 DEBUG: Done: Predicting
+2016-09-06 10:25:49,623 DEBUG: Start: Getting Results
+2016-09-06 10:25:49,624 DEBUG: Done: Getting Results
+2016-09-06 10:25:49,624 INFO: Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.455555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.995238095238
+ - Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:49,625 INFO: Done: Result Analysis
+2016-09-06 10:25:49,632 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:25:49,632 DEBUG: Start: Training
+2016-09-06 10:25:49,636 DEBUG: Info: Time for Training: 0.072704076767[s]
+2016-09-06 10:25:49,636 DEBUG: Done: Training
+2016-09-06 10:25:49,637 DEBUG: Start: Predicting
+2016-09-06 10:25:49,639 DEBUG: Done: Predicting
+2016-09-06 10:25:49,639 DEBUG: Start: Getting Results
+2016-09-06 10:25:49,641 DEBUG: Done: Getting Results
+2016-09-06 10:25:49,641 INFO: Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:49,642 INFO: Done: Result Analysis
+2016-09-06 10:25:49,706 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:49,706 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:49,706 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:25:49,706 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:25:49,706 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:25:49,706 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:25:49,707 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:49,707 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:49,707 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:49,707 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:49,707 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:25:49,707 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:25:49,707 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:25:49,707 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:25:49,745 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:25:49,745 DEBUG: Start: Training
+2016-09-06 10:25:49,745 DEBUG: Info: Time for Training: 0.0398440361023[s]
+2016-09-06 10:25:49,745 DEBUG: Done: Training
+2016-09-06 10:25:49,746 DEBUG: Start: Predicting
+2016-09-06 10:25:49,755 DEBUG: Done: Predicting
+2016-09-06 10:25:49,755 DEBUG: Start: Getting Results
+2016-09-06 10:25:49,756 DEBUG: Done: Getting Results
+2016-09-06 10:25:49,756 INFO: Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.571428571429
+accuracy_score on test : 0.544444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 41
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.571428571429
+ - Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:49,756 INFO: Done: Result Analysis
+2016-09-06 10:25:50,033 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:25:50,033 DEBUG: Start: Training
+2016-09-06 10:25:50,081 DEBUG: Info: Time for Training: 0.37574505806[s]
+2016-09-06 10:25:50,081 DEBUG: Done: Training
+2016-09-06 10:25:50,082 DEBUG: Start: Predicting
+2016-09-06 10:25:50,087 DEBUG: Done: Predicting
+2016-09-06 10:25:50,088 DEBUG: Start: Getting Results
+2016-09-06 10:25:50,089 DEBUG: Done: Getting Results
+2016-09-06 10:25:50,089 INFO: Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 18, max_depth : 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:50,089 INFO: Done: Result Analysis
+2016-09-06 10:25:50,155 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:50,156 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:25:50,156 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:25:50,156 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:50,156 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:25:50,156 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:25:50,156 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:50,157 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:50,157 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:50,157 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:50,157 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:25:50,157 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:25:50,157 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:25:50,157 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:25:50,204 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:25:50,204 DEBUG: Start: Training
+2016-09-06 10:25:50,205 DEBUG: Info: Time for Training: 0.0503079891205[s]
+2016-09-06 10:25:50,205 DEBUG: Done: Training
+2016-09-06 10:25:50,205 DEBUG: Start: Predicting
+2016-09-06 10:25:50,216 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:25:50,216 DEBUG: Start: Training
+2016-09-06 10:25:50,232 DEBUG: Done: Predicting
+2016-09-06 10:25:50,232 DEBUG: Start: Getting Results
+2016-09-06 10:25:50,236 DEBUG: Done: Getting Results
+2016-09-06 10:25:50,236 INFO: Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.514285714286
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.514285714286
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:50,236 INFO: Done: Result Analysis
+2016-09-06 10:25:50,245 DEBUG: Info: Time for Training: 0.0895159244537[s]
+2016-09-06 10:25:50,245 DEBUG: Done: Training
+2016-09-06 10:25:50,245 DEBUG: Start: Predicting
+2016-09-06 10:25:50,249 DEBUG: Done: Predicting
+2016-09-06 10:25:50,249 DEBUG: Start: Getting Results
+2016-09-06 10:25:50,250 DEBUG: Done: Getting Results
+2016-09-06 10:25:50,251 INFO: Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.480952380952
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5608
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.480952380952
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:50,251 INFO: Done: Result Analysis
+2016-09-06 10:25:50,403 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:50,403 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:50,403 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:25:50,403 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:25:50,403 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:25:50,403 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:25:50,404 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:50,404 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:50,404 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:25:50,404 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:25:50,404 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:50,404 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:50,404 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:25:50,405 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:25:50,454 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:25:50,454 DEBUG: Start: Training
+2016-09-06 10:25:50,466 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:25:50,466 DEBUG: Start: Training
+2016-09-06 10:25:50,472 DEBUG: Info: Time for Training: 0.0699520111084[s]
+2016-09-06 10:25:50,472 DEBUG: Done: Training
+2016-09-06 10:25:50,472 DEBUG: Start: Predicting
+2016-09-06 10:25:50,479 DEBUG: Done: Predicting
+2016-09-06 10:25:50,479 DEBUG: Start: Getting Results
+2016-09-06 10:25:50,480 DEBUG: Done: Getting Results
+2016-09-06 10:25:50,480 INFO: Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.433333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5608
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:50,480 INFO: Done: Result Analysis
+2016-09-06 10:25:50,487 DEBUG: Info: Time for Training: 0.084676027298[s]
+2016-09-06 10:25:50,487 DEBUG: Done: Training
+2016-09-06 10:25:50,487 DEBUG: Start: Predicting
+2016-09-06 10:25:50,493 DEBUG: Done: Predicting
+2016-09-06 10:25:50,493 DEBUG: Start: Getting Results
+2016-09-06 10:25:50,494 DEBUG: Done: Getting Results
+2016-09-06 10:25:50,495 INFO: Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5608
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:50,495 INFO: Done: Result Analysis
+2016-09-06 10:25:50,549 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:50,549 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:25:50,549 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:25:50,549 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:50,549 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:25:50,549 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:25:50,550 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:50,550 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:50,550 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:50,550 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:25:50,550 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:50,550 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:25:50,550 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:25:50,550 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:25:50,594 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:25:50,594 DEBUG: Start: Training
+2016-09-06 10:25:50,598 DEBUG: Info: Time for Training: 0.0494570732117[s]
+2016-09-06 10:25:50,598 DEBUG: Done: Training
+2016-09-06 10:25:50,598 DEBUG: Start: Predicting
+2016-09-06 10:25:50,601 DEBUG: Done: Predicting
+2016-09-06 10:25:50,601 DEBUG: Start: Getting Results
+2016-09-06 10:25:50,602 DEBUG: Done: Getting Results
+2016-09-06 10:25:50,603 INFO: Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 0.971428571429
+accuracy_score on test : 0.411111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.971428571429
+ - Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:50,603 INFO: Done: Result Analysis
+2016-09-06 10:25:50,604 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:25:50,605 DEBUG: Start: Training
+2016-09-06 10:25:50,610 DEBUG: Info: Time for Training: 0.0612750053406[s]
+2016-09-06 10:25:50,610 DEBUG: Done: Training
+2016-09-06 10:25:50,610 DEBUG: Start: Predicting
+2016-09-06 10:25:50,613 DEBUG: Done: Predicting
+2016-09-06 10:25:50,613 DEBUG: Start: Getting Results
+2016-09-06 10:25:50,615 DEBUG: Done: Getting Results
+2016-09-06 10:25:50,615 INFO: Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:50,615 INFO: Done: Result Analysis
+2016-09-06 10:25:50,695 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:50,695 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:50,695 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:25:50,695 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:25:50,696 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:25:50,696 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:25:50,696 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:50,696 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:50,696 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:50,696 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:25:50,696 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:50,697 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:25:50,697 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:25:50,697 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:25:50,731 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:25:50,732 DEBUG: Start: Training
+2016-09-06 10:25:50,732 DEBUG: Info: Time for Training: 0.0376601219177[s]
+2016-09-06 10:25:50,732 DEBUG: Done: Training
+2016-09-06 10:25:50,732 DEBUG: Start: Predicting
+2016-09-06 10:25:50,740 DEBUG: Done: Predicting
+2016-09-06 10:25:50,741 DEBUG: Start: Getting Results
+2016-09-06 10:25:50,742 DEBUG: Done: Getting Results
+2016-09-06 10:25:50,742 INFO: Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.580952380952
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 41
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.580952380952
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:50,742 INFO: Done: Result Analysis
+2016-09-06 10:25:51,022 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:25:51,022 DEBUG: Start: Training
+2016-09-06 10:25:51,071 DEBUG: Info: Time for Training: 0.37623500824[s]
+2016-09-06 10:25:51,071 DEBUG: Done: Training
+2016-09-06 10:25:51,071 DEBUG: Start: Predicting
+2016-09-06 10:25:51,077 DEBUG: Done: Predicting
+2016-09-06 10:25:51,077 DEBUG: Start: Getting Results
+2016-09-06 10:25:51,079 DEBUG: Done: Getting Results
+2016-09-06 10:25:51,079 INFO: Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.577777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 18, max_depth : 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:51,079 INFO: Done: Result Analysis
+2016-09-06 10:25:51,152 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:51,152 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:51,153 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:25:51,153 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:25:51,153 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:25:51,153 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:25:51,154 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:51,154 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:51,154 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:51,154 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:51,154 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:25:51,154 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:25:51,154 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:25:51,154 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:25:51,224 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:25:51,225 DEBUG: Start: Training
+2016-09-06 10:25:51,226 DEBUG: Info: Time for Training: 0.0744400024414[s]
+2016-09-06 10:25:51,226 DEBUG: Done: Training
+2016-09-06 10:25:51,226 DEBUG: Start: Predicting
+2016-09-06 10:25:51,232 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:25:51,232 DEBUG: Start: Training
+2016-09-06 10:25:51,243 DEBUG: Done: Predicting
+2016-09-06 10:25:51,244 DEBUG: Start: Getting Results
+2016-09-06 10:25:51,246 DEBUG: Done: Getting Results
+2016-09-06 10:25:51,247 INFO: Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.47619047619
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.47619047619
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:51,247 INFO: Done: Result Analysis
+2016-09-06 10:25:51,262 DEBUG: Info: Time for Training: 0.110822916031[s]
+2016-09-06 10:25:51,263 DEBUG: Done: Training
+2016-09-06 10:25:51,263 DEBUG: Start: Predicting
+2016-09-06 10:25:51,266 DEBUG: Done: Predicting
+2016-09-06 10:25:51,267 DEBUG: Start: Getting Results
+2016-09-06 10:25:51,268 DEBUG: Done: Getting Results
+2016-09-06 10:25:51,268 INFO: Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.495238095238
+accuracy_score on test : 0.633333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5608
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.495238095238
+ - Score on test : 0.633333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:51,268 INFO: Done: Result Analysis
+2016-09-06 10:25:51,400 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:51,400 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:51,401 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:25:51,401 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:25:51,401 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:25:51,401 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:25:51,402 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:51,402 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:51,402 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:51,403 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:51,403 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:25:51,403 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:25:51,403 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:25:51,403 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:25:51,474 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:25:51,474 DEBUG: Start: Training
+2016-09-06 10:25:51,487 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:25:51,487 DEBUG: Start: Training
+2016-09-06 10:25:51,500 DEBUG: Info: Time for Training: 0.101441144943[s]
+2016-09-06 10:25:51,501 DEBUG: Done: Training
+2016-09-06 10:25:51,501 DEBUG: Start: Predicting
+2016-09-06 10:25:51,509 DEBUG: Done: Predicting
+2016-09-06 10:25:51,510 DEBUG: Start: Getting Results
+2016-09-06 10:25:51,511 DEBUG: Done: Getting Results
+2016-09-06 10:25:51,512 INFO: Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.4
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5608
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.4
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:51,512 INFO: Done: Result Analysis
+2016-09-06 10:25:51,513 DEBUG: Info: Time for Training: 0.114230155945[s]
+2016-09-06 10:25:51,513 DEBUG: Done: Training
+2016-09-06 10:25:51,513 DEBUG: Start: Predicting
+2016-09-06 10:25:51,518 DEBUG: Done: Predicting
+2016-09-06 10:25:51,518 DEBUG: Start: Getting Results
+2016-09-06 10:25:51,519 DEBUG: Done: Getting Results
+2016-09-06 10:25:51,519 INFO: Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5608
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:51,519 INFO: Done: Result Analysis
+2016-09-06 10:25:51,649 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:51,649 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:51,649 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:25:51,649 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:25:51,650 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:25:51,650 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:25:51,650 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 10:25:51,650 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 10:25:51,650 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 10:25:51,650 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:25:51,650 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 10:25:51,651 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:25:51,651 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:25:51,651 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:25:51,688 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:25:51,688 DEBUG: Start: Training
+2016-09-06 10:25:51,690 DEBUG: Info: Time for Training: 0.0416340827942[s]
+2016-09-06 10:25:51,690 DEBUG: Done: Training
+2016-09-06 10:25:51,690 DEBUG: Start: Predicting
+2016-09-06 10:25:51,693 DEBUG: Done: Predicting
+2016-09-06 10:25:51,693 DEBUG: Start: Getting Results
+2016-09-06 10:25:51,694 DEBUG: Done: Getting Results
+2016-09-06 10:25:51,694 INFO: Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 0.914285714286
+accuracy_score on test : 0.544444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 18)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.914285714286
+ - Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:51,695 INFO: Done: Result Analysis
+2016-09-06 10:25:51,703 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:25:51,703 DEBUG: Start: Training
+2016-09-06 10:25:51,708 DEBUG: Info: Time for Training: 0.0598511695862[s]
+2016-09-06 10:25:51,708 DEBUG: Done: Training
+2016-09-06 10:25:51,709 DEBUG: Start: Predicting
+2016-09-06 10:25:51,712 DEBUG: Done: Predicting
+2016-09-06 10:25:51,712 DEBUG: Start: Getting Results
+2016-09-06 10:25:51,713 DEBUG: Done: Getting Results
+2016-09-06 10:25:51,714 INFO: Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 18)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:51,714 INFO: Done: Result Analysis
+2016-09-06 10:25:51,800 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:51,800 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:51,801 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:25:51,801 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:25:51,801 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:25:51,801 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:25:51,802 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 10:25:51,802 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 10:25:51,802 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 10:25:51,802 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 10:25:51,802 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:25:51,802 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:25:51,802 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:25:51,802 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:25:51,853 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:25:51,853 DEBUG: Start: Training
+2016-09-06 10:25:51,854 DEBUG: Info: Time for Training: 0.0547788143158[s]
+2016-09-06 10:25:51,855 DEBUG: Done: Training
+2016-09-06 10:25:51,855 DEBUG: Start: Predicting
+2016-09-06 10:25:51,866 DEBUG: Done: Predicting
+2016-09-06 10:25:51,866 DEBUG: Start: Getting Results
+2016-09-06 10:25:51,868 DEBUG: Done: Getting Results
+2016-09-06 10:25:51,868 INFO: Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.571428571429
+accuracy_score on test : 0.588888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 18)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 41
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.571428571429
+ - Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:51,868 INFO: Done: Result Analysis
+2016-09-06 10:25:52,153 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:25:52,153 DEBUG: Start: Training
+2016-09-06 10:25:52,201 DEBUG: Info: Time for Training: 0.401795864105[s]
+2016-09-06 10:25:52,202 DEBUG: Done: Training
+2016-09-06 10:25:52,202 DEBUG: Start: Predicting
+2016-09-06 10:25:52,208 DEBUG: Done: Predicting
+2016-09-06 10:25:52,208 DEBUG: Start: Getting Results
+2016-09-06 10:25:52,209 DEBUG: Done: Getting Results
+2016-09-06 10:25:52,209 INFO: Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.6
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 18)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 18, max_depth : 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.6
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:52,209 INFO: Done: Result Analysis
+2016-09-06 10:25:52,349 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:52,350 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:52,350 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:25:52,350 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:25:52,350 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:25:52,350 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:25:52,351 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 10:25:52,351 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 10:25:52,351 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 10:25:52,351 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 10:25:52,351 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:25:52,351 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:25:52,351 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:25:52,351 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:25:52,399 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:25:52,399 DEBUG: Start: Training
+2016-09-06 10:25:52,400 DEBUG: Info: Time for Training: 0.0507619380951[s]
+2016-09-06 10:25:52,400 DEBUG: Done: Training
+2016-09-06 10:25:52,400 DEBUG: Start: Predicting
+2016-09-06 10:25:52,407 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:25:52,407 DEBUG: Start: Training
+2016-09-06 10:25:52,425 DEBUG: Done: Predicting
+2016-09-06 10:25:52,426 DEBUG: Start: Getting Results
+2016-09-06 10:25:52,427 DEBUG: Done: Getting Results
+2016-09-06 10:25:52,427 INFO: Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.566666666667
+accuracy_score on test : 0.644444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 18)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.566666666667
+ - Score on test : 0.644444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:52,428 INFO: Done: Result Analysis
+2016-09-06 10:25:52,431 DEBUG: Info: Time for Training: 0.0818819999695[s]
+2016-09-06 10:25:52,431 DEBUG: Done: Training
+2016-09-06 10:25:52,431 DEBUG: Start: Predicting
+2016-09-06 10:25:52,435 DEBUG: Done: Predicting
+2016-09-06 10:25:52,435 DEBUG: Start: Getting Results
+2016-09-06 10:25:52,436 DEBUG: Done: Getting Results
+2016-09-06 10:25:52,436 INFO: Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.452380952381
+accuracy_score on test : 0.388888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 18)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5608
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.452380952381
+ - Score on test : 0.388888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:52,436 INFO: Done: Result Analysis
+2016-09-06 10:25:52,647 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:25:52,647 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-09-06 10:25:52,648 INFO: Info: Shape of View0 :(300, 20)
+2016-09-06 10:25:52,649 INFO: Info: Shape of View1 :(300, 20)
+2016-09-06 10:25:52,649 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:25:52,649 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-06 10:25:52,649 INFO: Info: Shape of View2 :(300, 20)
+2016-09-06 10:25:52,650 INFO: Info: Shape of View3 :(300, 18)
+2016-09-06 10:25:52,650 INFO: Info: Shape of View0 :(300, 20)
+2016-09-06 10:25:52,650 INFO: Done: Read Database Files
+2016-09-06 10:25:52,651 INFO: Start: Determine validation split for ratio 0.7
+2016-09-06 10:25:52,652 INFO: Info: Shape of View1 :(300, 20)
+2016-09-06 10:25:52,653 INFO: Info: Shape of View2 :(300, 20)
+2016-09-06 10:25:52,654 INFO: Info: Shape of View3 :(300, 18)
+2016-09-06 10:25:52,654 INFO: Done: Read Database Files
+2016-09-06 10:25:52,654 INFO: Start: Determine validation split for ratio 0.7
+2016-09-06 10:25:52,656 INFO: Done: Determine validation split
+2016-09-06 10:25:52,656 INFO: Start: Determine 5 folds
+2016-09-06 10:25:52,658 INFO: Done: Determine validation split
+2016-09-06 10:25:52,658 INFO: Start: Determine 5 folds
+2016-09-06 10:25:52,665 INFO: Info: Length of Learning Sets: 168
+2016-09-06 10:25:52,665 INFO: Info: Length of Testing Sets: 42
+2016-09-06 10:25:52,666 INFO: Info: Length of Validation Set: 90
+2016-09-06 10:25:52,666 INFO: Done: Determine folds
+2016-09-06 10:25:52,666 INFO: Start: Learning with Mumbo and 5 folds
+2016-09-06 10:25:52,666 INFO: Start: Classification
+2016-09-06 10:25:52,666 INFO: Start: Fold number 1
+2016-09-06 10:25:52,669 INFO: Info: Length of Learning Sets: 168
+2016-09-06 10:25:52,669 INFO: Info: Length of Testing Sets: 42
+2016-09-06 10:25:52,670 INFO: Info: Length of Validation Set: 90
+2016-09-06 10:25:52,670 INFO: Done: Determine folds
+2016-09-06 10:25:52,670 INFO: Start: Learning with Fusion and 5 folds
+2016-09-06 10:25:52,670 INFO: Start: Classification
+2016-09-06 10:25:52,670 INFO: Start: Fold number 1
+2016-09-06 10:25:52,699 DEBUG: Start: Iteration 1
+2016-09-06 10:25:52,708 DEBUG: View 0 : 0.434523809524
+2016-09-06 10:25:52,716 DEBUG: View 1 : 0.470238095238
+2016-09-06 10:25:52,724 DEBUG: View 2 : 0.505952380952
+2016-09-06 10:25:52,731 DEBUG: View 3 : 0.553571428571
+2016-09-06 10:25:52,763 DEBUG: Best view : View0
+2016-09-06 10:25:52,841 DEBUG: Start: Iteration 2
+2016-09-06 10:25:52,849 DEBUG: View 0 : 0.720238095238
+2016-09-06 10:25:52,857 DEBUG: View 1 : 0.666666666667
+2016-09-06 10:25:52,865 DEBUG: View 2 : 0.660714285714
+2016-09-06 10:25:52,872 DEBUG: View 3 : 0.744047619048
+2016-09-06 10:25:52,909 DEBUG: Best view : View3
+2016-09-06 10:25:53,053 DEBUG: Start: Iteration 3
+2016-09-06 10:25:53,071 DEBUG: View 0 : 0.720238095238
+2016-09-06 10:25:53,080 DEBUG: View 1 : 0.666666666667
+2016-09-06 10:25:53,088 DEBUG: View 2 : 0.660714285714
+2016-09-06 10:25:53,095 DEBUG: View 3 : 0.744047619048
+2016-09-06 10:25:53,134 DEBUG: Best view : View3
+2016-09-06 10:25:53,338 DEBUG: Start: Iteration 4
+2016-09-06 10:25:53,345 DEBUG: View 0 : 0.77380952381
+2016-09-06 10:25:53,353 DEBUG: View 1 : 0.732142857143
+2016-09-06 10:25:53,360 DEBUG: View 2 : 0.678571428571
+2016-09-06 10:25:53,369 DEBUG: View 3 : 0.690476190476
+2016-09-06 10:25:53,419 DEBUG: Best view : View0
+2016-09-06 10:25:53,697 INFO: Start: Classification
+2016-09-06 10:25:54,148 INFO: Done: Fold number 1
+2016-09-06 10:25:54,148 INFO: Start: Fold number 2
+2016-09-06 10:25:54,177 DEBUG: Start: Iteration 1
+2016-09-06 10:25:54,184 DEBUG: View 0 : 0.5
+2016-09-06 10:25:54,190 DEBUG: View 1 : 0.5
+2016-09-06 10:25:54,197 DEBUG: View 2 : 0.5
+2016-09-06 10:25:54,204 DEBUG: View 3 : 0.5
+2016-09-06 10:25:54,234 DEBUG: Best view : View0
+2016-09-06 10:25:54,309 DEBUG: Start: Iteration 2
+2016-09-06 10:25:54,317 DEBUG: View 0 : 0.72619047619
+2016-09-06 10:25:54,325 DEBUG: View 1 : 0.761904761905
+2016-09-06 10:25:54,333 DEBUG: View 2 : 0.666666666667
+2016-09-06 10:25:54,341 DEBUG: View 3 : 0.732142857143
+2016-09-06 10:25:54,377 DEBUG: Best view : View1
+2016-09-06 10:25:54,515 DEBUG: Start: Iteration 3
+2016-09-06 10:25:54,523 DEBUG: View 0 : 0.72619047619
+2016-09-06 10:25:54,531 DEBUG: View 1 : 0.761904761905
+2016-09-06 10:25:54,539 DEBUG: View 2 : 0.666666666667
+2016-09-06 10:25:54,546 DEBUG: View 3 : 0.732142857143
+2016-09-06 10:25:54,584 DEBUG: Best view : View1
+2016-09-06 10:25:54,786 DEBUG: Start: Iteration 4
+2016-09-06 10:25:54,794 DEBUG: View 0 : 0.678571428571
+2016-09-06 10:25:54,802 DEBUG: View 1 : 0.744047619048
+2016-09-06 10:25:54,809 DEBUG: View 2 : 0.738095238095
+2016-09-06 10:25:54,817 DEBUG: View 3 : 0.708333333333
+2016-09-06 10:25:54,857 DEBUG: Best view : View1
+2016-09-06 10:25:55,121 INFO: Start: Classification
+2016-09-06 10:25:55,575 INFO: Done: Fold number 2
+2016-09-06 10:25:55,575 INFO: Start: Fold number 3
+2016-09-06 10:25:55,604 DEBUG: Start: Iteration 1
+2016-09-06 10:25:55,611 DEBUG: View 0 : 0.577380952381
+2016-09-06 10:25:55,619 DEBUG: View 1 : 0.52380952381
+2016-09-06 10:25:55,626 DEBUG: View 2 : 0.47619047619
+2016-09-06 10:25:55,633 DEBUG: View 3 : 0.541666666667
+2016-09-06 10:25:55,664 DEBUG: Best view : View0
+2016-09-06 10:25:55,738 DEBUG: Start: Iteration 2
+2016-09-06 10:25:55,746 DEBUG: View 0 : 0.72619047619
+2016-09-06 10:25:55,754 DEBUG: View 1 : 0.77380952381
+2016-09-06 10:25:55,762 DEBUG: View 2 : 0.714285714286
+2016-09-06 10:25:55,769 DEBUG: View 3 : 0.767857142857
+2016-09-06 10:25:55,805 DEBUG: Best view : View1
+2016-09-06 10:25:55,944 DEBUG: Start: Iteration 3
+2016-09-06 10:25:55,952 DEBUG: View 0 : 0.72619047619
+2016-09-06 10:25:55,960 DEBUG: View 1 : 0.77380952381
+2016-09-06 10:25:55,967 DEBUG: View 2 : 0.714285714286
+2016-09-06 10:25:55,975 DEBUG: View 3 : 0.767857142857
+2016-09-06 10:25:56,013 DEBUG: Best view : View1
+2016-09-06 10:25:56,215 DEBUG: Start: Iteration 4
+2016-09-06 10:25:56,223 DEBUG: View 0 : 0.72619047619
+2016-09-06 10:25:56,231 DEBUG: View 1 : 0.672619047619
+2016-09-06 10:25:56,239 DEBUG: View 2 : 0.738095238095
+2016-09-06 10:25:56,246 DEBUG: View 3 : 0.702380952381
+2016-09-06 10:25:56,287 DEBUG: Best view : View2
+2016-09-06 10:25:56,554 INFO: Start: Classification
+2016-09-06 10:25:57,010 INFO: Done: Fold number 3
+2016-09-06 10:25:57,010 INFO: Start: Fold number 4
+2016-09-06 10:25:57,038 DEBUG: Start: Iteration 1
+2016-09-06 10:25:57,045 DEBUG: View 0 : 0.535714285714
+2016-09-06 10:25:57,053 DEBUG: View 1 : 0.5
+2016-09-06 10:25:57,060 DEBUG: View 2 : 0.440476190476
+2016-09-06 10:25:57,067 DEBUG: View 3 : 0.47619047619
+2016-09-06 10:25:57,097 DEBUG: Best view : View3
+2016-09-06 10:25:57,171 DEBUG: Start: Iteration 2
+2016-09-06 10:25:57,178 DEBUG: View 0 : 0.779761904762
+2016-09-06 10:25:57,186 DEBUG: View 1 : 0.744047619048
+2016-09-06 10:25:57,193 DEBUG: View 2 : 0.702380952381
+2016-09-06 10:25:57,201 DEBUG: View 3 : 0.714285714286
+2016-09-06 10:25:57,237 DEBUG: Best view : View0
+2016-09-06 10:25:57,376 DEBUG: Start: Iteration 3
+2016-09-06 10:25:57,383 DEBUG: View 0 : 0.779761904762
+2016-09-06 10:25:57,391 DEBUG: View 1 : 0.744047619048
+2016-09-06 10:25:57,399 DEBUG: View 2 : 0.702380952381
+2016-09-06 10:25:57,406 DEBUG: View 3 : 0.714285714286
+2016-09-06 10:25:57,445 DEBUG: Best view : View0
+2016-09-06 10:25:57,648 DEBUG: Start: Iteration 4
+2016-09-06 10:25:57,656 DEBUG: View 0 : 0.72619047619
+2016-09-06 10:25:57,664 DEBUG: View 1 : 0.672619047619
+2016-09-06 10:25:57,671 DEBUG: View 2 : 0.690476190476
+2016-09-06 10:25:57,678 DEBUG: View 3 : 0.732142857143
+2016-09-06 10:25:57,719 DEBUG: Best view : View3
+2016-09-06 10:25:57,985 INFO: Start: Classification
+2016-09-06 10:25:58,436 INFO: Done: Fold number 4
+2016-09-06 10:25:58,436 INFO: Start: Fold number 5
+2016-09-06 10:25:58,464 DEBUG: Start: Iteration 1
+2016-09-06 10:25:58,471 DEBUG: View 0 : 0.5
+2016-09-06 10:25:58,477 DEBUG: View 1 : 0.5
+2016-09-06 10:25:58,484 DEBUG: View 2 : 0.5
+2016-09-06 10:25:58,491 DEBUG: View 3 : 0.5
+2016-09-06 10:25:58,520 DEBUG: Best view : View0
+2016-09-06 10:25:58,595 DEBUG: Start: Iteration 2
+2016-09-06 10:25:58,602 DEBUG: View 0 : 0.75
+2016-09-06 10:25:58,610 DEBUG: View 1 : 0.75
+2016-09-06 10:25:58,618 DEBUG: View 2 : 0.714285714286
+2016-09-06 10:25:58,625 DEBUG: View 3 : 0.738095238095
+2016-09-06 10:25:58,661 DEBUG: Best view : View0
+2016-09-06 10:25:58,798 DEBUG: Start: Iteration 3
+2016-09-06 10:25:58,806 DEBUG: View 0 : 0.75
+2016-09-06 10:25:58,813 DEBUG: View 1 : 0.75
+2016-09-06 10:25:58,821 DEBUG: View 2 : 0.714285714286
+2016-09-06 10:25:58,829 DEBUG: View 3 : 0.738095238095
+2016-09-06 10:25:58,867 DEBUG: Best view : View0
+2016-09-06 10:25:59,068 DEBUG: Start: Iteration 4
+2016-09-06 10:25:59,075 DEBUG: View 0 : 0.720238095238
+2016-09-06 10:25:59,083 DEBUG: View 1 : 0.642857142857
+2016-09-06 10:25:59,091 DEBUG: View 2 : 0.660714285714
+2016-09-06 10:25:59,098 DEBUG: View 3 : 0.678571428571
+2016-09-06 10:25:59,138 DEBUG: Best view : View0
+2016-09-06 10:25:59,405 INFO: Start: Classification
+2016-09-06 10:25:59,857 INFO: Done: Fold number 5
+2016-09-06 10:25:59,857 INFO: Done: Classification
+2016-09-06 10:25:59,857 INFO: Info: Time for Classification: 7[s]
+2016-09-06 10:25:59,857 INFO: Start: Result Analysis for Mumbo
+2016-09-06 10:26:02,299 INFO: Result for Multiview classification with Mumbo
+
+Average accuracy :
+ -On Train : 76.1904761905
+ -On Test : 50.9523809524
+ -On Validation : 51.3333333333Dataset info :
+ -Dataset name : Fake
+ -Labels : Methyl, MiRNA_, RNASeq, Clinic
+ -Views : View0 of shape (300, 20), View1 of shape (300, 20), View2 of shape (300, 20), View3 of shape (300, 18)
+ -5 folds
+ - Validation set length : 90 for learning rate : 0.7 on a total number of examples of 300
+
+
+
+Mumbo configuration :
+ -Used 1 core(s)
+ -Iterations : min 1, max 10, threshold 0.01
+ -Weak Classifiers : DecisionTree with depth 3, sub-sampled at 1.0 on View0
+ -DecisionTree with depth 3, sub-sampled at 1.0 on View1
+ -DecisionTree with depth 3, sub-sampled at 1.0 on View2
+ -DecisionTree with depth 3, sub-sampled at 1.0 on View3
+
+
+
+Computation time on 1 cores :
+ Database extraction time : 0:00:00
+ Learn Prediction
+ Fold 1 0:00:01 0:00:00
+ Fold 2 0:00:02 0:00:00
+ Fold 3 0:00:03 0:00:00
+ Fold 4 0:00:05 0:00:00
+ Fold 5 0:00:06 0:00:00
+ Total 0:00:19 0:00:02
+ So a total classification time of 0:00:07.
+
+
+
+Mean average accuracies and stats for each fold :
+ - Fold 0, used 5
+ - On View0 :
+ - Mean average Accuracy : 0.264880952381
+ - Percentage of time chosen : 0.8
+ - On View1 :
+ - Mean average Accuracy : 0.253571428571
+ - Percentage of time chosen : 0.0
+ - On View2 :
+ - Mean average Accuracy : 0.250595238095
+ - Percentage of time chosen : 0.0
+ - On View3 :
+ - Mean average Accuracy : 0.273214285714
+ - Percentage of time chosen : 0.2
+ - Fold 1, used 5
+ - On View0 :
+ - Mean average Accuracy : 0.263095238095
+ - Percentage of time chosen : 0.7
+ - On View1 :
+ - Mean average Accuracy : 0.276785714286
+ - Percentage of time chosen : 0.3
+ - On View2 :
+ - Mean average Accuracy : 0.257142857143
+ - Percentage of time chosen : 0.0
+ - On View3 :
+ - Mean average Accuracy : 0.267261904762
+ - Percentage of time chosen : 0.0
+ - Fold 2, used 5
+ - On View0 :
+ - Mean average Accuracy : 0.275595238095
+ - Percentage of time chosen : 0.7
+ - On View1 :
+ - Mean average Accuracy : 0.274404761905
+ - Percentage of time chosen : 0.2
+ - On View2 :
+ - Mean average Accuracy : 0.264285714286
+ - Percentage of time chosen : 0.1
+ - On View3 :
+ - Mean average Accuracy : 0.277976190476
+ - Percentage of time chosen : 0.0
+ - Fold 3, used 5
+ - On View0 :
+ - Mean average Accuracy : 0.282142857143
+ - Percentage of time chosen : 0.8
+ - On View1 :
+ - Mean average Accuracy : 0.266071428571
+ - Percentage of time chosen : 0.0
+ - On View2 :
+ - Mean average Accuracy : 0.253571428571
+ - Percentage of time chosen : 0.0
+ - On View3 :
+ - Mean average Accuracy : 0.26369047619
+ - Percentage of time chosen : 0.2
+ - Fold 4, used 5
+ - On View0 :
+ - Mean average Accuracy : 0.272023809524
+ - Percentage of time chosen : 1.0
+ - On View1 :
+ - Mean average Accuracy : 0.264285714286
+ - Percentage of time chosen : 0.0
+ - On View2 :
+ - Mean average Accuracy : 0.258928571429
+ - Percentage of time chosen : 0.0
+ - On View3 :
+ - Mean average Accuracy : 0.265476190476
+ - Percentage of time chosen : 0.0
+
+ For each iteration :
+ - Iteration 1
+ Fold 1
+ Accuracy on train : 50.0
+ Accuracy on test : 0.0
+ Accuracy on validation : 50.0
+ Selected View : View0
+ Fold 2
+ Accuracy on train : 50.0
+ Accuracy on test : 0.0
+ Accuracy on validation : 50.0
+ Selected View : View0
+ Fold 3
+ Accuracy on train : 50.0
+ Accuracy on test : 0.0
+ Accuracy on validation : 50.0
+ Selected View : View0
+ Fold 4
+ Accuracy on train : 50.0
+ Accuracy on test : 0.0
+ Accuracy on validation : 50.0
+ Selected View : View3
+ Fold 5
+ Accuracy on train : 50.0
+ Accuracy on test : 0.0
+ Accuracy on validation : 50.0
+ Selected View : View0
+ - Iteration 2
+ Fold 1
+ Accuracy on train : 74.4047619048
+ Accuracy on test : 0.0
+ Accuracy on validation : 51.1111111111
+ Selected View : View3
+ Fold 2
+ Accuracy on train : 76.1904761905
+ Accuracy on test : 0.0
+ Accuracy on validation : 51.1111111111
+ Selected View : View1
+ Fold 3
+ Accuracy on train : 77.380952381
+ Accuracy on test : 0.0
+ Accuracy on validation : 51.1111111111
+ Selected View : View1
+ Fold 4
+ Accuracy on train : 77.9761904762
+ Accuracy on test : 0.0
+ Accuracy on validation : 52.2222222222
+ Selected View : View0
+ Fold 5
+ Accuracy on train : 75.0
+ Accuracy on test : 0.0
+ Accuracy on validation : 52.2222222222
+ Selected View : View0
+ - Iteration 3
+ Fold 1
+ Accuracy on train : 74.4047619048
+ Accuracy on test : 0.0
+ Accuracy on validation : 51.1111111111
+ Selected View : View3
+ Fold 2
+ Accuracy on train : 76.1904761905
+ Accuracy on test : 0.0
+ Accuracy on validation : 51.1111111111
+ Selected View : View1
+ Fold 3
+ Accuracy on train : 77.380952381
+ Accuracy on test : 0.0
+ Accuracy on validation : 51.1111111111
+ Selected View : View1
+ Fold 4
+ Accuracy on train : 77.9761904762
+ Accuracy on test : 0.0
+ Accuracy on validation : 52.2222222222
+ Selected View : View0
+ Fold 5
+ Accuracy on train : 75.0
+ Accuracy on test : 0.0
+ Accuracy on validation : 52.2222222222
+ Selected View : View0
+ - Iteration 4
+ Fold 1
+ Accuracy on train : 74.4047619048
+ Accuracy on test : 0.0
+ Accuracy on validation : 50.0
+ Selected View : View0
+ Fold 2
+ Accuracy on train : 76.1904761905
+ Accuracy on test : 0.0
+ Accuracy on validation : 51.1111111111
+ Selected View : View1
+ Fold 3
+ Accuracy on train : 77.380952381
+ Accuracy on test : 0.0
+ Accuracy on validation : 51.1111111111
+ Selected View : View2
+ Fold 4
+ Accuracy on train : 77.9761904762
+ Accuracy on test : 0.0
+ Accuracy on validation : 52.2222222222
+ Selected View : View3
+ Fold 5
+ Accuracy on train : 75.0
+ Accuracy on test : 0.0
+ Accuracy on validation : 52.2222222222
+ Selected View : View0
+ - Iteration 5
+ Fold 1
+ Accuracy on train : 50.0
+ Accuracy on test : 0.0
+ Accuracy on validation : 50.0
+ Selected View : View0
+ Fold 2
+ Accuracy on train : 50.0
+ Accuracy on test : 0.0
+ Accuracy on validation : 50.0
+ Selected View : View0
+ Fold 3
+ Accuracy on train : 50.0
+ Accuracy on test : 0.0
+ Accuracy on validation : 50.0
+ Selected View : View0
+ Fold 4
+ Accuracy on train : 50.0
+ Accuracy on test : 0.0
+ Accuracy on validation : 50.0
+ Selected View : View0
+ Fold 5
+ Accuracy on train : 50.0
+ Accuracy on test : 0.0
+ Accuracy on validation : 50.0
+ Selected View : View0
+2016-09-06 10:26:02,608 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..d211664d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.577777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..d51b4eaf
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 0.890476190476
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.890476190476
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..e3ecbf95
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.547619047619
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 41
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.547619047619
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..4b027c77
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..a7e7788a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.455555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.995238095238
+ - Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..30353e79
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.571428571429
+accuracy_score on test : 0.544444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 41
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.571428571429
+ - Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..b1fdee3f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 18, max_depth : 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.995238095238
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..d845e8ff
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.566666666667
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.566666666667
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..7979763e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.466666666667
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5608
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.466666666667
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..0d73c12c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5608
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..e7530b20
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5608
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..81b99c2e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..e1cc8a5d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 0.971428571429
+accuracy_score on test : 0.411111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.971428571429
+ - Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..28d885b8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.580952380952
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 41
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.580952380952
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..e4c62d65
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 18, max_depth : 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..524dffb8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.514285714286
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.514285714286
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..d606bc69
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.480952380952
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5608
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.480952380952
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..453b1235
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5608
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..7c633b5c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.433333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5608
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..672961f8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 18)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..0eb148d0
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 0.914285714286
+accuracy_score on test : 0.544444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 18)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.914285714286
+ - Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..bb5e97e2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.571428571429
+accuracy_score on test : 0.588888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 18)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 41
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.571428571429
+ - Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..c393d706
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.577777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 18, max_depth : 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..871e4290
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.47619047619
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.47619047619
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..a6d7a519
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.495238095238
+accuracy_score on test : 0.633333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5608
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.495238095238
+ - Score on test : 0.633333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..f7ae9cc6
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5608
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..1a3bc86e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.4
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5608
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.4
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..e5648785
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.6
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 18)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 18, max_depth : 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.6
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..a809878b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.566666666667
+accuracy_score on test : 0.644444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 18)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.566666666667
+ - Score on test : 0.644444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..805ad6de
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.452380952381
+accuracy_score on test : 0.388888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 18)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 5608
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.452380952381
+ - Score on test : 0.388888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102602Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Results/20160906-102602Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
new file mode 100644
index 0000000000000000000000000000000000000000..26f1647524b9834de293998d66cd7531623da06f
GIT binary patch
literal 53629
zcmeAS@N?(olHy`uVBq!ia0y~yU{+vYV2a>iV_;yIRn}C%z`(##?Bp53!NI{%!;#X#
zz`(#+;1OBOz`&mf!i+2ImuE6CC@^@sIEGZrd2_ci=IqV?>>sZ0J=zm{!XUUa=*nI1
zYLD$lg%Vp%SRFEuHcpdJ<mr`sEn%W8xQQ?IO;?TYjEut_Q@RwZw_N)6@9)L+pWj|v
zo0t0N<J`}G+`oUeoL_wI`JD65cReq*doIH$C}`357=ol4Klr9MKsh_=`R_42gh*Jd
zyZC{P<9MMA81=T9J3z(zvQ73d9)gO;EPlu<05^*_zo7u4Ugmm61#=6`ShvH{3Q+Om
zrc$F;NPz<CPV}J*7X;>7l{$HPa^5a`c1Dt;)#=PE)794=pPy&jEv)Xh;k=}z<b{hD
zR~FlC%e~#U(7FBEPmB8c`jR&{1jWR}3`|X1w{A7Pwl=!`(xppQ$3yjujfHD}eOYPf
zlar(4)-M<P-XtkGS#kR5g#j8DmU@eC$-OPc)yl-x%A_5>ZcX`~^@rPdr8ktljbd=9
zsj>O?_V)2_Q>RRE&=66wx8Hy6V_SRs;iE^FI#y<T)Gb=PIC5t9(WEmo44ID}J<5Bk
zf7zF>Ukm^K`s(JndS6phlR?sv4lDWa`&F@ft4!zFR0h5MH`6#B<U^;08?J4#E_oqP
z_3aI(u&{7NW#!e^zpflPJzam{;>FERo}^5DJ9FmDjGLQO=h;@b{rvoVb<ASkr|;kM
zCnqPDyuT;QEv{FS`?2WGe!={Pf;-Vp3IYcX9AIeYlTEt4E$3!a+}^6plA4eZkv@6*
zIMs8%o-MUI)+gJ2<3_}tJ(a@M-`*UYXIuT@sCfJpZ9Rq;cXk?|n`axHEWypq{pQxz
z)%P+ZA|e#*?Bogy3nTVa82<YDx_!qEiz7#mCVqQ!Q!BA^#l4qTjvtq=?>TSxo5vy2
zIrII!y;{FtNBi^IZd>>>P(x%#%}=98j~*3#eHHrW@Avy0JUlr8b^orLJlnfF>#CMb
z)t42w{w{XwJ=DrAzF_(C@MIfHOUoHXsa$;Wc0Es?q-@E*FXug7Z*6g%$;WrQ-ygbt
zTl(O^gIc$potz8`9yn~tzRm}d4tMsOdwIG4;|B+uf4pA5zu@gH(?9=yzqcrTbtSuV
z_TzuOpzL-}Nm$*_B_Ke6LBZNuT3kP_W%usgUNfd@haWn2jBVPqX%Yqr4Dask?LKnE
zCFj-_&V>sX255-9v9DD+w8*tPAu|&cKi3>0cNVEG^P79>QO(CktR<zTAOHOPe7#Js
zt*!0Grc`dTygMCp=EyJ{I5}DU;fog?aeJ!{9y`Vs7#OHiQ&3>=q{uSGNOH}ZH3iSl
z$xe-%GhIJE>B@>gflikRr=PBgIXBOiduPnKM>qG?S}zIGOfizQv9Xyjb0+74HR;EX
z9%a3E@7@Ftl?|IVIaO6vt$qIR`Ez!Cef=FfcRH#FJ^c9ixQCzLu?9xwgxp--nLf)t
zJ+@R+WBdE-YxC5pqB^>|g%1uiY85_v@??Q?JKvgekA(pVhgvup8n$dPnK5hDrY+CU
z%xpe$#z!Y|6U)Sj69Y6vemt4%&oJfc`(354K}ISoE4Q??tgvlgv`EQkwi&OQn%ah~
zTi0G%!Oh8;czw#`$->pAWJN?+#KpxcDk=`#xg+!U*Vn`M>;K<<@W&+k8c$SI6vL@k
zTNf-?5NAK<@87?PX=%r**1WsFfBiOfH+OgT@9*!Y&)=SV``Eiq)9h<2dUtQxB4V6&
zreWt!%O^#aYu2m@2nuRCbjWGP?%jt^o#GPeWO;n7_wecI`iu+{Cr+Gj`soa_T&dk`
z>tlDfeSCb}S!v>dd-vq{<?YsN%(=QU#=idVSMi2hueUCeulvDx?b@{s8#Xlb_VVh*
z@8e;J*ioP;XIs_6Z~sSttF`H9?8N3~W~=gdG1<(2PE1r@^R96dTS+NDyPRE3$3o|J
zC#|VYT2qrwPt(nrzi*$Nm~Iq{wY9Z`q@<#Pf<vy%WHnzW504J7eND~HA1}JgGcwfF
z)NDvR%*K$KYMOpd=EjX14hsVg+`cV+^X5&{d)?df@27o#bZ>99Vcs2!J)h56S3I9v
zZczB>2%GJX2RrI*A6dLxdtqyKxR;NQ%0v&BnKNf@$-JyK%RFCBH)>1Az8{acm6W)6
zd6#MiFAK=b)D#rVy}xfR8=s7a%Mzi!4h>t|xhzbKvNjbHIy(B~?fsT5Tjt{O<<HOJ
zs`q=pzc|`0?&7kg>TA}o7mNElIx=2gTWeDCB4BIfoh_Ndx-mNf-tIf~a(edVWxTPw
zN;>oI@AKWZZQGQ|lbu~$G&MD^>?}^#($QIBoPI7~d*0m_4-dDyxP$}*IIyy^PMJ5)
z&&tZm#iiuK1IMEB^7M~ssi}g3fgvFxC!ZE6DebEIs#W^xN~e*rac9Si-S77;{`dFy
z=FeHTx9O&)qzDLJG{0Zte0iB~@X6J?is#$c+r51K`toFT|Bj9oD^_&q=;*w-wl><u
zWy<vF?$4e*6OotqKR?e_xz)+BU(QxZX_0%s+{AtR?5x*!y?Oig<)K#YFHcSiPdfc{
zO&$|7bLO!gNu9Ur_x<YX=rGH_=cDY_lW<&ITzs;F!h-pAzdAcQ*2V62TN}MSEZEp3
z`}#Unb8~YoP0fXe4mrKLyW4!{jvWGm7jJG(KY93Yb5~baN5_jZGmTZIdPUAUJKH?{
z`L3LsMp6a|4$YsPU8c;M<rTDYO4PO-&*Wo0i7&HXeSCcU%D&p_s;{p^1*gUux3;#1
z#KibGEu8S_)2E0CSy|a#<?rPL1z)~=dGX1~$&oTuRaGLw!ojNlyr=6;jL=D&fBL=X
zqW^K#E-w4_?b}lH)JsXJtD|GVGT+&krfP?GbY#4{vvbMn)!v$#nl3ITrLRIjNkuR-
zZky@V^RrB|SFOE%>eTvprA7Atel)u%3I1NQe7XDe_3`1=F|JirRbQ^h*N5)@|NH&^
z<ui@bH=VzE^X8Q;nZa8V+xg}FxW#l7loqk`%UxiXuW{J8aih-M$jxbjfj4ch>?+MJ
z^AA;<eDcMKiOMc6CGYN7zPhxO`?h)6n}}aOpU>~?$oTl^=$4wFMceWp9%}va^Z9(?
ziD}=H{{Q=%`TyVFH$R-)`6h1OT)Z{)U}4PJE%!^9J09;a`tslZV@*wsfQ(GfhYtmB
z?(7t{E_;)Zc<9iff}fvKHAJ`=QvEZttBUTdi8QXLs$ycu$;~bN{VkWlVYXTBhllO*
zpj`Un&*$?S3m?1n-kZAaMt0Ek&sKGREP{fAFYYeS-{u<~EnWWZ&cTa|-5)-F+}tN?
z&7@JicahP`kgg?5RL;z?6y96?9aOBYYU%9ia{B-8@5PnD>I|v=3Db2V4_(^4b!%&5
zBjfMtzrVgp%{{!pvH8Nj+G+-epdg`1lO{cQ`t;}{T@8%`UteD@{QfTX&c0gdyu7@e
z`2iY7&dxT!xpQi9S=q6b!OKAv!k72=<vBSyJ-ob}TwGk{9@|n;UCqpJ=ImKUhJHEQ
zRg=O}QdExh%kwV`;8+;Y(J8F1WMU%XaC1jvOAAXxWaLI0``TYqdQTP<6f|tyXvlEj
z`t|MC!m~_H@-vjYzQ)VR$$8@R>E`X*%?}+uY*_Q7p!a;VyE}VZTib_^9~*;K9y!p+
z{PExK_p5srYPnCHcgKI8jU>Z`h0g31RaHtxMw?Ep?mKqun1+VNfhQ*?U)-J_&m&_Y
z;BeD+d(u%Z8S640RaMm!=gzq~Ix<?HyK+S&X`=)~LS|+sD3N{Th=`0_xkqiK=gn&&
z_RIa}uiF-<-QC&g=-|LG&$gN^H#b*BRdwNx9TK;0-O4)sV!oK5U}Nd)Ym5x{s^8m!
z>{n81s@>izY0Sp3VD;*5z1Up}y1Kl(cI^s%G}9zg2-MPHWM)&au$b{RE<{XBtgWeu
z>CMf}><*Fr9+fxuudAN;*y8L*Hjd+aQ)_B!76fQqSnSUK?eJ0=!NhlWcD@m9z6C0F
z-{kA+>Q0qUpI`HdlZ{`lN6xltLGtmw2_7ozpR;J~sQj!Jx38vC&bCTHO^r=YPfv??
z)vgz}wra17-`@vneq31@Ji|DhPe@q!wTS!Cw%%#erX6FRAH6N-<Uci~7q4C^>FKTW
z+Tq;J=cqMx)t;y@*`-09KR-Wz{C@xcV=pc)zPQkteU43~(Ep$3>pz^<-=8r>ztLeq
zk)PATfW^hDZe*w``OGrm>=x5q^>xRsx+T}t?RBHJG)$i^?&R$J`U=CvOP8E{e0a{y
zx9>MjKX>5PEvet%-ZpRDYRa%8=Jooxy{k;`tNYJ8ay`DDx6z>?aIstBxjB|Q>i$|S
zTC}L(*O$zi&u7go>i^j+TC_+&R<`%Wiwu+edoruT*B@);7LSOTy57)t{mxgXw&{Hh
zzI(VMe0^Nvkq*I*zP`2)E!VIxF{_dn4TlanU1O+}<>BEecz4J0o29NUuYApiMz_kf
ze<CjzNn4lcJUc(X|I3$>H~06;hp&qXl<w;Q)r()=-rj!7&i9=wGc$8aO3H@hOP4R-
zJj~Pg_*Gv&<HH@>eV(72D<~ka;M?AGe}8{a<18{VlA+<zqa+m-6$XZ=?RmZ8`f)2-
z*8f?&>`{7fa4;yn@7uTUjqJa+RTA}8RaSPtUMSc6`FI@EkiK*0j)0(G<LdDBn+&Gw
z#WFEeR8%A!?~|?g@PM&llU(bk@2^fj-c$Zwu3^)?4*B{&f(!-U-^EV-GI!3Lf<HeB
z#m+DDo&Dg=n>!B}uZzx{Idfs;<}@wdl~bmOq@<@GK6sGv<jIp-yc2%>sMu=Uc=Oh+
zN4K_ScXV|%?cHlTXYSmMX=kM*BqSE(-rg2^C_8BX)j+MOH}=(9dwF?%cy(1f=kBhl
zZ>5b=IGmiEB7O=9CM74|{P*HT#*$^rm>A6R@AcIE{bg!luc@i|hW+X4@bv;>V#lNo
zEM2<v#m&v?a`tsG$ugFfmK9%K2+GLFJb3<`U0PaNMMcFyLEylN6CCybeyTTYD*vu+
z?r`e-|C5u|Ir#b8H*YqMiH*IP!czR~%)!UU`zKDH&Mq!4t`omc=GU)Z9$sEa_V;F+
z=l8vN^Cs)~&WuI7HmzN;?AOYbaoVTOpLh54<dk-ayZ1R@o$qY3&{v!6*mxuwX3UUS
zvSbMZ1E@6|xY%ta@9Ge(kFVG7XVQ4B%FWHa`Z`aXm6a8!y}f78o`l@oUcJ~|D|ny3
zeAyYgI&9<nsek^|m|gRnTwQpiLy&`$Gx5If!Gi~{&XYb~xb-owKwq_Y$e)Qeb$=?p
zsccuu&(HUnVZi9;=QrW>(+Q`aS`<IyVMus!VPQvqf4F2z-0k3Fn!(E!ELr06Sp9rs
zW232ECoeB=YilcO)t48I6P4W$oj%RII&3Wy1E~FGVj>c}+^;o8uYKoE%h^fSE?yMm
z7T0T;JzF}(Nb=XWx2NkGrak}p>gwtZ+qb7*-rgr|&ZjidL$$WJxcJY9!~8n)UcAWo
zru@A0;<anX9v*Jrn0{XFM2b;|i_(u@zZNZ6!0`6g*25t2ijPTUeV?D755LWD@!GYv
zwQF^EmA-CM^PT0OH1WaH)6)aAre<9(Ei81*%hP*OWSMPnEo}eOWxli7n3<Wsytye{
z`}-Tn3%9Q?_m|g+-p13+&fgZJ*Y2Tm=-=PpH|wgatIy0b<yM-=0m|~ndZnf3%<t&n
zDEk`~73CBkFaP=3S!M>CiVqCu=i7(h=2)$vt=-L=G|fXrfUEVx$B&EwW^3Pkd3iZ8
zGqY3LJa2*TY%{IOtn2G!b#!$T)6?7a_x}-^Hf>tyqw^<DGz6_|@lk7bQEGHr*syxF
zwwlk3266qk182_g$jQlV^%IVr{OZ)UMYCQlUsko&!`;2z*X`o!aQ&%rwhTYMT=q9i
zJk*lKzR-WB5$msCzbbxwP@LkWTJ`PC$!}-p+t24!$iKVGwC?9qaULlX4mCBkia$RL
zS6@GMZGC+Ij~^8tOM`ao*s-CS?ZO``E%VAxDMyYS3wtTKuHw#~%Fj1g#Di9fl$Dk3
zD0?f#aQ2;RdU`sKtd)q;L=L5i9CC7U1*N5H4dZ2whx$A8{(Yo=yfDAl-|i>NT&vPU
z+wa%?Ubgwr-qxn3rh-RDI<+pp{WrPn{k>l6@^=a6=2-Sldp7%n+No)eE#BoGJl;@n
zXMaFini{Bk{O#@S)jDw}3iGAv*S0>kcvmm~o)Kx#$Pmv^&)&qgwzd~nR!%;(i#7e+
zoW=l+BWofzGcfG`|Ia$@{5;+TIc0LWxw#r5TqjbDPCPA=v8|dSyF2%`8Aq$rn>#xv
z=UNv%;g~aLPRJ!+Z|~-5)5P}uez*I2m7VBk^@$z{X=!aMgO_j0ba|b9YKo>s^|zef
zL^fWjmgwzySFL*+nc1Bd1~}+Vch(RA^=|a0n`bOreEs43{r}rGZ#Hh&lo|2&@88gC
zt{*;rJlMu7{YrFuUmxGQ`}^bH?=dy8_gXsXY{%8@`S-J~7gpT;^XJdKUybinRaFJJ
zT2Gumzy9;auPd(`r=NRpWo7WycmMx>zYj`T<@am1U)xmu<AdUzJ9iS2l9*P8biH`-
z!s_W#y=8uLyLJ{o54&lXaY12C{C+t@L&Kc5E!PuoY)Gv5|M&arSaX@<;QG9fsgZ$=
zjm@Iug~07?si(!JPMv9MW3!>?sn@rirLV7bbay*DIWbMsi|x9&*nRbygP@@vornz%
zPu&xruGHC{bCW4OJ)MP}egC&&t@HP%YKIp*JtbQ5>I!FBS=ot`Cs&rw+5hiX_FCS?
z!|nWwtG~bd>i77>L}ie%H8nOYOpO12Jmx=g{P^nJA1k5@f6rgEe0lqc6CTsn?cZ<D
z#myb>eTO$zdFjHRmIV(O9zA|6C@<eX(>Oip(h^VJQ|JHB`gP5}w$Hlumx<pzo5;8S
z<ZP=FUSC`L=Aeh~vokZB?P`B1SXxT%|MQ7^&)&UP{oMar`OLGCv@UxS@RM&({eL^q
zc!9jUd_-hq;_q*7*X!<;G)~*_o}=&at9Ek-i+8#Pg^yfVSXmhvHf`Qq`0<hJj@`RA
z|Gm8}_wa><&R6ALzj@Ph=T3}G^|zkf$tP0+G(;ly*X_NP?B(sfvGjG=n&|C(YooR%
zWq1A&-m-P;!}ss|`R#r*=-&JO;h}SnjHQs7nb`!dr4btv7`f{oALHGzd$+UJR428`
z8>e>6oiiswIrsCkvj*no?M@3DLbOu3rOMvknriFl@9W#z-OaruNK=H1_0ZwNky1_z
z1x`=bZ{NDrw5O-%irxb^7cKjYpy1$zt5<ig-}j44Oib*?mQ3NUt}X-g&NIU)u~V~+
zQn_++aug<?G)O+iv$c2HL}m9)rLv%rzqGTnHnx8L_2p%Ax48bMEsP1jzPuFRVA-&F
zbF<UJgzliY-DSMz&Yd$bH9dM_qVnoo1zDR40o&?tEiroQH@P_%6&bPdNHAn)XP-zh
zVqs%r`tg494_0n52fgWw-TUQoe%)w$w!HN9wKwmdpP%onvF3NZ_w)1f+gn>%UzgtA
zQ)%3=%GNNx<|FGg-RNU>%a<=-9KXK~R8P0@$ws|Rs(H$-I_-G;y~jTe^V@@(Yhq$z
z8#ZoCyspUb;`Qs~gH5a(wr_Wzt{1zZ@^cysE9=IuYIVQgZqGVhc7C3%&b+Sc38i0|
zuf(huRaH|vbn&9#v}w}>I$eJJ`~9AgVdKV)H}+H-Z_B^W*Xq>x^z`&_%Wc))-wAZO
zT)1>;mD#_d-{0OI-jaElf#KS<YZtCwJt|-SXQ5e+g9F2nBtsP;&SU-Z{dexfFr@Ac
zxp(iLgh__LnKNe|yn3~2Ti=>BYZfeC-2C`>|K{A=+j5)R+u3hz$!tDx!ec7C)$u;r
z=H_PRs;{qDqobpz>HEHOUGT<wyK_6=)@h8#3r!XJ9^YHBE_QcNQj$`qi_)nxXO{TR
zHoLf|@^i+;MXq0tipL8GUc9z8I`hSag)S~rAOi?*eoUAk5TrHrO>yOoCr?sdU0Eso
z^vRP84-dDWJbALS)w1e~#?4!|gaj}8+y8aBckf>5sn36ZfA`dzzWQ#sW&KmT-*1d(
z878v{3hvyw^J4wK&+<x2T6%hF<>lqK_Sf5sii=O~?BslVb91}w_b)Glm6TrH-mc%;
z-YzT{INL1u#ldFwH$V35u~AYoGBH_l<cN!s(xz?Omc`fqo!ZfncXyZT+uPfhTbI8R
z5S-|su;A+IaACoVz0&3@LRW`{1O+Xc6yNCJpdYs<VSaE>kcWc6g?D#%-`sYzTm15;
zr>Ac|Z{-%hls>=K&BbN1n(w06-DMkVh1Gl(m}XxK(VQM%_p^1G-&`*j7dbh(l>B`E
z<$iM)-MJHU>FU*`lO_pyd3q|w#>STI1C1Pf{v0Wje7sM!x*9yLFm=k53BA3%Z}0El
z?-j)_XS1N}?X8Pzqqlc-WE^heRT1HOd2erZNJxl_n3z~jd|aH|%-OSpYievjq2Zvg
zK-GKNhD{mQ*UA3<`~7}SeC?;;yfZTlmn>iI-qzMOWy+M0+#RoYWGn(y|2;oHe{;>B
zKcCO%{C+;a-tT_xcUeI}P}O~}{(o)NpC5*Tf<J%#x^!H=evMgOadGjknx96$=ggh!
z>*C@+&nEFUsQeV_ba}IR-aNUpGmYCjI`Z!A@jN@rba6uigObv&f`?3{@9xa(@L0U*
z?CI01H`ZmXx_+vtsA$E84HG&#K7Ib|sWmkyCr3wn^7?(hR^2Mn3y<E>&MzNydYW$N
zHePA7o^AQ}<5b_g-~Zn)Y2%Ed;^NK~Z}$ECa@jx9>e#VkZ|r0YlUV-#c-)_LIc9H_
zY3+}P?Qb@Bc5=G9xd{mdy11}>eRZ|F<J3OxH*elVRGmC|5<K9gr+4i|oKAS1{IvS-
z1v260HL?PI)rVjFpI`9n#}5Sy3yFshAKutmto~XzK;7C}nyZz`f4<$(%gg->e|>qW
zmAI??eV?Lpn}UJ@!?tbPw0<`=HQiXPx7fYEZT@`uxIGmMHzk$$T9v=+*|EdI*I)2J
z{dr@1d-;hICxY7bx2H{?-agwr|IxFvvn#&765X?B&yB*jx3;bpnjW<B$c>H34<9~k
z{P*{Fc;#Cyb93>+!ooK)y}i8;A3SI%etz!Y?c34}H})@Ewk+e)5>7driiYp^>-Qfk
z&bYQl(ydSCq+NS!>%s*K8f-V}X=ycqMiCTjY-BcW-1vs`^XcjO7Z<s5Z+-sh@AvzQ
zmn>n~o_Dut|9*SFxmK-*4msVpbt~+)vcuH00C}?<iCJd3TnsO+t`-jp4qh!&{O?a?
zL{!u!n_s_vJ-X~~&&r^ps;a1|$r%?Hr}M6<i794(oh)d?;D*;_H#fHpn>QccS^OL{
zZBq4odCbqPaxyYK>-YbQTDsj!7&M^0f4@CwaOKULn5R|i&n<9lzA-r`K0dxj(%3E6
zVrft(s2z8>jkkIAYHbxEP6kkaosp5T;@ca^EnBxvoHC{5(W9h{?CF26N7b#JF=NI8
z&&g_QK;XlJgUnB!KYu*8{2pUK@l7Vv>;6$uQCq)8MaIR+E%Tq>cjSl*N2?QPD#OFW
zqrqw6g%xMwws&-J^hlX@-Icz)-2eEF!pArMfd(?;jHIQdmn>PbqWAdm<LunxdPg>=
zp9f7%h=__VjNe}udPvr~jA!M_l?5dwDNk3dUE906{Qb4xaZfDPy#voz2-(@$1!#!8
zxVV_Tr>7^2C)(~96T>u}$fk+P?x5Iz!ydo0m0R3NYwDp37bfIZe|o~%Cu7;<JzX#H
z=B88yB@uD)<mcz+{`m2@KmK>+)ZHa7g+QZ6N=*(52j<uR>s0fdwW4Kb-QQhqg>Ucg
zmoF?V?C9z`)WFF6;cof;l_pUQtKJ_7|M%zJ?)RJaw>m96RUujWkTE3FK|s(f=SIW6
z+TUTE-Yy*6+`*dFrMt@Baxrl5@`C2vrg*8={dmYOA}YGE@%gJq>()H_nUR?pYB~Sb
zBSp|K$g13kY5VO$E$_~{*SOxlPR(ylN9N^ait6g@YooTVGOgJexj8Kn)Pd{iIWotx
zI3Y2y(KP#-Lv(cX)2r*J>&IV{@c;Sq#*G^fuC5NxxVVTFRD`UF+{|`;ef;s6#_0xS
zZz4bgC7{UY5L6bBmE~O-(zRxdPRWZ4jIFJ$YZ!xmN@@R(`RBZC$Br4M4y&|pf2z-3
zHNTmie^pIbdF_<n&t~Ue*jb$3($eCfAdqlkg5uU`-m#@`ZU`=2x)hX#sv9QEo5#n@
z+#J=v_NCq0msVN#_u1aLdv{~<alRwRk0(Dm(wTKwQ1H&(yI1#>MXuBPu5`Tc>*3j-
zWxh`XO|daCF$o9=1Z3anvN!ni=g)%Gt5=s@TX=4+b@Hhxnp*GoPVZMgdE&%@x3{-X
zoHnhk_V>4q-=D>|cm34X(gJ0-cXxNQ=jZ2d$+*bm=jW$YdFsrWBY%H?Pt3~ddUkg9
z>OIW=zq|~dy2IPsd*ZBFT&+%xk(<*V9_F`q@$u>LnlVi`+G%0H2F7oinwlMbed{jg
zguSXe5_<0$wDT=r@bl--HG)rHzU*vhU@-e&rmemDRvbfmdb&=;1_nJnJy5OC!NJZi
z*AldH&7^J9rin#u$q0N~(K_qK&6|pPdVRL8_x4m8pPoN+rsOQs>}8iePn$I9(6KEH
z2~TIvn$^`Uu7Bv_#f^?XEC2nml(VTgVE6w1e);$N|J!BP)cpN=J%=a2chcm^7q@1I
z*L=C?{^Qx~{2RM}9XodH$LI6*j8o#I<+j4s0+{aUm$hD2bZ4Tn`;D)nA|gjl{eE<`
zyQ9B9J()T0-X2b4V`I>aZ18fwqer{NC(fVWKWUQCi<d7MHKdI%2)sHa_9K7q^$>g0
ztJ$fMk&zd!UvGC%0FBe$e8T2%vzgcT^~Pmxd<;DDb~4iDc`N~8^Ze)8G&VOoPc86T
zDpdXLjbmJ#9D{;^fk0kfp48KYn>;70DH<3Eu=C5U*~a+j-MhZW$NN{?yjfvU=03Sv
zaD|my&*L4tKq~^O4{O<dl(w(4S+jQS>k=M@KR+J#M{G=Ly`?@gzW%RiQBlzi!-EG8
zhQ^r|78W|DrKt%C37MQKPD@McU7z#h*)vcCHZJ}C?(X5ApP%34KRwsFTuD{6^{u>J
zjm12>S}t8(-In(D<kQo1CwQoUhC~V;G@P;&7Z*>yw8S%~J|;$H_SEG`JKx;c$h^D!
zeVdP3^YrQBO-)T74zkOy(6ul1|73d8EdQR(i4>!V$Vf-6sfW&;<Ga2tmYHEq>~68B
zsHh1iQzm$*6qJ@8y}P^o>NHTrGuOKOMsQeqdb)_1*r79Ld~$DXPCxH<e_!p*1^K?|
zk1OBr{SIo!R#a3t=uLn8?(Xh4Tdi|*b7kymB&xr?Y4lNR_E2eBzh0k<i|fY6*4EYu
zr=Ln#6eyVG+&B;yxjMgY)22-Z6(17J?tWaq|6kYr`u}s^`g?ddEcct6)ti2HmZ?Ce
z3#bFLr@|0a_AngSl6kq{{XN?b7bQ@asjRF_>$s(b#evhOxoxYzxum73P1B2AW%kdq
z_}K=>7od6YL#^DP8F$xiv4fW`3GJ==>I7;+^sRg=wffJ_U7K|I!-M}`30Xf?H(~=r
zV`JlllPM7!5*klU{`>H7yRw?vvRjTX)cxnR?AvEo_v@wlpHHXt4GattnD-|qB|Uig
z^5mg)5gVBT0|N~V4I9_5*LQMuzPj&_NyY_*9!X<1P*2K3rQ+u&)oD7Bi_G3{Ei5f{
zjf;~5H98J&FcKCP2F2B6b$`$p&(57YPo>Dty3_sc-8<0eGYb==kFW2;SFfVh|0%k&
ze`Z64@Vv(seXAWkJvnEYWH#;DV`GwiZOz3?TQ+Y_ww3t5`ku?pBAMfRGxxBA)}XAO
z>Y$KuCERbzmM#CDOUpl%+MQ$B_xPU4J?2A&GMiIQ3dvX$IHW#U?VxZVfB)ZOY~IB(
zphYjHEbkc~?l3=b;lc!6xoex#{moLf8B$VG95h6dPEFCwiH?h#ckBC#-DPjN#Kos;
z-4@r6<JlO&vonUz*w`49(M_KnKYyP8<HwI3-QCIBO~;QOy}J72#@yRxpoJKq1sul4
z%`tk%A06$Mwt3v=v@n4=?CQxgXVzR!C@wvI`e~7d2p4Gm!`a#34yMz>18;6_el7d{
zHE2Zim4U7<@BV+kR-dY?t*z}%7h0`hXt>c)`-HiP{mPXqH*DFG!u_{b&NeFb!*BbO
z7cWlK)%me))t(9Smc5Ez4;pIpE9YkLp03xra;4^$EnBj5cXNyDIcQA<%_)LX{o!{0
z*K_23zrMJ*_>|YX*4EY+7Zx(Fi{0JED{YqW_SV+*s%twsKyx0BI%3W`V#)9B>|C!J
z*Z2GS4|Q?LhZgJ7+S=Mmo}Lm_QUcBNZOy#grWd=*#U+*d*Vc@SO?E#XFmJPn&dbx|
zY;~Gyp`@hbp*KDFc%Q7Mt?k_0`}^zdZ{ECladr6mj*b;emb5IrDxsw}&%@=*kB`Yx
zHWdcEayC0Ie!3747#Mg}Z%4$sPfTsSPp_;DPRYz%xyjexzyIH_*Xym0Pn|Hq;qLD8
z`sAOQIyx!WH3cu8nQ8p;wEq5_fI5?>4<0zU_sL{V+45@jBd4m;!24Be*ZTbX^V#3a
z+gsV){kYWOsnezfU0)ZgX=OF5ql1ZwDdWqFi>2rKmxZl~a8!2h3%R*1`?}tyO`EFT
zW*$9$JlM^RO;B)e)z_|^+}zCP=jN`rmF@c-f3(`c;vMhe|MlFUHl=&Nob7~(6Aykf
z&Ah}?{QTTe^ZPZyZ$COZI)3>2wRQV;b5KM1+y4Lmq~G4!nw;It(dx8qflPqe;@-uJ
zl|g+@X=!N}SJ#cT-D0{;ot>N@A$$A%*FXIVZReBSkbB$A#l=NIQ<IYcG<RrWZ4FvR
z02*D`UvJM{|Kx-qsI*g??6@#s!%Cx8C&f*hH#@q_uq;+PbM~y_^wXeWOA#?KC1qu1
zD=RCVdBW;`2M!%#GMdTb=jW&Mc60i9CmpfmV?B}+PNoQSAC=NwCjnUv!6R+P6SPv~
z<HwJnDF)DxcXBfG%8)MCZn2FYPM$m|VUWPEX6;(V$tMLQBvzQ+`+mP(-hZAAW5BkK
z_)7hAr>E<KhC(Jy5(13^9DNCDhb~#7q9MY?BX2k7=;reGaW`(>Jb2>7ies8{|Ni-N
zVQ+OgXh`V(zFMu{eX`bU?Ck7cUR~whwr!h5`8%10P3j+wX7bqC*$H&IY}m9ZX>*;a
zioNeFlf>(pg@uMi#l@!cJXbF|aKK^4?Agu>102*QZ~V39j<E9d(}n;3RDSz;q*Hjq
zrcF(qou0jWTmJp~XJBlcY@77`{eAX;Z8Mf{-D<idNVAPc^3W?oQ&Z7hyLQd6uea0D
z(E&}l#q6mNoL~PBw4P&0kY?VU9f8?5!@^?!GMHY^cC{{h)39}`sg91$f&h&hB1an=
z8$CQc4lJKv$JOf87`)ss)biS?Q(SFrZCm@Ie||o1e{oZ)x7qI-UtV4=d~kqq>ek3p
zPoF(YdUT|7jr+sh@AoDDmXVb;Og|^{<=tKBySvMetIw}lWVW~P@v(<5E-tS4_s8<K
zn2gLBvC1!>KOYW{uRZ#0ZNx?<Gc&UdTecio;MfeRKNx1+?KYa(vo`zEmds#}r9n%U
zFTWlkCv!aXLz}dN%=h(a85tg(ot-W&Q>ILDxx2f3xw`*6fxt-qj=nxVP+xlDM8UcC
z_4~Z|Pi#&<zan;b*_63+eZ$wqC^9oMZ+m~RnLQ;f&F${4(#3_3k6oOm8@*z|f`*O`
zT&pY&xARZ-Pyr1AC@D!<m+6$gy%n1Kk6+fxC3u-n;O_NB{2rDoL%e=}d%O7g`T3J$
z^p>;p%LxcxoUHDD;=qB1clY=EgSxOAlij`i{M3{dJ$jUM>fAXsC8dP{9{O>67X0{8
z@#^Mg_3rI8^_4pX1#N47Ot`f@|NOi|@9*rKeCEuVjumUl7lZmr$1^W4^L_H{*|JID
zrM3#Q&GVOqt&IxI%F??1?9CgW=jZ3ED=k{INGV8bs)(4Fo2#p9N@Akp_Po1FN{jaF
zu`x0?U;g9A53Q$0X=fyEZOs-3jaIB&sR<f`sH?M^Yf;$5#wT;)lAK`@i{Q$7t#!+n
zyKhcA3t7_<`1;z~mlqeihhF01=T~1D;#FB$>EdFNbw%Ubl0VnhMyI5wAAe<UX*tuX
z{GHFyqen#q1s$ChPB=H$+TF!v;lhO}d3pC9{1Ffo6%m=>qoyn?D_c}nmUcZYc1y;^
zCOP{$AD7hYK0kkkzU!QLGUd(6J<ZL`J9q93%*@n$`t<3<7`^4SzrTfc{)VpMxE357
z5a7_-+Unx+W}d22)UFcEvvVvbcbs~s-RR)p-Y=)B1j<QSS67L8SO5I<^vT<|zAj4&
zZ(a+rPfSd_dM!KEf4*I6{r!Elv#m;Bxg0%ubd8V6OVL%jcjnnvXIxm|sCE0+_I&^H
z_xIKcm3@12(<Jc_i;~i*lP48BU7XnYWD;IZiU0od=g*Xc1c&MR@nN%<Enlww_s{3^
zE-q6hPILr~-`?l_{QUfK$Wj+4C#Q^GUtYf1@!&y%lu-(YU}l+qSjqc)wxFdz8j6Yw
zZ*6*D;U@~N+H;<qm}p>Z-0Y!pNPT`y6KG=N&Gk!{4oy<^zA-s+)rTvE&(F<$aeu#k
z)YdH4jS)S4veu7|bP6+^x>f_4aVdLuhqKeAiC5YTv<&O$(W4fKH5VtI7S;TC*nZ*K
zwPSOw%R!UR6_u46L-#NDn;VsT&(_AqBuZo5qECP3+t<r&&%f{X|KDHGLd_5@MG>yT
z&(D14Se0`9{rx@t{rc?8%#Eflr~2PtTPyA5<z<k0Nu{NwWx<9G8@!?n4GkxFsO0d~
z=jZ3&*<U~3cBh${S=-X3stgCNT@%}$fB)JG85tRw3u~jz865ol_|DC<?FJ2?&zsTH
z!*gdxA+x8ar-zqU)AZ@$Pl_zxynP!O5U`*ure*VHW6+St>FN514;^Bv{q@B$Bt%3>
zN$J%VuHv6k`nz`RdU0tf_oYjhR<(5Y^c*=cQTgJY%3_99r8RGE7%o}13^Z6<v{NQ7
zF7Cp$YiZdtB&4M`vp#j}mjlf%t<X`6nE!_%RXgtZ@#Bvl9&QIMa+>;4WmbUJR0-=c
z9Z=(P$@1l!Kd(Of<KtsR0gelMDvLpFNI^lt&_gd?yf|>-f`FZ!-H)%=<3GN+X>5{t
ziN)b&g}S%5w}*;Q&h2fyot>R;c7&~sT3G-89|ME8xA&IZ+ta`+G#*}C8?CcOEj2p$
z?;N|@UAL;%>X@2}zI*php!=wVS&qb}O`Afh;{R)g_^$VKabb~ue{EB$_m3YxR(#pl
zBW23PaA8fPvB%O&S>j*kn1B~$FfrWNRjS>_D}8LAwH6mQ_u-S1)fK0oR-Ar%W7XHJ
zvfk@Y<7)o>v9z(V0Zmn}42e2^>A{YA{kP_G8T)>3zrDL$|45RdlCtu{hYuU;{{A|6
z=T403nHLuqe|+3;uViBr^YrSJ1q&3uHTQN*m_56D!2*SClQuXgH1ziJ=H%pjIH^8=
zgBz#d!J|iAQ$wE~n=o~1>y#-X9WF|sML0?mJJy=T2Kq8jKYf0G>FaBtBnwIamzVp4
znronna?6%2Cg;xYe!uVZziXE+ITaRebUPjzQ?hQ$77?d~0@L;5*BLn;n5rEP>cC%I
z9qYAJ2-NP_xX~~sCg#F2UujTdLrbe`?WDDxzvcy7DmCriZM`k;Zd7V~sr|34s@PTQ
zer8@;0v>DkxBn{=U-PjwZRWrF#Kgpo-d@*`5RsVJ*o{d?LAAWhty{OW*8ja?CI}k5
z*_wU*!Q;owjSda>_EsmKon_k5*?I8i=jVmb&PYZ?M<?IkR|^^$G|Ro!(k-qJ8dbKa
z_^{xVQr-JKpZiYzyuQ^&YkIEl*WlY5MJp>S6YuUS{qW%f1H+cg%gYW5yM&}ABrNz8
zb*Woif5QCv{paRdU$4q}x+gm-kC}}J)UTQ_V@3yP`9idxOUuvC&yx?eaE5-%tkO?Q
zPd|M3t}JLdZ;zyLmaTf<<A2@X8w&17hy8Q8aPy|5eElE8eSg2**80t$prF9;`T6<u
z`N_%2AD^5QzO%nx9+cCgqNIX?f<VhGY-)drY}~j}%m2&dNs|I%Vt8t6Yj^D2si>sH
z6uv$#_4n!H5gJpy-rU~KzjNnKg+*1ionh1eAMck>{`BNzL20RLznpDCdV2eoEha8*
zZkr4`J3BS>^x9_6miC)t(fH&^3Pb8+rlO*vkViG2o^Tcw7cUIa+L&|GNJ2_#Q=50C
zgtWAD%-*W0x%c;0n}7N8<%ZVf_0F@+avwZ+(2#n1+Q!`P@9(>(rKz1ca|V>^r?$m;
zd2z+<sR&e+tF5iw^ZVUy(EQ63FV#77<_L(1wf+A7-dSm42<QD>rP*6QW;m@3NqT>8
z?}^i=oij5v&GPS^`L=WS?#DkqKGy0z@F|;FBfG`L-JMxO`@CN4t_OR+-^&iDGcq!|
z(SLu^B%wFAxBEYxGk4CM2@@s+B+DE<dUQwaZ!;Yo9fRs`IXu#4Jx_O6e}9+bJCR#l
zFJ$J!%-`SMYG`UY*45dSy}iY{G>CJe$C0$g=*?-oMMXs)UR+eRu&_9A{ye{#nVE-&
zheKGH*sX23+_JK=B4T1;F9p}U>)da3b!x7w=~e%yRcl|~tNr+hm4}y?amrVbC(oX3
zs^wjIx@*mvH4~;!KYlLr@v&YBtCAJB+F87oPKs^cxpU`@jmhlQ6N1E}qM{`1>ui?!
z%w)>V&9$(yS_NM5!7FXH;iHnPtLvKQUX!a0)z#V8ty?Eyl+tl(hfv>R6VT>}?`)xU
zr_AgB)wsC3E8E%aJ5?txE?)BbT5s;{O{v^&{c>xIYCv)G>Q&d<+uI+%yu4h(IE_cz
z#5(nq$eUYRx!>O2-ac`npp&z+Vd<-o6Hkj~%$~jZrRMMN@B1MOewo6;!d_fj>b><R
zXm0x9>(|k5|E*Y|5fKrwApQKjg3{7fAGO2p?(Sw}$i2Po=tO1r4LLWBrnT=Ze!ghS
z77;f$Hx53&V>1ks6(*ldNKS5^ZJxhs%E`OC%O5^^bck(2_tB(ptGp{`@X1;wJU=)0
z!^e+_%m=ciW<<yDE_>?~6eOe~#Hk|0>DD9Bn0|hqV|281JHPy~dA8LGlTRuLbv^(&
zXL8!opw4B!vz`3?`Eze?YhAZaZ_b=KSK6kYerjN5))t|Y*1S(U-v6pC<M$6A4m>#6
ztT_F2Vsi3gqmvDtot$NFZZw{ptiHJ_a(|ueZGExskEiwbJLrfdUtJZ-!ONRlP$wiL
z1X|*~Glp-bPurU}IiT@WhseXAwM$#gq`JGh3=$8qcq|P%@w6yrZ<Xl$`hS)Un=+Hb
z*T?a;IyGLn5b(__u5zA_uWw>@Hup>)HgR$Bsg7Tsf<|phUIch74f0SC;%IgH@#9Cr
zg9D8QCMGSNot(eEykzF)=I%WgIrmiR?(FO9v=Xyc&7bvZ>!z*Q*ONXxI0$Np_ez_C
z#-%pY1~2z(-M_znYx9M_fB!CAzrO$UbbV0SV_;yAz&j1p^L}}0DZ?qX9#Dc?=0Be=
zI5^nF#U<hCsi|3?H)mW_5)eF+WSEni`|#=0Rz>GF2JoVh!bdJgjvY(-^yK8#Hc9h5
z8PM(syV_qHGPj?fXM6hF^)mf+aeJjWIXQ1^&zHBZJK7~``mFo$j{lFi1&d_D7w`Y~
zi(5`kPO88E!M}1>H@BpBcXoDkc4lrd(U05HF=2v0&W#NVFJ7t<h7P)D*6@I~4Sm0+
zC4cxxq0I5W?;qGAEeWw>0}aW5b_VSLZwvZ9O-oa=asPh%s&8*NJ6)Jsof<(y<<{2H
z>F4LQ#^|v#q@<;7np%}@oN+;6YAL(asS6jt6WBg#&7GZ`ZG5s_+~RsB8s`oiIFQA@
zYFhM;f`zvjcg>30QJ^^c|NhX`VK1(&m6rDSWp#SF-LLQO&tE&m!N(UibK}ZKUN1^b
zOI`$+CG6QJHEI6N;^$6E6E_sC+wtnJy{3^7Xn-th$`&b5TgJU#Ztds7uPd)_*|J3{
z{lu=?-)3{>%mLMa5s{HsCBic^HSPZWNS2V6K78Z|%WSjURl#wulCH0d1??W#^tWBU
z?m)YIUB{(MK^h`ld#b*^+PcKN?hj~jkA?`>jT<)<w6tDrJ#xBG=7-k{neW@A%=2V;
z<!ole?Ws7p?d|>j^DkYxbY<OzziJXvQlRzEH=Z-I^99`AmaD0z_Kf%0s=PUK=DYwc
zczCBDFZ;`P#hyzgN2gwRed_Y%$z|{E{5<w4_@mXn>hF46b8b$WXJ3DB!JiDLyIgPc
zm3_nI*RQerTJ`FR=B6!M)|CCfyxjlgpP!#gUR+ohbvp0#G~Flf-ks~aeEs_J+2;Az
zH0@_q{rsf5HUIv+ygNG<ZrYoEeqPC&8ym0Bi#wYA?b1^3D+`_5ug_yU{x|xgoIqbS
z_tIs{9&Nv0clg@c=!?5bv$xg$`0z02-M=EpzNW<atxk&5^yB%mv$G{6C0FVkxw<tw
zJZ5JRtHaGGQDwKD1{bA9AGO1WTDf0O4PRx@lk?)jLeLI~x<4P?8B&8+oSSPcZf0f{
zpdlh*nk6!Oow2pGbnr5t#t5CZ7`@}}^0h1ra=Ok(Zcby=n9X?d(j_6TRwkiNmd3_L
zhEvOK#%#}%Wl+%9=Ld~CM(DJ;C>?xsv^%nH&YU?Pu7<}iEPsFR#l5}O%lzlBGy5Ns
zsWE5YKD%e<=5l9eXPZq;_weAz$;nB`%;Z#8SO4?%dc4k`E>Z0p`y89u9^Ttq9eU~J
zjzZ<=HE&K$)fV7tebf2sP%F2ffWU!E`|AE$F)=ewoG_um-~Mk%s@0V@Z*qF%Y^8)c
zS<KALb`(GFyH(}s<<+!yt?q2G?KQHZ3vzgyHG`KOIChN9XeN(w+L;9Iua;I;k2asT
z1MPe9@$va^*8Dz$L-Xh0k5=c-oofrwaLLSEd27Sf$}cYjV`5_!r+O()K6yjr!qn=d
zLx&H8%4|DvF|k9(kFzUH<OmB3(|UbBq~!B6-y64YJL^q%)|z_g{(X6df>&2GZ`{1O
za!py>x#P##FE96R4_etWaiU;NZSBYB^XnJo-`{6oW_Ij)eEr!&VS2Z_erv5-37Wm`
z;^O9h`0gE_wzjs1icpWVd7tlpo7!I`DypiE3j<cnE!q*gyKKYOtyeQtf7(`m+aP-8
z_SWonL1niME4OJ|SAEgY(a}+ueDcAYH)obz>+9)hiP1a$<>h6?$tM*idR$re`_nn=
z_n^Hcj*gBww|{<qK5_D7W^;4%DX`6J@(dH^%;{ldW;<}})-2z{ycu=+VSCc%+t<gX
zE)48@EcJlbyuqR`IQjj(y(*JWuDTn)kzK{sc5X>Y$ql<k2Zz<+>(!JNNt@>dtPWcX
z8h`9KRiz^&EZn)`&B_1Y@7JI2tN!$)^VSwN9tqI?fUNJKJ?rlrY-T@s=gyptj%m8l
zZr;=NQh!g=iF7hfJENeqh*MZ?!+z~$`!g;o{d&}`-_enAVuIq9>hF1C%l>r!Y~d8n
zczbKBi;GG6IT_HHhoIn7NI!gU^>-n`!2A1ZGjD8242g>JVr6B$vNbz=SH;IgUUk2}
zz4d<h@S)c2FJHc7+}e`)<};Sv8l4>)Dk?3@{O6xL#Kz8kd4i(zswtsGMMg7c%@Pv4
zcxtNl$$R(a?fd&JT1jcs)~!oVPuJhP<^QkO`bt6fA1_$E*tz`uJyoSe`uqP(N;@|v
zQdL(;X=jYz?d|#MN{fyhaml*1MYB4{F7uMgt*zPO+}4SQSfVzk@p@lAd$!lh+dH!I
z-R<q`5B^kCirQOcI&;R1fYZ}-K|3L?wyi$LZ~te3P<>dK*lG8p^JZ+=U{F+8sJLj8
zoIs<4!}D`<m6aA<xDZfQ8)lY#jOW+K<MOMI{QvW_SW8z|b<w8RR}LI#kh7_9aB%^x
z2yu6J*V58j6uv$#@cO#g%p)CwAwfY-mX?-FR<Ax?#bTm+8?rWYjm>}fhLSL~Szov^
zh1R8?pZDgLs#4VUJXzm)Haop`u=C3W{QmY<Q&Y3Eqhnj{ZMUS2GnQW7k{R69-><H;
z=<Dn2mk%^DUtK0HFYiCssx;|-rhM%e@OqGrQ&rlnPKr;TJ`D^E6pW0BxUe>Qd&i15
zyAR&EGw0Fc$Ju+u^<#G(x%B(h>h&w4x9441=*(WmTPSnAV2|bb!vD>C_U>h5K&)F>
zu*qIupTGXkNA|Sy^UiMDntwkoH^#kBrqOBP1&iEO7iNQ6-LlqY8$OEdE`RUl=El~&
z{SnupSC`6*iVjUu_1=(jQpm;KeKBa&@*z+UL4Cv7+2)|x`w%gIY0DxN7AD4fwcle;
zq!=w(wk)eePFUS9rM|trU0G3)F)S=>!s(|!9(C(~`0_GXL|j~1M~6q<e_qP-9aUem
z?(D7J?o|UCa9ka}9<+2E)Dcur0Buq`aq{HBwb9#6^pe@x*+Em0pzSJG=lO?)iA|a`
z2{d!f(BPsJSXpjoV<W@J$f$E(Tt6;E^0r~!p9&5ZCeVWX<YPUKE-o(3pMu|gK5u{g
z>}>PGcXupx;`Zz~w>tOsty_=2->*MD$FjKK$A`o%85f-vKi*aTo{ycKo#DWXi;F>H
zHd^0V7(hd!v(0jw=FgW`Qc}9{_1lY!%AlFfq>U0@ONAmMBeiT3=FOXT;o3E~%gcNf
zg*qR+d&jph0F-|hx%bPJ>fL_zN?lu<`}+F$_1ogKwRCllc8O{$Oh2tS(F3$#HY$Ab
zfA!cM1&my+Otw{D7y`BlBt%6?$=O!1EO@iqOKmcv#_H#<uE|b1neyX7Gk?LC7lBWn
zKL_<(XP9ILO)bDO^wRM9`ugh?`pcFqS)rr0GwCSTl4Z+48(`w=eyYCid}OgM@rRXy
z%y%&@d7imbr!M_eHC63Yea>`Yu2u~_y)fy%e~pnll9Q4YggP0fn7V<+Q8cEWs#^O0
zX8QbtmoEzo3JT6J&zC!rwDHucJFi0GRCgvtW@Kir)a5RXg6!b%f$qK#>gzD5|5xKP
z!(ibxi@9^>7Ct#4cxPuZJ0Blk#m`Twd@>dc?(Xir;b+#p{~6LVze7MwEbM8ioo`>Q
z?9YiOQ(oNIsQmBq{Qn0IA9h}B!_csIuPtb0sGwlu-s<m%Aai0Du3ZcJnHX``>+@3<
zCPo$}#<X*DI$ylV;9y~DX=?+mSl?Uy-Oa^?<=VQ~?)i1UI3GTI_~Gl<qZ<+r7d$w?
z$j}g?73#UTNb<^3pDR;yOTSK@I(6yCRS#}&&v!2=F<G>DF=#|;P2ApH2Zdc$Bqt;^
z^z`t2TWo7<E6c{lR@%2*FV!sbl1k3aO|1U&?OIz~TW^Wp@8sCEYgb2icd)d3>Gws=
z&CR!rN(&1Qf_DGDzP{dBN9_8~KNfw#;3*y5l$4Z~hlkriopCMyPJS7ShA&^fXg$>a
zH{tq{K+r0pFYoR~r*2!R?my2&=dwn1k+g(F2WVNj!_CPLudWVXykG$X!wq2%1__%A
z15k@*T72nCb#?W{-qZDF*i;%lJ3G7G?Yo#dY%M^yxPBPt;t;Ky)(<NyD?uA7?%cT}
zRih`$D`~`{sHm8e!!D?3W;Si^<2GLD4}X3ZGdKhW3JM7eUp@Bt&6}S0`~T0=UGx0O
zlNQ%*v6Z<C7*pRbxW08d14BtkNkU#;pR9Em1H-zwy{kSwzI^$zFe4*l4v$+{*fg*6
zN58$j9T*hU^yg2_n_FAEdzY`suWQ((e(7o6-CeD#!`B}=bB2fE!|nY28#kJndU|_1
zpPr^`m~?~#w7S+j?~X%Von23F@5C81Iu0Fj5)l^<pQ)&;s>*ut;zfprySvK|A2`79
ze(!fV=z{knM~`lt*Y*8gHGlH)K34|^2GEpTnBCLfM$qc&TU)cgYB4YPwQ22&UB6bY
zjnh7L>XcJEpDbvIFeN=*Sw*Gg?d2_Uo@aloI0fo$#@GK91ubd$^+ogB+uQ9EmEFTQ
z)3dX?fBdL0dzXB?Pu9T5$mw{WZ1!3AR8X;#lA>~LO=R%XU7F|DLsoYjJH@53n{U(R
z&5T#-^u@%)R(&}Xv!j49GBPqt+FIuLRax++^eo6qgbmxawMFQ(?c8Yz8a^$1dn=SP
z`eSFTufK=(KUpCm(84806(Prk0S}(fuMeBu-^k1!_FE)SK_KDD3DEXl14Bc{f&v53
zjFz{zH^YJJ@%6o+{nfW_Nd>JG*|u%lo8v!Mg|7ba>8W?e(IgEKuA1L(w}(E8-(P2Y
zdj9<R@?mSELZ8}zR@|&z%eygxCptR1qN-|B<JMU=y4O;o=iAkSy7fGAHWEcUWt1j%
z_$piH-LdGAwHB-W^(8U*%skubu-r;ZYiscG1^4CM-Q4f)>|{<)Pv4s9Ki5k1-@kua
z&HGHZ*8VO7FNjR2dw*~5&3#+5uRD2rck})zEiDbL%sq4E3dpS!PNozT79QM?cvx!A
z{K(B|8#&pJ^+-1R&Ne%E;sl37q;v4y*QHbE+SN*liHS`({q)7<<^0#yL^eM=JNxjx
zdvab&h0@Z}UR+-<@8jzm+Bexl<;T<M@d{$y8?&yi%38km?%rzi(~JB2`OWg~a4>)t
z1shg>%h3?wdUI!IaB9}ATkAHSn!j56cGp8lj~mnqdvLT{95mqH-rjyH<ZjVUnPbO}
zP4H0pfBar;yKCXvdWQe!F3b*b4dF4H65-&IlyrDq;2x<fH=1G>n|Dej`v)g?tIT?G
zOvPMk!;vo8Wb-6$v966Qz8hUO3UdYqw0!=ZTyH(Sl-s*d?8)y+Il02;XJ(eCmA6+u
zpJRFaf%A)*GiQQIum(ovf?r=UL)OQ|N-j~-*XQq*HV?~_0L4mOUEPY{<$SYDv)eK+
zFMBCl&+y^fw_{zR+86fM*R!y)g68}V?<#%Gb1Y6EJUqOmz5Vg2soGT^v-=ls-D>*i
z(W4DzZ=+aPSr<Asvq@MMEh#cyvf%xqvIk6WFI=3g=6mSb+1aI0Zk;P$SO{AQ3Tj4e
z%jsMhy!_GA>G5H?v;Y6PzJEi-$E1+8QK4INqncFvbOi%5kEA<vba*7b+pM9Qd3u`e
zs{G4BI)yo%9bU@Y8UzFd1qCN&{ovsc4Agw|{CRsr1A|H49f|Yy|83a#WLA8M*6&I^
zJ?-J4R_@j~eM3XRzrVg}e=7671sc6Mz3CkDzyE(e^Us+(_pH>RAD^C{KIKvp^;7+s
z$@R&?^+JMy4-FX=m9$({JP++EeGO{S7e7A->RVZDz9}B&<HHja9L&8pZnB#17q`iq
zR6HjM2njX4-}l?EGWYGRtt;a9?~8mLzo$YlzW#4%=TRpY7uVw~EgdVST>RE^_T0H+
z&FuUo?{?IGI;jp?8S3QZ^x@NKeeK1{xwp6RHa0eL@bS4VcIyRAsq4h<5&>;8`tk9x
zpo~n<T<h{zIq6qdg;sqoxUwpgd%6F7x0)IoDf7HFd-hd-&)br7Q^<F=nd|1Xvr{JS
zW8;x%FwMTkz#yg{$75}6J$GUopS)d6(($F~=jVYoS@`+!F(_DBMdb@wB^UO|*-DA)
z#W)lc7*u_Ev2e=$4T;QgRWDUR_H0f+&$b}*FDD1bg_YYYKR>&%H9NfXEw^Cc&SkIm
zymoO(nKXa?{1xHr=W%dx3FYPGEvo(fP08FmoI~-*s?gO(Is}zn;^O8BICY3>hk^Ef
zb1AlP@bHLma&mgvN=iyPaVQ=-bm+)5-DsXXZEbDukTns3clJ~Ub0{9^xBsW%<mA*M
z-~?JJW0H3#f<y6$eElEAxSEfy6KBrUY!Lu;^M&o~>>gcQ?EYx?`+dTP+jxbYI4*8Z
z_XkzPpml>E9~@+U`1Gl&oPFJz?RVer{~yP~%G!CXSNio|owz+W64o@c^NY=|{Wep=
zG;2!4_Pn_szP_SoXPcW_pZoLlll<$A$;Y`ATR{Eeg3?ma%*;$D4#lV~8H(TD+*EE6
zP}0=&{PFuYw_?kVl9!VT%FE3qlm!K6wl7W)6r5`G`r2CO!)?41r%wmf0idxk(54R1
zZo|py{#xg4o;-c3dAIS_*6j9;$;XwHl$d0#%Ulu?5*lxB+`ir2#f2qoO++K7uo`HY
zt#cdCMeXgy&;39LaAaIvHT9BJa&B&~cKEslwZFfC7E^rs^a->(^vq0ScF@W%oztLY
zqf@6&jjR3p`@8%9e}B76azF!Dpjxi6v9W67tgg!vxia(S&07(<Sxql)4~M+G{GPAZ
zqCxHN3;SxTb>j9&Bp>V14BF%3vZOzDLq~^~<lCd0bxwD5bOh}3<5V;#eHEgjuC8op
zDY@Klu9KG+7lZHhH7{OdSXf#nURdDR(bZ*?9qru4<5*H+^6vir{vA6knmZc>m}IR=
z9D;%-ZF&oGe%RV5&@z;{b7lAa|5pu~WsKcj_T%Gm`Hki8<K}XTeU9FmB??OC--Tsl
z*7*EO_sPi6xOC|fsMPuL_V)ER7QGv{Zx65AzG>5;$H)5_8P@Ol#N{{Nu6N25k(}Gx
z&L$+exwyC<786if6qNJ--``VbF4<On0WE*%>geFuv3vL9kB^UA6hGqu?MeIhr;>$*
z1vHBi78Z8O=c%Yxh=ZG3+r?kb&d!Q02dv-kY37r)I&k)^?|Pr*esc|qpZUzOtCixF
zHe+F!V^PTDJKKzPL1t~`|G(cqK5UmiboHvJnVDHfZ|~7(XJ_-~ZMM)=VEFL!`TWP{
z=2{mN6&YEdYw9~ZdCi(N23c1$K<lnRj@JrVu|P*yX79X?6%jKU1O%rlO;UMyH9Wqx
zxtY1BsOZ7-=gWKUUD;n>zhwDxP)BQm3TMB({jm!RodrciS~4#$1C@OnHg0U3JbCh^
zNd`qvJbY#tFoIS}&#^29t-#%z%O`2XGFjdKTD3>)>aev@5nHoF-Fl^3*R0X8srtgv
zD{X%4)KqOohPSu3AHN=7zjjN-7RZS)u8ZAzy}s<o*qV8H*@Z&0tScN}Utf1W+{PQY
zI!rg`-k!*Olhx|~?Q?Dz_{_JH&ENB}?PQUQONhmsjtLzdCmL?thyZPe_R1C#6r9Qf
zF3W8fdQaB_&8lw9ysYNk?&R|2Od>Z2sEij}*#^o-;L;ux3~<IwMLRpWO*+ogQe|u^
zE?jzO6Ff~fnk{l#Vrpt?!KDKmHW-}P)Uz%3c9g=dPr1i>Bu`a4FZ&oFIJ2LVW2!eh
zzg&o(T56=5lT*`D=S4<M&Kp+sAD`3l;^rZimQzb-%#dK1#VzeQX+zD=qSI_HE^3+k
zS1+*(KK?H<f3DVw(s!PJ<_OJf=j51bee>qc8ygatRXm$EZ#Hfbh}fPdTU1n}BP%5+
z`2jSMYHlw6``g>iB^po9vCG#qfVPCy{kid=D6ZhYzvJ<)j(=cNW}D?YrKPDcJotRx
zo}B?Srgvjo?rhb=N=jOjEoRvZii!yfXq9idyOxQW*)ZdR!j-kr;m=Ezl$4aJs%A6<
z>@0eGjQ7pm-Qtr}5+59BWM}~GIOJ%YYhC{6-QC@3*Gs>=2<(wCWcvE*DrnXIn>TMB
zynfyN;X?r^cS}l2x?SH}^V4XV&&;L?69g(MD-T|~CMK#K=CV3$Eoj?U%$|zC^?SZG
z{%jIbwYRrFv8iXFbNegZ%iq$crAmgci#fR1y?;Z-MWsDI9(AvX*r=2edF;$g<Efu4
zEiF5`yARLK-`9EJLICL4nrqj@;^N{!wO-np8HwKvtG~Pu1PuX|yuQYJbya9HWE=Oz
z1&+)h=k6|lA9m{A-fHo(va$)OlAvN}!@C_jEK>I;B`2TldhU7i=1tI<08LFz58k}t
z(bv}pt=smVrlWa9_THX7HlV$`4<9~U5w%t8&D*!2nTct-(Mb;vwVpV4&TQAa$$wv-
zpKsqkQQ3V_+SyqJ1qBXMwL(9<TD|_!jg84^M~gl`^97|}-`QrZ%a^NzmMHA4D&6zz
z)oQif#*)AGyuG=Z9n>@Y__1;CUfZl|YdQ^+k3G1xHJkNV;rn~GJ~NG285tQ1N=jO`
zZ#TCpd6Dp~I`x%h^Q1mgTU%MsDK>knzCL*IqT|$5?ZwH*`#@8g4TtQ76HbAa*=JqR
z`0@Mq;gcshL4{8<8}Em2xAPx=dV1RJy7tRjv!gdo_%*Zhx9Obj=<jD|0M(7@3H*`M
z0vEgSHkbbU`~Ch5>vFxQEg6koQ?(ABJI5EgD#Y>oySp0`53^1FnDP4Byyf5C-|xS-
zw|a5a*H;_1Zgo97%e3^=eA{X<(8}WH=jJ|q{hGVFx_ZZs9S82;mv3h0H~STPsjtdB
z<%9sktT4H^J5B%l{}cRpMwa#Ur>CbubB|Ic84X;bS_e*^<ox&VU&Y_A*9)JWnd$WQ
zf2Xkeh4u0FsrOffu5LSV!ehy@WwX|_pLoO)Xt^ojAd{l!qz&7)i9u>ry|_IZDUo8E
zbh`ET{aAFsYSX4oF8k~Lwy1bIO;Sm{`^SEgN~ff8nndrxYuBzl*_C^1%f-m&mzH{a
z{Y*VQZD!B;^XL6fY|?4tkzBN0M@`Lb<?|;eCo5@ac<A^2Ddd+jIgv5vR>a;tdyY(0
zcK10pWzHO(tSc)Li{__Bih&mRTzh$WdH)0z&x(qQj&^?eS!@1J4UY><s{Ha|;>k^I
zcXkvyv#_v8^!QIwxwx}9ePi|aywu8?nmq@r^y2sFFflXxzDu8LReGeATbyT|yqK6;
zN~Bostu2DqWp4!h{QTHnZ`ia6w2Z-Mr@waiI+1hd&bfW}oTLIeE@a~D*{ip|n>>HJ
zX7DnJwabH+dMTNjUd@nser~RC=&BIIUH>jDbS|}<{?neht?%^Zp0Cg6*Nfe|chBv*
zylGZQ<HlLp=jK=*I(6#Qi*0Y-zCHTs>1p0|(UX41OyIZw6R^^H|G{SV*R}QY|E!GM
zyezJ^`ujW4dGqF}*-ww%T_*bd{e5$Z{xZ=1R?uFNmsf)qyYVi4)5y%uW#jKXNoASu
z>}A{MGPCo&m|9&_tF5g)x43a*^3?mAQct(+-MjbV-|gGBi4{LTXS%EI-{1eAPV2Y!
z_VymEnloq4k%i9fJ=dI5GJPCY#BlQPh-}j7{`U6v>pk}txpFIdPRh8oW#!4QpFe$a
zY3GxjV$W3i`kE-H8D>%Rq{G9{Pt5+$hvtg!cgtJ%@85rrCFR+fnWuKi$;mz1`~9Bq
z?{9B~H8nLK9cW|*ZF&;bjoKoRdwbi}$oH8l3XP48hfbaH;*m6Rd2?f<vx>TUw{G;d
zj=9$5T2I;eWHhp_uhUiYouzVZeY}33thJhuu<+4iz0yZ#7$#4#Uvlr>y+_~P-e!Aj
zS^jQLL1AIww%e=2^*7f2t(rJvhDM*fy<XV57)>Q*W!F|N(GtBEpW;8PkKBCh#lL&|
zYNK!5y2TYa?dXoe$3Ak>&T{s3I<rhN7v-$>p01a4dYUe%u?VUnOWxcFJh4f~$=TUg
zO`-h#J<)yp_Bl;bdHCRg!m&PCZL^#k3PQrdt~E7#X4uv4nj~;#U2HUHXH->bhW?hn
zfB&}b-o3k^yj=YA^Yh}IoSaIEijHUIT8Ho0xl^++=jNuP5A4>)?he~2u`+mhU&NM-
zi8uCEZwCeGY_nXUySqw*r}@t?aLoVvXa4qQXJ>PJPI9@uE%)dw(`=WZph-9O*Y7{a
z`odd(Z;C<R{{R1?fBgET_4L>G_xfe;?r2V$H0cOPVMfM^dmBxsHAr}!@iw|v{rz31
zR_H2?r)>OkI%RKfse+X4Dt+zp`Ptc4ozsgVH>Vx-bJ+LillM}-{QUfl1rMDH-`p@P
z{PH5uu=ttJ)89#{sj6jfZhXA*?WW8zy;YYlT{?1ebGqy0WxlN{o=PSrA#-f2!=69C
z9$$Yo^7)5{hfkGXJ$~H%%uHkVocsG?K{5RH_V(@*Cp=W2#qF(<>~WX&UhLM}qT&h4
z{yNdyeCF6x2FX~Jcsy6TbZ>8UYj=0|i|gy>|9D^jUz<<PMk6RF=*YXfyN@1f<#z3r
zGM)1F=#e8Ya-JV-W`FeN=H{etZ*HD4`~L3k>h1mfayA+tA~KI2J*s(ob-===8J5M%
zKq=Ou?9Ggb9R&+lgshyj>EHGE`rM-b@iiY^6H`-Fo0^-CZc07vl9aT_qUOhj83u_>
zph9nx4!6C%{lv+WlmDDm-K3zV5R$iK$r6dZ9shs5UOzQ;|DR9Y7p?4mzcHT6of%wy
zd#kQa)X9@4K}q|>CLJawrZ3Z;mw$Z~I`{wd5~f9KwXLjfrNq|%`PhE)Y5KW2f_i#-
zPSa9B#|}yBfSS#BZbom<n|pEBrOTIH%gVNW`oCw7O=`SV>8q5Iy`TxfoAZO0`_1gR
zKH>kL=k}+5Eq3oWi%9$N;i1*>vjI$(_Se@ddQMsqxVY^i_vI%~QXal}qmvRT_I}^*
zb@z(iwr*4LJaqSN>>T_0xb<glZccAqy?XV8)W{#desvx1mw&CZzxa7yL2<G0CY^3U
zWjBeOhXPI=FE20Get&whuF+5X{7pLD*W>H&Cdewg^#q(+x@5@<-TLQq%cng3{QUg&
z=zl*xCSP0`tUfn6dJfAHi;aEK=6;cRD^yMN_5HuRxVZR01b5NzZ@EiTS4VBl0xhhl
zO8r#t$Fuji>elnmG+r#ab)|Z8yq{m+r`mV7wyr*L>XcE$yW9EuukL(MKdEe{isz!_
z<9%x94}vnHq@<);@~ayg7k}D&_Vnq?+QBo#^n;IUsjIt#h9^Aj>i+#q-)jV_E~2>S
z&YcU|rStXFll%Wad?;w<bo;j?^YgQ_YRPJwL_o3GBX1v<IqTe+Ggl&~&6}t9=FOWH
zozqFLuB>Fce*X07;CIi$*2mq=5NAx@a(COBHEXzP7ydDca<D3S5wJp{=c3$ow_d52
zx~K1VCBE>hcHE-tcJ11=D$kkIzWHTLR`cbG-7nyDWqo|S$0U`Snwp+Ck-wkM+gp{Z
z&-ZcAi`}*4PU)YA?eac(`JbMgbeg0RxHigkZ|bdX%gAY<RNi~-<HzIj+E2}LZY<dB
zQ+;Mt`1-t0@18$C-tW3K>ne|$^H2Ny(A8n4RTInq{i$3m{<dGvR!B}x&S_ezpomC{
zN#E-5^{+}ib^Mruu0+V&)o9$gd$(2R^rKesI0LEIXJ?zYHa9zO6q@ko{oP+*U-wQs
zT`wppExmg48kKK1V^pU$tclp@<S|J_Mpl+LF5>z3L)`itk<*SIXk`96EmPPbJ3G7Z
z=_%3A<wrV#m-}h{o<3#DlI?qz2u&z`eNFSUm`+5(mOfBNUQ=9Dbm{h(-T(Xg`rM-D
z8XFr=-JkAi`Ce#cBmctSouH+QS67ESPf}@VZ$EtVW@P%#-Mg*h>VBqjD1P|+{l4@X
zWmQ$y@bz)544@WgUteEZe*B;R&hI9vDC+3&^h%j({W>>g!oR=2#jEC4e}DI|-~Qi$
zoyE@!zP*W@V_PkjmzT!?+WM<#ZXUk8CMIUir{6a=CUfuIyU@AaX<F*jgYVQ;RaGB;
zd3kw)3g_WAUhR^<=6QEOCMb9cftKi0S64Sb1+C~<?k~UiYtH?BvO9O~EGQ{)d46uL
zZ{Ft6)nVW>B%gl%0vcwLl+4`Hr|8_KQLO*w?(XpPo7-}4D|ia+DtpV7ot?e7G$Jzc
z;ZgDU76B&vzh5R_EZST3wFy)ir~do*?^OBAe*1qHBB#xm(E(an!(fncL1FRN@U>B*
zvesoLyY?7#NxeQfSsm1PWe8wfZm=Y1sn?4uD}y&CUD+3(mzVeA=H}%Wiw?K*UynXM
zVbQzIx3=Zo<zmRVyUWzZ#>Qt_?$uROPo7_@;+b)8k7Z45Ew||5#qRwdp3kp;G(Em9
z@LYUlW#y?)!OQ(bSM57=VPE?s6;1wa(wBCZ=kM6H>(!diDxQ<T2clJ0O2)i<^QOnD
z^cBN~KfnD_c!UMC#LM2^YMpJK&%_W{{noTOb&I%M`1-h`{r3M>Y`3wrjQqJb_4KsX
zTmO!Bi%;dAb7<Y~Ei-1b%(X0Lb8hEzovIc3;Ne5i7|UYEX10tQ8x*C?ayme((;Cvg
zsJ~@hKJD9#(DiY9FNkwDJbSbyc)6c|lg8%j&%fU-zt70<=FOX~DFJisYNLMc&Ahx!
z?Q~mPn^bJsrzf5a2b$UW6H-%`ng{v)o1b@gm*(rcI|>gc)S6~pF{qsQ?CDchdwcu6
zwV;)VpmC80uh;K4lWd=pf5afUkilVZ)z=<9W|NcF45@jtYbRN4YYn^-@wwmT$`ViE
z$)znVEh2`y{^xp6Qek0dpDm-iGH9vSsp_1plqI0B_nT+K={adb@$+*`_R|9c0~vhk
z_0HPX+g5yFD1Ls<H6TC$w3y^h@%e>ye}A3&scm5qp;`U>+}uZRmvG+tzQL_m>cf{W
zCQ;{(9b@a2Hg6MH6!Cs{dpmoJfLQ(g&Ynajr>9M2E4=*u{TG)T>@It|>dsknlPkIZ
z?EUQQ>{@Tt6<({2+9IyDOec1i$iagLL2G2s&NiPK*+2i!#Z#wFU5fmFZm#tNm6Z{{
z*X$Mk^ZWh&$Dk_vb-$c#P}J6{Z*NxKG1ck$@}=bIcdw~hGj(3SeVbcyb8YnYDffG2
zm7R~jIoQn3_F7v*EZ4?~V{Wa7zkhgIqM13L=cF(9w*TSEdO11u?EH!9(fhq#-4JsN
zOg=l`B79B6#S~dqUHy)4J7#>A3ttx#`QElJDDZn^?ydJ8iFVVMwXHFG{-^Zkr&N=S
z3k;wQ%r7r5pE!Mb_=?Zro?ia`?JAyW``_Ox?a|bJZ#~iQ*zM<?^91Yv|1}3CbI(bj
zGXxjk75#L3N1-xkK$V@HT~ujHPt&ng_Vxd4Knu+8+=;2Et_E!b&A7ab_s^d{4<0>Q
zv_@W5c5O+$kbk1vlMf#XDypj=Ut1g9(bwnJEvC!BFxx!;SbY88(1hu1#ZUJOXzIL`
zuhiGqm$4`~@aAzs&b*dI$0q$xO8)WTVOn|h=B-<GFMT@`Iw$$}8m;`}F28TSxwMqq
z?%$8cN!P<K{N%o{B`bbciRVo5msi8%K@(V7VQW_Wkri;#D7EeVe0}n%+mPH;^=xhI
zZn2v;Z&sD>?h$l)Fu(ra!DGGBh6x84o__uFH+Oyfe!lwp`pwA|MRyNfx_6do!R7Ru
z6MlbxFTShJIVmaW!;g>25z*106M?<vKT@B$%lrA$r>$46Tv2+vdD_;krb^1n8xsyP
zfsPi6uldL-BP%N?E6eL}dGG4l-``wUS^tj>$rZOv_R+mww(`W8GcKT=Yqb*t19WZ~
zO;UMTa`n{z-2zPK?f=(oe5BE*-VYj6@116vUO#`?pN;qS)!t57vpxU5-nDHuHZ}#1
zkMVB)_VZY;bk+O9NYKXW*VorSzPQ+ZRgE5~bFb{)=P*eH)QvYzJ7W-W?#j>ADw(@o
zX8zuneO<9CY$Lar?dPyH5sVI(zh-`Yb(QsB$m%fOhC1^#e`;GlpRddAlQPZHxVxKc
zw;sce9XmAk+y9*7T37$~Yxu&nV)LhOZf<61IB>vW@k#a%`=876T$J0a9lq|tdHer5
zMLM_gBEG-BfBe_i*M)CynSu^XoTQTY;lV-Ib=wmUx1D;g?l;FE^4gV8$9koi8RYD0
zGCozzc>U;Tx7z*Z*5&UWTw3bQ(7?tkrLq3@jT;N<{{9khdT?T*^3lG<7GAGztO952
z_nn=cYNy4%|INLyfU!lu!^_L4()i)Gx3^E_y!d(l@ug#TQlfvyfHnq9&1>?X{Ou3x
zfhP<?tb6CHUQJq9%da1|C*zaM-ya{7Rn*m;CoKv3yH8kDTv(uLS?sP7&z;<$gCgx}
ze~J7M78GPuWcl{~{`%tQb_NCl`}XY<a7s8iN!9K5d+9~8Q$08ufBg92)W7Y~vuDdx
z43_;%SQoc<*O6U<8Tvv#TUW&FuZxwudu+mo4+T%Zw{nY%Sx&PmeWk(w-re0j`R1ln
zulje7R21y&>^L|$8U&bLUtfRxa63QmwCWEJ7_Y63PTw*Qq|+(&;v!eqK6h`9#1ED#
zYhS#`cv@Sx>Fe_9@9!8Iu3QOub93|Z8yiJJC#(5Rna29om%$mdY4q;y^3|f#92FH6
zPZ_&thp#h<dUtJYG-v{2<F=)>``_K&-QFv0E~UFY<)qN!_l22@WG&C#D0lb%z4Oe8
z69G~B^Oybc>|0rUBRsw~b<?~*%8y02^e@Y}@2uEz@-Jxe)5yrE<eFlYm?7iB^jn)!
zPfxLbw72T3QMPRE&OLi<=5`BuP68bi#V~8)CM9KMP)mOUpJGeg_QmD*D%m&bICC`a
ztNjgXu(~B1Z1J%+c{)uuy5Qp@*S$}lpPzsH_xt_vN#$?0td-mU@0YfakkFTD>8Yup
z%_VB`BDvSs$<8v#WKv`Sol-nm{Nu})FV$X4+0|II2!M|40*Nv>L_|o;tA1x$Tz+!F
z#l`N-7fPx@osWY?kB{|UuDtNa^WVRJFYfL(ZxM*toaWo;d19&e^bKier6#AQot@Rn
zEw1-s@4xO>w`RiD0-V<04?3fb!G!b5{(oQf=RQq7-q#8q`jjwA;W#(fI=#pm)NMa`
zv8?OVwQFL|Z9I(vOq!aSr%t&21nnR^(jn-!-#u&N=FQG2DJm@j7KM*mKK(r9&jC6v
zQ0%pm$OWE-Z(h9MP-FoecDH4V$zG%7esd4q*;&lUaCKFvbHAKz*VePkJ*^M8I3C{1
zCuQQXv+?se>-P^XEOb^oEpJ)0#AEHzsGrffyuAz9GbZuB{oHu$iM+$(@AvEVi)?JG
zzJNyg6k0$#ntAJ<|NHaV|7m{D;>F3GHn-hEvTWCKx^;Lp-`biD>JvXbHT7(qy1u@D
z$jTts6Pt9T%yL$2Uz2>i@08CL^sx%iAkX{#|Mz|P{$1QS?aYPMzZdu01zjsGFBb=O
zk#20ypYP%6Dag(zBhW3TE3`Ihs}N|oBD(zgy4X`HA^Z02OL}=}DQI{=#-<`*&;NhF
z51l>h`{VcTZcq(rDwyavIcu&(p;LO;JImr{9nI|gU5}6VcOUH*@3ty^#SuBp_4G7d
z-)lcl>+jdtywrP=ijtDjkzZe5yMnr$b$@n5Y|WY~VUjVSpsdVHGB_-3n$h9yW!0<0
z)*9{Pe^&A5$3~;*-721okcTO5ZgQQeX=!3|<%!RWmoHx~Dtmj&t7dKVb}{eidZM5~
z2TRLHP&2gV(@AyElpm;H`sLl-==FUYHW(<L-UJGYckkYTx`k(EoBOM%sC2Znuo#xV
zi+T9$*|H;`vHs@YTM8e$fqKX)o<e+ld`VwkTugd>Z7nz)tjm0IZftP$nP(GuWldzT
z$0U_)`S;gtuJiP*nK^NyVoh!B)a2Xy>+3fbKlhtBWeRBhgz_x2T&<MIYnI!NsVPlK
z-Fcvsqgyz;s%qDVj~@e*dV6|Ac9*>s^7Qmfy1T3N(bDO0LYs8D)6UM)ysiDpr2@3j
zWn1vhqSTEU7nKVC{3rwuPb44XdHCqjBA=*T#m~<z-**0)MuyTMRqtsUr;qo`>w|`l
z^7sD*9Uo@C_q&mzqN9a{1xMtx)~#E&N>~&u=;-Yg?d<G)bfi-l<grK1{B{Do(q<x)
zR66zd{|U;kzLhI!wcpM&x?jcf(be#H(Za$)B}2o&oSU0mzr4EY?J;Rd@js)G^t7~P
zLd6~)9XB?o_wOip*tDttr@FrGi-_F&`(iEaLskSho|$FpEn{01B4b_VQ}gTP@<X?8
zM@Rmjoxjg8qJ8J@{8!e_AyNj63)8PXJlsBYGH8E2_ttm&|NqTjy!mljYHFul?JthV
zX;bah{Qu4P#SDt3#O&<sqF86n4gsegY4dY0%CfJlPz(wVZk<1W{sfhkH#j>4W<_r8
z&Gny_YM6ORW%DZ^FE63<w%_Mmti2YOBPw!vhGBBaq(yJuykYu&P$n}oQ%OPLz>B)F
zmzP}M+}wOTg-NL;ZP}Y^Yoo<~+?<da$s=zUlmGXRI4`IG0j1L&`?hY~dUEEaRskhF
zJ->>|%9%FLHtBFnOG|_HB+o77u5QeJ^I5G$ApQKjP@yX4+wx{P5smW|i_@L-^VTf)
zn=5qh-aWPCzwdUxpYs3yuF}&_zWL0wE?>7{+cvXZy6o)i8;hQLHNT$r#7=z4g6>!K
z|9-k(SrvNvh2=}n<9)K)A7U2#?E`gF^Zy=FQP9-XJaqZ;<rjv0TwFrtd3Q86oI5#L
zUHY%zb9-@-k~J*?Nk2b5^>Rvn^V_2SU(Mpz;%bbB-{vZ6@t&#`U-SF>`^U}vb`I0^
zVjsMG$r(AV_uF1SUtd?yNc*ElO#c4<E^cm8b9ZFCytK69-_Pd{A3R{Fudn~}-2Q*%
ztlf@mAXiBK)KXGf6!6;3#YN-nC;vLRH*eo=EPERTI`4P=-fvP_*Vb^lySv}Gd2?aa
z*H@qg<o|x(|1b6SoN?+YkvZ1oe2<Uyf~KM$zIw$aEiE0gDugpJF>!`%^|p)W9~^4s
z-k5b&YfIKuu666yfjXdjtG*iK-ZD{9Q(KgGcNarK(GmUN)APREt9}nU4-vG`&c?<j
zAw8WvK0f}-tE=3gb3Xq3{CsxR=EBFvKxZA>{r^)8x<umMy?dZ}$nI`#DbuVL(AmiE
z@9kX?w|CdciY=2pye74Pb|0QQcW%vJjgJ0){z;Q3f4t}}zcBG|8))(b)LpOn=vwpj
zYWR<b?eYo9$-xim!d8WFvh&F}#Kg#eRyjm%%{qAUB<Hqm+g60E6q;q8-v^p7x3iPG
zU-x_ML8~MG%OycAWxshgoeQ1YLCXNk%E~@``{uSjZZBvlCn#}0u`7LhOH^Dx&Lt#7
z1T-CgYO40(YuCj3`uadq<A)9({`hkF{6~+Dc4yq(we{Yw)}M1`X#M{4`TXNIH#g5P
z$rSqY_iti;et(y!_M+U|+amT>nNHJ*OuF{X>y>fts+`4cy`YJCpBV-RGuG7q|GPbC
zpGNWd4BweXtS3*NlrT(U$-2Id*WKOS!rJ=r&*$?&i)AOwnzhQ9D?ahtVvAJn-TNj_
z76vV>1g)6@EpwSOXAaM~jSEGkq`E+B&Eo6-nu3nGT@|_-bfOF&AK!|&y;fGGuUc+y
zPFFTHz4~O$muDP2JT6{dTygb(OMm?Q37Y1cH*a3W!&Y(7e$EG=X|J=6#`*VbK#RLT
zr#ai$+9u}a@_J4xaZ|sd^&(>bw_DjBpfyp`H*YqUk(CYHU6$J;V<~j<<Vn!d(v9D4
z--+nZ@f31uZCvlY@$_{4#csV)74LRF57?aMD`Qc>aPi{Bj-H+?n&oeAnX<659=v;3
z7PQzdElo{KC!*o`ylTGV{qpTwzy5bK`S)ygzCqQOj4$u*O3PZ8f#woG)3Imf+sD7W
z@9EMCYO#Uro}}{d#>V8M^QP_s?XlzqE%y`?Yy12Ad$g5|QNoc9!H8WYnxMKOa8*ag
zih@&rZ4^RRg&gdaHV;@CqzX#jnU|NbojyCyRyr~=(!<l!@#?BjMh4J2nsz=}0b$|A
zJi@c)&h0I~Un~Cb;lqgCWx5OrH#Q`mID7W!n#j!tMNdv7-8EI#I=}DtJ8Onnin{Z+
zK56#a|K?3j&dp7%lT<)QSWn|??^tn0>wD~mEwS-jn+hMhE!`QhI(B#2hwtC7OWu5b
zT)y6=s><qEkL2Rb|3F3ZpU>y*Z|tkBKFR7I(kEk6A;8JWIYGr!a%a~Hm6=i}HTCuR
z*YE$g>(bk87KiPH1*euj-N$+aRKo5keLd}<^}2-%7v9*I-2Ul5Xfx(4!w;XcFD>Cr
ziF7L}GWzxX{r?hXwJJBzb{BqmyCXA=(?PR~%nVxL>;Cxg`=7MCJ<T}#nvRTpolNcT
zZ%4WH_cVA-)l$^f=C=R;#~3tU3))UGNu}h)1;*H2B^yh2xVWT>2@4o!JTE?BFSlb_
ze?R}F%a<okp3Dr|>AAm7HY_ac!>3P|G}Dulnf?9!o0InmPfT1Jwe`?c?eHo8y(hJR
zMx0f@eW=U~IVXR5^NuT5LTWx7WUpGEE&n3-;v&|t^>Mk4CptRj$@(~)nJ{%KD+8N;
z)t1dm(o$0o9y`Vs85y}@>sHf<{=Iwm28j!b`b<mpm;|~i#U$s3z~$xs?$6K7)s?*#
zd~{;APWr7anJc2V>wSB7x7&NV9;ml;;^fK3<;&HboSZIf&yNSq@J{2K+_B<JGE)-h
zcn<~x3+8oY&(d-w+1c4`s=k~^u)TTnrkADjyE{9ZV|SMwJb(UtishnDN@iwZ@3$^l
zqQdR6sL$W-r_0a3k@wDD0<{-k+}ODI;?tV=4h?N>?&*55PRsq~eyQ3sp>jsU49DAF
zy>5$Y?|*%L{o%uhna#}1yl&pP6LV*0vHNr9nUg0s?=FA8$ZM*W+xE@b*ViTWSNplR
zxVZS=KQ&d`_uO^oV}^`|&QrBQ12!hPf~Gj~Z+?7yTu?-0$sc<S4Gt#`kl6CoCpv}I
zCrqEdJouYuU0of6iTsO(hK5PE8;cVD{P+l(v{-srBe>#BSKINmA5f?Q@cGa(76Ci+
z79UDxN?O*@!J!ksPv-i%*x<HDcem%u>&5I?VEnlB_O@KmPV)8petD@g{(QB1{eve@
zTGqwxmb%-v?RKbx14C1D^ZZNmY-Y#A##Wx;>O8(Cqv8%%KQh(TcK2y_A16lxXst5n
z1g@>w*BKb1x91(5U;mGjq2l%0?GGM3Y65N2GcyxQOiVoG^CB=Xkb{TkiX{KNd-vov
zr=2|{oxi8iuJ+f0;N^Z66(1DB)<(5j7C$?1_Uu`q-5#Kj3(+!QWE2n;UCQ%S(^JSS
z_g0JVY%@mixZ;i-I~GK3&FbjyKYlxZfA7lR<%h0a6T7>s)Hx(%%BMf=*Iry)oS2>6
z?Qi#!1#~>?`t|x^x=}5llXSk;Dub3|X!*`)VAxUdaZ%(`CMKp5HCdC?8ygZO%yJ?M
z4}s*Bs-B&g#@Dby<;&~q=PxNvv9w8ZJ^VPV0-18ln#{8NuCD2%Ns}%t@f223Q~Q)?
zproX<D5&Fl_R-^F0$_uJc!ft9!#EHoIC=Q^xPaP+uU>U=iE4opbm^-p2RE%;xe|2J
zVN6WSgO@KmPn_@o6%|iAIy*W#I=sZQz1ai8R8&<Lrk<V#Y5}C3nbCOXP7G+JPhw&s
z=$w#OS5_{bvbXH5RMfT{&hql|GqcV4XU?1nT9$Zjj%D-X<Nb$k&1#<uGVYXm9Ovwp
zd%xdnUc6XYNJ!|xvuA7!pc9q;{QYZ~d&^`^{Qfvi>*{YgDXFQ7s;aD^tHW3sOmc6D
zfEM@p`0%vx%jZq3RRAT}O(tarjV?bt+<tLiZMEu8h6d2O7sw&vhnv~?r(8GByVC(0
zLqEyRuqFFC-@kwVKr1m<6@oU82u@Y=aX9nk!$W5a3yT+f-@SOz;odLz@XSnO@a)K)
zU8UTLii%aK&d$yg=gj$YC0P|@rPkUR4Kr5e-QDHk;n5)ARH>`3&VIa4mU%(tJ7$J;
z@%#B0K79OmarNQj$Jt-Le0k#J$w0*<kiJEi+BrLBf>w-ki|c`II{+>JE_ilEa?iJ0
z**gj!voR=WYkNPBx3;$4YxU;s+tO3u<;YXbeH_l1WL?n!-QTr3e7)P>Uta|!Bs#jq
z^^-0w@nis<^T-geE7P{-N5J~NH*a$08iC3qm#4h$2en!vH>W+ETYm50&CTf#U%l%3
z_4Re~uP-kdK)nEl|F5f-zU`WN|Lg1P4<9~cl$MsBd|T4K&L$={wlwbb_4V?<zP=6@
z+6~&e<dOzzd4{czTl+c6&8@9<QDvEj(tl^(kc@>37lLkC(Tm@=X3w*?Z+p#hZzY_a
zWoniB`s!*>8y?gnONjOK^aLGVD<mu|C?oSH<gps4;5qd8Yt+Y$++l`mVt0!{*CUt4
z^>=hQfM&cBtQg+h-p&tNJUrj7_6px*P|8_xB|%)rSk|h9!#Mq%%ke(hhc91tf;Q6_
z8X8vod^-K(wdnkpQ|6lGN+l&H3yO)gZA?C{WNa)9DbJ5QIM}SanHd~BOBsX(j6>H(
ziC(*Qt)r_8v`#MT%8EwI;%5neetevedbe#!*40&@)qwf?|C%lHo68j)9SvGg78n@F
zYWE);C|`6yRo5)j?4;v;vT4)#IXE;zD#20xso^|qF*(ZeLijqvQO2+hACW`(`~SA>
ztNnfG`gL(XKR*r*jst5VH&2*4b!o7ksj28(>+-hlZf+Y}TR}<5PV4e_5gJ+GM04rz
ztxJqkM8ns|f!3!yeD|);{C<tFoSfW`m&@ma4pac0gE!kOmx&=}cNwp4^tP7WyRAXH
zsz5gYMr>e6KR4&#$H&JLbI&@18*@*^*SDPoUET#+;R7-wV11k|!+{SE4^NyuyIWK{
zOhH+hdEL5oGi<BPHf`E8VbY|gRiUey7-pH}cJawtftH3JZs*_rBoAB;MT+cBn5k)O
zEX)Av;LN?Q9lnkyavJFD2!<Ow3YB-2zFwvx&v0Q~to4uIzmxCoDt*Q31IlS3T!xH>
z!4VM>Edml&B^sc8M61KsC%wJ3^~bN*>kZ4^L|9l@fUbtxo_7~?1>e123l}c*@blZ|
z@C2mYWhwjagqbUYm;22y%@zY)v9LCJI}?M7=b<kzFN4<jFdX>$`Z^<not<6P>)$_q
zDA?M{T3T9e*tjuJzW^MpK`opfia&n-Tv+=08t8Ou8ylMqwZF^G%(IoAsvQnG17ym@
z4R7AO`EqUR_U-HpDXFQTZ5m2Si;B8AJ7$75@PH0)-hRI>ILZQa|ID2`F`)BF|Ni<4
zIyax;!NbGtRj<?2)0rEB+Q4O$O%hYmGSDpu2M->sy1N>5ba?9NX^NJXk&?3Bh&K7*
zHP`w-K0bc<>ebNaTkHP*0v#}Y^aeA?&00NISBEEmeRb7peQHWdgvP9Nc3}bIyL&1@
zsiA89^CwRnI2w=l%P;ntss&ma>o?D4W%#=vA0KmYaT#StbBpV_6ciL}Oa>RTi;PQ{
zEi^SbeP@|$3~)Km*4LD(R$pJQqOSgU`~AAhm9<w_g>Kli$!W1$F9U<1pde^*?bht;
zQ;a|t4zzNMUs&!h9||@~Nh|jTQ_`|dVfCcv=jMXWalTjan75*;D(UvN+#6f7#X&pN
zPEFNjW?1GsyY0q}h?dsY#5XrKf@b4FLPS8Tj4M7QoPNgwT7o^XbtCH$AKmC}M=maQ
z7Zeg|0)^P)<Ne2vc8eRPpOevv+Y_M~zBbDA$noQg%ii9aVO6TNXwf3j(O+-w?VY_i
z9%M-1&f{VeoH#f+9b;l*8bOWY4avv(L_uMwq@)#ng9&sin~Te;#6i#4xUGc2C56$6
zp<gc2ic4wHq||I~k4Z}xD=cC%U{ut~m$PNsRnA`ac2kLpi;L>&VD*I8VZKUEE=$FP
z1r~jB;dt=%dZT<j%f25>1;v|7TwGjKAK$Z0IK-0hB_rXa(6$zVsYyN#9j7L<ayPul
zNO<a%aDc(v-Pt8%)15mF*W;L9U1e7D+friUlEMNqFX^&c!YPpld%w@95xBTH^MS*I
zKa~leQWp5hE^z1fc6V`MD-h9H^hv=maB|6)cf2(}7A{?^s8m&zc7~zq>!mrLCr#3k
z<rS1Xz|!&}vi$9=XA3{K@ixwoaCmloUdOz|O=~Q#UtQ1N(w=>;(IsTn2d|bFk!An)
zJ>%anRdhot_k#xsFFQIq=GnQspUsE}3!5cv@cn(Xm6Fn_c216qo3-@)FU$YB9^bgb
zVnI<V$T3UiotoO1d%J;K+~oP8JvJbr|Lc8J6h1#cf85k;p^)ggD=TMNH)LO9=#`4J
zl2TIo%F4#HIy^h7VY%<-5)+p%|4+Fv8P)zOvH5<dIOFy<-9?KRPn<b3^W}sNukG*m
zG28u<`0+$|L3TJO#8NEp?#evZ`uTZwo1(f>x3)0niKnNh8<xL|S+ab2@{J9ND`IvU
z{rdK{`|a)R%I4<RFFh2Ts>ja9u-GkAO-9E0+NFyEf|2)j6wdnmXhony&7P%;6}7&f
za$ySEnic9Z$3jq3Q}f5q=kq}=8AU}!M#x(5gP_&!$;bO5t$dVD|Na{OU}|{727?Em
z&sz!`2nsH+6tDZpdSTVnXEHxuub<Y}u_8uWnA4+<SIXqTot?#nFE6QrGEwU3X`mZ{
zLEEo>{QmuTalhT6wb9#kWYs4Ax^&s}?%F+j8^6A0l$HjCUr5!B8yV+%MYXebr7m3_
zZKbrRX!RLY1%?T8=k}UrUwiPl-~QOP+}jDMsjYgkyB0V$vw@1|tgEY9eP^42T=~_e
zYRSwBZQEGmM^k6dUaG95q*Z(8ZsXhAjl0WDpP!i{<L&MeqBnIWiwj5N-QDHM7Zx~z
zhED(g`wKcjz2E+y1gIX2iID+aNaj6VZ=vU8HP95}QfIZGc7X#k3=cd!d|;Wc!`i56
zeH|SuzN`x6zOf^)_?(QfTQQ%iQfOqf6+>~4yuDoZ^>s(j&NhGe_;K^~_<Gri6DNXp
z(#1u}%gcjy&rh5<QLJOi2`0OrED^gzBDV2l+`I&eqN#OjqZsekar4OWTv^TjVVUa0
zRi|8-KE2(3e-UV(TThS6?{9AvML#6uc8lo>oL%^Bh0TpUk~2&^jrZA3&QntQ%qzt*
z$DZG34#$;synkNDgJR{5hA`&|kXxQSNhzzex3~ZC^QZ0Jh7PahCn*UB7!;J4W|*D^
zTW%{~yJV*3-uimq>1&Sl>dFcR?hH_y$5{E}gCc05;NsK%^X+=~|NG^Aw4*~uN0*m_
zpM73^+~u!cdcyIkN{cSJPHTL6y0L-btfi^$*-L_gQ@K+gtxEm#<0EJj<;AC<^WLoA
z?^&GW;v!b^-|oWR-Dxqu*G4nCyMyDt%+f0B+|o%)UbcOG(704tsp{F(PL`Da|Ng$X
zw|Do&r=XiMc9*a35)_Q|h@1B;vj0j*!fmqy$B$nU6cn6V8@ejv+{*ob7FiyD_B7H;
zX;FIAOb>^=`}=tH_4OB@wzajj{HZXN|MK$ewp+1#r8u}Ym6*7=obOwpkZ@J&tiay)
z`?6nVbj-_4Rbu@3@uOo=(WZ+}J3Bj9L~L9HveChZ#~@!$Ky>NL)jpttu(~|qAXCD-
zn6sZtUA}}&>10{r-Y*9lo86Xoch&Zs)6;Z8t76X2v(*)loapuFapOegMnUC<kB@a_
zc?B<Se*JcP<HN(I&u=%g>&o(8G+yb$bNDyt3exxY_P)5eS)GZQ8MJSN;lNDebkOlJ
zEG#Su$9g2i`X;^5{Q1`W!PIb*$vkyGmp*g!R8p$4QsIo)Bk|)g|AKV?2R97Q+9<k&
zB%N~M?pVDVG|m3u-R}2F>gwzpH*N$S7wG55cdz=rt&DXUPhMW$G``6zp8WkPeqpWj
zgQu)Vjxm{;fl~I1o9W4mEuU9aW}Z7J^J@MIm#Lj%0t^L3MTdGMjc;6XeCqDUcVV@7
z!S8L)_~z{14bEuxckY4;$4KJ^<@pK6`R=hOX(^^IVr^M*N06!FG4F#5if21?a{j3+
z1;x*`npOPaVDqfvO#;*UI$q2SNQ-jVw0SdVM+fM-4`pTMO8=gVx}HMQ^kTCXzUc6}
zaj5k`{eNy3cjmRx(N<DQi$1L`&3-UDzhR+sW7Ss+0ggaUP%3|Vdiup>zS1$fN;vcK
z@=lyM(a_$`4q6rgTFz8idGO}Vo0k%WruO9CZYaOUxU77BoE&T|fDa=Z|29yXFiGJk
z_-A8~*tE=bmh(bkQSX^AnoJ8Hxum3|C@3m2itEKR2rz-NxBdS=%|Aas2VLa28&pHi
za5*}C64RI0%OxvQzvX}mZI`IxXAHJg8Rsr8_lvZWT4c1=M@8ZH_xHzV7$yryNpVG9
zYtd<Gm^)V%bhAa-+gpiub`*;BO<FNyUHtVsp{v9BBlgKmIDO>$<c<|J6H*h7axKV<
zJ-hSQuToGcmNvChdk#Cl9OytkhJb}mtR9nEdU|+Le|GEd>p0xbAFW}vNa(`@#t-Z3
zGvx~I>=G>~+61bhzo@9IpS83y4=+A<?i5HQHE5=X!>aK0pf#{epyQ<jw`PT^sHkY1
zK0nu*y}G*EyVX6!rn2(D0>=Y4H_x)q30)OorKF@}v@9$1?B=TU^I7Na`tx)-yRd?a
z|J&PgH>REzd-}hbo&VUOR&K+jBOIV(twD!x9Qx@XF*Wr4e*T;r3^o641!QtwW^{C{
zP&jySR&iO5&WD5S)B4(0oY53soB*0jQumv4;BY&CVtRTzXqZ%2mv`gFji5H;|9^i$
z^H)(;K8p@T?~D1o;pcJrY`HuYrA3<-y7MR8vq|`ra^Svv#amNwKuzfsoAB!DYGoxQ
zrn0g!28QbDYMsbUETC(?L8IO?W=Mpr2za>ixM9i(ffJi@J{&)FitGAk+X>TV&FVTg
z*ZT0$qpb7h&FkptIWpI}d|p*gNSllm3y(Y>kMuIhvgsWir>1oZn|^NQk>~pm8KSg#
z>MyYgpm@1{T|9W1&%q;|!n#tYU$5Wa$0uv`VvkzOffa#^K~qRSe*9oy04?j8rW<|e
z?p@i>&(1nS=Z-*UH2nDZc*8cmukCZ^ZM$E8ooDOqMXVbt3>7><*|USods4&teN6v;
zI2Zfbm+`EKynHE8aB9`ke_l5(UKG4`?OK5r>td$89}aO>JZu#&i345u2fD+Dk(o_n
z{aKu|L(LBlx388mpKA2|9pl|yjB;|b{vF=74QylAscT`MW#Tud%_{zVadE@!*-IBI
zX<a?#BEF`hgX74tV@@22F9W{R{QvuXLaOB7UtfdcK#L18`)AWQCVO?yky%il|KORg
zf}Y>x76HM)o6jCDv|N6F2jiEQi!HAUPYhhTkClZ1)SNL)KDNMjwwZ)s5({V%;)4eZ
z!u2Aa3bx4E*Y$jTeSLA|=VzdSK?bC*T-?tK7Xm;#Wv)uB6cJ>q`0V@O0As@UytB^T
zE-qhkK77BvrKRKz$DXfZyRQq)3{VrAuwjG2)9=phd@psyog5~qJbCtPm4xVq;^%&M
z_Ev{4|2_4aW69gk3O|3M^z(H6v{dc>%lvpIop4fUfv+?;!>g|Kl|E~%sO-kT$T-WT
zrQ^$#P8Jm_t6P^8CAMT;)w*%()}r$F_fBO;{<qg&Rd!_EpDmMB(?DH6&HQco?Dy)T
zjgwAoNSxN!*3mI<+QY+*pPx5=eB2ndwP9T>s5)L5tR^(Uv6(H>ip3VR9uIUN+|J_k
z9lLjHug~7y1{#dZp9>z03-SB&$N0i_eNZKPrb0=nYTMb_?0<f(es-t+i^hcw3zsS@
zX@!E?e^R-(ws1y9Mq1rn-|)Y5?<srrj%$#ixxj?i$A5f${P5MQRiC5w*ZnP#D_L&z
z=os(W6&j_l7_`Hgie^fDZ|vykI(*8j_+0+YpwH$HZWw|a$F8m$s=j8eiQ60Xd}~h+
z54Y>*HxpbkWujhjI=A!bZs<|*jM!DOlIvnd$;(Tu?)`G0rqa}Pp;7nl-OISVjQ84_
z$i<(lKwAo5zwWlJ{wA@;7}Aq;OiEhxivP;x;0aS%TUr^z*E1dOXa4Ets;soA=)tQ{
z1rw1Kak4)?x;NM=%;Ve>r@hbUBGab&|8*YzJu0Blhu7EFE1R2(i)x33l+K#~x}x*`
zzFG-`goe0Jj2Ugbar<g4*TwE$#&dLc{(ZZc9R-ZSYCZwFew!BTu}S!l@Zd}4*`DV<
zb{!q3=4V}PSR36Kwzfghxgm7*4I#f4qe%@hyGl+zcx`EE3A&61w25@_S}y;?vrMxm
z%$~ja`d_c?TU#<gRd7Uv#IZiv?lWh6TwGm2H^DTs^M`?~;kReGw)Qg5$s0>LK|LEK
zZ%)pOCC9ru7+zgtO*^xp#KlD{eA(p!X=&*_ACF0cmMws~!dJKL&bq2qQC+<_c6S-u
z^zhYTydOV)ToJn)G=SK(Vuc210C9Ku`)4o0pC0^vpLugyrkqpFQ&Dh=o%ZdN_JKP)
z51g4fYrmk&)0mkVjG7(^2?}9rA{O5I_OE~M@4odf|NUq9XJu|ZNx)N}vrFO9t=U48
zZisGFauQv6@tS$s_3LYtmYRC6yvnt-Y>kF#chpLiIxp$y5TT|MN}WBQe?P7%etnA9
z{PjHRxbw%A`D@PYc>nw3N!#bO=hNp{7C+nid(Y2jvtvpwNX)h4-SdCnmlc0EpML_%
z0wqVE&u8Aa@sjDHFx>}lw?FOc@Gz_T%d+Ia!^7=~xw*RK5-D%;?lS%Q!gy;7W7xVU
zTsvG`zUVtLgsq>pPO$bD!}k2svVs$HzC7YQu_ki!m1)bDEP3$c<m8UNzGL<Of5+>(
zyMAf960#s%e?gF{f{l!U`E+X~C8bNI&z@bnalkBh$;-;~a~R`hmCZjP@xqnKsQ8)B
z)~DaTeFKGT*t(d`z{PHv<{FEbcJ6dYP?#`*VU9Jsg~bV3K|#TnTJG)&N=zBI#Uv~O
ze(hcvcc)Kqs^s2`k3WC@ENXXHGEal)@p0zkeT?k<UdD3-1O@kYEmz-=%zk05v_T@%
zhr8uP?k-D0*$mlQJk0Lbvag9y{3~LpEhQ)zs5sZk^LpRY(=!_;T)f!L^5nzA!!wLh
zy(ZpX?l-q9`FLNZp^_2<JD<n>nA$&v2lN_TTtZ?NyE9K#^Yp*OFE_<nN@?xIZkCjD
zb1Z*6pI_f~M^0Y8-?I2whM|&C)pULD+Va1D8rJWhW-X<pbSeCP4fB^bn!T!fY9ild
zbZl|b7n)%I|Bvw6=<Rw%)u*TFg1UIRZZ2+b4>D_hEGVBYHpR$rA~*=c)zl80xA(p-
zVsEdY>G`gs<J8<Z6@{BOZ&v*K^HY1fef2k=Zo!EcPo0_aEWiGr%#H2*8#Y38mYbPf
zGF7#X-gPV`^(3eVa;ckTiDNU{jjh?!Z{;r1P_nU+$=~~R+O)+=i<P@Bac%qmz5c+x
zy$-!nMeZ&xE^Uk4cwgQU5>}4Qb_oR8+Z(5%kbJ!F=*!E?L2JVQ{CqzD#M!g1d3kzz
zvAbFrnb{t^xw%=wx@^sz#7PT(fAUT^EtYVYZGkKI&c%vKO3s%zEiLK2xkpkXNia>x
zd4c%r1|CTx7W4djN8a4rEGQ?(*Xnd?_NULE6*-tdqfd#4+fK@;N^Y57KTT-Dg#ZNw
zhj$qr9VY^KB^b`kWcHcOR#Cyw%m(t;%*)*@EDWG!tsnz;m%Rn`iWwNTW?y%kZ&w?z
zyDWF+V&&$tZ)?6-)Vv5t_>=;1k9jIr!B;H-8J;VvShr*<m%F&6dgTiVICxpu{d^+4
z^RPm4(R02FyUj1G65UW`s=xsW6hDCm)9i-Z+m~iv`1!eE_Ut=>pyVjQEF>%2E2<qP
zva|MRmndkaS4>%H@q}q=zMUD_+%4_QVq#$Z#||B0y0O)Jmg4Gg{g<;`QWU-@G0wCq
z)#{V640`e{`FLOAtu2{RJsmu8dF!@Zc(Awnz`wr@ix=MkO*d>TEMZYnYN-Fm*wnNn
zTX-Vlg>DuVEiJCNxH#9m@1H&$YULK+A|WWb$8&PS;r2#ZYX*ORuve46KAU~u+uH*R
zoevzBKP}4}I1{AP)>d}eGFSfaKn)QOU*D@qE-4qL>9bGMW#0Fj@5LQZGVM5Vqx7Xx
z?Br>BtZ^4r=X%7yaAgYGo);@)RkGs5ThMi6yLayvQ&ws&{h(lwCj-iF58I2}on2g1
zetvLH_*3yx>g((EMefcaT2&JpKpUxke}8{`iRa`AlO`?F@%Quuwde{zJaFuhFmyV`
zaZ({>r&90l7Z;f$Hh?3$BSbfbqvSmw3!B$<9VOeP;+H>wF8>zSk2?Z7O|ax;P{{f?
zS%wJ{CN%u{Q?uv$z3Lejg^SJvPKxvPnv}MusCaVN0#isfQVLa5yJWg5*81fvQAth<
z%agLAfg4}CGAVH|fiA6G=-dt(5k1w)QT_d0@4vsl58u8m?LW_^aqr%}XC_XHJ9Ucb
z*Ed!p<3?fiC7I=&9UUbT*XnM_m%p&f^uluff-f2gnVRJxE=L!Mb9yk!%gg`x@k8VP
z+_`gm_f~&j<UijIwB%sZrcEh5eP1SNFe$q+T)Y_c>EkhJaGLm%Wtz=Y{f*(?y#p%(
z547>dXiWdo6{o7epwOk%CuiH`JKHSr`np&TZ|~;GlZDy%<#ft3mh?Y6!&vuE^2V;v
zsJC-EIy~gJ<ngYF*YAy86VCsjDr1TJ7d6JtuC7DNd}nV+JuP-;f4zK@f`WzyhoYh)
zsA0g8_AewRdOMSG8pGyvZ{}Ye9UWgh6BG)*Y8lj8J$QI9Tk<9ETt5fUiE1GGFE8`m
zQTNv>CME{d2VNAqIt+AE*st&J`?uxZJ|(O6a&htVhQ!05I(%jDQ&5ewB4wHH<Vh0}
z4=-64xi<Q=tl-7$mpM$Aw&g~<xVeFjFlJ<AjM!bKduw|>Kf{j7&&$qSoEX>L&0O`J
zEouwHwRNfcBbAgc#WyL$p3b{JZC&p3^WL2t6ZtORWO?%E=jRontHnTTEaur(?<-O*
zQR-c~)H7H-Yz4!cTdWZg;F=@wWki(M^)1)dHpcC3xV+qQvGPlkxv?D|cE8`(ym_-R
zs7ai4b=AXWe!Bx7A0JojT6AaO#C@kuGqdqBy}QS%q-0TUB`7G_rxm&+J33}g$=cJW
zCtE8mQhJ%g1iC^6bkq)L-W0SNJ$Sj_!r<k8ptHq#B#jj~nAqn9f2{dq_~G?>P_~QC
zKPk&AC>VHiRlNL-ZM+s00zaMz?_8{?HR+t10xM|94RkEa`u+b7$=CmBT(Cgl&$rw8
z0<MC|$9fLFxVTtQQ1GCs!;7o?{>%ON7M*a@XhC*3I9qGU-`&ONJ)OyW8iRKDDOui$
zQC9OC_N=J<{48QihTy-S&*f#TN;vNBDg~W({P6vIes_2GorjfUOJ4{Wl<64c$$Yrz
z4$iG1`;PTYTvx5Ac;Mk-P;TvrQ&nIU;bH|{hq_^dflb8+(1s+p<$iOY@O775jIU>8
zX7jkup&O-AZsOwNa%*G8#4ktc4=_A<zyE1pn@4M$nu7kOEnAKpXk^})eqL^;vtsP$
z4+~8mUzN^hm~GCirUp*-OJtWVJ1|YRVfE?~-I^~R?>aiBe6eFx4AQoiesNp8;#bHl
z(Ie;l-hpOhwwylaw~H+;#pC*sH#s|%m6#5&wD3&SU|Q_PD6Yp){jI=JO-acpE-&xE
zySoSE>lqfidtdiZnrX<Wc=+?x@B{w#jERX3v(3PsRaw6+x3Tnf1Fv+$&SH>r%fp@2
zwI;s3-I#f~L4Q9>Qxmws<YCSu#S*id&1WuGMFj&RW0AXyTMs9PWZwyogckt`9}*IN
zm4IX6lCE(YL-=~8_j}b#{qt@Hz3VvPv5{L$vTb)cV}1RC%-{#@@(hj>Ky|s&T_uhK
z{q|nhz3S^9%&&ja*U=*)EO4p*<x7Y4aSc<anp`+{DG2Q0lF3U|H)M+^Br3*Q8ykTX
z3J42a%CD>pn!ft>_Qu-Z4O*d3`Z_u~Jhq3f)$%=G{d!sn?^#d+H8S#X@YrAbOY_5N
z{R7)_9h%v|zFs0PWx}v6kM-MocDvs`?=nuDFkn>tyERL6Lxtgox7!`Qzk?`?ulm9;
zO@~oTmnm%hG;1j%r8%tKUoKBnK2ZOUxuwO!-o?cwq$c^emvQpn=k^Y7Z#gXY1BFb3
zzk|pBBgecxPcMI|WXq_iq_k*K^d=WC8z!bD**bxi$xKeao?cWwaGt-QlyzGUqt8!)
z{*I0nAwIrKKPp^Z?S1{m@&2cM9Xv9^0+-}(-gMYs=MXi^<Z<ZHE^tywk<bd8l6K6?
zcj~&WH*XfXyRh|ea=c^@WU>3<5cRg^d)|UD-JQyyffSvsS)SK#6hB{*y-wqlte~WZ
zn!*zKXJ?tOt)CuNQ2RR<9A!p6Z*O^CzxDNXBe;EL6!6Bue^>r<zl2jF3CDOc4}$hc
zXxYEtr+v1^w9@FoL&egQN?Oh}OisJr9%6m4_j|(vg@k855Tp3w>o|F2ICvzPTwG7e
z@=8jmDJ<Fj?k@A6zm3OwmmWXZq)-l;1wG~{&`|oi!L7F;Zts$8qd>_-CZ}DR%Fe$I
z7#mM!I}#ar64cn8Sh#W1q;&_=(k_`=Dm%aHIN@NxsCaksboH<HdkdXs32n#<g?Q8N
z>MGCR`}cP!mL5L^j&%kf2aoT6zV8ouw6RqC!iI%+E(!<=N^W=SU2?Sk``gKE%Y>&2
z2~SAs>g#LUzu#U)Mg}y@1zHAqzxMmwQ+69SZUik1&&kaN-C4ZQxgB(Bnvv_8^lR%_
z#dH|N^x5uJx`WzZ9UKf*-&n+SnVy}WzV52a(gT?l-48D;bpG)DJLtM7(6K<ETVkJ`
zoxS7xp#y7v{HOq(8+7lU9B4?Us><rw`T3wb1eA@8gqAH^^GQk8nyu!`#4pBWYrrnP
z*nO;*IVpMR>yo#(8v6V12u_^$=<bHLOP4Pj=G-s<EhoRdJzu|Q|Ah+yDQRh-Ro4ZD
zg^Ubaa&8L6?kaJNh>&pWlUccT!q?MVvmO5bYgn(pp+qx%54bDRq!7C}|K5~!H=m#L
zeBB}-D!e-(Gvo3yUk@)Yr;H2@h6gt{rwh1lyif<4vHAQtSWTYc!pdOvii(Pa+uL%Z
zy2IDo)alsXDlMCKtoDN@s6m!zWX!xRcQV`Z-#-~oo-A^Asp{bDXzP=+<+8T61})Y7
z@a0QOZ!hngH*XBm&Pb%Br!zAM2?<S@GNnaKH%i26U+C(vj?T`DWjA)c|5^Mpddd`$
z%m<ZAy})VSWqr`fNhN3f{HCpYcz*tAS;3c*Y6?=j>i$|46%}3BU7r8t#YN_a4<CZY
zb3k3F@AvEF879n_!NE{aR;E|7XY*#`Ns}i}oIH8))g7g?G??V;IOf=Lzq!S_WA|xL
zS`*AXbT>9i`N<i{OrMTfaf&XkiSgpRsVON6#>T>+1EfK_Ss7kjUd|61POkczb!L_+
z_sp3y85p>^yPb|lM@2C)7#SNUrll?0d!Y2@pP!esR@?t&*^;jht`tIK-ri=;zQ%C4
zjd9+*1Iy>1k`;W(X~%eR!qxv_kNCK_f;8oADhyl#L8p04{Vh^*vMMkvY}%>Ho_p(J
zFICB3UgpUy4GQCbl2#%!tVDnODSp|>F_G;Di;MHs)#1nE>;JZ{(qDTjcBz*r!-2`_
z{!?PtbL~pnt6^v;I8{6R+KZRRk8}zLZcK8Wy2-Jb4Rqq1DsTLD7gta1x$G<q@pYOI
zS4wS8V+>#Kb^TlL@`kjtPx@Az$+@>ja<+MXn5MU{?^I2x!v|ShoX^fSKR!{}T|i#G
ze^=>it<%CnLJS_A9$#NwefZ=_3%9tQh?Sj@k&y2!lZ!95is{Goq_KTDy;{jm?!))#
zVHWZG<pMq^$_NThj0@ISlD%uY`GwWu0+O7245U}s1%GEPC@C?SWuC7W?9$d)UN6PP
z!_%VC1=`dQa_ah;NM>VW<B~Tw1Q|dlZh}s=wzG@ddH&*JchL4x7B;pl)3mvtzpVUn
zFeQoU%}wSF)>49k6B9RVo3`$)n%X7tUVm`V8`v>k44ACu`{Cd3_n@^MCr+MpJl-b@
z+RXIk&Q9UrWj>Buv#v5QfQDK7WUU{(ecSu-@o{BaTUl-~9f!xqdO@cVRo-uz^Xc=_
zjdIu5GcWh|{@gAsC^+$9K^fb(x6HS;GT*D_zp=5|R@OsTP2uQ<tgBjA)<g=wzP1*0
zi%a0<G+)q2aq{uL(07ktz3LKH_e;36#1nL`@R_q`A3lHH&dMz&@%>HsWOeWRJ#X%E
zn`A*+7a?*|=4@v9Y*pXcmiaI~J3HB0>5`+5L)XD0M_53+wZFW)9KZ8EXnK{Ioi9UE
z%l5<L{sZRs8M?(hS1%I+Iq=n0?uZQxKYk=Hh_h9&h<MlGaom7$;is>+^AE7gd)N!B
zod7k@1TzmG>AZ9%LcT^pH|od!jhkE;*cS#@?y8f0aovBGX!1=X2LW)E+p)z_pyB;K
zW~-73?^xHxPq$XOr0C<YqDDoX*|wS~bT!lM?aaq){<U^=oRF9~)AKunpI?ypn<ptd
zm6c=<vb3DK_xbrHrRI|-9b8xfR!#!->;xyuym`B{y3xIVNp<ra8IZyQEG?&|X)q-p
zW7z+n@69dNE9-Q@^@z%HtI{RaP50}kzY_%Y%01G299HD$#&G=kz`P-i_rX(DaJpW?
zZB@b$ww~?Ux~XYLRTdwM+_2)!o14rv-)3%kc6>u3xUP5EmGYw?Gi{B(J?pD$Q^S1J
z7avhq2r)}aYB<=ubf(_3GZR5W7fP307cO+TzweUm;iKI}?#@PuOioLuPf*ac`f@G$
zKpSsE)7h?$j*cq<8V&R7m~3p9&fF*@^rWvN<$$%T*4*X(OWjvAF}%6U9RcbBfW~u-
zva?^#V`QlM&Nk0>vbo$PD>a3XqMttxTv+Ikqq8Aj{=pj^uuqrpuL__3PWIm~?_JYR
zoCk%n5=iCcWuBK`{rzKnVM!;r#+YcAc`IaBI|o1eGXLq;QkOuAj1m(;<8Xg|&N<oy
zTKu}KJRdZ{wSFJdwe=u1LVkz6kCuN@F{m;zC{i)VoCG!L_mAY2*56kIHte>(E@;eD
z!{l_d_;H&_?Y!yY8<JX0e$43r-TunrxNCXF{kUDncN99m>sT?zkWulhZKaa$%;zsS
z-rQiEF%w*s3raE-7r(rB=%C}S>lL3|-gT^y0V#a)gn^Oqz~OcW7nX#4eXvJUJhZ~6
zy)&FNsezGsNj1NbhmXUHuP%ZdoQ!^RC#9`g5giV05}pvax>oyc_8c3{yVV_w?g$22
zCNMdz-=4y`p~`eamgt3ziy^*H{QPa&JIk>3Z2SI(y~|i3Qy`{OwYB#5lAD@#|7AG%
zz~fO$Mr;QUUOE#oS&gyumB(YJ6A2$UIYi(7|Hm(3!tvs=`>gr1L2b+EXXmC~<K*O;
z^v?4{f&ruAS0Ul0`O>ehGS~g$4A`(zKu~a^n5M{qW4#US>=#x~FUfaeYb>7{a$|G)
zC3C)?KS5(jtz3}G?AQt?)|J*3aq~*@C(YC>cL`ye(J*1vpPGal1`ke&z7)TA)D_~(
z89R4)d{+ry=kZ<R=g%T{m!(HoT0+jwV0?3jGv^kINwyj|eY;$;J$bD6Qc`Ptjbgb;
zh?kGUi+E`>hJ_0cT#xr=J`Y;+T;h6v-+?`q2j*B_`Vn#|dQRJPmF0en%I-|>_w(0$
zf+QY6(Qhx8U;45~*_~-?7USF7r)34FGR$ufDEPeW%UY-1We2|BXNET5Tv|DqHWaB|
z*tWLhebMV_cLW1D4Hyl}G&L`k?4GLaoos*R>{C#KykkYivuBrVy_b4jvi0%mx+559
zXvk=oW@+h=pzz@Vqky2Isv@XCxa-_O$6e)~K2zSM78O73>*#9a?AR#ensCSB!H>e1
z(JcJ@;Fg}!rI@`{mu$~`e0<5a7u2H7?BML!7_}?pmGjOb)d!E2A;EF<`D^Xo_`m<E
z6K)#qT&&1jD5jIVrQY5kbCSvABOQW}{2o%I?9LRvo=I4pX=^sKZS^Tx!M$wr8zg3X
zPj6VDupnGN;av>4QLCi2xAOB6#+Qzc2j<tGlI7jYCM=L#^nc%!iYqIX*Q#YDDS;EN
z%hL2Drkq<UcOU=w=_&kX!nq`-BpoB;rB}}u7d6!WW{{SKctOkB%<Ph_pC8|a-P=pv
zJ6m;fb{L*G&)&vAeOLV3+w41bodUN<0&f<VvVMCv^<8CCv!(I0z9gn3p0sl;+j1xG
ziZ#vGgLw4ma#!vL%jY+&jb2)<aLLlgf#>cr-ve)MUTXMU_ZQr8>sayU>wEs1uc8Lo
z(@OMRtXeOBklJ*4dE@u{(|5(!{@V%8C4!mf>;G%}p8x&tx5L_~1Ghn=+kg4zH%P3C
z-oC`~dG0NS)nSaVn7^gWamhB|^|ed37vJ74a(DTEnB|DT(q&$k--fN>cyoty#_ZEz
z4@~?P8Nu-IVZ+?HCbs!mtL_L2&S;RBcPB={K)}O;K}m_h-5nAjllm7gF7X$U=jV~p
zxT`&J&5Q<#S)9TR$;TTIV|GfD<}Y7fl3((K!=P}{or@B-Y6@*BH;od07J(+OgViAs
zbxC%vwfA?A`?czSC*)XgPg`hF;869;FjL4NYYN1pFXvrc+;}4*;ZFr<?N@dAPGuvn
z8m1%;z4&RrOuoJ0tohsvRk-fw=f?7T6OWyV+ow}*;?~L8v9J1r!>p?Z@5=u8nSS7i
z3pj&zluYmJEIA*tdRhtp)F~?EE^h6d9d@TrO?tNV+&NG~ASY)_yO1SpEdZp(^!R@B
zme+Sb*6%@)kwxw)Au}3YXa`OS`?jY-@WyWO6@ktWqrV)N&iD8pU}$*BcB;lH*@+hu
zm=>*1JIiF2&sO(~Gvj8^yA051z!(43VF%iHFWFvwbF;`j*<?n;i^XSWH_kL}gbx#U
zbbPtqz<6Me<pJ?{hUjf4WP1Z2nC$uW>`kPCg@lBa$PAmvcNre;ok|m?v8kww|2_O}
z-4b6lwM+d@3;+4&=4<yCb2oi)cB#rZCGy}^Xu^+z17~?NF1x+!;E@s*s0EJ;Z@=<1
z>uXlr%N~a~xe1d#y>fB7dw#O|rH%T=rmR}w)9Ti&+48^cghl6~D{`MdU;Q6BC->*i
zqW!bOwS5mMhZ`;HHk|sUf}8v0dzYhX90z8Z9#|WFY45e4pM#W~o;GoI?DO!RysT^U
zR@Sn&lk8GG^KI(a*?CsPDovcU=wFSPSVQyS{U=?Xo;nbnzw%%7-Tq$Q&taGH7yZb0
z3J&w^U3UA_=Q~U9PMfV=doJU5X=(16bqXHSPj-nmdQA=a9}>PSZ}wDCd-dLFt*s^f
zS9h6$mY@7GNk~^O4`J)%?65m=cB+`vo^t<JO;2BFl)JcjiZq@%^T@u}!;fvAU1+^u
zbo5o0gZJWo9eP^Y$ud7~!bYtZ&z1x*q@@+PJ1<%h%^8=bHer@lZRO`j^S{6Ie0^4Q
z%F?Bxe@)JwSyRE;y~`;0UhGSKb#=(B4hPqyw3CnOGiE0DX1zGlDg5E%N6ipD4kpmO
zpkZsHG*2fbCmSXnVga3`{Os8?(2^c8-6)phlhpVot1-6oGqdwC<nN2TwB6yqa+gC@
z)k&ewH*XF-7iM|6=yOt2z}j1{=X->#Vwo{J`~P2I!KtE-Kek&>u@iJvP+nZHDCooT
zU7L=$T-}s@zRRw@mFuW?MBKm0{;N~JU5h^O?d_$km-6*IHNV&15tNisQ#fjpc!&jb
zz01-yesir(?LU6}_~Fy1xxan;2D-!q)JLuVR}-_ZMsniBi3YlPVtZ>WH{?iu`1{>q
zx?bu>F&?+Lc_oS(o=OYzYFauyAF3QY%bRiS&DBnqqg}tsKdLGjrLo3t&B^uN*AwP<
zZvV+HHBX-)p@O%wUTwP*^YwX{2A8_#(ORX$^KLD@vUPRIf73Lcfc5jfs85wtQ#g8M
zUv2f5*Vosdc)M`n!V_oC968!8{_xQwCWefgn^bp|ylmRA!JtRRGU&7T@2}wre<~6V
zu{=1;?=8LS+^Lfnb8K36eH484OMdojaXI;iFMs~Z^WC=X&#}&q4wK|GwXYp|na|sr
z^7hWMsnz`Pdu{Zp+m|kP+wE_4J$d|K$SRds;og6zxAxmjN;@WH&bJ~~`o%ThcO5)z
z!V`o*>xQOI74^CDC#|i${qXto{xfSB9G3gd1s%MVdwW~vyoQ*aOe}0ncXu;$i%kiA
z@pRRa$I3=VSLUyGYCYt6NIE~m%&w`EMNsgikt4&JwM*x}y6Rolx5q}4^P)<p=8w<s
zVy_yN#&&bxxHYXt#%HM)Taj(t4uKtYvAfi*tHWN+b6t9jWyy@D=H`j>=J5#&3s+QD
zCjR^L6Lc0C185DBr>AG@t_((p)nRK39vomi8>eC^DPhXBA~HCtB%nLqQRGXvuBTGq
zm0d#m{rxL~o__ULdGprNuBoHrMW~O@lI)zNUaI!)W#3i{Pn&-F-QCIilxqvC1DSvM
z`g+^(Eqmbp?WCHI&yu5@x3^ACQ?{*-d)C0iEG)n~bM|ay1qFupdp`5Y$jW}a9$%k$
zu!;4>g@w$Ztyaf+rIig01sNW^c@y)ZB0W7Fbdb8bI(yps-?_hjB)=>?l)v9I-hPTh
zUERs!?kiqTUKOySUh3kKldsvMr2i_%C@Cq)rY0)Z8b&PjI@woW>9AkT^UUAht8RDA
z(iEC7PhhHAr{?5$SGR3Du&>r(b(q6^yQ<5^uG0+}F9zJ+mV0qqZuHarpgZmN*Vmh5
zUE!Fl?te^w|DUEaXM7kMT4zm+pEhk8L&KRfXRfO&Y}|O@<Yb4lvre4Vz4@a<-Tz8|
zzk#u~joqQc&YM39#@qq5#NtB2n15ZqWb)c++AJ=c8jd?V7Vdf*%KYo?8!x*V`;%M`
zToMXeKgG71AwNIi+nY&Y;(z&tCj^0}hO556>y6!A<|yJC@i)J)Eyq+=YxBSB_9wlX
zS!M2(+H|a0aLvPSTAkw6job9*sO;Fa&u-#|ZDtMsX1KVxq!@7RlPN9GxcQNl`IqV3
zAWKux9ktP0*R1%h>rmyTqN<u3aM4t*i#eJh<Hp)~SCW{5yiBvM9QgeF{KqdZgHx}s
zZdw<i@8#p;^7Pcy`2Sy+9adQgo27L>e5KXfY+%e>^+lrPzyZfo9|iwcF90o4Pq`8&
zb7B25@jY%_Y+v@z*&t)HLvp!auA5)qmnlyj`97SnVyrl7W$>lS$HD96v$L}|Y}o=D
z8_LPa0quwdjUn99hm9f35ftpcxF&M*Ccdel3hzoqp+o)IS^uZ~{>{&1;E{SCbjT5l
zONh~%tLqoK_sfZD{oeoow>@aL)!n!m;=Ahq|Lf@JaHy%-v+2!?i;F?mEHg1PZxzx8
zt+=1K?vbznFRzS+Kv-B<#qYP<1DAS<g7$o8Ut81I+spgx+}zc7@=s0C1YPO5XYXFn
zHCUj9(FdDYGw$p#Y~z>LyDtsix_61u$HC;v_WbyqTU$80#r55yqNG4q?+OSsNE)Xd
zxO+Et=ls>HwLy3PdQMhL{Pg6cfw_76{Q2@?IuQ#l{@_;v0q0{ZF3yUIiX5Dr7c=gI
zw#<HZWe262jvkKV&@D#S3_v5wN=iy=6QSDxq7JjPfI_BYOB*NTJorr5zSm1qY6_qR
zw#yPz9|y1+k8Ic$E*cnQIMteA(I?P#?lv_)1QuzWlI0Q<oT$U%*f4pra7=9MQ@&OX
z24MjsP|kAB$k5P>-KA2l;o{<AwJ>n8+YGBxEhc8>r+lqFGQt8z7Pq(MItK&@fOe_8
zyu5sI+SyqKhK3WZb(EBpBA+~cs;H;O_xjpekP-$1PQ^$=M#W}fDXA_w+p2{7`)UI;
zEY5UxbgW1{Jq>hA&7VJi3{p>tnB?7&D1CM1p?HZ3_r19j9e0+$m(z>g)$-s$f||UG
zi;I4Mh6v~`-LtdJL90eVHx*P?Ru;9px^<Liqy)&>)o?JZh}^vF%tQe}!IyI8=H?7R
zMna;Zt=8r5PDt@fs;eoa1aPJ8ex#hLq_pS|*Sh{`)24wI2E@e7IU_jHFbN!IDJ^Ht
zp8fdbBxqt?`3N}nPH=EG9WYc-;Z{>{3DKKpSIgDv#8_WnztdS!iMjCeGhYo6t{yqt
zsHHV0CMX7Ih<JE-T=)^QZr!>MA3kWj76=s))t|UAnaL?7L4?b7W1U&fjexw398C<4
z0;k^o3E5Y&QkT&ut2EM)X+zP|Q`g_!-kKc_x{T-M=H+MN;ssig&eueU&G)UWtPIc)
zd2ws&Ywh^<`OPk?cJOgbY@BIXoVIY&(Qa{NU0vSC$NSxvdQA=4W}10vNz9`a0SlS_
z{rjDN<Hn6Cr+=>e^U^C?>8^R!6%Ei0M4%~6yFVWeD}C?o?mm2RvHQbEkDB84R!zP6
zT0dd~=zKx<tyx!3y_u7G@a$RM`u~5+7v2|DN<Plga^k?58HS4kZtg05Jz?6kHcnx+
z2fgO^E=1`(y}P&Z)O5YAst@i*zVrAF8Vr<^lLMWt2s-7-$L;#MSnfcP(=}_CtqNVe
zDC_E~)G|BYz3Y5u&6x3_c!CFS*+C^G28V7jUC`a!Yu4z1_Mw)(zSi2>%6hC<nw`PJ
z&+pit%FhqpzU37c7mtXDSP-=}i-Uv1p`@f_;b+mSvCk&^+ci2ZbSNn?0qu(y(~UZC
z>z0(CpPz(P$%-Of@k{yra<+%m=huJ^tci$>Tv+t<6lglZZ;nM^#{D&GbaWy%fDX=R
z-MiNoRNlRN*Z1%5Z_v?O><l{b`((<>$^=*#1qB7QV)u*bMlE@}-8~C*x&0!Iq_eY3
zv$j8f{v34KA6F}rvRe-WgNlmEgVXx^U3Qnf1)cBD&CR`I*RG@!6BKt;eO+~I{ohrF
z3f|N8T>t+1nwXi%86F-E+C}j4<;$$|lV{E9%HQ{sZQs6q1*N5+09X~awrgMQZzUt6
zpw#>Fbw3!NJ$oj=!sy;76PP(UzV@qVLwG-<r;pDOcllZtb93{Es3=efb8v7RINB}F
z{r2}^etQSV?ODe_k^S~9Z*+8Y$f^*~VfQ=A-p)E!>bivc2TMx_gU8<~pFe*FjdW&T
zU$-!NdtO07L4e*X31Q)8(7^`x?%e|owrhp0>A1Z;A9NFFM@PqjiOTMv>sET>+yz)_
z{(ilFV}HGU)%SP2@$vDX7O}K>-jS)=;RO#5v3{F*E&2Mo*yi=?^@W6mA3lD}Y;SMB
zWa-jG&_ehtD}_PZFcvIOc>1rgu@O`|Y)(5XAS1)W&CPw}`0>Y2PENKcdn0jcdw&15
zwb99^rfB|nwR%0Mm3FY1UAw>j_uK7HD`tP0lz6x;@zs@;pj!#V^kO)?y}hTt-T&tk
z_oK&;FRqO?fBO8?$H&J(r#$}n@nb>c<}^@VSr@;*544v?Nr?%x!!bI4@7G(`^|Iz1
zt^fB~9<<CAG;!W8U&mo=Z2aclUg?PwCxVXNm^yXpsejY;<B#=78eiC$?Ed5T@5Mn&
zy+FI$?(MAx-KPEEKqK>quU}aiO!Du^7^j^%uqJZzgLm)x7CN_Uoxi-$xjp<?_3LZ8
zpl#!xo}Qr1@u1r!@9eD>zr8*G_`kouK}YqMyuP;fLiL@k+2Y^c+-#m_Tb*=fhT)Ey
zpGHbbN}zBB-MaDSmFq8OmAQhVqN+>+ix(|=!snX5k?(48aj~!{SF41$c=!`L&_VO8
zjsnr!^K_RjT`GF(-;<NVhN`Md1AeZJ-tOY=?!GZ}b(q#uNlD2?3LHu6<MxJ}vj6!+
zI8cD);>C-BdkP=7o!WM9Z*@0UtCLN^1Bb$Ua+wp=a#vP<ezs`l%$X8`f(wJ@&6%T9
zRaMmmT4xj-9PAn>a`ea%7mx|ZkGsF!Icf6bsrCA+d?&sG9Wb^cK;y{Q*Vn~wJ$wIN
zUrU6`(8eYva@v$BDt5KMwp`dJt{*3&9kN2<*YDq}r`+FB=q$qGC}Wy6#p~w;MQ4{*
zC&gpOk87Xyn`fiR)ud2WRi$-%wnkS{SlF~1t3tH{1z0xb-QA`2+|$=LG_#oX-Mziq
zVXMP@bFQqI=vA4Qw@!nj>BdrT@zSe-DSP(q+qcMRp}_O=^VbLc`~7}@x0;$-(p0U`
zBWKR|r2f3MHQUuuz@@mj_~Vn4leLb6PACH@7uSzlW7alZG)DdTnVHIZF*^e8>?&RD
zcKqW0iuHk2HB3${4Cm)DZrpgQKGxs=z^$m?>!Vp51q|}<+1$8wOYBzI74z(^Sy!9(
z@3%j6=n!bV(M9uuCWQx^&)Ye_y|p#;lzX2{=ich?N=8OPcXyZT-<&<+=H~SC0@}{e
zpT2x)*~+(Nsd4%_1uHA5g9i_SPB=bgR$W~UIxrM;Ga%@Y7|?Oc+h@+6z1nvE-o3Ru
zk7=tr|G6P)TV=8>_jcRX?CYy+1b5W`x8swsXwV8>wW8#7Nl{Ty^}J)f(u<Ri_gPeZ
z(QpaWxhQ;5zW$G3lS0C;FE2N2-W)tFIwIo1>h=4art8IW$$oxRr@tz2G25@NuiK|;
zhd=uF_xDn%H*(_Q?V!^b?%oC67ZP;y+PYY47B)7}F>;{e{7+4Ga(I>X?CH~`L1tB7
zGISy~EC>)cREj;u(!w#}9n&oH>;GQ6xiX%emHEFk9&{XpYo2x8AB$<a(QWqselUA_
zdY(9WGB8h=h4JIZk5c7~8S9qq%f7OLk)dj@QGQO&np@w)?t%7k`mo=+baQh$BZH@>
z=Z{aP^|^ERm%o>D2|RMY{y#6nim<g(H8nM$W3)iGuYfk&PM<CwwKa>Co11&>v30p^
zKQ=yo{kr?s*6hb$US8f&^;PTD)z$0{UTj6h#ey<2Ji@}lQ<Fgf9lAP9(aK7yU*6s=
zFK=DZYy}PvKfgB6A>zrMot=ye_F0Ft^T~F}u++c0qNx+ROXT_a`R+kMLi6nF*9rO8
z)YPmLnjro-NE}o$Zp*pZG-JjLC(e$Ab$OFMCNep(c*JwMxt{#I%j(FjTT-H0ApyH{
zA0O+LFw2q1$<4jE)LZ<@s!(oGt&j!<4$w_Rh41cIrlhB5|A~4fBpMw2QrUBU__MEH
zzPL=`$vqkDe(MtGnkkRJf9rof72o=sqp6{%hv(`3?{9CngSN+l4z2m}^0H!=(wenv
zK^HRp{QO+{O|XFT-y4#aMJj6k^Ln;sUr+k?=O@EISr^-)CmvI4#r0x3{O$jW@bU41
ziZIY|Zra+~98C?Ndwf1UJ$>|7Ol4qJmKMW`xw5Cvobk!seQRH>H5;!KOLlfPXjS;T
zcke*Oum~3`Xf@gW`u}^6eB2{5ee>qcx8AZo)zs9~dfKEQ@#eXjq`aDfOT!g;NA`WT
z({8WcWeI92l$Di%mNOPHWHx4BU-$6N&f=%C^Wrva+jgx}U9f0n#6~3+#{>QL|2h&6
zw`G>}Y~5-K+T(3m{0w}4aHp{Phr|5#4?rhbB_%0+dw)OvT;S=$0rq80&CQ0%$9QsX
zY+x*Ye(q?euzJDoZ@DG!?nu6S_wLl+qT*u1>}xtZcI*IcWAE$h(|T@M`6*@PqZJVw
zl^7lzY-TSgEe*XnZSv%1b^m!HUQWyW=Yy{I2Hh|0H`hw*_Pcv~SI?QcR_D>K`v3cG
zz3uGm)cP$bspNOq+hzUK>C@Xm$0vDCR%@Nqf9Foj9Lr+2WhXqYZ^^vOwn$^zk7pa*
zdZi?cQaXw@?s9JD)7)RE_hQ%K{HoIEgBJbn{c@o7c%YUZi=&2#=-Xy?{t5Hu^?iGL
zTina;(&fujW2btpdeF^K8f-t`qLAt5&!4TQ^6&0yW#g3+@v{5$`SVpZdDhq!U6<8n
zo8@vjyn3a&D$@PQjg83yEC*NYKcR3KbS+@q=_5y2^z`(06g}<Q)Xl4)d+6)^|Nq3x
z-`$z0Z*fTzbRFF0w6hxuAG?8$gnVmfXlQsUPviUh`{C9Z_NJzypi5+T?%Zkg5!BI~
zI#u-Dy}i+O`3<LB4$e~G7S{t^YVq*Jiw?WmUn|}OZ+LecwDv6h{5;Lm#YIH|LP9~W
z40#V8JSgBQxH<iN*fw(p(4f+lmBCl#f;Xr6#_X*U<(IPw(422qyX)3gGZx2&y?bjn
z-by(;%M`RxbGCUt=-5)N+n~GFa&K=tc=4j(-s<nye#~+C)fT&<!$VnE-S5cq`E^}x
zy;7ik^8p$n7KM*k9zA;W;hgn*P{m%<?qQYm``cSknrGve>uKc{UzB=!+J>!LkM6Dh
zE+8!2ygGdSqx1IvU5<8%f_e)Tm6Z!KFE7iuuz)cpCI)mELhbKwil(Nb!OQ(ZzZJ4K
z_|3IS{QvJSsAYS&jW;nX3$!I^ap~)8M*EVhBCoHDefaDd=prJHrUuaFoj>+Wmo7JN
zOg;{}8VYne{GY#nC(fPQn|!=aQCF8&*1Ak5)LchjU%%$dMfZrEMXIl^ukUYYV3;v$
zR?_!(cUJ^0<ysZ8GU&w&(02PbH#UOKPx<xzJ%4a;@EogBuIuaL-PgzM6%Z5*d@%#$
zwCCsMf*OFc%yPMcgM*=a5L}OK18qpCtW3PL#M7eS0mCfw{JtGKEZ*GM$efmz#=*_4
zy~V9_*K$>tUtRtV9X%~-mm~IN%?I_jpPrgpP*QSaSLtie0?>-8Do|aQzwc+-#OK=Y
zzRXm$Y}@j>^uPVqtgAtKF*B!6ZwDR2a(cSHdrFFmjg8HR$NlzNyLVJnRXys~-v`=S
z2P!>|c8e#!zqj|pmoG>9WUURdujx!JULUoUi;<DhqV$yrDC<iar#<-c^77MfaVwvI
zR^$Ast*iV{*;x7c*~8iS`x=)nRSjDo*K1q-Z9(qsZ9g9O+dp{qYSp&2qF*H!uFLuP
z>1o8K6i!epICOPb;_GW`KYadtc%gH9LRwnbiy5G+wA$tC9JXd%1>H41zy2Snv;dtd
zDP@|q<eP2H_q*jBTwI5C7C)bo`{`(2{HsT6gO~FyTC@m!{DzsC7%1s4bZ)<}I$R&r
zfVQpra^c0_z3cosN~S6pExjgr@#4jddwX`C^I5*KKcd~m#l=;7hu^uG#_VBXVFjh7
zt_cYX?j*hxy_Y>jZa&4s6DJmk=p30ceY$$Fii?ZOuH_GYooMCu^z(5zvB1L8l9A!j
zqem%yEgc;m-$8d>GbE&^tLH1ET$#}@!LhKk6m;L-hP=C0Po6(NEz2h;IPu(x^XHH6
zDt-On*|Rp#DGm=N3ruQXd52M8>eiLP%at@VIO^)^IQaP5X3v)P@$q@W*WJ<KAy!#g
zxuf_wA7~kLGdsVMii%5QUSgtRaY;(gjD`sZIeB<o^78aRRnvh34m%GkDk)t8Z3zXP
z*)ZEISD{O(sJQsyqen{R5h*Pomr2=F7~HvYXG8vdJBEg>TTMl{zFDX!DLq!^V0!cB
z&6K<AHf`QKamo}Hc6Rn1J9nOxmXTCdQ&=h>CDjEQ@^EZs16`MSYTJz)5ulq1m;28L
z-CkVv?M<g^x7fjp7Xz1N^!mTPwszv=$<3fG6q1sX0<IHlV>>%eXr!j3fR03ANXW|S
z%DTGh;Pva`e0+RH?rz6AI6XLz9zUM^?#|8)8#e~-K8thU-ZXV<c5`PZr&ZaTj%R0Q
zg9c(PEiDCH1K(}v=;%mU9H5bqo!y;vb=AkZmXyQO!ky&n{}|rdmfH=v!!IH=IwM2F
z$k@2><0IG9`=_SqMuQGd=HTW&d}5;VQ;wSpa_{YtY-ZyP+I{xeF}Bjz*RB@1?0O$s
z>gJlfS5*G>nv{tAqsOhZC9g?mN?RYDe&FO}bwN3~K2`5&EAGn4$?@6S+fQwbooiLv
z)ZWg%E%)}awFbSXo}Hcjl+W?~hI3wRU&CtFY@U<|Giyn%k3*NHy}kV7W4(t#+a>qc
z{dKx6j97(s>)NVN?ITByWGMT9#^aRc9?^?9`sMHU`;Xt<-3_|C2wZo9PN{nCbbHR+
zxfAEi>6tS}X2#5!Q?uuQ2ikUZ_&andT3SkiPW4YaGh^YLQ}0qiE5#O+CCY<(h_xLc
z(8D{wLFDk2mB9}mJXjFp7$U2!t<7*r=C=OnQ>TJ%K2K%sJbAzVf9$P*Ucam?twoC$
zKjm{?kHtqvgA3*#)1Ttq$Jx=AcV|Z<=rn`d`TJdKYHS!BL|j2fj;X7&7Zw(R+S6rw
zwO(|6U3BD#OUSyIohROfm%qEi$iO3Mw4~-<OJnxEJv;9xzL32Zd2++OuKv<6qcz(f
zCC{D8^Lp(M(0<mkx3^Bpw#5m#==Cg^D6H;x;QDoOhAZn-6*-uW^~?9Wc8e{{y1MGa
z$Bzf6=|+cq`}pOHORtnEsLi`|{l^y<mH+&Dy}qEhSo@PECur#Pq`I-OaQVACj@$F@
z?l@4wX~=l-!sFxpg}=VMbb1=R+|Sj`jcuA<ELVAX`IYtY@}TwKZM@P2Utfhj4cX(~
zFXuX4FBY_}sz=Jy>-IzN0cbB-)D)IJdHlHfc)$GPOG~{^ZL0qM4s^6AsF?(E<CiZb
zpwXB7Js;VWl$9-+#Xt^`)IHY}%Esat@N+wKfYz$?Rl=tyCqak3{rU5!;Kc<+@YtYB
z$`#Pq1t|KqwD}{3Q!@^Jet!Pr&*$?&J9@8Oy9U}(&?jTL=;FqeD_2U`R++2{S;-_V
zE&b!yFC{g#Wpk9L#e2Ta`TOf@fQCqGosW;tgeg;&w4QkS^~=lPnvX}tUtC<wE+Zqe
zAV6b7;$gPle)D7B!e&_+U;9?}@DMBL+E>SBwhbFMI-Z?nYFO~V0d#Y~;>W!k=Q1u`
zZJwN)4cdRq#LNsjb~a>10HdFuA813Ll9G~%)xC(@QJJsKM%<odwQ%9W01c6hD=Q|t
z{g%$I*5+KtyomL18?SRkg@u~moDQqfR|+~hJfKOLy5Dcj_x$~K+f`1zOX*(ab6L=4
zryp-NpMUV+L4&Av*n!)(r7vE*xFUA<HnIDyWmC7DW61P5_vYqi&<W;2aVvwC7WG7!
zgRV;j-J5uJmg&VMp2DDI{ZjJTYu>IkDP@yLum1h(_4>kRXC!$f3>bQ)Ob`A2{e5H3
zO(T=+Ydp`-&p#fWzc=*B#$&h6HC(zXoiHa?S5J>mQc_aFuEqki#XSA|yu|0{=6?A8
z{rIb^s||~uoKWgFeYP!ZRuyy4=Cylu?Cj)<pPvKWx##v<y0}_faN?<}KR-Wrbrd+_
z+AXGedef#&N32R;b-cZ`wfgDC=<Ru;td0VQ4<DYI$}evx^7if9qviK&x8F&c7M=M@
zM@PpeL_;JgBqXG=edXkN_Vs#p6(1Z#Tm$zMJZ#e7XxdTtx2m_#^cpPIzP*VwR9AOj
z`leQD=>Y}?f$N?wjv>FkzFxF+>Czwfs^9PZ6p@^pz9?c>mGQN0%lzu<_T5<GDg5!Q
z`Td%wb;s^pyK-^My&Kn5nA!P6tjpi&R8?1Z^Gci5y#1GxfAp|k#M7MPeX<6orlFC)
z4<9-N8WlWnb94IC)vH!-E`II@x~rr1_qWh#mR43u1_lDv-`+U-`T6ZUrZ@f4m7Cp0
zMn)6n&ySBwe5GP+Y#g#OXsOF_sqAWP-?@8F%(xXj+blOBYt=7?y?%48R>beO`}gnn
z`*6AJHE($|V)uen%(7}ySm4$x<>BdB33qH<%9`A_Pfkwe;O9R+N!8oLuI=sGiWv=4
zHg}8bA3A(^@df2QpxNy0;;L$DY-eYgYKPog6{>Ah{f+1Px>)Vo38gNXuXrZNHCI(v
zA3k@E@7lF%pxYPn?(7K6`zXbgoSVP&=(lg{+4<%6l!Yd2+kD7mrTKv!w{LAuKmXy!
z$7Ik!huz}(#~PX0FYGLS9xiuyTiB*5=APYapFV${d}l|YglU$@tLy96e=GzAprOK3
z9YaIGbLY;ro|-#%u40#xkdRPZB`8p@%5gJra&jL1_4I=H(v`;9+1V`Y?4Xkn%gV|+
zy1EV>YURGTF4o$`-Ca9;+44g@TlY?7yM22<XmIhzkBT!hjh|2by>af!OIz;s>|I;?
z=i~8+ty!WB4hsVo)c^k%u`TDOc7I~DcH~~k>geD9e!nmL{w`L=x@=9v@r`>$`GqGG
zO`AEB6SThg>9lFnPQ6vN3==sTF1Px;S8zHr8_$6TM&>F1zkUDietli+#f8r7Ep2T{
zN4rE*<u^1MT>BO#m6JaSbZAve%YxY5WfE2;FSNg>TK^6&e0BE5?OR7Wg$)x9fJTo&
z>$yRfFO`*jgE^--+S||Xn0S0mqoi@#f}*FVj#^E7ySCy6>*_g?k&!F^rk+1_Dkx9?
z<i;k2J)a_K!)93pFOS__20HWmSg^dbc5-fh>e(B&Za1^@U)Y@Pf8+M;<lo=k3a~t!
z`g`+S&#vhFR}#U|pFezPSifGsjYqNxH1uR^EBo)?zx6h4xocCtF6-O41=LDAbnqaf
zqN3taJCOA=8m1V7rhnDc#I*D^R>$qF+9|X8{He#a*H;8GpFDYTY2VoilG)YT{Kqcc
zyL9Q&g}v3~OO`Hu_~2mkim<h}vilRQwIlb69*wwl>ogM+(}&~o^?QnVMYnA|WU_Mm
zB8jT#-Gz_YK<A?sK0XFID6z1x5Y!R`9VIt;a`TQI7B-cimYjHe^Ilgwr-vozsOWXE
zyW4ttc&2_TetynTfW;*9(vzvj-Q3;9wd~8^$*fts_Tj^afzLj^YPxhKGBEG%E>?B*
z<(s%JDnJ~0;ohdrn?VEM(&l*y|Ns33T@CMcT(a6)J9Dq-QHfi#tXS_x1_cLq-#LEc
z_AIMeSE2)X_O3m8{J67-Ymc-!-`1?FPHUsKf`)iMKR^HY)z#Gn<>klU+}zx4GY^{G
z8X6d$JbRY(_0`o=o1UMa51KZ}y|pEf@7b>U|90!*_IB;rV>2~b-ln4A!-s;@n~UB1
zLFbKu?$kYV#-}>prQ0hwy<g5&t7Kn%-A~rQz`z$57rXytU$1`SURVFyxA8xJ{^a1{
zIr8)KbI=9yPkBo9ldXe8XMu{bZyz2Wc1qp3b7#c<I@>jC)=cR(-?Vn6mhWE4?C9(Z
z3m8FHmBsEZOZ@)s?y32|zP<*{?%mj1ZSK}1!PxM2tq#W`jjkg0#20LQG97n!mnR=;
z;XD<(S3hP)L-O&yg|WNK7(m_9<il;ephyDkR%f_$bXD!|Z##;fa)Gw2T3A_uasy~L
zGAODS<lWs>v~O)u=q0PqA3iu-Ugmpokt=t|nh3$bz`%&@d9pDvF#?j3nHTQg6N%VY
zV;K|_1X|!eRXco9{QkO(TU#_467K9M+^}_PYIU`z=j$^Ow{N#_3U4TR8T8}#@6|kE
zx}dFqOb?#lI_(vlf9%#Z&?x1WEheDjzd_>`_5bZavqhk@0xvFd1ziQqux;z~smt#5
zY+WmQ^!Rah(7ohKy{At}&B;%7=)20_>swZ~?Z?gZ`JGo+hl{UWv*yUPwb9+}{PJNp
z(|>$;xTv$UQ$k#PdC<H`la%_TOub5qiiC1=a$Mr$;|;B>qGmrly=Y0PS?#Zqjggzv
z7VX`;HzP4oao5hBor;QzNkKuAw%u>axpU`^ORLkuZ_5{~yKygM>XvgoTlZF0SBpo+
zbG16{l)pW5)uk)5H*VXyHhOzk?CvtrxpU{b78e&^Jb2LY&8@AgLBsHy<ZsV{s#;W0
zUESR)Wg4WJT&-<u2b-mSC;>TwnO0NLFSh#4v0!X<YMiX@e@fPC<_ybXH4&~jrp}Hh
z2mIrelysx_*V&42u`XJ?cxoy;pUi>j@pYQXA(P(k|1bCY`ugzQz5*<|?#ds(eREsv
z)_dy9oF;`6-|9L#dSrzKl$0d*)O`L8y3gm@nn)wp&+A3C!&dk@fVx9x&iItPxZwEO
z&@fOz$w&t@y<XCw(e<?G-kLRQjI5{c+*vtuZg)pVk2uJjnXK&W$w#|H85mlf7+akf
zg@uKmmIaDzOgSk8x@j#eEDSV8Z1?Ab^O7Y?R?Inm_AKwefB!%?6h&>z;nddFUK6)h
ziUHKYEqQf?Gjw%W>(Zs*F_x*?;j3igSBL3>RxRxR`)#%nUwnK#gKIzI&3(1jQCl(?
zv$L};EG-}I{eI8c$*Jj--Qjlr_O)wug@lAaXCZ=)4)L0*_2bKBfA2XJnj&1FvIW#g
z0F4S|W_GgiN<Fw9U$496{rmm@*R26>`FQOzw^!O+>wbT8Gcy~nR7>^ucbeOeA3JvF
z)-9>S?fm**q?RpPHp8Z}sPi7b;6y{vssJUW<nIsL<&_i_LDPu`m-)`-x;v#`&Q?m=
zEa$-Q_xnL-M?U`c_IAX!97(;nJsp>q`)f}YbrcYgmhL{-%nrI2<iLxIiv!n#&ip(z
zRr}%sM`qBa2g{Z%yRkiA{#cJBbD#+5N(@E@(AoPfEiDJGT@$;!%(q#iYem%7RYrZ&
zcRRJVwm$s({l5F_YikYj@7aL1a(YkGIXKfeT|iiv+1lFrY2nYGKVMv1D}8HAW^*gI
z_@fgOl|kD_UtL+*%y0K&fy>vjS64J!+S`-Q%rFEEzFk`CEhr$+u)F;Ip?mk_7z%!V
zN(CK?0lGV3P2}bYlP5RNG)@N{Oy7D=TuQ1-(l|{)LxTf!VA<hz{*5IsgFsgt|E#mI
zwKc5%mQ(ZXW;*D4ule)k=h;@bJv`k0c<uIkmpb!GTwJXDRg{$0a&dEmC)q?rm+EkH
zatexaxh|~rpKGPc)ufPjbCc`RKNl7{FH+!m^z7{Hsi}E+d7#-NetEl?7dDZRk*%k8
zmA}`!wldg#=^DpownhE@{tIjCf4?;kTo}-y?l(ukHBh9usOZS5(A8bN(&l2DH*cQ$
z`|bVx@ggjab1aLOZOQ?aJWdM*oSmIR&)r-TX}l41qh#Fc)O+{tB^_?#4L!x;D4?sW
zd-V49eC^Yq)g4?-3VQMTVj{0C_m{tT>sHjB^7r#L>6n>?nV6b}u6(~^hsDJ!S3>5P
z=g&L!I%a2)tBJ9(@bhzXSL>WUe_nj=o;@z<>FFOoK0f{kG(CFp&YhT^zP`G*)rv|=
zfhpoTN=mZZl8^I&W|-$%l^%M0ynm`a=qmc&Ufw==`*oY@*G6pxwE>-;Z``mU;GeCo
zE^oiQy<0n<Y{Bbmx=WTX7q?!=Ev^STi8lFIPoSjqr*qct4_v-1{QLWR{gN}M{jK)w
z*;DZP+S;W3WsQ2VyAGT^>-+P48?W@CbLadj&;9vR1KLzv`}<pKcXxNu{;IF97DjK+
z(|UeuXR*3V;E@JK=8#uE|KHx-JYmH=hQ7!9w@0N!w+n>x>%Pj0dLXCbFE&kKmV%s_
z$1V|xtqLJZ9Z8RZmgI<Ni0x=<Z13R9JSH@2!*qui60v7^mhvfaUR|V-^0w^9<wv(;
z|4;j0^K<?C=Uejcz1cIf{I{WTz6@VGpNvI9)%mu>jDmsyZ*T8a>(+%S2n0k&&+h8(
zHn$aH_%V9}3pc~IjW1tj*8cjU`ErutVS_LK{#FM^=H=yGDpONad-D0`mp?y?b@laU
z-{NRLd~0|4d;hPm-`?JC#N*7$%F6kjPtImVXD4Uy&2_Q6r_7s|r*(UK{(QT-KR<%c
z$DV#!qBdo1ZgFCwqqes8sq^RkSA<7Ithjjb;*GDf&GSV%T_#;K<xx&#-go-xr1Q^%
zi;Fkk$=iPG_HA=pK87E+1soU>q`O^|PMtY(<?rwBlk;wzJ8{AwcKOn!s#j*toEhlq
z+R7(uwdCEqJj=O$Z}033_Mc}H=<naJH1Wi=um5*%o&9S2uCpocGnFTLxR}lMO-@es
z<kz^fIo;ps=H=!7sS<6^o;^$2tjf^PD+sbF;{BTLJ$r0gofM}|n>K4&;NPAEgB9oJ
z+iOek%$lau>a<Y)o%^0QZ*p802m1Q@zWnj=@v`dR;NVl|&fN=%uV32J!?Seh(#)zV
ztB3bihp+d!URhb$<+k|L$&;Q|+(P;S92z2AD>rWrj*X4|RJFJ2%ZrQpudNyWoV>xz
z#BjUeZr=1S=l}I=^_y$8R6l;7%HLg=uCI?5<!d+3`Tg7H;H{Z`1r}=#9z3|@Z^!>V
z`9`X$sznwuTr5mcv9Y~V`}_K`ii(0vO--e@ZM>ReHha~oRVGG8Nx!DNd-7z-vE#>m
zOKs))bIfk<T7I$q#|Oo4)n#R7-$d73yB2nLSE+Zuy#2Mk)!(<JDKLC!Ph?|ZsB8X_
z>I7<8m`Ghc+ASU$8oG4K6p^b}udZCP#wTulc-{VZ{kS~=si{lL-rQJteSQ4vhlkr=
zy?s0N_~W0~(`*0x`}+3oiCZrwE^caIu;9!YpR?!BtA9}z;8?MFb1>*+8VeaMeSQBI
z#aUUa^6u<d`0H2I)TvWnetCI$*8KV5`T6T(^rlNmOV?kV{VRW6+}>3iHyX;?R)z5L
z@<zqRUY%=Q9ug9=1hn?y)y^2b*!^{}xw*NZ%Vy?=8yOofo;Xpka&Mfesj26rSFc}h
zt^WS*#!X#uy_gFZE=&NO<KET9m71FRciKj#1OHhW!M<JUC)e*jT`%@hGdq7$dgGO=
zS3M{F`t>U$C}`2Ye|51ti(FT(TzTr`$%`|M(@UP55DcDua$10>h)708hR55tZ?p39
z!otF)Nl8gfnKy4;@$+*tUzL@Vytuo&d{^b?Wn$vurEhL*j9}=@|NZ9X=1sTXe)|14
zD>rxQ-Ms53C#ygG{yRCnVU78{-c#nQw%?0*eEM$Q_G8D7U%hfgW&Zi)e}8{}eQRs>
ztl6`TSp__^GBX1c6Bq8<y*t_1&u@9Ky5F3D+}yP)FTcILUG?qF%(nJ+-`TI;-`lIb
z{r1|=&(CX1@cg?lgW*Abs*CyNXnqC;1_rZ6TLloU<IKRwz`&3o$;1KTo)&NbQ4wtn
zObiSR2aH$*K-_7H4IpX*4<ic$14Bb92MJn#vRxNRz4Um?#~_dqp00i_>zopr0N-BZ
AfB*mh
literal 0
HcmV?d00001
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102602Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102602Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..0873f3f6
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102602Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
@@ -0,0 +1,235 @@
+ Result for Multiview classification with Mumbo
+
+Average accuracy :
+ -On Train : 76.1904761905
+ -On Test : 50.9523809524
+ -On Validation : 51.3333333333Dataset info :
+ -Dataset name : Fake
+ -Labels : Methyl, MiRNA_, RNASeq, Clinic
+ -Views : View0 of shape (300, 20), View1 of shape (300, 20), View2 of shape (300, 20), View3 of shape (300, 18)
+ -5 folds
+ - Validation set length : 90 for learning rate : 0.7 on a total number of examples of 300
+
+
+
+Mumbo configuration :
+ -Used 1 core(s)
+ -Iterations : min 1, max 10, threshold 0.01
+ -Weak Classifiers : DecisionTree with depth 3, sub-sampled at 1.0 on View0
+ -DecisionTree with depth 3, sub-sampled at 1.0 on View1
+ -DecisionTree with depth 3, sub-sampled at 1.0 on View2
+ -DecisionTree with depth 3, sub-sampled at 1.0 on View3
+
+
+
+Computation time on 1 cores :
+ Database extraction time : 0:00:00
+ Learn Prediction
+ Fold 1 0:00:01 0:00:00
+ Fold 2 0:00:02 0:00:00
+ Fold 3 0:00:03 0:00:00
+ Fold 4 0:00:05 0:00:00
+ Fold 5 0:00:06 0:00:00
+ Total 0:00:19 0:00:02
+ So a total classification time of 0:00:07.
+
+
+
+Mean average accuracies and stats for each fold :
+ - Fold 0, used 5
+ - On View0 :
+ - Mean average Accuracy : 0.264880952381
+ - Percentage of time chosen : 0.8
+ - On View1 :
+ - Mean average Accuracy : 0.253571428571
+ - Percentage of time chosen : 0.0
+ - On View2 :
+ - Mean average Accuracy : 0.250595238095
+ - Percentage of time chosen : 0.0
+ - On View3 :
+ - Mean average Accuracy : 0.273214285714
+ - Percentage of time chosen : 0.2
+ - Fold 1, used 5
+ - On View0 :
+ - Mean average Accuracy : 0.263095238095
+ - Percentage of time chosen : 0.7
+ - On View1 :
+ - Mean average Accuracy : 0.276785714286
+ - Percentage of time chosen : 0.3
+ - On View2 :
+ - Mean average Accuracy : 0.257142857143
+ - Percentage of time chosen : 0.0
+ - On View3 :
+ - Mean average Accuracy : 0.267261904762
+ - Percentage of time chosen : 0.0
+ - Fold 2, used 5
+ - On View0 :
+ - Mean average Accuracy : 0.275595238095
+ - Percentage of time chosen : 0.7
+ - On View1 :
+ - Mean average Accuracy : 0.274404761905
+ - Percentage of time chosen : 0.2
+ - On View2 :
+ - Mean average Accuracy : 0.264285714286
+ - Percentage of time chosen : 0.1
+ - On View3 :
+ - Mean average Accuracy : 0.277976190476
+ - Percentage of time chosen : 0.0
+ - Fold 3, used 5
+ - On View0 :
+ - Mean average Accuracy : 0.282142857143
+ - Percentage of time chosen : 0.8
+ - On View1 :
+ - Mean average Accuracy : 0.266071428571
+ - Percentage of time chosen : 0.0
+ - On View2 :
+ - Mean average Accuracy : 0.253571428571
+ - Percentage of time chosen : 0.0
+ - On View3 :
+ - Mean average Accuracy : 0.26369047619
+ - Percentage of time chosen : 0.2
+ - Fold 4, used 5
+ - On View0 :
+ - Mean average Accuracy : 0.272023809524
+ - Percentage of time chosen : 1.0
+ - On View1 :
+ - Mean average Accuracy : 0.264285714286
+ - Percentage of time chosen : 0.0
+ - On View2 :
+ - Mean average Accuracy : 0.258928571429
+ - Percentage of time chosen : 0.0
+ - On View3 :
+ - Mean average Accuracy : 0.265476190476
+ - Percentage of time chosen : 0.0
+
+ For each iteration :
+ - Iteration 1
+ Fold 1
+ Accuracy on train : 50.0
+ Accuracy on test : 0.0
+ Accuracy on validation : 50.0
+ Selected View : View0
+ Fold 2
+ Accuracy on train : 50.0
+ Accuracy on test : 0.0
+ Accuracy on validation : 50.0
+ Selected View : View0
+ Fold 3
+ Accuracy on train : 50.0
+ Accuracy on test : 0.0
+ Accuracy on validation : 50.0
+ Selected View : View0
+ Fold 4
+ Accuracy on train : 50.0
+ Accuracy on test : 0.0
+ Accuracy on validation : 50.0
+ Selected View : View3
+ Fold 5
+ Accuracy on train : 50.0
+ Accuracy on test : 0.0
+ Accuracy on validation : 50.0
+ Selected View : View0
+ - Iteration 2
+ Fold 1
+ Accuracy on train : 74.4047619048
+ Accuracy on test : 0.0
+ Accuracy on validation : 51.1111111111
+ Selected View : View3
+ Fold 2
+ Accuracy on train : 76.1904761905
+ Accuracy on test : 0.0
+ Accuracy on validation : 51.1111111111
+ Selected View : View1
+ Fold 3
+ Accuracy on train : 77.380952381
+ Accuracy on test : 0.0
+ Accuracy on validation : 51.1111111111
+ Selected View : View1
+ Fold 4
+ Accuracy on train : 77.9761904762
+ Accuracy on test : 0.0
+ Accuracy on validation : 52.2222222222
+ Selected View : View0
+ Fold 5
+ Accuracy on train : 75.0
+ Accuracy on test : 0.0
+ Accuracy on validation : 52.2222222222
+ Selected View : View0
+ - Iteration 3
+ Fold 1
+ Accuracy on train : 74.4047619048
+ Accuracy on test : 0.0
+ Accuracy on validation : 51.1111111111
+ Selected View : View3
+ Fold 2
+ Accuracy on train : 76.1904761905
+ Accuracy on test : 0.0
+ Accuracy on validation : 51.1111111111
+ Selected View : View1
+ Fold 3
+ Accuracy on train : 77.380952381
+ Accuracy on test : 0.0
+ Accuracy on validation : 51.1111111111
+ Selected View : View1
+ Fold 4
+ Accuracy on train : 77.9761904762
+ Accuracy on test : 0.0
+ Accuracy on validation : 52.2222222222
+ Selected View : View0
+ Fold 5
+ Accuracy on train : 75.0
+ Accuracy on test : 0.0
+ Accuracy on validation : 52.2222222222
+ Selected View : View0
+ - Iteration 4
+ Fold 1
+ Accuracy on train : 74.4047619048
+ Accuracy on test : 0.0
+ Accuracy on validation : 50.0
+ Selected View : View0
+ Fold 2
+ Accuracy on train : 76.1904761905
+ Accuracy on test : 0.0
+ Accuracy on validation : 51.1111111111
+ Selected View : View1
+ Fold 3
+ Accuracy on train : 77.380952381
+ Accuracy on test : 0.0
+ Accuracy on validation : 51.1111111111
+ Selected View : View2
+ Fold 4
+ Accuracy on train : 77.9761904762
+ Accuracy on test : 0.0
+ Accuracy on validation : 52.2222222222
+ Selected View : View3
+ Fold 5
+ Accuracy on train : 75.0
+ Accuracy on test : 0.0
+ Accuracy on validation : 52.2222222222
+ Selected View : View0
+ - Iteration 5
+ Fold 1
+ Accuracy on train : 50.0
+ Accuracy on test : 0.0
+ Accuracy on validation : 50.0
+ Selected View : View0
+ Fold 2
+ Accuracy on train : 50.0
+ Accuracy on test : 0.0
+ Accuracy on validation : 50.0
+ Selected View : View0
+ Fold 3
+ Accuracy on train : 50.0
+ Accuracy on test : 0.0
+ Accuracy on validation : 50.0
+ Selected View : View0
+ Fold 4
+ Accuracy on train : 50.0
+ Accuracy on test : 0.0
+ Accuracy on validation : 50.0
+ Selected View : View0
+ Fold 5
+ Accuracy on train : 50.0
+ Accuracy on test : 0.0
+ Accuracy on validation : 50.0
+ Selected View : View0
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103115-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-103115-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 00000000..edd1146e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103115-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,1624 @@
+2016-09-06 10:31:15,988 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-06 10:31:15,989 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.00010290625 Gbytes /!\
+2016-09-06 10:31:20,994 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-06 10:31:20,996 INFO: Start: Finding all available mono- & multiview algorithms
+2016-09-06 10:31:21,043 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:21,043 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:21,044 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:31:21,044 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:31:21,044 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:31:21,044 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:31:21,045 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:31:21,045 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:31:21,045 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:31:21,045 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:31:21,045 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:31:21,045 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:31:21,045 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:31:21,045 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:31:21,080 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:31:21,080 DEBUG: Start: Training
+2016-09-06 10:31:21,082 DEBUG: Info: Time for Training: 0.0389211177826[s]
+2016-09-06 10:31:21,082 DEBUG: Done: Training
+2016-09-06 10:31:21,082 DEBUG: Start: Predicting
+2016-09-06 10:31:21,085 DEBUG: Done: Predicting
+2016-09-06 10:31:21,085 DEBUG: Start: Getting Results
+2016-09-06 10:31:21,086 DEBUG: Done: Getting Results
+2016-09-06 10:31:21,086 INFO: Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 0.938095238095
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 8
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.938095238095
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:21,087 INFO: Done: Result Analysis
+2016-09-06 10:31:21,095 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:31:21,095 DEBUG: Start: Training
+2016-09-06 10:31:21,098 DEBUG: Info: Time for Training: 0.0554401874542[s]
+2016-09-06 10:31:21,098 DEBUG: Done: Training
+2016-09-06 10:31:21,099 DEBUG: Start: Predicting
+2016-09-06 10:31:21,101 DEBUG: Done: Predicting
+2016-09-06 10:31:21,101 DEBUG: Start: Getting Results
+2016-09-06 10:31:21,103 DEBUG: Done: Getting Results
+2016-09-06 10:31:21,103 INFO: Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:21,104 INFO: Done: Result Analysis
+2016-09-06 10:31:21,196 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:21,196 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:21,197 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:31:21,197 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:31:21,197 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:31:21,197 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:31:21,197 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:31:21,197 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:31:21,198 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:31:21,198 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:31:21,198 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:31:21,198 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:31:21,198 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:31:21,198 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:31:21,230 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:31:21,230 DEBUG: Start: Training
+2016-09-06 10:31:21,231 DEBUG: Info: Time for Training: 0.0347671508789[s]
+2016-09-06 10:31:21,231 DEBUG: Done: Training
+2016-09-06 10:31:21,231 DEBUG: Start: Predicting
+2016-09-06 10:31:21,238 DEBUG: Done: Predicting
+2016-09-06 10:31:21,238 DEBUG: Start: Getting Results
+2016-09-06 10:31:21,239 DEBUG: Done: Getting Results
+2016-09-06 10:31:21,239 INFO: Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.552380952381
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 40
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.552380952381
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:21,240 INFO: Done: Result Analysis
+2016-09-06 10:31:21,497 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:31:21,497 DEBUG: Start: Training
+2016-09-06 10:31:21,540 DEBUG: Info: Time for Training: 0.343922138214[s]
+2016-09-06 10:31:21,540 DEBUG: Done: Training
+2016-09-06 10:31:21,540 DEBUG: Start: Predicting
+2016-09-06 10:31:21,546 DEBUG: Done: Predicting
+2016-09-06 10:31:21,546 DEBUG: Start: Getting Results
+2016-09-06 10:31:21,548 DEBUG: Done: Getting Results
+2016-09-06 10:31:21,548 INFO: Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.990476190476
+accuracy_score on test : 0.4
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 17, max_depth : 8
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.990476190476
+ - Score on test : 0.4
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:21,548 INFO: Done: Result Analysis
+2016-09-06 10:31:21,646 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:21,646 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:21,646 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:31:21,646 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:31:21,646 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:31:21,647 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:31:21,647 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:31:21,647 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:31:21,647 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:31:21,647 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:31:21,647 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:31:21,647 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:31:21,647 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:31:21,647 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:31:21,692 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:31:21,692 DEBUG: Start: Training
+2016-09-06 10:31:21,693 DEBUG: Info: Time for Training: 0.0476639270782[s]
+2016-09-06 10:31:21,693 DEBUG: Done: Training
+2016-09-06 10:31:21,693 DEBUG: Start: Predicting
+2016-09-06 10:31:21,698 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:31:21,698 DEBUG: Start: Training
+2016-09-06 10:31:21,710 DEBUG: Done: Predicting
+2016-09-06 10:31:21,710 DEBUG: Start: Getting Results
+2016-09-06 10:31:21,713 DEBUG: Done: Getting Results
+2016-09-06 10:31:21,713 INFO: Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.538095238095
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.538095238095
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:21,713 INFO: Done: Result Analysis
+2016-09-06 10:31:21,721 DEBUG: Info: Time for Training: 0.0758979320526[s]
+2016-09-06 10:31:21,722 DEBUG: Done: Training
+2016-09-06 10:31:21,722 DEBUG: Start: Predicting
+2016-09-06 10:31:21,725 DEBUG: Done: Predicting
+2016-09-06 10:31:21,725 DEBUG: Start: Getting Results
+2016-09-06 10:31:21,726 DEBUG: Done: Getting Results
+2016-09-06 10:31:21,726 INFO: Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.495238095238
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2481
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.495238095238
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:21,727 INFO: Done: Result Analysis
+2016-09-06 10:31:21,797 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:21,797 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:21,798 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:31:21,798 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:31:21,798 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:31:21,798 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:31:21,799 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:31:21,799 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:31:21,799 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:31:21,799 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:31:21,799 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:31:21,799 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:31:21,799 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:31:21,799 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:31:21,869 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:31:21,869 DEBUG: Start: Training
+2016-09-06 10:31:21,877 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:31:21,877 DEBUG: Start: Training
+2016-09-06 10:31:21,892 DEBUG: Info: Time for Training: 0.0960838794708[s]
+2016-09-06 10:31:21,893 DEBUG: Done: Training
+2016-09-06 10:31:21,893 DEBUG: Start: Predicting
+2016-09-06 10:31:21,899 DEBUG: Done: Predicting
+2016-09-06 10:31:21,900 DEBUG: Start: Getting Results
+2016-09-06 10:31:21,900 DEBUG: Info: Time for Training: 0.103469848633[s]
+2016-09-06 10:31:21,900 DEBUG: Done: Training
+2016-09-06 10:31:21,900 DEBUG: Start: Predicting
+2016-09-06 10:31:21,901 DEBUG: Done: Getting Results
+2016-09-06 10:31:21,901 INFO: Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2481
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:21,901 INFO: Done: Result Analysis
+2016-09-06 10:31:21,903 DEBUG: Done: Predicting
+2016-09-06 10:31:21,903 DEBUG: Start: Getting Results
+2016-09-06 10:31:21,905 DEBUG: Done: Getting Results
+2016-09-06 10:31:21,905 INFO: Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 0.985714285714
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2481
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.985714285714
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:21,905 INFO: Done: Result Analysis
+2016-09-06 10:31:22,048 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:22,048 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:22,049 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:31:22,049 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:31:22,049 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:31:22,049 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:31:22,051 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:31:22,051 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:31:22,051 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:31:22,051 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:31:22,051 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:31:22,051 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:31:22,051 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:31:22,051 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:31:22,102 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:31:22,102 DEBUG: Start: Training
+2016-09-06 10:31:22,104 DEBUG: Info: Time for Training: 0.0569620132446[s]
+2016-09-06 10:31:22,105 DEBUG: Done: Training
+2016-09-06 10:31:22,105 DEBUG: Start: Predicting
+2016-09-06 10:31:22,108 DEBUG: Done: Predicting
+2016-09-06 10:31:22,109 DEBUG: Start: Getting Results
+2016-09-06 10:31:22,111 DEBUG: Done: Getting Results
+2016-09-06 10:31:22,111 INFO: Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 0.838095238095
+accuracy_score on test : 0.433333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 8
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.838095238095
+ - Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:22,111 INFO: Done: Result Analysis
+2016-09-06 10:31:22,121 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:31:22,121 DEBUG: Start: Training
+2016-09-06 10:31:22,125 DEBUG: Info: Time for Training: 0.0774049758911[s]
+2016-09-06 10:31:22,125 DEBUG: Done: Training
+2016-09-06 10:31:22,125 DEBUG: Start: Predicting
+2016-09-06 10:31:22,128 DEBUG: Done: Predicting
+2016-09-06 10:31:22,128 DEBUG: Start: Getting Results
+2016-09-06 10:31:22,130 DEBUG: Done: Getting Results
+2016-09-06 10:31:22,130 INFO: Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:22,130 INFO: Done: Result Analysis
+2016-09-06 10:31:22,194 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:22,194 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:22,194 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:31:22,194 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:31:22,194 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:31:22,194 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:31:22,195 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:31:22,195 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:31:22,195 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:31:22,195 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:31:22,196 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:31:22,196 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:31:22,196 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:31:22,196 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:31:22,248 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:31:22,248 DEBUG: Start: Training
+2016-09-06 10:31:22,248 DEBUG: Info: Time for Training: 0.0551888942719[s]
+2016-09-06 10:31:22,248 DEBUG: Done: Training
+2016-09-06 10:31:22,249 DEBUG: Start: Predicting
+2016-09-06 10:31:22,255 DEBUG: Done: Predicting
+2016-09-06 10:31:22,256 DEBUG: Start: Getting Results
+2016-09-06 10:31:22,257 DEBUG: Done: Getting Results
+2016-09-06 10:31:22,257 INFO: Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.490476190476
+accuracy_score on test : 0.4
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 40
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.490476190476
+ - Score on test : 0.4
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:22,257 INFO: Done: Result Analysis
+2016-09-06 10:31:22,555 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:31:22,555 DEBUG: Start: Training
+2016-09-06 10:31:22,602 DEBUG: Info: Time for Training: 0.409327983856[s]
+2016-09-06 10:31:22,603 DEBUG: Done: Training
+2016-09-06 10:31:22,603 DEBUG: Start: Predicting
+2016-09-06 10:31:22,609 DEBUG: Done: Predicting
+2016-09-06 10:31:22,609 DEBUG: Start: Getting Results
+2016-09-06 10:31:22,610 DEBUG: Done: Getting Results
+2016-09-06 10:31:22,610 INFO: Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.985714285714
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 17, max_depth : 8
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.985714285714
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:22,611 INFO: Done: Result Analysis
+2016-09-06 10:31:22,751 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:22,751 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:22,752 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:31:22,752 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:31:22,752 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:31:22,752 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:31:22,753 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:31:22,753 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:31:22,753 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:31:22,753 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:31:22,753 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:31:22,753 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:31:22,753 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:31:22,753 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:31:22,823 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:31:22,823 DEBUG: Start: Training
+2016-09-06 10:31:22,824 DEBUG: Info: Time for Training: 0.0737769603729[s]
+2016-09-06 10:31:22,824 DEBUG: Done: Training
+2016-09-06 10:31:22,825 DEBUG: Start: Predicting
+2016-09-06 10:31:22,828 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:31:22,828 DEBUG: Start: Training
+2016-09-06 10:31:22,836 DEBUG: Done: Predicting
+2016-09-06 10:31:22,837 DEBUG: Start: Getting Results
+2016-09-06 10:31:22,839 DEBUG: Done: Getting Results
+2016-09-06 10:31:22,839 INFO: Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.538095238095
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.538095238095
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:22,839 INFO: Done: Result Analysis
+2016-09-06 10:31:22,852 DEBUG: Info: Time for Training: 0.10190820694[s]
+2016-09-06 10:31:22,853 DEBUG: Done: Training
+2016-09-06 10:31:22,853 DEBUG: Start: Predicting
+2016-09-06 10:31:22,856 DEBUG: Done: Predicting
+2016-09-06 10:31:22,856 DEBUG: Start: Getting Results
+2016-09-06 10:31:22,857 DEBUG: Done: Getting Results
+2016-09-06 10:31:22,857 INFO: Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.552380952381
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2481
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.552380952381
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:22,858 INFO: Done: Result Analysis
+2016-09-06 10:31:22,994 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:22,994 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:22,994 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:31:22,994 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:31:22,994 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:31:22,994 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:31:22,996 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:31:22,996 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:31:22,996 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:31:22,996 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:31:22,996 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:31:22,996 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:31:22,996 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:31:22,996 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:31:23,070 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:31:23,070 DEBUG: Start: Training
+2016-09-06 10:31:23,077 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:31:23,077 DEBUG: Start: Training
+2016-09-06 10:31:23,093 DEBUG: Info: Time for Training: 0.10053896904[s]
+2016-09-06 10:31:23,094 DEBUG: Done: Training
+2016-09-06 10:31:23,094 DEBUG: Start: Predicting
+2016-09-06 10:31:23,101 DEBUG: Done: Predicting
+2016-09-06 10:31:23,101 DEBUG: Start: Getting Results
+2016-09-06 10:31:23,102 DEBUG: Info: Time for Training: 0.108783006668[s]
+2016-09-06 10:31:23,102 DEBUG: Done: Training
+2016-09-06 10:31:23,102 DEBUG: Start: Predicting
+2016-09-06 10:31:23,103 DEBUG: Done: Getting Results
+2016-09-06 10:31:23,104 INFO: Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2481
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:23,104 INFO: Done: Result Analysis
+2016-09-06 10:31:23,107 DEBUG: Done: Predicting
+2016-09-06 10:31:23,107 DEBUG: Start: Getting Results
+2016-09-06 10:31:23,108 DEBUG: Done: Getting Results
+2016-09-06 10:31:23,108 INFO: Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2481
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:23,108 INFO: Done: Result Analysis
+2016-09-06 10:31:23,242 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:23,243 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:31:23,243 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:31:23,243 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:23,243 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:31:23,244 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:31:23,244 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:31:23,244 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:31:23,244 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:31:23,244 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:31:23,245 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:31:23,245 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:31:23,245 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:31:23,245 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:31:23,282 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:31:23,282 DEBUG: Start: Training
+2016-09-06 10:31:23,283 DEBUG: Info: Time for Training: 0.0416178703308[s]
+2016-09-06 10:31:23,283 DEBUG: Done: Training
+2016-09-06 10:31:23,284 DEBUG: Start: Predicting
+2016-09-06 10:31:23,286 DEBUG: Done: Predicting
+2016-09-06 10:31:23,286 DEBUG: Start: Getting Results
+2016-09-06 10:31:23,288 DEBUG: Done: Getting Results
+2016-09-06 10:31:23,288 INFO: Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 0.785714285714
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 8
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.785714285714
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:23,288 INFO: Done: Result Analysis
+2016-09-06 10:31:23,292 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:31:23,292 DEBUG: Start: Training
+2016-09-06 10:31:23,296 DEBUG: Info: Time for Training: 0.0540850162506[s]
+2016-09-06 10:31:23,296 DEBUG: Done: Training
+2016-09-06 10:31:23,296 DEBUG: Start: Predicting
+2016-09-06 10:31:23,299 DEBUG: Done: Predicting
+2016-09-06 10:31:23,299 DEBUG: Start: Getting Results
+2016-09-06 10:31:23,301 DEBUG: Done: Getting Results
+2016-09-06 10:31:23,301 INFO: Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:23,301 INFO: Done: Result Analysis
+2016-09-06 10:31:23,391 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:23,391 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:23,391 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:31:23,391 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:31:23,391 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:31:23,391 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:31:23,392 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:31:23,392 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:31:23,392 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:31:23,392 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:31:23,393 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:31:23,393 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:31:23,393 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:31:23,393 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:31:23,430 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:31:23,430 DEBUG: Start: Training
+2016-09-06 10:31:23,431 DEBUG: Info: Time for Training: 0.0408799648285[s]
+2016-09-06 10:31:23,431 DEBUG: Done: Training
+2016-09-06 10:31:23,431 DEBUG: Start: Predicting
+2016-09-06 10:31:23,439 DEBUG: Done: Predicting
+2016-09-06 10:31:23,439 DEBUG: Start: Getting Results
+2016-09-06 10:31:23,440 DEBUG: Done: Getting Results
+2016-09-06 10:31:23,441 INFO: Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.614285714286
+accuracy_score on test : 0.544444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 40
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.614285714286
+ - Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:23,441 INFO: Done: Result Analysis
+2016-09-06 10:31:23,693 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:31:23,693 DEBUG: Start: Training
+2016-09-06 10:31:23,736 DEBUG: Info: Time for Training: 0.345639944077[s]
+2016-09-06 10:31:23,736 DEBUG: Done: Training
+2016-09-06 10:31:23,736 DEBUG: Start: Predicting
+2016-09-06 10:31:23,742 DEBUG: Done: Predicting
+2016-09-06 10:31:23,742 DEBUG: Start: Getting Results
+2016-09-06 10:31:23,743 DEBUG: Done: Getting Results
+2016-09-06 10:31:23,743 INFO: Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.961904761905
+accuracy_score on test : 0.566666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 17, max_depth : 8
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.961904761905
+ - Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:23,743 INFO: Done: Result Analysis
+2016-09-06 10:31:23,843 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:23,843 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:23,844 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:31:23,844 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:31:23,844 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:31:23,844 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:31:23,845 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:31:23,845 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:31:23,845 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:31:23,845 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:31:23,845 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:31:23,845 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:31:23,845 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:31:23,845 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:31:23,914 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:31:23,914 DEBUG: Start: Training
+2016-09-06 10:31:23,915 DEBUG: Info: Time for Training: 0.0723230838776[s]
+2016-09-06 10:31:23,915 DEBUG: Done: Training
+2016-09-06 10:31:23,915 DEBUG: Start: Predicting
+2016-09-06 10:31:23,917 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:31:23,917 DEBUG: Start: Training
+2016-09-06 10:31:23,927 DEBUG: Done: Predicting
+2016-09-06 10:31:23,927 DEBUG: Start: Getting Results
+2016-09-06 10:31:23,929 DEBUG: Done: Getting Results
+2016-09-06 10:31:23,929 INFO: Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.538095238095
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.538095238095
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:23,930 INFO: Done: Result Analysis
+2016-09-06 10:31:23,939 DEBUG: Info: Time for Training: 0.0961401462555[s]
+2016-09-06 10:31:23,939 DEBUG: Done: Training
+2016-09-06 10:31:23,939 DEBUG: Start: Predicting
+2016-09-06 10:31:23,942 DEBUG: Done: Predicting
+2016-09-06 10:31:23,942 DEBUG: Start: Getting Results
+2016-09-06 10:31:23,943 DEBUG: Done: Getting Results
+2016-09-06 10:31:23,943 INFO: Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.490476190476
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2481
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.490476190476
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:23,943 INFO: Done: Result Analysis
+2016-09-06 10:31:24,085 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:24,085 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:24,085 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:31:24,085 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:31:24,085 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:31:24,085 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:31:24,086 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:31:24,086 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:31:24,086 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:31:24,086 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:31:24,086 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:31:24,086 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:31:24,086 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:31:24,086 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:31:24,144 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:31:24,144 DEBUG: Start: Training
+2016-09-06 10:31:24,149 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:31:24,149 DEBUG: Start: Training
+2016-09-06 10:31:24,161 DEBUG: Info: Time for Training: 0.0771129131317[s]
+2016-09-06 10:31:24,161 DEBUG: Done: Training
+2016-09-06 10:31:24,161 DEBUG: Start: Predicting
+2016-09-06 10:31:24,164 DEBUG: Done: Predicting
+2016-09-06 10:31:24,165 DEBUG: Start: Getting Results
+2016-09-06 10:31:24,166 DEBUG: Done: Getting Results
+2016-09-06 10:31:24,166 INFO: Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 0.6
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2481
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.6
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:24,166 INFO: Done: Result Analysis
+2016-09-06 10:31:24,172 DEBUG: Info: Time for Training: 0.0878200531006[s]
+2016-09-06 10:31:24,172 DEBUG: Done: Training
+2016-09-06 10:31:24,172 DEBUG: Start: Predicting
+2016-09-06 10:31:24,178 DEBUG: Done: Predicting
+2016-09-06 10:31:24,178 DEBUG: Start: Getting Results
+2016-09-06 10:31:24,179 DEBUG: Done: Getting Results
+2016-09-06 10:31:24,179 INFO: Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.455555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2481
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:24,180 INFO: Done: Result Analysis
+2016-09-06 10:31:24,332 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:24,332 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:31:24,332 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:31:24,333 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:24,333 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:31:24,333 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:31:24,333 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:31:24,333 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:31:24,333 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:31:24,333 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:31:24,334 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:31:24,334 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:31:24,334 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:31:24,334 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:31:24,384 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:31:24,384 DEBUG: Start: Training
+2016-09-06 10:31:24,386 DEBUG: Info: Time for Training: 0.0551080703735[s]
+2016-09-06 10:31:24,386 DEBUG: Done: Training
+2016-09-06 10:31:24,387 DEBUG: Start: Predicting
+2016-09-06 10:31:24,389 DEBUG: Done: Predicting
+2016-09-06 10:31:24,389 DEBUG: Start: Getting Results
+2016-09-06 10:31:24,391 DEBUG: Done: Getting Results
+2016-09-06 10:31:24,391 INFO: Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 0.919047619048
+accuracy_score on test : 0.411111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 8
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.919047619048
+ - Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:24,392 INFO: Done: Result Analysis
+2016-09-06 10:31:24,418 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:31:24,418 DEBUG: Start: Training
+2016-09-06 10:31:24,423 DEBUG: Info: Time for Training: 0.0905458927155[s]
+2016-09-06 10:31:24,423 DEBUG: Done: Training
+2016-09-06 10:31:24,423 DEBUG: Start: Predicting
+2016-09-06 10:31:24,427 DEBUG: Done: Predicting
+2016-09-06 10:31:24,428 DEBUG: Start: Getting Results
+2016-09-06 10:31:24,430 DEBUG: Done: Getting Results
+2016-09-06 10:31:24,430 INFO: Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.433333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:24,431 INFO: Done: Result Analysis
+2016-09-06 10:31:24,595 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:24,595 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:31:24,596 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:31:24,596 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:31:24,597 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:31:24,597 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:31:24,597 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:31:24,600 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:24,601 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:31:24,601 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:31:24,602 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:31:24,602 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:31:24,602 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:31:24,602 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:31:24,652 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:31:24,652 DEBUG: Start: Training
+2016-09-06 10:31:24,653 DEBUG: Info: Time for Training: 0.0590059757233[s]
+2016-09-06 10:31:24,653 DEBUG: Done: Training
+2016-09-06 10:31:24,653 DEBUG: Start: Predicting
+2016-09-06 10:31:24,664 DEBUG: Done: Predicting
+2016-09-06 10:31:24,665 DEBUG: Start: Getting Results
+2016-09-06 10:31:24,667 DEBUG: Done: Getting Results
+2016-09-06 10:31:24,667 INFO: Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.614285714286
+accuracy_score on test : 0.4
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 40
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.614285714286
+ - Score on test : 0.4
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:24,667 INFO: Done: Result Analysis
+2016-09-06 10:31:24,960 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:31:24,960 DEBUG: Start: Training
+2016-09-06 10:31:25,007 DEBUG: Info: Time for Training: 0.407965898514[s]
+2016-09-06 10:31:25,008 DEBUG: Done: Training
+2016-09-06 10:31:25,008 DEBUG: Start: Predicting
+2016-09-06 10:31:25,014 DEBUG: Done: Predicting
+2016-09-06 10:31:25,014 DEBUG: Start: Getting Results
+2016-09-06 10:31:25,015 DEBUG: Done: Getting Results
+2016-09-06 10:31:25,016 INFO: Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.990476190476
+accuracy_score on test : 0.4
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 17, max_depth : 8
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.990476190476
+ - Score on test : 0.4
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:25,016 INFO: Done: Result Analysis
+2016-09-06 10:31:25,149 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:25,149 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:25,149 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:31:25,149 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:31:25,149 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:31:25,149 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:31:25,150 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:31:25,150 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:31:25,150 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:31:25,150 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:31:25,151 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:31:25,151 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:31:25,151 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:31:25,151 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:31:25,220 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:31:25,220 DEBUG: Start: Training
+2016-09-06 10:31:25,221 DEBUG: Info: Time for Training: 0.0730149745941[s]
+2016-09-06 10:31:25,222 DEBUG: Done: Training
+2016-09-06 10:31:25,222 DEBUG: Start: Predicting
+2016-09-06 10:31:25,226 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:31:25,226 DEBUG: Start: Training
+2016-09-06 10:31:25,234 DEBUG: Done: Predicting
+2016-09-06 10:31:25,235 DEBUG: Start: Getting Results
+2016-09-06 10:31:25,236 DEBUG: Done: Getting Results
+2016-09-06 10:31:25,236 INFO: Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.538095238095
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.538095238095
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:25,237 INFO: Done: Result Analysis
+2016-09-06 10:31:25,251 DEBUG: Info: Time for Training: 0.103055000305[s]
+2016-09-06 10:31:25,252 DEBUG: Done: Training
+2016-09-06 10:31:25,253 DEBUG: Start: Predicting
+2016-09-06 10:31:25,259 DEBUG: Done: Predicting
+2016-09-06 10:31:25,259 DEBUG: Start: Getting Results
+2016-09-06 10:31:25,261 DEBUG: Done: Getting Results
+2016-09-06 10:31:25,261 INFO: Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.528571428571
+accuracy_score on test : 0.622222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2481
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.528571428571
+ - Score on test : 0.622222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:25,262 INFO: Done: Result Analysis
+2016-09-06 10:31:25,546 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:31:25,546 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-09-06 10:31:25,547 INFO: Info: Shape of View0 :(300, 8)
+2016-09-06 10:31:25,547 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:31:25,547 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-06 10:31:25,547 INFO: Info: Shape of View1 :(300, 10)
+2016-09-06 10:31:25,548 INFO: Info: Shape of View0 :(300, 8)
+2016-09-06 10:31:25,548 INFO: Info: Shape of View2 :(300, 5)
+2016-09-06 10:31:25,548 INFO: Info: Shape of View1 :(300, 10)
+2016-09-06 10:31:25,548 INFO: Info: Shape of View3 :(300, 13)
+2016-09-06 10:31:25,548 INFO: Done: Read Database Files
+2016-09-06 10:31:25,549 INFO: Start: Determine validation split for ratio 0.7
+2016-09-06 10:31:25,549 INFO: Info: Shape of View2 :(300, 5)
+2016-09-06 10:31:25,549 INFO: Info: Shape of View3 :(300, 13)
+2016-09-06 10:31:25,549 INFO: Done: Read Database Files
+2016-09-06 10:31:25,550 INFO: Start: Determine validation split for ratio 0.7
+2016-09-06 10:31:25,553 INFO: Done: Determine validation split
+2016-09-06 10:31:25,553 INFO: Start: Determine 5 folds
+2016-09-06 10:31:25,554 INFO: Done: Determine validation split
+2016-09-06 10:31:25,554 INFO: Start: Determine 5 folds
+2016-09-06 10:31:25,561 INFO: Info: Length of Learning Sets: 169
+2016-09-06 10:31:25,561 INFO: Info: Length of Testing Sets: 42
+2016-09-06 10:31:25,561 INFO: Info: Length of Validation Set: 89
+2016-09-06 10:31:25,561 INFO: Done: Determine folds
+2016-09-06 10:31:25,561 INFO: Start: Learning with Fusion and 5 folds
+2016-09-06 10:31:25,561 INFO: Start: Classification
+2016-09-06 10:31:25,561 INFO: Info: Length of Learning Sets: 169
+2016-09-06 10:31:25,561 INFO: Start: Fold number 1
+2016-09-06 10:31:25,561 INFO: Info: Length of Testing Sets: 42
+2016-09-06 10:31:25,561 INFO: Info: Length of Validation Set: 89
+2016-09-06 10:31:25,562 INFO: Done: Determine folds
+2016-09-06 10:31:25,562 INFO: Start: Learning with Mumbo and 5 folds
+2016-09-06 10:31:25,562 INFO: Start: Classification
+2016-09-06 10:31:25,562 INFO: Start: Fold number 1
+2016-09-06 10:31:25,604 DEBUG: Start: Iteration 1
+2016-09-06 10:31:25,618 DEBUG: View 0 : 0.538461538462
+2016-09-06 10:31:25,626 DEBUG: View 1 : 0.550295857988
+2016-09-06 10:31:25,634 DEBUG: View 2 : 0.520710059172
+2016-09-06 10:31:25,642 DEBUG: View 3 : 0.508875739645
+2016-09-06 10:31:25,674 DEBUG: Best view : View3
+2016-09-06 10:31:25,752 DEBUG: Start: Iteration 2
+2016-09-06 10:31:25,760 DEBUG: View 0 : 0.715976331361
+2016-09-06 10:31:25,767 DEBUG: View 1 : 0.715976331361
+2016-09-06 10:31:25,774 DEBUG: View 2 : 0.704142011834
+2016-09-06 10:31:25,782 DEBUG: View 3 : 0.751479289941
+2016-09-06 10:31:25,819 DEBUG: Best view : View3
+2016-09-06 10:31:25,964 DEBUG: Start: Iteration 3
+2016-09-06 10:31:25,980 DEBUG: View 0 : 0.715976331361
+2016-09-06 10:31:25,987 DEBUG: View 1 : 0.715976331361
+2016-09-06 10:31:25,994 DEBUG: View 2 : 0.704142011834
+2016-09-06 10:31:26,001 DEBUG: View 3 : 0.751479289941
+2016-09-06 10:31:26,040 DEBUG: Best view : View3
+2016-09-06 10:31:26,244 DEBUG: Start: Iteration 4
+2016-09-06 10:31:26,251 DEBUG: View 0 : 0.692307692308
+2016-09-06 10:31:26,258 DEBUG: View 1 : 0.573964497041
+2016-09-06 10:31:26,265 DEBUG: View 2 : 0.686390532544
+2016-09-06 10:31:26,272 DEBUG: View 3 : 0.633136094675
+2016-09-06 10:31:26,313 DEBUG: Best view : View2
+2016-09-06 10:31:26,583 INFO: Start: Classification
+2016-09-06 10:31:27,038 INFO: Done: Fold number 1
+2016-09-06 10:31:27,038 INFO: Start: Fold number 2
+2016-09-06 10:31:27,068 DEBUG: Start: Iteration 1
+2016-09-06 10:31:27,075 DEBUG: View 0 : 0.514792899408
+2016-09-06 10:31:27,082 DEBUG: View 1 : 0.538461538462
+2016-09-06 10:31:27,089 DEBUG: View 2 : 0.473372781065
+2016-09-06 10:31:27,096 DEBUG: View 3 : 0.497041420118
+2016-09-06 10:31:27,127 DEBUG: Best view : View1
+2016-09-06 10:31:27,203 DEBUG: Start: Iteration 2
+2016-09-06 10:31:27,210 DEBUG: View 0 : 0.674556213018
+2016-09-06 10:31:27,217 DEBUG: View 1 : 0.727810650888
+2016-09-06 10:31:27,224 DEBUG: View 2 : 0.644970414201
+2016-09-06 10:31:27,232 DEBUG: View 3 : 0.686390532544
+2016-09-06 10:31:27,268 DEBUG: Best view : View1
+2016-09-06 10:31:27,409 DEBUG: Start: Iteration 3
+2016-09-06 10:31:27,416 DEBUG: View 0 : 0.674556213018
+2016-09-06 10:31:27,423 DEBUG: View 1 : 0.727810650888
+2016-09-06 10:31:27,430 DEBUG: View 2 : 0.644970414201
+2016-09-06 10:31:27,437 DEBUG: View 3 : 0.686390532544
+2016-09-06 10:31:27,478 DEBUG: Best view : View1
+2016-09-06 10:31:27,682 DEBUG: Start: Iteration 4
+2016-09-06 10:31:27,689 DEBUG: View 0 : 0.644970414201
+2016-09-06 10:31:27,696 DEBUG: View 1 : 0.692307692308
+2016-09-06 10:31:27,703 DEBUG: View 2 : 0.650887573964
+2016-09-06 10:31:27,711 DEBUG: View 3 : 0.698224852071
+2016-09-06 10:31:27,752 DEBUG: Best view : View1
+2016-09-06 10:31:28,021 INFO: Start: Classification
+2016-09-06 10:31:28,472 INFO: Done: Fold number 2
+2016-09-06 10:31:28,472 INFO: Start: Fold number 3
+2016-09-06 10:31:28,501 DEBUG: Start: Iteration 1
+2016-09-06 10:31:28,508 DEBUG: View 0 : 0.497041420118
+2016-09-06 10:31:28,514 DEBUG: View 1 : 0.485207100592
+2016-09-06 10:31:28,521 DEBUG: View 2 : 0.491124260355
+2016-09-06 10:31:28,528 DEBUG: View 3 : 0.568047337278
+2016-09-06 10:31:28,558 DEBUG: Best view : View0
+2016-09-06 10:31:28,633 DEBUG: Start: Iteration 2
+2016-09-06 10:31:28,640 DEBUG: View 0 : 0.674556213018
+2016-09-06 10:31:28,647 DEBUG: View 1 : 0.597633136095
+2016-09-06 10:31:28,654 DEBUG: View 2 : 0.692307692308
+2016-09-06 10:31:28,661 DEBUG: View 3 : 0.751479289941
+2016-09-06 10:31:28,697 DEBUG: Best view : View3
+2016-09-06 10:31:28,836 DEBUG: Start: Iteration 3
+2016-09-06 10:31:28,844 DEBUG: View 0 : 0.674556213018
+2016-09-06 10:31:28,851 DEBUG: View 1 : 0.597633136095
+2016-09-06 10:31:28,857 DEBUG: View 2 : 0.692307692308
+2016-09-06 10:31:28,865 DEBUG: View 3 : 0.751479289941
+2016-09-06 10:31:28,903 DEBUG: Best view : View3
+2016-09-06 10:31:29,107 DEBUG: Start: Iteration 4
+2016-09-06 10:31:29,114 DEBUG: View 0 : 0.656804733728
+2016-09-06 10:31:29,121 DEBUG: View 1 : 0.639053254438
+2016-09-06 10:31:29,128 DEBUG: View 2 : 0.674556213018
+2016-09-06 10:31:29,136 DEBUG: View 3 : 0.792899408284
+2016-09-06 10:31:29,177 DEBUG: Best view : View3
+2016-09-06 10:31:29,444 INFO: Start: Classification
+2016-09-06 10:31:29,896 INFO: Done: Fold number 3
+2016-09-06 10:31:29,896 INFO: Start: Fold number 4
+2016-09-06 10:31:29,924 DEBUG: Start: Iteration 1
+2016-09-06 10:31:29,931 DEBUG: View 0 : 0.479289940828
+2016-09-06 10:31:29,938 DEBUG: View 1 : 0.479289940828
+2016-09-06 10:31:29,945 DEBUG: View 2 : 0.479289940828
+2016-09-06 10:31:29,951 DEBUG: View 3 : 0.479289940828
+2016-09-06 10:31:29,951 WARNING: WARNING: All bad for iteration 0
+2016-09-06 10:31:29,982 DEBUG: Best view : View0
+2016-09-06 10:31:30,058 DEBUG: Start: Iteration 2
+2016-09-06 10:31:30,065 DEBUG: View 0 : 0.627218934911
+2016-09-06 10:31:30,072 DEBUG: View 1 : 0.686390532544
+2016-09-06 10:31:30,080 DEBUG: View 2 : 0.686390532544
+2016-09-06 10:31:30,087 DEBUG: View 3 : 0.751479289941
+2016-09-06 10:31:30,123 DEBUG: Best view : View3
+2016-09-06 10:31:30,265 DEBUG: Start: Iteration 3
+2016-09-06 10:31:30,272 DEBUG: View 0 : 0.627218934911
+2016-09-06 10:31:30,280 DEBUG: View 1 : 0.686390532544
+2016-09-06 10:31:30,287 DEBUG: View 2 : 0.686390532544
+2016-09-06 10:31:30,294 DEBUG: View 3 : 0.751479289941
+2016-09-06 10:31:30,334 DEBUG: Best view : View3
+2016-09-06 10:31:30,537 DEBUG: Start: Iteration 4
+2016-09-06 10:31:30,544 DEBUG: View 0 : 0.615384615385
+2016-09-06 10:31:30,551 DEBUG: View 1 : 0.603550295858
+2016-09-06 10:31:30,559 DEBUG: View 2 : 0.644970414201
+2016-09-06 10:31:30,566 DEBUG: View 3 : 0.662721893491
+2016-09-06 10:31:30,607 DEBUG: Best view : View3
+2016-09-06 10:31:30,876 INFO: Start: Classification
+2016-09-06 10:31:31,332 INFO: Done: Fold number 4
+2016-09-06 10:31:31,332 INFO: Start: Fold number 5
+2016-09-06 10:31:31,361 DEBUG: Start: Iteration 1
+2016-09-06 10:31:31,368 DEBUG: View 0 : 0.491124260355
+2016-09-06 10:31:31,375 DEBUG: View 1 : 0.479289940828
+2016-09-06 10:31:31,382 DEBUG: View 2 : 0.479289940828
+2016-09-06 10:31:31,388 DEBUG: View 3 : 0.526627218935
+2016-09-06 10:31:31,418 DEBUG: Best view : View3
+2016-09-06 10:31:31,493 DEBUG: Start: Iteration 2
+2016-09-06 10:31:31,501 DEBUG: View 0 : 0.668639053254
+2016-09-06 10:31:31,508 DEBUG: View 1 : 0.656804733728
+2016-09-06 10:31:31,515 DEBUG: View 2 : 0.656804733728
+2016-09-06 10:31:31,523 DEBUG: View 3 : 0.715976331361
+2016-09-06 10:31:31,559 DEBUG: Best view : View3
+2016-09-06 10:31:31,698 DEBUG: Start: Iteration 3
+2016-09-06 10:31:31,705 DEBUG: View 0 : 0.668639053254
+2016-09-06 10:31:31,712 DEBUG: View 1 : 0.656804733728
+2016-09-06 10:31:31,719 DEBUG: View 2 : 0.656804733728
+2016-09-06 10:31:31,727 DEBUG: View 3 : 0.715976331361
+2016-09-06 10:31:31,765 DEBUG: Best view : View3
+2016-09-06 10:31:31,969 DEBUG: Start: Iteration 4
+2016-09-06 10:31:31,977 DEBUG: View 0 : 0.633136094675
+2016-09-06 10:31:31,984 DEBUG: View 1 : 0.556213017751
+2016-09-06 10:31:31,991 DEBUG: View 2 : 0.568047337278
+2016-09-06 10:31:31,998 DEBUG: View 3 : 0.639053254438
+2016-09-06 10:31:32,039 DEBUG: Best view : View3
+2016-09-06 10:31:32,311 INFO: Start: Classification
+2016-09-06 10:31:32,766 INFO: Done: Fold number 5
+2016-09-06 10:31:32,767 INFO: Done: Classification
+2016-09-06 10:31:32,767 INFO: Info: Time for Classification: 7[s]
+2016-09-06 10:31:32,767 INFO: Start: Result Analysis for Mumbo
+2016-09-06 10:31:35,218 INFO: Result for Multiview classification with Mumbo
+
+Average accuracy :
+ -On Train : 73.9644970414
+ -On Test : 48.5714285714
+ -On Validation : 51.6853932584Dataset info :
+ -Dataset name : Fake
+ -Labels : Methyl, MiRNA_, RNASeq, Clinic
+ -Views : View0 of shape (300, 8), View1 of shape (300, 10), View2 of shape (300, 5), View3 of shape (300, 13)
+ -5 folds
+ - Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
+
+
+
+Mumbo configuration :
+ -Used 1 core(s)
+ -Iterations : min 1, max 10, threshold 0.01
+ -Weak Classifiers : DecisionTree with depth 3, sub-sampled at 1.0 on View0
+ -DecisionTree with depth 3, sub-sampled at 1.0 on View1
+ -DecisionTree with depth 3, sub-sampled at 1.0 on View2
+ -DecisionTree with depth 3, sub-sampled at 1.0 on View3
+
+
+
+Computation time on 1 cores :
+ Database extraction time : 0:00:00
+ Learn Prediction
+ Fold 1 0:00:01 0:00:00
+ Fold 2 0:00:02 0:00:00
+ Fold 3 0:00:03 0:00:00
+ Fold 4 0:00:05 0:00:00
+ Fold 5 0:00:06 0:00:00
+ Total 0:00:19 0:00:02
+ So a total classification time of 0:00:07.
+
+
+
+Mean average accuracies and stats for each fold :
+ - Fold 0, used 5
+ - On View0 :
+ - Mean average Accuracy : 0.266272189349
+ - Percentage of time chosen : 0.6
+ - On View1 :
+ - Mean average Accuracy : 0.255621301775
+ - Percentage of time chosen : 0.0
+ - On View2 :
+ - Mean average Accuracy : 0.261538461538
+ - Percentage of time chosen : 0.1
+ - On View3 :
+ - Mean average Accuracy : 0.26449704142
+ - Percentage of time chosen : 0.3
+ - Fold 1, used 5
+ - On View0 :
+ - Mean average Accuracy : 0.250887573964
+ - Percentage of time chosen : 0.6
+ - On View1 :
+ - Mean average Accuracy : 0.268639053254
+ - Percentage of time chosen : 0.4
+ - On View2 :
+ - Mean average Accuracy : 0.241420118343
+ - Percentage of time chosen : 0.0
+ - On View3 :
+ - Mean average Accuracy : 0.256804733728
+ - Percentage of time chosen : 0.0
+ - Fold 2, used 5
+ - On View0 :
+ - Mean average Accuracy : 0.250295857988
+ - Percentage of time chosen : 0.7
+ - On View1 :
+ - Mean average Accuracy : 0.231952662722
+ - Percentage of time chosen : 0.0
+ - On View2 :
+ - Mean average Accuracy : 0.255029585799
+ - Percentage of time chosen : 0.0
+ - On View3 :
+ - Mean average Accuracy : 0.286390532544
+ - Percentage of time chosen : 0.3
+ - Fold 3, used 5
+ - On View0 :
+ - Mean average Accuracy : 0.234911242604
+ - Percentage of time chosen : 0.7
+ - On View1 :
+ - Mean average Accuracy : 0.245562130178
+ - Percentage of time chosen : 0.0
+ - On View2 :
+ - Mean average Accuracy : 0.249704142012
+ - Percentage of time chosen : 0.0
+ - On View3 :
+ - Mean average Accuracy : 0.26449704142
+ - Percentage of time chosen : 0.3
+ - Fold 4, used 5
+ - On View0 :
+ - Mean average Accuracy : 0.246153846154
+ - Percentage of time chosen : 0.6
+ - On View1 :
+ - Mean average Accuracy : 0.234911242604
+ - Percentage of time chosen : 0.0
+ - On View2 :
+ - Mean average Accuracy : 0.236094674556
+ - Percentage of time chosen : 0.0
+ - On View3 :
+ - Mean average Accuracy : 0.259763313609
+ - Percentage of time chosen : 0.4
+
+ For each iteration :
+ - Iteration 1
+ Fold 1
+ Accuracy on train : 47.9289940828
+ Accuracy on test : 0.0
+ Accuracy on validation : 47.191011236
+ Selected View : View3
+ Fold 2
+ Accuracy on train : 47.9289940828
+ Accuracy on test : 0.0
+ Accuracy on validation : 47.191011236
+ Selected View : View1
+ Fold 3
+ Accuracy on train : 47.9289940828
+ Accuracy on test : 0.0
+ Accuracy on validation : 47.191011236
+ Selected View : View0
+ Fold 4
+ Accuracy on train : 47.9289940828
+ Accuracy on test : 0.0
+ Accuracy on validation : 47.191011236
+ Selected View : View0
+ Fold 5
+ Accuracy on train : 47.9289940828
+ Accuracy on test : 0.0
+ Accuracy on validation : 47.191011236
+ Selected View : View3
+ - Iteration 2
+ Fold 1
+ Accuracy on train : 75.1479289941
+ Accuracy on test : 0.0
+ Accuracy on validation : 49.4382022472
+ Selected View : View3
+ Fold 2
+ Accuracy on train : 72.7810650888
+ Accuracy on test : 0.0
+ Accuracy on validation : 47.191011236
+ Selected View : View1
+ Fold 3
+ Accuracy on train : 75.1479289941
+ Accuracy on test : 0.0
+ Accuracy on validation : 62.9213483146
+ Selected View : View3
+ Fold 4
+ Accuracy on train : 75.1479289941
+ Accuracy on test : 0.0
+ Accuracy on validation : 49.4382022472
+ Selected View : View3
+ Fold 5
+ Accuracy on train : 71.5976331361
+ Accuracy on test : 0.0
+ Accuracy on validation : 49.4382022472
+ Selected View : View3
+ - Iteration 3
+ Fold 1
+ Accuracy on train : 75.1479289941
+ Accuracy on test : 0.0
+ Accuracy on validation : 49.4382022472
+ Selected View : View3
+ Fold 2
+ Accuracy on train : 72.7810650888
+ Accuracy on test : 0.0
+ Accuracy on validation : 47.191011236
+ Selected View : View1
+ Fold 3
+ Accuracy on train : 75.1479289941
+ Accuracy on test : 0.0
+ Accuracy on validation : 62.9213483146
+ Selected View : View3
+ Fold 4
+ Accuracy on train : 75.1479289941
+ Accuracy on test : 0.0
+ Accuracy on validation : 49.4382022472
+ Selected View : View3
+ Fold 5
+ Accuracy on train : 71.5976331361
+ Accuracy on test : 0.0
+ Accuracy on validation : 49.4382022472
+ Selected View : View3
+ - Iteration 4
+ Fold 1
+ Accuracy on train : 75.1479289941
+ Accuracy on test : 0.0
+ Accuracy on validation : 49.4382022472
+ Selected View : View2
+ Fold 2
+ Accuracy on train : 72.7810650888
+ Accuracy on test : 0.0
+ Accuracy on validation : 47.191011236
+ Selected View : View1
+ Fold 3
+ Accuracy on train : 75.1479289941
+ Accuracy on test : 0.0
+ Accuracy on validation : 62.9213483146
+ Selected View : View3
+ Fold 4
+ Accuracy on train : 75.1479289941
+ Accuracy on test : 0.0
+ Accuracy on validation : 49.4382022472
+ Selected View : View3
+ Fold 5
+ Accuracy on train : 71.5976331361
+ Accuracy on test : 0.0
+ Accuracy on validation : 49.4382022472
+ Selected View : View3
+ - Iteration 5
+ Fold 1
+ Accuracy on train : 47.9289940828
+ Accuracy on test : 0.0
+ Accuracy on validation : 47.191011236
+ Selected View : View0
+ Fold 2
+ Accuracy on train : 47.9289940828
+ Accuracy on test : 0.0
+ Accuracy on validation : 47.191011236
+ Selected View : View0
+ Fold 3
+ Accuracy on train : 47.9289940828
+ Accuracy on test : 0.0
+ Accuracy on validation : 47.191011236
+ Selected View : View0
+ Fold 4
+ Accuracy on train : 47.9289940828
+ Accuracy on test : 0.0
+ Accuracy on validation : 47.191011236
+ Selected View : View0
+ Fold 5
+ Accuracy on train : 47.9289940828
+ Accuracy on test : 0.0
+ Accuracy on validation : 47.191011236
+ Selected View : View0
+2016-09-06 10:31:35,405 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..47001d62
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..03adc80f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 0.938095238095
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 8
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.938095238095
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..c7e9d432
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.552380952381
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 40
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.552380952381
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..461f44c9
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.990476190476
+accuracy_score on test : 0.4
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 17, max_depth : 8
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.990476190476
+ - Score on test : 0.4
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..207b9a7e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.538095238095
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.538095238095
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..ba87e538
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.495238095238
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2481
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.495238095238
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..3c15e799
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 0.985714285714
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2481
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.985714285714
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..8f4eb2ff
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2481
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..a51fc411
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..a89da4e8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 0.838095238095
+accuracy_score on test : 0.433333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 8
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.838095238095
+ - Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..f62702a6
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.490476190476
+accuracy_score on test : 0.4
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 40
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.490476190476
+ - Score on test : 0.4
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..2ca48124
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.985714285714
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 17, max_depth : 8
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.985714285714
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..5dfa38e3
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.538095238095
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.538095238095
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..7a807ce4
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.552380952381
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2481
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.552380952381
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..c696ff7a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..12a61d1e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 0.785714285714
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 8
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.785714285714
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..7afc9baf
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.614285714286
+accuracy_score on test : 0.544444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 40
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.614285714286
+ - Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..8b74e159
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.961904761905
+accuracy_score on test : 0.566666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 17, max_depth : 8
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.961904761905
+ - Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..5368fdbf
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.538095238095
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.538095238095
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..66e58177
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.490476190476
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2481
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.490476190476
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..4959e4f4
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2481
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..e1f9847a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2481
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..474fe79a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.433333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..e82c8d88
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 0.919047619048
+accuracy_score on test : 0.411111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 8
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.919047619048
+ - Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..bb1b14bb
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.614285714286
+accuracy_score on test : 0.4
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 40
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.614285714286
+ - Score on test : 0.4
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..b9e72cea
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 0.6
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2481
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.6
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..c25b7951
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.455555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2481
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..06e42109
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.990476190476
+accuracy_score on test : 0.4
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 17, max_depth : 8
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.990476190476
+ - Score on test : 0.4
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..ec5c4978
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.538095238095
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.538095238095
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..39374ee4
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.528571428571
+accuracy_score on test : 0.622222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2481
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.528571428571
+ - Score on test : 0.622222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103135Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Results/20160906-103135Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
new file mode 100644
index 0000000000000000000000000000000000000000..129780daaf421ae71fa74885fd4baaf13677ad38
GIT binary patch
literal 54649
zcmeAS@N?(olHy`uVBq!ia0y~yU{+vYV2a>iV_;yIRn}C%z`(##?Bp53!NI{%!;#X#
zz`(#+;1OBOz`&mf!i+2ImuE6CC@^@sIEGZrd2_ci=IqV?>>sZ0J=zm{!XUUa=*nI1
zYLD$lg%Vp%SRFEuHcpdJ<mr`sEn%W8xQQ?IO;?TYjEut_Q@RwZw_N)6@9)L+pWj|v
zo0t0N<J`}G+`oUeoL_wI`JD65cReq*doIH$C}`357=ol4Klr9MKsh_=`R_42gh*Jd
zyZC{P<9MMA81=T9J3z(zvQ73d9)gO;EPlu<05^*_zo7u4Ugmm61#=6`ShvH{3Q+Om
zrc$F;NPz<CPV}J*7X;>7l{$HPa^5a`c1Dt;)#=PE)794=pPy&jEv)Xh;k=}z<b{hD
zR~FlC%e~#U(7FBEPmB8c`jR&{1jWR}3`|X1w{A7Pwl=!`(xppQ$3yjujfHD}eOYPf
zlar(4)-M<P-XtkGS#kR5g#j8DmU@eC$-OPc)yl-x%A_5>ZcX`~^@rPdr8ktljbd=9
zsj>O?_V)2_Q>RRE&=66wx8Hy6V_SRs;iE^FI#y<T)Gb=PIC5t9(WEmo44ID}J<5Bk
zf7zF>Ukm^K`s(JndS6phlR?sv4lDWa`&F@ft4!zFR0h5MH`6#B<U^;08?J4#E_oqP
z_3aI(u&{7NW#!e^zpflPJzam{;>FERo}^5DJ9FmDjGLQO=h;@b{rvoVb<ASkr|;kM
zCnqPDyuT;QEv{FS`?2WGe!={Pf;-Vp3IYcX9AIeYlTEt4E$3!a+}^6plA4eZkv@6*
zIMs8%o-MUI)+gJ2<3_}tJ(a@M-`*UYXIuT@sCfJpZ9Rq;cXk?|n`axHEWypq{pQxz
z)%P+ZA|e#*?Bogy3nTVa82<YDx_!qEiz7#mCVqQ!Q!BA^#l4qTjvtq=?>TSxo5vy2
zIrII!y;{FtNBi^IZd>>>P(x%#%}=98j~*3#eHHrW@Avy0JUlr8b^orLJlnfF>#CMb
z)t42w{w{XwJ=DrAzF_(C@MIfHOUoHXsa$;Wc0Es?q-@E*FXug7Z*6g%$;WrQ-ygbt
zTl(O^gIc$potz8`9yn~tzRm}d4tMsOdwIG4;|B+uf4pA5zu@gH(?9=yzqcrTbtSuV
z_TzuOpzL-}Nm$*_B_Ke6LBZNuT3kP_W%usgUNfd@haWn2jBVPqX%Yqr4Dask?LKnE
zCFj-_&V>sX255-9v9DD+w8*tPAu|&cKi3>0cNVEG^P79>QO(CktR<zTAOHOPe7#Js
zt*!0Grc`dTygMCp=EyJ{I5}DU;fog?aeJ!{9y`Vs7#OHiQ&3>=q{uSGNOH}ZH3iSl
z$xe-%GhIJE>B@>gflikRr=PBgIXBOiduPnKM>qG?S}zIGOfizQv9Xyjb0+74HR;EX
z9%a3E@7@Ftl?|IVIaO6vt$qIR`Ez!Cef=FfcRH#FJ^c9ixQCzLu?9xwgxp--nLf)t
zJ+@R+WBdE-YxC5pqB^>|g%1uiY85_v@??Q?JKvgekA(pVhgvup8n$dPnK5hDrY+CU
z%xpe$#z!Y|6U)Sj69Y6vemt4%&oJfc`(354K}ISoE4Q??tgvlgv`EQkwi&OQn%ah~
zTi0G%!Oh8;czw#`$->pAWJN?+#KpxcDk=`#xg+!U*Vn`M>;K<<@W&+k8c$SI6vL@k
zTNf-?5NAK<@87?PX=%r**1WsFfBiOfH+OgT@9*!Y&)=SV``Eiq)9h<2dUtQxB4V6&
zreWt!%O^#aYu2m@2nuRCbjWGP?%jt^o#GPeWO;n7_wecI`iu+{Cr+Gj`soa_T&dk`
z>tlDfeSCb}S!v>dd-vq{<?YsN%(=QU#=idVSMi2hueUCeulvDx?b@{s8#Xlb_VVh*
z@8e;J*ioP;XIs_6Z~sSttF`H9?8N3~W~=gdG1<(2PE1r@^R96dTS+NDyPRE3$3o|J
zC#|VYT2qrwPt(nrzi*$Nm~Iq{wY9Z`q@<#Pf<vy%WHnzW504J7eND~HA1}JgGcwfF
z)NDvR%*K$KYMOpd=EjX14hsVg+`cV+^X5&{d)?df@27o#bZ>99Vcs2!J)h56S3I9v
zZczB>2%GJX2RrI*A6dLxdtqyKxR;NQ%0v&BnKNf@$-JyK%RFCBH)>1Az8{acm6W)6
zd6#MiFAK=b)D#rVy}xfR8=s7a%Mzi!4h>t|xhzbKvNjbHIy(B~?fsT5Tjt{O<<HOJ
zs`q=pzc|`0?&7kg>TA}o7mNElIx=2gTWeDCB4BIfoh_Ndx-mNf-tIf~a(edVWxTPw
zN;>oI@AKWZZQGQ|lbu~$G&MD^>?}^#($QIBoPI7~d*0m_4-dDyxP$}*IIyy^PMJ5)
z&&tZm#iiuK1IMEB^7M~ssi}g3fgvFxC!ZE6DebEIs#W^xN~e*rac9Si-S77;{`dFy
z=FeHTx9O&)qzDLJG{0Zte0iB~@X6J?is#$c+r51K`toFT|Bj9oD^_&q=;*w-wl><u
zWy<vF?$4e*6OotqKR?e_xz)+BU(QxZX_0%s+{AtR?5x*!y?Oig<)K#YFHcSiPdfc{
zO&$|7bLO!gNu9Ur_x<YX=rGH_=cDY_lW<&ITzs;F!h-pAzdAcQ*2V62TN}MSEZEp3
z`}#Unb8~YoP0fXe4mrKLyW4!{jvWGm7jJG(KY93Yb5~baN5_jZGmTZIdPUAUJKH?{
z`L3LsMp6a|4$YsPU8c;M<rTDYO4PO-&*Wo0i7&HXeSCcU%D&p_s;{p^1*gUux3;#1
z#KibGEu8S_)2E0CSy|a#<?rPL1z)~=dGX1~$&oTuRaGLw!ojNlyr=6;jL=D&fBL=X
zqW^K#E-w4_?b}lH)JsXJtD|GVGT+&krfP?GbY#4{vvbMn)!v$#nl3ITrLRIjNkuR-
zZky@V^RrB|SFOE%>eTvprA7Atel)u%3I1NQe7XDe_3`1=F|JirRbQ^h*N5)@|NH&^
z<ui@bH=VzE^X8Q;nZa8V+xg}FxW#l7loqk`%UxiXuW{J8aih-M$jxbjfj4ch>?+MJ
z^AA;<eDcMKiOMc6CGYN7zPhxO`?h)6n}}aOpU>~?$oTl^=$4wFMceWp9%}va^Z9(?
ziD}=H{{Q=%`TyVFH$R-)`6h1OT)Z{)U}4PJE%!^9J09;a`tslZV@*wsfQ(GfhYtmB
z?(7t{E_;)Zc<9iff}fvKHAJ`=QvEZttBUTdi8QXLs$ycu$;~bN{VkWlVYXTBhllO*
zpj`Un&*$?S3m?1n-kZAaMt0Ek&sKGREP{fAFYYeS-{u<~EnWWZ&cTa|-5)-F+}tN?
z&7@JicahP`kgg?5RL;z?6y96?9aOBYYU%9ia{B-8@5PnD>I|v=3Db2V4_(^4b!%&5
zBjfMtzrVgp%{{!pvH8Nj+G+-epdg`1lO{cQ`t;}{T@8%`UteD@{QfTX&c0gdyu7@e
z`2iY7&dxT!xpQi9S=q6b!OKAv!k72=<vBSyJ-ob}TwGk{9@|n;UCqpJ=ImKUhJHEQ
zRg=O}QdExh%kwV`;8+;Y(J8F1WMU%XaC1jvOAAXxWaLI0``TYqdQTP<6f|tyXvlEj
z`t|MC!m~_H@-vjYzQ)VR$$8@R>E`X*%?}+uY*_Q7p!a;VyE}VZTib_^9~*;K9y!p+
z{PExK_p5srYPnCHcgKI8jU>Z`h0g31RaHtxMw?Ep?mKqun1+VNfhQ*?U)-J_&m&_Y
z;BeD+d(u%Z8S640RaMm!=gzq~Ix<?HyK+S&X`=)~LS|+sD3N{Th=`0_xkqiK=gn&&
z_RIa}uiF-<-QC&g=-|LG&$gN^H#b*BRdwNx9TK;0-O4)sV!oK5U}Nd)Ym5x{s^8m!
z>{n81s@>izY0Sp3VD;*5z1Up}y1Kl(cI^s%G}9zg2-MPHWM)&au$b{RE<{XBtgWeu
z>CMf}><*Fr9+fxuudAN;*y8L*Hjd+aQ)_B!76fQqSnSUK?eJ0=!NhlWcD@m9z6C0F
z-{kA+>Q0qUpI`HdlZ{`lN6xltLGtmw2_7ozpR;J~sQj!Jx38vC&bCTHO^r=YPfv??
z)vgz}wra17-`@vneq31@Ji|DhPe@q!wTS!Cw%%#erX6FRAH6N-<Uci~7q4C^>FKTW
z+Tq;J=cqMx)t;y@*`-09KR-Wz{C@xcV=pc)zPQkteU43~(Ep$3>pz^<-=8r>ztLeq
zk)PATfW^hDZe*w``OGrm>=x5q^>xRsx+T}t?RBHJG)$i^?&R$J`U=CvOP8E{e0a{y
zx9>MjKX>5PEvet%-ZpRDYRa%8=Jooxy{k;`tNYJ8ay`DDx6z>?aIstBxjB|Q>i$|S
zTC}L(*O$zi&u7go>i^j+TC_+&R<`%Wiwu+edoruT*B@);7LSOTy57)t{mxgXw&{Hh
zzI(VMe0^Nvkq*I*zP`2)E!VIxF{_dn4TlanU1O+}<>BEecz4J0o29NUuYApiMz_kf
ze<CjzNn4lcJUc(X|I3$>H~06;hp&qXl<w;Q)r()=-rj!7&i9=wGc$8aO3H@hOP4R-
zJj~Pg_*Gv&<HH@>eV(72D<~ka;M?AGe}8{a<18{VlA+<zqa+m-6$XZ=?RmZ8`f)2-
z*8f?&>`{7fa4;yn@7uTUjqJa+RTA}8RaSPtUMSc6`FI@EkiK*0j)0(G<LdDBn+&Gw
z#WFEeR8%A!?~|?g@PM&llU(bk@2^fj-c$Zwu3^)?4*B{&f(!-U-^EV-GI!3Lf<HeB
z#m+DDo&Dg=n>!B}uZzx{Idfs;<}@wdl~bmOq@<@GK6sGv<jIp-yc2%>sMu=Uc=Oh+
zN4K_ScXV|%?cHlTXYSmMX=kM*BqSE(-rg2^C_8BX)j+MOH}=(9dwF?%cy(1f=kBhl
zZ>5b=IGmiEB7O=9CM74|{P*HT#*$^rm>A6R@AcIE{bg!luc@i|hW+X4@bv;>V#lNo
zEM2<v#m&v?a`tsG$ugFfmK9%K2+GLFJb3<`U0PaNMMcFyLEylN6CCybeyTTYD*vu+
z?r`e-|C5u|Ir#b8H*YqMiH*IP!czR~%)!UU`zKDH&Mq!4t`omc=GU)Z9$sEa_V;F+
z=l8vN^Cs)~&WuI7HmzN;?AOYbaoVTOpLh54<dk-ayZ1R@o$qY3&{v!6*mxuwX3UUS
zvSbMZ1E@6|xY%ta@9Ge(kFVG7XVQ4B%FWHa`Z`aXm6a8!y}f78o`l@oUcJ~|D|ny3
zeAyYgI&9<nsek^|m|gRnTwQpiLy&`$Gx5If!Gi~{&XYb~xb-owKwq_Y$e)Qeb$=?p
zsccuu&(HUnVZi9;=QrW>(+Q`aS`<IyVMus!VPQvqf4F2z-0k3Fn!(E!ELr06Sp9rs
zW232ECoeB=YilcO)t48I6P4W$oj%RII&3Wy1E~FGVj>c}+^;o8uYKoE%h^fSE?yMm
z7T0T;JzF}(Nb=XWx2NkGrak}p>gwtZ+qb7*-rgr|&ZjidL$$WJxcJY9!~8n)UcAWo
zru@A0;<anX9v*Jrn0{XFM2b;|i_(u@zZNZ6!0`6g*25t2ijPTUeV?D755LWD@!GYv
zwQF^EmA-CM^PT0OH1WaH)6)aAre<9(Ei81*%hP*OWSMPnEo}eOWxli7n3<Wsytye{
z`}-Tn3%9Q?_m|g+-p13+&fgZJ*Y2Tm=-=PpH|wgatIy0b<yM-=0m|~ndZnf3%<t&n
zDEk`~73CBkFaP=3S!M>CiVqCu=i7(h=2)$vt=-L=G|fXrfUEVx$B&EwW^3Pkd3iZ8
zGqY3LJa2*TY%{IOtn2G!b#!$T)6?7a_x}-^Hf>tyqw^<DGz6_|@lk7bQEGHr*syxF
zwwlk3266qk182_g$jQlV^%IVr{OZ)UMYCQlUsko&!`;2z*X`o!aQ&%rwhTYMT=q9i
zJk*lKzR-WB5$msCzbbxwP@LkWTJ`PC$!}-p+t24!$iKVGwC?9qaULlX4mCBkia$RL
zS6@GMZGC+Ij~^8tOM`ao*s-CS?ZO``E%VAxDMyYS3wtTKuHw#~%Fj1g#Di9fl$Dk3
zD0?f#aQ2;RdU`sKtd)q;L=L5i9CC7U1*N5H4dZ2whx$A8{(Yo=yfDAl-|i>NT&vPU
z+wa%?Ubgwr-qxn3rh-RDI<+pp{WrPn{k>l6@^=a6=2-Sldp7%n+No)eE#BoGJl;@n
zXMaFini{Bk{O#@S)jDw}3iGAv*S0>kcvmm~o)Kx#$Pmv^&)&qgwzd~nR!%;(i#7e+
zoW=l+BWofzGcfG`|Ia$@{5;+TIc0LWxw#r5TqjbDPCPA=v8|dSyF2%`8Aq$rn>#xv
z=UNv%;g~aLPRJ!+Z|~-5)5P}uez*I2m7VBk^@$z{X=!aMgO_j0ba|b9YKo>s^|zef
zL^fWjmgwzySFL*+nc1Bd1~}+Vch(RA^=|a0n`bOreEs43{r}rGZ#Hh&lo|2&@88gC
zt{*;rJlMu7{YrFuUmxGQ`}^bH?=dy8_gXsXY{%8@`S-J~7gpT;^XJdKUybinRaFJJ
zT2Gumzy9;auPd(`r=NRpWo7WycmMx>zYj`T<@am1U)xmu<AdUzJ9iS2l9*P8biH`-
z!s_W#y=8uLyLJ{o54&lXaY12C{C+t@L&Kc5E!PuoY)Gv5|M&arSaX@<;QG9fsgZ$=
zjm@Iug~07?si(!JPMv9MW3!>?sn@rirLV7bbay*DIWbMsi|x9&*nRbygP@@vornz%
zPu&xruGHC{bCW4OJ)MP}egC&&t@HP%YKIp*JtbQ5>I!FBS=ot`Cs&rw+5hiX_FCS?
z!|nWwtG~bd>i77>L}ie%H8nOYOpO12Jmx=g{P^nJA1k5@f6rgEe0lqc6CTsn?cZ<D
z#myb>eTO$zdFjHRmIV(O9zA|6C@<eX(>Oip(h^VJQ|JHB`gP5}w$Hlumx<pzo5;8S
z<ZP=FUSC`L=Aeh~vokZB?P`B1SXxT%|MQ7^&)&UP{oMar`OLGCv@UxS@RM&({eL^q
zc!9jUd_-hq;_q*7*X!<;G)~*_o}=&at9Ek-i+8#Pg^yfVSXmhvHf`Qq`0<hJj@`RA
z|Gm8}_wa><&R6ALzj@Ph=T3}G^|zkf$tP0+G(;ly*X_NP?B(sfvGjG=n&|C(YooR%
zWq1A&-m-P;!}ss|`R#r*=-&JO;h}SnjHQs7nb`!dr4btv7`f{oALHGzd$+UJR428`
z8>e>6oiiswIrsCkvj*no?M@3DLbOu3rOMvknriFl@9W#z-OaruNK=H1_0ZwNky1_z
z1x`=bZ{NDrw5O-%irxb^7cKjYpy1$zt5<ig-}j44Oib*?mQ3NUt}X-g&NIU)u~V~+
zQn_++aug<?G)O+iv$c2HL}m9)rLv%rzqGTnHnx8L_2p%Ax48bMEsP1jzPuFRVA-&F
zbF<UJgzliY-DSMz&Yd$bH9dM_qVnoo1zDR40o&?tEiroQH@P_%6&bPdNHAn)XP-zh
zVqs%r`tg494_0n52fgWw-TUQoe%)w$w!HN9wKwmdpP%onvF3NZ_w)1f+gn>%UzgtA
zQ)%3=%GNNx<|FGg-RNU>%a<=-9KXK~R8P0@$ws|Rs(H$-I_-G;y~jTe^V@@(Yhq$z
z8#ZoCyspUb;`Qs~gH5a(wr_Wzt{1zZ@^cysE9=IuYIVQgZqGVhc7C3%&b+Sc38i0|
zuf(huRaH|vbn&9#v}w}>I$eJJ`~9AgVdKV)H}+H-Z_B^W*Xq>x^z`&_%Wc))-wAZO
zT)1>;mD#_d-{0OI-jaElf#KS<YZtCwJt|-SXQ5e+g9F2nBtsP;&SU-Z{dexfFr@Ac
zxp(iLgh__LnKNe|yn3~2Ti=>BYZfeC-2C`>|K{A=+j5)R+u3hz$!tDx!ec7C)$u;r
z=H_PRs;{qDqobpz>HEHOUGT<wyK_6=)@h8#3r!XJ9^YHBE_QcNQj$`qi_)nxXO{TR
zHoLf|@^i+;MXq0tipL8GUc9z8I`hSag)S~rAOi?*eoUAk5TrHrO>yOoCr?sdU0Eso
z^vRP84-dDWJbALS)w1e~#?4!|gaj}8+y8aBckf>5sn36ZfA`dzzWQ#sW&KmT-*1d(
z878v{3hvyw^J4wK&+<x2T6%hF<>lqK_Sf5sii=O~?BslVb91}w_b)Glm6TrH-mc%;
z-YzT{INL1u#ldFwH$V35u~AYoGBH_l<cN!s(xz?Omc`fqo!ZfncXyZT+uPfhTbI8R
z5S-|su;A+IaACoVz0&3@LRW`{1O+Xc6yNCJpdYs<VSaE>kcWc6g?D#%-`sYzTm15;
zr>Ac|Z{-%hls>=K&BbN1n(w06-DMkVh1Gl(m}XxK(VQM%_p^1G-&`*j7dbh(l>B`E
z<$iM)-MJHU>FU*`lO_pyd3q|w#>STI1C1Pf{v0Wje7sM!x*9yLFm=k53BA3%Z}0El
z?-j)_XS1N}?X8Pzqqlc-WE^heRT1HOd2erZNJxl_n3z~jd|aH|%-OSpYievjq2Zvg
zK-GKNhD{mQ*UA3<`~7}SeC?;;yfZTlmn>iI-qzMOWy+M0+#RoYWGn(y|2;oHe{;>B
zKcCO%{C+;a-tT_xcUeI}P}O~}{(o)NpC5*Tf<J%#x^!H=evMgOadGjknx96$=ggh!
z>*C@+&nEFUsQeV_ba}IR-aNUpGmYCjI`Z!A@jN@rba6uigObv&f`?3{@9xa(@L0U*
z?CI01H`ZmXx_+vtsA$E84HG&#K7Ib|sWmkyCr3wn^7?(hR^2Mn3y<E>&MzNydYW$N
zHePA7o^AQ}<5b_g-~Zn)Y2%Ed;^NK~Z}$ECa@jx9>e#VkZ|r0YlUV-#c-)_LIc9H_
zY3+}P?Qb@Bc5=G9xd{mdy11}>eRZ|F<J3OxH*elVRGmC|5<K9gr+4i|oKAS1{IvS-
z1v260HL?PI)rVjFpI`9n#}5Sy3yFshAKutmto~XzK;7C}nyZz`f4<$(%gg->e|>qW
zmAI??eV?Lpn}UJ@!?tbPw0<`=HQiXPx7fYEZT@`uxIGmMHzk$$T9v=+*|EdI*I)2J
z{dr@1d-;hICxY7bx2H{?-agwr|IxFvvn#&765X?B&yB*jx3;bpnjW<B$c>H34<9~k
z{P*{Fc;#Cyb93>+!ooK)y}i8;A3SI%etz!Y?c34}H})@Ewk+e)5>7driiYp^>-Qfk
z&bYQl(ydSCq+NS!>%s*K8f-V}X=ycqMiCTjY-BcW-1vs`^XcjO7Z<s5Z+-sh@AvzQ
zmn>n~o_Dut|9*SFxmK-*4msVpbt~+)vcuH00C}?<iCJd3TnsO+t`-jp4qh!&{O?a?
zL{!u!n_s_vJ-X~~&&r^ps;a1|$r%?Hr}M6<i794(oh)d?;D*;_H#fHpn>QccS^OL{
zZBq4odCbqPaxyYK>-YbQTDsj!7&M^0f4@CwaOKULn5R|i&n<9lzA-r`K0dxj(%3E6
zVrft(s2z8>jkkIAYHbxEP6kkaosp5T;@ca^EnBxvoHC{5(W9h{?CF26N7b#JF=NI8
z&&g_QK;XlJgUnB!KYu*8{2pUK@l7Vv>;6$uQCq)8MaIR+E%Tq>cjSl*N2?QPD#OFW
zqrqw6g%xMwws&-J^hlX@-Icz)-2eEF!pArMfd(?;jHIQdmn>PbqWAdm<LunxdPg>=
zp9f7%h=__VjNe}udPvr~jA!M_l?5dwDNk3dUE906{Qb4xaZfDPy#voz2-(@$1!#!8
zxVV_Tr>7^2C)(~96T>u}$fk+P?x5Iz!ydo0m0R3NYwDp37bfIZe|o~%Cu7;<JzX#H
z=B88yB@uD)<mcz+{`m2@KmK>+)ZHa7g+QZ6N=*(52j<uR>s0fdwW4Kb-QQhqg>Ucg
zmoF?V?C9z`)WFF6;cof;l_pUQtKJ_7|M%zJ?)RJaw>m96RUujWkTE3FK|s(f=SIW6
z+TUTE-Yy*6+`*dFrMt@Baxrl5@`C2vrg*8={dmYOA}YGE@%gJq>()H_nUR?pYB~Sb
zBSp|K$g13kY5VO$E$_~{*SOxlPR(ylN9N^ait6g@YooTVGOgJexj8Kn)Pd{iIWotx
zI3Y2y(KP#-Lv(cX)2r*J>&IV{@c;Sq#*G^fuC5NxxVVTFRD`UF+{|`;ef;s6#_0xS
zZz4bgC7{UY5L6bBmE~O-(zRxdPRWZ4jIFJ$YZ!xmN@@R(`RBZC$Br4M4y&|pf2z-3
zHNTmie^pIbdF_<n&t~Ue*jb$3($eCfAdqlkg5uU`-m#@`ZU`=2x)hX#sv9QEo5#n@
z+#J=v_NCq0msVN#_u1aLdv{~<alRwRk0(Dm(wTKwQ1H&(yI1#>MXuBPu5`Tc>*3j-
zWxh`XO|daCF$o9=1Z3anvN!ni=g)%Gt5=s@TX=4+b@Hhxnp*GoPVZMgdE&%@x3{-X
zoHnhk_V>4q-=D>|cm34X(gJ0-cXxNQ=jZ2d$+*bm=jW$YdFsrWBY%H?Pt3~ddUkg9
z>OIW=zq|~dy2IPsd*ZBFT&+%xk(<*V9_F`q@$u>LnlVi`+G%0H2F7oinwlMbed{jg
zguSXe5_<0$wDT=r@bl--HG)rHzU*vhU@-e&rmemDRvbfmdb&=;1_nJnJy5OC!NJZi
z*AldH&7^J9rin#u$q0N~(K_qK&6|pPdVRL8_x4m8pPoN+rsOQs>}8iePn$I9(6KEH
z2~TIvn$^`Uu7Bv_#f^?XEC2nml(VTgVE6w1e);$N|J!BP)cpN=J%=a2chcm^7q@1I
z*L=C?{^Qx~{2RM}9XodH$LI6*j8o#I<+j4s0+{aUm$hD2bZ4Tn`;D)nA|gjl{eE<`
zyQ9B9J()T0-X2b4V`I>aZ18fwqer{NC(fVWKWUQCi<d7MHKdI%2)sHa_9K7q^$>g0
ztJ$fMk&zd!UvGC%0FBe$e8T2%vzgcT^~Pmxd<;DDb~4iDc`N~8^Ze)8G&VOoPc86T
zDpdXLjbmJ#9D{;^fk0kfp48KYn>;70DH<3Eu=C5U*~a+j-MhZW$NN{?yjfvU=03Sv
zaD|my&*L4tKq~^O4{O<dl(w(4S+jQS>k=M@KR+J#M{G=Ly`?@gzW%RiQBlzi!-EG8
zhQ^r|78W|DrKt%C37MQKPD@McU7z#h*)vcCHZJ}C?(X5ApP%34KRwsFTuD{6^{u>J
zjm12>S}t8(-In(D<kQo1CwQoUhC~V;G@P;&7Z*>yw8S%~J|;$H_SEG`JKx;c$h^D!
zeVdP3^YrQBO-)T74zkOy(6ul1|73d8EdQR(i4>!V$Vf-6sfW&;<Ga2tmYHEq>~68B
zsHh1iQzm$*6qJ@8y}P^o>NHTrGuOKOMsQeqdb)_1*r79Ld~$DXPCxH<e_!p*1^K?|
zk1OBr{SIo!R#a3t=uLn8?(Xh4Tdi|*b7kymB&xr?Y4lNR_E2eBzh0k<i|fY6*4EYu
zr=Ln#6eyVG+&B;yxjMgY)22-Z6(17J?tWaq|6kYr`u}s^`g?ddEcct6)ti2HmZ?Ce
z3#bFLr@|0a_AngSl6kq{{XN?b7bQ@asjRF_>$s(b#evhOxoxYzxum73P1B2AW%kdq
z_}K=>7od6YL#^DP8F$xiv4fW`3GJ==>I7;+^sRg=wffJ_U7K|I!-M}`30Xf?H(~=r
zV`JlllPM7!5*klU{`>H7yRw?vvRjTX)cxnR?AvEo_v@wlpHHXt4GattnD-|qB|Uig
z^5mg)5gVBT0|N~V4I9_5*LQMuzPj&_NyY_*9!X<1P*2K3rQ+u&)oD7Bi_G3{Ei5f{
zjf;~5H98J&FcKCP2F2B6b$`$p&(57YPo>Dty3_sc-8<0eGYb==kFW2;SFfVh|0%k&
ze`Z64@Vv(seXAWkJvnEYWH#;DV`GwiZOz3?TQ+Y_ww3t5`ku?pBAMfRGxxBA)}XAO
z>Y$KuCERbzmM#CDOUpl%+MQ$B_xPU4J?2A&GMiIQ3dvX$IHW#U?VxZVfB)ZOY~IB(
zphYjHEbkc~?l3=b;lc!6xoex#{moLf8B$VG95h6dPEFCwiH?h#ckBC#-DPjN#Kos;
z-4@r6<JlO&vonUz*w`49(M_KnKYyP8<HwI3-QCIBO~;QOy}J72#@yRxpoJKq1sul4
z%`tk%A06$Mwt3v=v@n4=?CQxgXVzR!C@wvI`e~7d2p4Gm!`a#34yMz>18;6_el7d{
zHE2Zim4U7<@BV+kR-dY?t*z}%7h0`hXt>c)`-HiP{mPXqH*DFG!u_{b&NeFb!*BbO
z7cWlK)%me))t(9Smc5Ez4;pIpE9YkLp03xra;4^$EnBj5cXNyDIcQA<%_)LX{o!{0
z*K_23zrMJ*_>|YX*4EY+7Zx(Fi{0JED{YqW_SV+*s%twsKyx0BI%3W`V#)9B>|C!J
z*Z2GS4|Q?LhZgJ7+S=Mmo}Lm_QUcBNZOy#grWd=*#U+*d*Vc@SO?E#XFmJPn&dbx|
zY;~Gyp`@hbp*KDFc%Q7Mt?k_0`}^zdZ{ECladr6mj*b;emb5IrDxsw}&%@=*kB`Yx
zHWdcEayC0Ie!3747#Mg}Z%4$sPfTsSPp_;DPRYz%xyjexzyIH_*Xym0Pn|Hq;qLD8
z`sAOQIyx!WH3cu8nQ8p;wEq5_fI5?>4<0zU_sL{V+45@jBd4m;!24Be*ZTbX^V#3a
z+gsV){kYWOsnezfU0)ZgX=OF5ql1ZwDdWqFi>2rKmxZl~a8!2h3%R*1`?}tyO`EFT
zW*$9$JlM^RO;B)e)z_|^+}zCP=jN`rmF@c-f3(`c;vMhe|MlFUHl=&Nob7~(6Aykf
z&Ah}?{QTTe^ZPZyZ$COZI)3>2wRQV;b5KM1+y4Lmq~G4!nw;It(dx8qflPqe;@-uJ
zl|g+@X=!N}SJ#cT-D0{;ot>N@A$$A%*FXIVZReBSkbB$A#l=NIQ<IYcG<RrWZ4FvR
z02*D`UvJM{|Kx-qsI*g??6@#s!%Cx8C&f*hH#@q_uq;+PbM~y_^wXeWOA#?KC1qu1
zD=RCVdBW;`2M!%#GMdTb=jW&Mc60i9CmpfmV?B}+PNoQSAC=NwCjnUv!6R+P6SPv~
z<HwJnDF)DxcXBfG%8)MCZn2FYPM$m|VUWPEX6;(V$tMLQBvzQ+`+mP(-hZAAW5BkK
z_)7hAr>E<KhC(Jy5(13^9DNCDhb~#7q9MY?BX2k7=;reGaW`(>Jb2>7ies8{|Ni-N
zVQ+OgXh`V(zFMu{eX`bU?Ck7cUR~whwr!h5`8%10P3j+wX7bqC*$H&IY}m9ZX>*;a
zioNeFlf>(pg@uMi#l@!cJXbF|aKK^4?Agu>102*QZ~V39j<E9d(}n;3RDSz;q*Hjq
zrcF(qou0jWTmJp~XJBlcY@77`{eAX;Z8Mf{-D<idNVAPc^3W?oQ&Z7hyLQd6uea0D
z(E&}l#q6mNoL~PBw4P&0kY?VU9f8?5!@^?!GMHY^cC{{h)39}`sg91$f&h&hB1an=
z8$CQc4lJKv$JOf87`)ss)biS?Q(SFrZCm@Ie||o1e{oZ)x7qI-UtV4=d~kqq>ek3p
zPoF(YdUT|7jr+sh@AoDDmXVb;Og|^{<=tKBySvMetIw}lWVW~P@v(<5E-tS4_s8<K
zn2gLBvC1!>KOYW{uRZ#0ZNx?<Gc&UdTecio;MfeRKNx1+?KYa(vo`zEmds#}r9n%U
zFTWlkCv!aXLz}dN%=h(a85tg(ot-W&Q>ILDxx2f3xw`*6fxt-qj=nxVP+xlDM8UcC
z_4~Z|Pi#&<zan;b*_63+eZ$wqC^9oMZ+m~RnLQ;f&F${4(#3_3k6oOm8@*z|f`*O`
zT&pY&xARZ-Pyr1AC@D!<m+6$gy%n1Kk6+fxC3u-n;O_NB{2rDoL%e=}d%O7g`T3J$
z^p>;p%LxcxoUHDD;=qB1clY=EgSxOAlij`i{M3{dJ$jUM>fAXsC8dP{9{O>67X0{8
z@#^Mg_3rI8^_4pX1#N47Ot`f@|NOi|@9*rKeCEuVjumUl7lZmr$1^W4^L_H{*|JID
zrM3#Q&GVOqt&IxI%F??1?9CgW=jZ3ED=k{INGV8bs)(4Fo2#p9N@Akp_Po1FN{jaF
zu`x0?U;g9A53Q$0X=fyEZOs-3jaIB&sR<f`sH?M^Yf;$5#wT;)lAK`@i{Q$7t#!+n
zyKhcA3t7_<`1;z~mlqeihhF01=T~1D;#FB$>EdFNbw%Ubl0VnhMyI5wAAe<UX*tuX
z{GHFyqen#q1s$ChPB=H$+TF!v;lhO}d3pC9{1Ffo6%m=>qoyn?D_c}nmUcZYc1y;^
zCOP{$AD7hYK0kkkzU!QLGUd(6J<ZL`J9q93%*@n$`t<3<7`^4SzrTfc{)VpMxE357
z5a7_-+Unx+W}d22)UFcEvvVvbcbs~s-RR)p-Y=)B1j<QSS67L8SO5I<^vT<|zAj4&
zZ(a+rPfSd_dM!KEf4*I6{r!Elv#m;Bxg0%ubd8V6OVL%jcjnnvXIxm|sCE0+_I&^H
z_xIKcm3@12(<Jc_i;~i*lP48BU7XnYWD;IZiU0od=g*Xc1c&MR@nN%<Enlww_s{3^
zE-q6hPILr~-`?l_{QUfK$Wj+4C#Q^GUtYf1@!&y%lu-(YU}l+qSjqc)wxFdz8j6Yw
zZ*6*D;U@~N+H;<qm}p>Z-0Y!pNPT`y6KG=N&Gk!{4oy<^zA-s+)rTvE&(F<$aeu#k
z)YdH4jS)S4veu7|bP6+^x>f_4aVdLuhqKeAiC5YTv<&O$(W4fKH5VtI7S;TC*nZ*K
zwPSOw%R!UR6_u46L-#NDn;VsT&(_AqBuZo5qECP3+t<r&&%f{X|KDHGLd_5@MG>yT
z&(D14Se0`9{rx@t{rc?8%#Eflr~2PtTPyA5<z<k0Nu{NwWx<9G8@!?n4GkxFsO0d~
z=jZ3&*<U~3cBh${S=-X3stgCNT@%}$fB)JG85tRw3u~jz865ol_|DC<?FJ2?&zsTH
z!*gdxA+x8ar-zqU)AZ@$Pl_zxynP!O5U`*ure*VHW6+St>FN514;^Bv{q@B$Bt%3>
zN$J%VuHv6k`nz`RdU0tf_oYjhR<(5Y^c*=cQTgJY%3_99r8RGE7%o}13^Z6<v{NQ7
zF7Cp$YiZdtB&4M`vp#j}mjlf%t<X`6nE!_%RXgtZ@#Bvl9&QIMa+>;4WmbUJR0-=c
z9Z=(P$@1l!Kd(Of<KtsR0gelMDvLpFNI^lt&_gd?yf|>-f`FZ!-H)%=<3GN+X>5{t
ziN)b&g}S%5w}*;Q&h2fyot>R;c7&~sT3G-89|ME8xA&IZ+ta`+G#*}C8?CcOEj2p$
z?;N|@UAL;%>X@2}zI*php!=wVS&qb}O`Afh;{R)g_^$VKabb~ue{EB$_m3YxR(#pl
zBW23PaA8fPvB%O&S>j*kn1B~$FfrWNRjS>_D}8LAwH6mQ_u-S1)fK0oR-Ar%W7XHJ
zvfk@Y<7)o>v9z(V0Zmn}42e2^>A{YA{kP_G8T)>3zrDL$|45RdlCtu{hYuU;{{A|6
z=T403nHLuqe|+3;uViBr^YrSJ1q&3uHTQN*m_56D!2*SClQuXgH1ziJ=H%pjIH^8=
zgBz#d!J|iAQ$wE~n=o~1>y#-X9WF|sML0?mJJy=T2Kq8jKYf0G>FaBtBnwIamzVp4
znronna?6%2Cg;xYe!uVZziXE+ITaRebUPjzQ?hQ$77?d~0@L;5*BLn;n5rEP>cC%I
z9qYAJ2-NP_xX~~sCg#F2UujTdLrbe`?WDDxzvcy7DmCriZM`k;Zd7V~sr|34s@PTQ
zer8@;0v>DkxBn{=U-PjwZRWrF#Kgpo-d@*`5RsVJ*o{d?LAAWhty{OW*8ja?CI}k5
z*_wU*!Q;owjSda>_EsmKon_k5*?I8i=jVmb&PYZ?M<?IkR|^^$G|Ro!(k-qJ8dbKa
z_^{xVQr-JKpZiYzyuQ^&YkIEl*WlY5MJp>S6YuUS{qW%f1H+cg%gYW5yM&}ABrNz8
zb*Woif5QCv{paRdU$4q}x+gm-kC}}J)UTQ_V@3yP`9idxOUuvC&yx?eaE5-%tkO?Q
zPd|M3t}JLdZ;zyLmaTf<<A2@X8w&17hy8Q8aPy|5eElE8eSg2**80t$prF9;`T6<u
z`N_%2AD^5QzO%nx9+cCgqNIX?f<VhGY-)drY}~j}%m2&dNs|I%Vt8t6Yj^D2si>sH
z6uv$#_4n!H5gJpy-rU~KzjNnKg+*1ionh1eAMck>{`BNzL20RLznpDCdV2eoEha8*
zZkr4`J3BS>^x9_6miC)t(fH&^3Pb8+rlO*vkViG2o^Tcw7cUIa+L&|GNJ2_#Q=50C
zgtWAD%-*W0x%c;0n}7N8<%ZVf_0F@+avwZ+(2#n1+Q!`P@9(>(rKz1ca|V>^r?$m;
zd2z+<sR&e+tF5iw^ZVUy(EQ63FV#77<_L(1wf+A7-dSm42<QD>rP*6QW;m@3NqT>8
z?}^i=oij5v&GPS^`L=WS?#DkqKGy0z@F|;FBfG`L-JMxO`@CN4t_OR+-^&iDGcq!|
z(SLu^B%wFAxBEYxGk4CM2@@s+B+DE<dUQwaZ!;Yo9fRs`IXu#4Jx_O6e}9+bJCR#l
zFJ$J!%-`SMYG`UY*45dSy}iY{G>CJe$C0$g=*?-oMMXs)UR+eRu&_9A{ye{#nVE-&
zheKGH*sX23+_JK=B4T1;F9p}U>)da3b!x7w=~e%yRcl|~tNr+hm4}y?amrVbC(oX3
zs^wjIx@*mvH4~;!KYlLr@v&YBtCAJB+F87oPKs^cxpU`@jmhlQ6N1E}qM{`1>ui?!
z%w)>V&9$(yS_NM5!7FXH;iHnPtLvKQUX!a0)z#V8ty?Eyl+tl(hfv>R6VT>}?`)xU
zr_AgB)wsC3E8E%aJ5?txE?)BbT5s;{O{v^&{c>xIYCv)G>Q&d<+uI+%yu4h(IE_cz
z#5(nq$eUYRx!>O2-ac`npp&z+Vd<-o6Hkj~%$~jZrRMMN@B1MOewo6;!d_fj>b><R
zXm0x9>(|k5|E*Y|5fKrwApQKjg3{7fAGO2p?(Sw}$i2Po=tO1r4LLWBrnT=Ze!ghS
z77;f$Hx53&V>1ks6(*ldNKS5^ZJxhs%E`OC%O5^^bck(2_tB(ptGp{`@X1;wJU=)0
z!^e+_%m=ciW<<yDE_>?~6eOe~#Hk|0>DD9Bn0|hqV|281JHPy~dA8LGlTRuLbv^(&
zXL8!opw4B!vz`3?`Eze?YhAZaZ_b=KSK6kYerjN5))t|Y*1S(U-v6pC<M$6A4m>#6
ztT_F2Vsi3gqmvDtot$NFZZw{ptiHJ_a(|ueZGExskEiwbJLrfdUtJZ-!ONRlP$wiL
z1X|*~Glp-bPurU}IiT@WhseXAwM$#gq`JGh3=$8qcq|P%@w6yrZ<Xl$`hS)Un=+Hb
z*T?a;IyGLn5b(__u5zA_uWw>@Hup>)HgR$Bsg7Tsf<|phUIch74f0SC;%IgH@#9Cr
zg9D8QCMGSNot(eEykzF)=I%WgIrmiR?(FO9v=Xyc&7bvZ>!z*Q*ONXxI0$Np_ez_C
z#-%pY1~2z(-M_znYx9M_fB!CAzrO$UbbV0SV_;yAz&j1p^L}}0DZ?qX9#Dc?=0Be=
zI5^nF#U<hCsi|3?H)mW_5)eF+WSEni`|#=0Rz>GF2JoVh!bdJgjvY(-^yK8#Hc9h5
z8PM(syV_qHGPj?fXM6hF^)mf+aeJjWIXQ1^&zHBZJK7~``mFo$j{lFi1&d_D7w`Y~
zi(5`kPO88E!M}1>H@BpBcXoDkc4lrd(U05HF=2v0&W#NVFJ7t<h7P)D*6@I~4Sm0+
zC4cxxq0I5W?;qGAEeWw>0}aW5b_VSLZwvZ9O-oa=asPh%s&8*NJ6)Jsof<(y<<{2H
z>F4LQ#^|v#q@<;7np%}@oN+;6YAL(asS6jt6WBg#&7GZ`ZG5s_+~RsB8s`oiIFQA@
zYFhM;f`zvjcg>30QJ^^c|NhX`VK1(&m6rDSWp#SF-LLQO&tE&m!N(UibK}ZKUN1^b
zOI`$+CG6QJHEI6N;^$6E6E_sC+wtnJy{3^7Xn-th$`&b5TgJU#Ztds7uPd)_*|J3{
z{lu=?-)3{>%mLMa5s{HsCBic^HSPZWNS2V6K78Z|%WSjURl#wulCH0d1??W#^tWBU
z?m)YIUB{(MK^h`ld#b*^+PcKN?hj~jkA?`>jT<)<w6tDrJ#xBG=7-k{neW@A%=2V;
z<!ole?Ws7p?d|>j^DkYxbY<OzziJXvQlRzEH=Z-I^99`AmaD0z_Kf%0s=PUK=DYwc
zczCBDFZ;`P#hyzgN2gwRed_Y%$z|{E{5<w4_@mXn>hF46b8b$WXJ3DB!JiDLyIgPc
zm3_nI*RQerTJ`FR=B6!M)|CCfyxjlgpP!#gUR+ohbvp0#G~Flf-ks~aeEs_J+2;Az
zH0@_q{rsf5HUIv+ygNG<ZrYoEeqPC&8ym0Bi#wYA?b1^3D+`_5ug_yU{x|xgoIqbS
z_tIs{9&Nv0clg@c=!?5bv$xg$`0z02-M=EpzNW<atxk&5^yB%mv$G{6C0FVkxw<tw
zJZ5JRtHaGGQDwKD1{bA9AGO1WTDf0O4PRx@lk?)jLeLI~x<4P?8B&8+oSSPcZf0f{
zpdlh*nk6!Oow2pGbnr5t#t5CZ7`@}}^0h1ra=Ok(Zcby=n9X?d(j_6TRwkiNmd3_L
zhEvOK#%#}%Wl+%9=Ld~CM(DJ;C>?xsv^%nH&YU?Pu7<}iEPsFR#l5}O%lzlBGy5Ns
zsWE5YKD%e<=5l9eXPZq;_weAz$;nB`%;Z#8SO4?%dc4k`E>Z0p`y89u9^Ttq9eU~J
zjzZ<=HE&K$)fV7tebf2sP%F2ffWU!E`|AE$F)=ewoG_um-~Mk%s@0V@Z*qF%Y^8)c
zS<KALb`(GFyH(}s<<+!yt?q2G?KQHZ3vzgyHG`KOIChN9XeN(w+L;9Iua;I;k2asT
z1MPe9@$va^*8Dz$L-Xh0k5=c-oofrwaLLSEd27Sf$}cYjV`5_!r+O()K6yjr!qn=d
zLx&H8%4|DvF|k9(kFzUH<OmB3(|UbBq~!B6-y64YJL^q%)|z_g{(X6df>&2GZ`{1O
za!py>x#P##FE96R4_etWaiU;NZSBYB^XnJo-`{6oW_Ij)eEr!&VS2Z_erv5-37Wm`
z;^O9h`0gE_wzjs1icpWVd7tlpo7!I`DypiE3j<cnE!q*gyKKYOtyeQtf7(`m+aP-8
z_SWonL1niME4OJ|SAEgY(a}+ueDcAYH)obz>+9)hiP1a$<>h6?$tM*idR$re`_nn=
z_n^Hcj*gBww|{<qK5_D7W^;4%DX`6J@(dH^%;{ldW;<}})-2z{ycu=+VSCc%+t<gX
zE)48@EcJlbyuqR`IQjj(y(*JWuDTn)kzK{sc5X>Y$ql<k2Zz<+>(!JNNt@>dtPWcX
z8h`9KRiz^&EZn)`&B_1Y@7JI2tN!$)^VSwN9tqI?fUNJKJ?rlrY-T@s=gyptj%m8l
zZr;=NQh!g=iF7hfJENeqh*MZ?!+z~$`!g;o{d&}`-_enAVuIq9>hF1C%l>r!Y~d8n
zczbKBi;GG6IT_HHhoIn7NI!gU^>-n`!2A1ZGjD8242g>JVr6B$vNbz=SH;IgUUk2}
zz4d<h@S)c2FJHc7+}e`)<};Sv8l4>)Dk?3@{O6xL#Kz8kd4i(zswtsGMMg7c%@Pv4
zcxtNl$$R(a?fd&JT1jcs)~!oVPuJhP<^QkO`bt6fA1_$E*tz`uJyoSe`uqP(N;@|v
zQdL(;X=jYz?d|#MN{fyhaml*1MYB4{F7uMgt*zPO+}4SQSfVzk@p@lAd$!lh+dH!I
z-R<q`5B^kCirQOcI&;R1fYZ}-K|3L?wyi$LZ~te3P<>dK*lG8p^JZ+=U{F+8sJLj8
zoIs<4!}D`<m6aA<xDZfQ8)lY#jOW+K<MOMI{QvW_SW8z|b<w8RR}LI#kh7_9aB%^x
z2yu6J*V58j6uv$#@cO#g%p)CwAwfY-mX?-FR<Ax?#bTm+8?rWYjm>}fhLSL~Szov^
zh1R8?pZDgLs#4VUJXzm)Haop`u=C3W{QmY<Q&Y3Eqhnj{ZMUS2GnQW7k{R69-><H;
z=<Dn2mk%^DUtK0HFYiCssx;|-rhM%e@OqGrQ&rlnPKr;TJ`D^E6pW0BxUe>Qd&i15
zyAR&EGw0Fc$Ju+u^<#G(x%B(h>h&w4x9441=*(WmTPSnAV2|bb!vD>C_U>h5K&)F>
zu*qIupTGXkNA|Sy^UiMDntwkoH^#kBrqOBP1&iEO7iNQ6-LlqY8$OEdE`RUl=El~&
z{SnupSC`6*iVjUu_1=(jQpm;KeKBa&@*z+UL4Cv7+2)|x`w%gIY0DxN7AD4fwcle;
zq!=w(wk)eePFUS9rM|trU0G3)F)S=>!s(|!9(C(~`0_GXL|j~1M~6q<e_qP-9aUem
z?(D7J?o|UCa9ka}9<+2E)Dcur0Buq`aq{HBwb9#6^pe@x*+Em0pzSJG=lO?)iA|a`
z2{d!f(BPsJSXpjoV<W@J$f$E(Tt6;E^0r~!p9&5ZCeVWX<YPUKE-o(3pMu|gK5u{g
z>}>PGcXupx;`Zz~w>tOsty_=2->*MD$FjKK$A`o%85f-vKi*aTo{ycKo#DWXi;F>H
zHd^0V7(hd!v(0jw=FgW`Qc}9{_1lY!%AlFfq>U0@ONAmMBeiT3=FOXT;o3E~%gcNf
zg*qR+d&jph0F-|hx%bPJ>fL_zN?lu<`}+F$_1ogKwRCllc8O{$Oh2tS(F3$#HY$Ab
zfA!cM1&my+Otw{D7y`BlBt%6?$=O!1EO@iqOKmcv#_H#<uE|b1neyX7Gk?LC7lBWn
zKL_<(XP9ILO)bDO^wRM9`ugh?`pcFqS)rr0GwCSTl4Z+48(`w=eyYCid}OgM@rRXy
z%y%&@d7imbr!M_eHC63Yea>`Yu2u~_y)fy%e~pnll9Q4YggP0fn7V<+Q8cEWs#^O0
zX8QbtmoEzo3JT6J&zC!rwDHucJFi0GRCgvtW@Kir)a5RXg6!b%f$qK#>gzD5|5xKP
z!(ibxi@9^>7Ct#4cxPuZJ0Blk#m`Twd@>dc?(Xir;b+#p{~6LVze7MwEbM8ioo`>Q
z?9YiOQ(oNIsQmBq{Qn0IA9h}B!_csIuPtb0sGwlu-s<m%Aai0Du3ZcJnHX``>+@3<
zCPo$}#<X*DI$ylV;9y~DX=?+mSl?Uy-Oa^?<=VQ~?)i1UI3GTI_~Gl<qZ<+r7d$w?
z$j}g?73#UTNb<^3pDR;yOTSK@I(6yCRS#}&&v!2=F<G>DF=#|;P2ApH2Zdc$Bqt;^
z^z`t2TWo7<E6c{lR@%2*FV!sbl1k3aO|1U&?OIz~TW^Wp@8sCEYgb2icd)d3>Gws=
z&CR!rN(&1Qf_DGDzP{dBN9_8~KNfw#;3*y5l$4Z~hlkriopCMyPJS7ShA&^fXg$>a
zH{tq{K+r0pFYoR~r*2!R?my2&=dwn1k+g(F2WVNj!_CPLudWVXykG$X!wq2%1__%A
z15k@*T72nCb#?W{-qZDF*i;%lJ3G7G?Yo#dY%M^yxPBPt;t;Ky)(<NyD?uA7?%cT}
zRih`$D`~`{sHm8e!!D?3W;Si^<2GLD4}X3ZGdKhW3JM7eUp@Bt&6}S0`~T0=UGx0O
zlNQ%*v6Z<C7*pRbxW08d14BtkNkU#;pR9Em1H-zwy{kSwzI^$zFe4*l4v$+{*fg*6
zN58$j9T*hU^yg2_n_FAEdzY`suWQ((e(7o6-CeD#!`B}=bB2fE!|nY28#kJndU|_1
zpPr^`m~?~#w7S+j?~X%Von23F@5C81Iu0Fj5)l^<pQ)&;s>*ut;zfprySvK|A2`79
ze(!fV=z{knM~`lt*Y*8gHGlH)K34|^2GEpTnBCLfM$qc&TU)cgYB4YPwQ22&UB6bY
zjnh7L>XcJEpDbvIFeN=*Sw*Gg?d2_Uo@aloI0fo$#@GK91ubd$^+ogB+uQ9EmEFTQ
z)3dX?fBdL0dzXB?Pu9T5$mw{WZ1!3AR8X;#lA>~LO=R%XU7F|DLsoYjJH@53n{U(R
z&5T#-^u@%)R(&}Xv!j49GBPqt+FIuLRax++^eo6qgbmxawMFQ(?c8Yz8a^$1dn=SP
z`eSFTufK=(KUpCm(84806(Prk0S}(fuMeBu-^k1!_FE)SK_KDD3DEXl14Bc{f&v53
zjFz{zH^YJJ@%6o+{nfW_Nd>JG*|u%lo8v!Mg|7ba>8W?e(IgEKuA1L(w}(E8-(P2Y
zdj9<R@?mSELZ8}zR@|&z%eygxCptR1qN-|B<JMU=y4O;o=iAkSy7fGAHWEcUWt1j%
z_$piH-LdGAwHB-W^(8U*%skubu-r;ZYiscG1^4CM-Q4f)>|{<)Pv4s9Ki5k1-@kua
z&HGHZ*8VO7FNjR2dw*~5&3#+5uRD2rck})zEiDbL%sq4E3dpS!PNozT79QM?cvx!A
z{K(B|8#&pJ^+-1R&Ne%E;sl37q;v4y*QHbE+SN*liHS`({q)7<<^0#yL^eM=JNxjx
zdvab&h0@Z}UR+-<@8jzm+Bexl<;T<M@d{$y8?&yi%38km?%rzi(~JB2`OWg~a4>)t
z1shg>%h3?wdUI!IaB9}ATkAHSn!j56cGp8lj~mnqdvLT{95mqH-rjyH<ZjVUnPbO}
zP4H0pfBfFu<Dr3n|1;O0-H@-eKuLp5Pa{u(D{AG#b(~@HKCZmwzWb+6dKxuz)-2D=
zubyj7uHU&Fa)#G8Mkqw=`W3P1M`B|<90kw4@Bh8~^TR4}y#>XQap#XWw_6maJ+n3b
z&i8p<@iUob{e|giX`n`T?XNEfKR!MlxY&(%>EtfZ>V?cpE|m!v_Se_{`1Na%-&`xu
zqAxX{84Jq)Fdn#kS@`rcUFY}r_6mxKw1BpDXJ1=mC{gVg6B{ciDA?F7uK()jdfUEh
z*Fbv#4!^p(8l<j`S32p%g@vaMPiWfLeXEaqORRgpq%oWQ|391k&T0sX%DOwLD=E3I
z4qIzb_orgdpHHV(<$j(T9(VB0&f>teQKnb7WpYll*yiG*DtMn!QAw$F=lexAzS8D-
zA+@uoC3GfxyMzdBWNYaFfsn)H4jmm{2J!LnEG#UbrG_iw_uKvY_ICA_Wc@Bx?`aR-
z+}zw6r)Oy?85kJodhgxl4OL&WT3T8_W2B0TiWN_%hEI7{m>L-tXTitMe_X!)PvC=J
zYQD3Ud}p#26tt~f!>FjFb@0!hKN~VHt1W$f{=<iYlDD_6K3MVYS;pmMyh%w(pr+Px
z$z`v^bfa28+YcTe>pkUte&x!QpgoONB`+AXwYBf;tBp=K$yQR*l8j(>atS$<IbS$6
zJ^k^)W_GLO!!`eYJ_iN+EYs|^>G5@uFLg6NKRe68!{bs=U?63l*K>BZdGf<UtrkU3
zIFgc*K-+b5Z*O~<lYX>IwCZuul~tkK$NS~oot&6vnPe{VxxX=)J!)%K>x2mcpf<|O
zpR4`n+eK{65@kqucW38@&6_X3tjzfT@2~&U8K7NzKR%t-FRhBny0oP8((1r}>DhI4
zbrxlBBp7^|zWw|CesSsRYb%16_xUNACmdj?`Fb^+!Q`%HzofC-OVMuqK4C#y+ZxWf
z9V=R1-`uSJ=IvY1G`30hH6BpIgkeq0PNDR3a}FMCW>@qSnx-G$2U^ghr^hE>_d{{t
z?|0UqMi&EUE;wXG0ORTD`sPv0pXUGn!wwqf{`&fQ;8HJ9&{orT@7`rxSs^GSBs69E
zit}@=+xzYRNm!S?Y1q8k_}{PB`dhNDa*6B3IQ;(h7PJc_Zf}*Mot>Pbq9SOH+gpEc
zOY-qPNtvD1N4rF4m}XBqIDJbykL058+r^KM@y6^b@svEgaKpcUzw=d8R31D$+zv`Z
zlO|1?@_319?XQwQACJqQa%kV4cUSAuwvG-Dd0_#iMM8OJXPJWB^y$+lgOV2kpptBJ
z+Sx<r?f>^EyZ0$rSV%Oa^{~D5t=V1n)}ZK#hl`urqU7U!96UTa1=iobee3AyIkGAB
zv_aLE44t?=63lEo7qqwM-Zleu=riu_+8T5@Avd=dG{sQ+`y1%gj!&OHfx5*$^X+7<
z%ip!=oCd83ojP^uy<b(|-}T<xTP^-vK-BB}JX=Lip*L^ef^y>if4{u-8E<Y%<+l6%
z#yBE6`=3u$Ma2p+dBMQQ<7yEuE~<0Bsl`uAb#Zgk@?G8{02)FL3J#t)YgX4{_kJZ^
zUEYRenb#J%c0YLXq^0ifFGVx6Yin|OB@7t4#dHt-{rz1~Ts+;tP{0Xv=!J);XW+Kl
zkB?kI>Cw*44z$j=-~L}m+SyqPV|SN<=F9};<<}cONUGBgTjMZU&G$=PQ*-mwgAAWn
z$Lus(6}~>NMC3!$joY`i_eV!Yf{xs9jp7#+6y*hlcTmdrcXv-!^RM6cOAEBm_4f9B
zW(GO?x-}{a>F4Hv_Bw+qT~O*WG!$eASm?xhXK(fO7r&gHofTUSRKMTb&L?ZNAn9mV
zRjO6#tCk->DrD^IVrG{8_>lPYd)eDt4;S~_fvWQduU>IUOG|@V5N~g9H{Wsg-rj2M
zPM?4Cr%Vx9tbf~omI>$keZTe2&9PkU<L&M3TvM~>Vy$*h-rZdfPwVeLve><!!9nup
z?2eAEe%}qClF4(@h0W>y78Vu?>gw$K_U!{L*kCx@TkYn?c56!}v*)A_KRzadc6&QI
zI?gaiWI8v`mRnj{+QQOO)0ES>o$u(2i;F?k3n<>_T9;qD{j7nJ8Pu8CJN?SmtgEY9
zMYY2cZfr<Aaq85e#qRwVmUs$-c9$|-*jHP9X1;y=%8wcM_t}c+#dI9&mA2pd*r?!v
zgN2pVBLDezJL>=2ty#M^aCMmOnHh%8mU%z>7cRW-dsf=IOb4`Z_`!pO8@FzO&V?x~
zEcDl{Q(E+?=k5m~K|w*miCH&Tx<LhSN629oSW%3Xae{}FlXF6<<R%@^pvN@6<vx*{
zQaDp0+onzx)r;G+!rWd*hi8+{;@sQY#5&g1%(kiARFrqZL{3}jvmxWcBG92Xr_L_X
zIekik(}lHilXQ61w~8~J9jC<96hicL<Mv22JUiw#Nku1i*Og6-f)hP&z1LcK{>n!E
z*IR>Gub#8*oFA<8*?@6jk-D|DwEf>N!YZCkn>QP`2t+`pXmn(y1SLPbUcX<-++2Kj
z+1pJe8c)wH@th2r^E}=!FFohH^t=C>FCqjl{s)<oez=YI#F;ZJkb<z`_V)bb&(F?I
z<LT~L5%Q?wm`g~6kHd;P7tOYF@$k6pulpOYJui0dOBWXxmoINX`k%hKy1Jvkzg@-i
z#qI6-3=8t_?-OuJxV<fRN9pTnH(&pHyZydF$_arhtHb%%ty>4$OV!`cucV}8Q2s6k
zoS-*}>%|<{QTP}%F&45agmc@rZ7X7S8i96cpPa1z_|DGas;67^&xlXa`1$LX)3j93
zMw0w_+iW-MoZeCKQ3-Soi)Hb%2S1<BcYk_ns^V#*Wj-?(O*wz^q+?8s%)Pqbxu7o4
z-Mg|Nb3;}NMQzCdO>WGtJp<DDe*b^DhYugFh}&!R=Iz^uZ{GBP>iQEC6mM)yZV&o)
zZEZBj#X6@Y43k_MU-xx)2Y<`HyQ?(q=;tMG-sFH%u%n}6L0OsGQm?5OHl=#&L~K|P
z;9v3i(<ktVfs4z6sI6I`jaeZ1RiWBv=GjW$ym`}YdhI6Lzo6CUB`*T5tceun<Kz4A
z?VH>AdA1X$Oi2mTxxOy;@R3eoK_MX~et!NNw{I_Y>y>(ObMx|(&wSeK@~z9?xwyEn
zfYyfP=l9Pt%}zSfA?V@nuW!%?+H!hlM<E9XN5kU9$~LyP7Z*CSZ$3YD<#(QWWoslQ
zB|(#2udc3UcXxLWSsNuPYh8Bay#0Tk1s~qC%qV(vgcG#<?BBnC1w}<ptz4oPHY7TO
z<|lU)JZ!r7blx|+*-2~Cm;d|w+j&|lcsKpGH#Zkw-q@~l`orhX#yiece}9*FtVc5A
z<|fruAuE}zt*tAns~1Oa&-?Lm`Fx*ai(|Kcv)WqyJ#WvyU$0jLE@sQg%?;d~<~#Qj
zXy@^k%*&va^gn+504)O1)6@I#{k!}Be}8-Hrf=DM4^&DwESu(DoqP8G<o)h8#nlHv
z`+YzSmJNx{pz7Ao&+o(c@5g87@9Pv+_Y=wb&o6J+vtort>it#W>-%23$e3eSd+SM6
zhmN1XROQWSXN4xIaAs#`7ykT|s@Nj%{M=mOO*-6>(~e$`uaA9TwQ18PmynPt6I47s
zCN0sK{oj3(iecral*NWOZrz#{v(0z5nbqkz_Vsa^`%_K|P5$`v*DtQfX-69vnZG>K
zjoT9ud48g@yGua8ggBX>kKbKiKmVe|){SASLOf+G3J#?BtX#SB(E0P{pV*z=q@x$J
zV}Z`)#qRuyo|6j7%f<QS?M!z5`~T;;y_ZhEoUKvhwF#@k*LO8EFf@PkoTOq^{!XXt
z&5ea;{5?DjcFf}z*XuZNz+v+5$mM>rh2P#pHlJ>9ZRL!d<~rLf_sF%i(XEpwPd->0
z5gFNOS^R9qo#jtYPv=fgPhb37#dDH`O~r;2XU|@Z{&w>G?Hz@Wd)6)YnyU5a%gf7d
zk1a}HP5JQco7t{E2O616?WX^<XKw2|y}9S>jg85NE?>U<;?v<aUTuT0V&D08u@RAx
zl6R-=-McqpSINqQsq>%Ni9dPVZy$DTZv6j$f4}bA@Bi=0n#kb%+K(SU9y)yZ@RR48
zvahdu^zZL)w#OIsK)bOG6A!WM{rcnQPtDZtZ*B%ZD2<w=0v3Dw=H_Pi^>-$pj*g6c
z8N=NpyZX;Gok*oOZ{PO5+rM?|))|J$Z5N+@e*fS6evNWYP7cqy<0nsgzPY{q{EMm%
zonB6ksoIT=jZV{2FYc?Yp8HZadYi~3mCgwh1SX$;{`6@lpRAQeJ<F?WYqj_N`Q!~+
z8#r;+EG^J3@{b4E<vHr>>zg@GOwkOUl6v><-J|XDbsA-FZ>e6obm_=E+v+3Y@imH3
z+j2B-ZOdJ~`Oan!2PS6b)~QpcN*E+G+}KsR+QZjZRN6dG<o34Q;8Wk<-VVRAE;bsp
z^Jq=%?l2Wq)y^kRQa0B8tujnH!ts=U<?`jrcN9KuJ6Pd2*Ge@eCT7apviJ95!D`jj
zyPezlI-j1N-u>c5M&Lpx*16KV{+Rz+9k$kJ*M9T7I|AFbZF8EIYM6CJWAgE16OQ-G
zcQ5mut#$j=rKR0Bb`&o5@bnY}^;urr*tmE?YGg}W+bkc4yt}(pYiep*R6GwIJm}aX
zV;S`4%VmEhOUp<V6&28_F<P9CZ*Ofqy0`lKmun06Ui|j`yLet+-l4N+eb3CX3|3K7
z>pF78#W498PvFKRSJl2=X>+MN%df5u*EUx!eRV~!_}Ljj>#{cjD_5>eI^4z!T8A^!
zzUoWH-lw1I@5R-8bXD}6lyQ68+K8=LQ$Y>{?P}V|So^S5oab2K`+K$zA3t6^<@Md&
z+iz^koefg3I&7`b!i5W~j$eylO42Z_pSAJc&SLk9>T2z$zrMfM-<E%0FDNMJ$Sl+B
zBi;J@G*TkPbfdOtlreoeYkptj^@lHCL^h|N7xVP=e6;<3opA6npTK?kVPRoOudl5=
zpELh#;+ti^K0Q7C=*h{+N-8QY9+Om@oSl!JoUC422iiF_H}%{5`|E4M_wLzq<jc#;
zN2lpVyJTgpy0Iy>yQ8l!Ea~07y}J)qY}sg%cPGMQl1fuk(~(K4-bW@XyC2zC`@2L=
z>FDv}+~VTmf}*0TpzgxP^7nBQr%h8UdvilE?fg95KY#vAc?&8&(yjUBYy{N(=ZS#M
zwAv;qFTZ|!Kfj!f#)pW^qeqWw-d-KBuxW--YL`X9g9Z;TFCmbxLRW<ZPMdFEzwhMV
z`hP#4gVG@fAD@`_G#$Z{Cr^UTPD=Xn;^LuOx1vsL(y6Jfb@lV>`#9M<#Sv7;dw6>r
zR|(tK|Eo#8_kREXyb{^?x}QtW^=?_Y{Ov6%v9(vOge0b>s-{GWF)}h%eg3?=<mIGu
z|L2!6EsDLq$hF&N-Rt-J|C?F<Khi0D=;%?`6PtAGe!Wm`P6aiI)vH1M>|N`6dUz(z
zoxApry}rKx&FOw~tt4aLb?fg7`1bt8#l>omO`q)l|9AbSKOY_*=A9;KQ?cP(q4AUk
z(BPlPB$YpZ{`B0Nv258guJrVD#nYQWBf(#uW#?~E^%Tm>%X@TYrg2rO-#i=5nAq4>
zozsVoA9uH@`xBA=@9b=I*P~sctvaWZPE1f_%eH6{04@7HRav>oP3`}4-)X5IpUuvf
zia9<((RoTyW@cv9=lj*~ZBzf9nQ8p>nSI@#h>}-VJfGhXQ)krlTeH}$H|Ux4D$cW~
zPYbW#_baP$qu7&Uz0zK1!@j<{>RMH`D{khW`NGc6e`LPhY{|82rCDj-r_(1-2JSl_
zySq#@GBT3wbnXAY->uC5_+L6VS;e#D&5essOn+Qk8~y0r-Q8-*uWoEy{Ausm)2A<M
z2hWhw4?eD?uI?VPI?UJ8u20r_S@^R<t=wL@3pa1x3~FJ;&HVFQUQjU6;NXe*CZLs^
zYRPJwMAQ^Q@{Sxiq7iy|=FFK^pN))+TISB3J0Uf)N8bM4gHM%}m8UM>kuuGa$W=<{
z*>-c<)TyBh^QX*r<@IRiljRa^HoW!t){esDy_G++4S8%c^*gp5J8|n)l)mQWOLm;O
zx3*+1ysqCOz$;}UFiC||UtfQ*x$FObzt^9;d{SL_!sqAbwf$x5e?AqTd|TSOY|R7}
z&ye+TvHA19rTI-u1-1Mazy9%N^Z6-7_J6-jK4<gRaCP9~wv)@}_siRhU0oe+K7;4!
z|2?6r!_L0&y*JZ1z31K*`>HP<J9h8pj-2Ki5HR7Qg<kBgC6`q;T5x&<CfEM>u&|)G
zSa_38_p`IJrF*~L+M0dn;K79voF4y*x0k(*TKx3WeV297+x^n-UA~cCY`c~v<>sbT
zr%5UgU%bfpaLj7|v$L~7Cg0yz`}J9-umh+?@bQuB=kg;Ri{1OReovn?X_5K=OjU*K
zYik0Zot<TR`h^u}jCpBHPR^QpHE;h}TUkl1Ej@SUOvt}^qEB~uFJar);`h`kFHbLQ
zT@0t^BmqG|#`N^`xj*yr@+y8lovzq&px^#qMuBW*X6BBPmqH8)CnhKsl$4lM|9<p;
z{}Vk=p+kobUD#3hc+vYa!IyTIzjx9vetu3Cw2joF^p%L;Je$tR>i&z8j&?CL1TXhf
zG&c`lUK0~D=hN>K6P3C5?ggFBF)j7!(Rb=gmn?bk<KyEADxA%1yjov=t&iK=6};Tf
zagxfH7Z;hoef!3C+S1Z;p?kla+x5+<r^TK;dj?vfG1t1>Z0Ff)YonPN^y2reshKxV
z#Zzd};>Eq|k`FX6p1Q&P@#9CU&u01ecswV8P8y7ej+TyXpE^}^Uj4sH4#k2G4;&Xi
z-BtdcPg+{K`SYK@f2Y2`bie-pT4Q}nOUdo|_t_Y}yuN<^#jn+&tK0fyt)=G1OStxa
zJuy*vNAYt$293GF5<yG7L`z;>Ss7uNbw2!Pm*~e^+3O{B%_}}6>`mp-d}X&f_wq7d
zhKA{JRh;kMy;D2AE${BFC(nZ>sqFdjsQbgmkD4Jo{c^U4?En91zF+@8c4ytwr%yFM
z_siKvt@tY1_D#lX(vm$l@=m;7zn?EWJbZ2{$b+CGsl29YNzQry=1q@P=_`f}e=60D
z+Bi9;a^K#be|&59bq0p~Js;Z+8r_oa_nxlz@Opgx)!FlB&06*6-m1{mPPzZ@>?}UT
z>z6paHtdXV6KLUJBQrawv-$4byA`p!&7{n7I^24t76vYM1MRY9%&4lrbgv*Qvp=#|
z%9N{iGebt6pLOXg7R45$s@gjHKOdMG4xBr;&V%#n$H&K~-gkC#@|niV%WG=&{=`IO
zh6Z+ixdW$8d97bQ=a2ovhYwE`g|Ck@-C<{MZ$H^QbE;No)B5%6@7X<h`czR@mzM!F
z2Q|6y#Lp@oV=;DyE33oLzp!zbyoWj9*tLsQQOCC#bV+6Zo7X<eEH|r2_UtTE?WB_b
zx2LIia`5q)-HZ;Jsuen=IA>M*l11+}-|CaGWb&M}A@}w+Cj03dH*RF`sn<KZx6ZcW
z1A}qe8Ha!X0noaSJH_W0IySSN`l@YV5usWA`Ptb=Z<lc1Dt__&IXgc;|Kg`PIXR%t
zJ%?gf>Akym?<%%z_yG&>TkEuJZESk%4uZ~={bX`KAbk7J{}z>%m7-a{k4Aorym)WM
zj7<d(omkk}lRrK>x})svETdN+C)-CwMn+nlzQ3nZc#?`}_WZ5>O`r+*H~06)&;0k|
zqVm$P*ITd01<#Wbb8~ZR`&2zuE7WJ&*Dqg8tlt0n`uf!SV|^1BCVzWykh%G_wuV@)
zjT6V*+7l;E1h^%dne%y0`f@J1j`ix7lbfEMKQVc0y#MPPVs3%SXXjgluZy|)#ENTH
zyujN+!{;*LYokKn%h<16@q3Ng+wvs_^Sy&(!)6!%1$9?IiJ((h{n3w)k58OFt$pR|
z>&#OpPBf@^rp+&ZYjkYNw9kGiJs*EnE;0fg@p)rQrm%|Vp|i8id(Fa6<p2Hkb%u4h
zo{Wr4NXG>uu2-u;Rg{EXjYUy$@xzxdI}MYME%2Rf2HOAB-QB(E&m!0EDbM^LN%nNe
z2nsU#`}>17&UcIHf;KBMGT8tBW1M$?Uu?o;w&JJz1vPcv%K!A5s+D+to^6^ud-7uy
z5z}dZ%%z*z`OS9UoV|8!?!;~B>mJG6-n43utnl0P&EC`XK;?MX-hEB$;v1HQ{r>aw
z^Qj#kw$<NU92^+5!q=_&BP-yf@w!Uv`g--L+duoI@CXZNeGXe6*Zby8&fev*$2b%}
zyx;#n>F=+vFD@_dH?pm-zO5a;?!f)~_uoj&i`lkei~nYZ7h59Dp8Wg!yESgVVz;>d
zvB$^z6LWKW_f~(`%I%t;@=7yysn^tkUteA-z1=)*>sHf6ix(SaU(*5YJq2x#m64U*
zm~l~wVb-(Qx3{<JU)uHA-u0^4MdM>vYH}xcojK!kWp()a8|IR%M;GZTdrtBTUi)W1
zw_?Ely1z+B17-Z>Zr|FPEv=h<?w_6a&*Z&@kK0a0<los5xF#300{7L`)oE`(UtJyk
z^;}`*(}xcmwZqpvxVYGTRgK>HIhM`L?0gQBRD9-G2u@b_H;Xt2itI~fokrjHWKUPr
zT7841`_!J))6*C)nAAq?EJ|&(zq&e{eZhSm?fU44e-_`Lb@=>w|IB-#H&Pi796jp#
z^Y6*;I-hpe{w`bSR&4(C$;rtK4F?W5EI!HpVgGk|o{MsuyT$b%&9DEbS)_9-E#mw8
z`^Rr>&7L@KUf*2na;HfuU*6x}f8urSl@${|{eF13-FKR)>3+}2Y77nS{PMGG<epiT
zzngRNa}8+2gnhjn1E}Ypx~sRBSIQ)VL6L<|#v<U2Mc3m@*=SRfj0+AwZ7nTtrrdlq
z?|vJvv=c|;lqn*U`GrAC*?QXM{g;_;@=Wdat?yEOGM0->glnd4xvtNkW<5dUMUCBB
zDekwHHXzHgzdSzP588I_ImzowrMi}$nnK93x%TyOm0V(ylAUt4RU$uxB_%l(S<2qu
zTl@Kmoq+*pvx0zA!oen1x7+Wf7rmY8!O_^#(&F@eTM(%G(P;RUz4+;=ss3vv8^5U?
z4Cy^JRXcpjy<DEoj*fu3`L@+%6O&g4FYo*G>E9yPZqOLbQrq%EApw1TeHIoL2ad+A
z+1DREJlxK98dR8p+pP0CIyj!XrJkE(>DuS+&5`)QR%NYgx0vShpC@n4&%3*ek-=}i
zUGH4$axv>^i?VKQSsAo>K`lpuM^%*-sFNNR!DgC$&8M<G<L9TRZl`1S*U8@Ap09sx
z+m`3A&&{<4ouPEGh+9m@psLSXDEEBfv3=H2x6VJkySseqt<&k6^^=lcNr84(Pkw#!
zzHyx6?~JZ*GF*y2@BZJ~nhiQ4bZ%<LDJ4+2=|*mH`Kf$wZ*}?RX&0xRK7IP~PRWTX
zoS?n(48H!67cX8saq^_$4nD<}xXp_}bHbZ+oH-if_Ess{+TP9JX?vErd)b{Gh05pV
zS}!;Me`cofj~9#k=iL&v&Rq+dL~1;8#AWZ#$jHbScXw|;DeekdWd>Sd%+a`h|G!&L
zd~4j?+D?}8%GpRbalE;`ogb9<7!-7Lc+T5?pR;p!%7qUP4>M-Wvh9(xy_GOCaIxFV
zJsJO0>g(%4BS8XA30GHzvZ^UQKR^HYs?gOw(`H(gYMq;ByW0FOXo1R?mzNncgjR&d
zRl27CJ26rD;hQ%+iY#nA5}@|a%sc1w_y0-Sw0-WPO<T8u24ECg9HOFTUD_O2w{z!C
z(Bklm?<YpRdGiKzxPu}KpS)e3V(n|#T7We*`#uRffsOz?x{H(5Bv!-P%F2nO@#13l
z$Df{_u1f8bv+a^JPGevIjUk1{*KUotT{CTqtdGc*yV?8e{!U@H0L{36dwV;5)1O9W
z_O6iVHB<LryB5G-)u8tC!an&7pMQKNpiQ(&$3MSXy&lvaaN_WpZFcrS`Mde`{{sIV
zv#_$Vn_7Om!)r~t=>d*F&w$-!xr&~XRzz)Gb@TL-r%yTa^Yax?Z_2o`V&c6$eX`bC
z%AiA!!fSp$o&M-RBeRmJX=u*fU7?@@5DwkHAMZ2ICNlr-{F+alo7T*pJsZ?bI<hWy
zw@XyitQ(ut`)3%Z_kpHkK%MS;n{IE<4`*BW>ElPx!G$84nwp@E29Fl^+X;zkhl%|A
z_ivHcRIM~#mxv1sqT2c8uSrg={`KW0SkZ-r&Png?>;z4*g2q->g=$ZJ4QiuKy?9C0
zQ)uGEiAis7Z3PV^$XJzltclp@^yT$+e+z5t**%)t+TM~6qj&EW)ef6+hrJloO_N+3
zJ4xl@Qg88%g^%4PPMV}-mUm}G&lDLcsjMQEFFidxfxF9cRrgJgtMb%|*%1I5cervT
zL{MH{-|qjPV$jGAsB;=u|F?8+X$yGxt9AeW{h*EksBd`L-(Iw{vs1~?Fc35`5V9sB
zurv8^8!y}U%)h_B9y)c(%VUy?jEu~Y4-XF?UFJL6#mlP;)TsvbP$aC&)~tx!yljGs
zXU@${uIWFQn|=W0&({9_{-T`f@9%WC<=$2UjXA6eUG3uG(J>)4^7#!Z>5hPDPnp=2
zbo@Z0u7^&a_U<&bvWm*Nzb|&lk|iCc+1EP6<0>3aY|@F^lCdyn{R-xXXXjdn-}Jk>
zD)i_a%i<#oo!gJ(@Bgc&=08sdG!o?Ky*>B#w=>^v%CPdf>_|Aslv?NG)8paiCszOO
z=W@`27hlh%CM7K@C@2uvqyt)73p#dpW8Ph>iF4-Y@X6chnC0G5QS+UpB4wJTqT(rZ
zzvgrAy>DA?8<o`7PAapVq|!Ohwz_M_4hxWav%EVRyUO3|sd!%6Stl8lo|d*usCaYQ
z*(2xXS|7c+IelvF8T*P%$K#;Y8mGQ4bZX^FdUa(bcuWN}w$iP?Phj4>d8a<zulv3B
zVCjtA|CjAEc2Q*%7SP%Z8bOI!)6Op+w<7ib`~UyWzj(1DGBOghQp{;u>eK#{lkJ!N
za{$E?c%=1O0FzRSz+$&vqg}D~_V$IpzGOap{aRbaQ>%<gsU<BWH~IGD)W{x5<6~<|
zmn~V+@qAwOxfgcX`P;a<GLLi!T6qSRm6bLAPUt&5UBA1bfno9Qo1kP?{M>IcXNSP7
ziJRi9UM^*_m)@k)eXyDRweI}?%N|aTuM<3X?i}0n?c27QSqAMAa5{4IXs6dyt(i8@
zHtB$-vp{Ec&Q0a6Zq9!5S*-;$P!l3l<$PP-BqN}4zGAVvlYZVB_kKCidGqF}CI5Zc
zE<fde{QkPTPrmugG)!*Wux*>!E?stZ_Kh_^i<(c{ovqdLa5TRP>O`*yT|Mn$_NB>_
zCQahnBbKpW`Sdj1%hCG`I6b1GW_|eZ!Jw+AxvA;M)z#tLG0$&oOzy2){yCnT%PP!?
zV`It7Ak~G2-)iSr7B9Q_lzVc6#P4$gQ(2zw<Gl9w_jkk0ODZKVFR{wW$xTSTnQ{B|
z<>mg1i=Uq>C@gGjZ)XQ}@c3f36g+ggvOZoOG$Zl-`|<k!zwf8-lukLYDfRS}dFz#w
zmASX(q@*kf(*6JB`N4bl<Ul9>zPr2o<1y*{1!-qz6_k{;%$+Nnb!9~(Xa;fj`+df@
z=A_@*Q3&d}y7fx6y7fvOyn0pC%*<>>&{D2LhYlI!-ZEJ{|KQEd>B&dCL^JO0GL4Ci
z1zm<v_4QTap%zXLKR?ixXT}35{qf75*}go?Z+~Q(ZuEtX$?l+|{m-4_v$L}UEiNy6
zcc*ivar&&S=hS>>Jpff;uU4<WurAiRqN?i9v14p{dU_dmb{MwtO1J&^QE_EO;NcXT
zTaGHKo=zblB5&TjIdiMOsiLw{(bQBFbfmMCX%>sSyZervJ0JdhKL7Zh%FiE8>+e5s
z?AWpcf7b*p<@)yKX7i367GGXoX5X51wQ2HXVbEO0QZG?Z7gWNw%0x&==)>Rd_oZXn
zfBg4l+g<XqN!h&*w4VjkJfAyP7Bn}rx4Im3xG^Yk@AQw|U3PS5@pA!b>F%<(w-#ny
zUA1BB*4D*~m9ws`>Fns>sQGwQ9JJFguIi=g-lyE3Ee%7r)&2ctQTa*bSdS!geSJM>
zqNwQUsf^p(bi>xgbh2`b&Dgqk`PZ3dq1y@{9|K20zg(`#wd(KhG@o`Gee1clqww*B
zS+lwno!b_ay}bpBz0I4AUtM3{AGp}<;g^?}t@eHpO|$O`-nQYiwWa052M3!aEQ?fL
zU0=_yuCCskoRiVo*7m61{@;N=KR*k~$@OJjU3Ku>IX*r<KG5tAXkt3^@-mP?YVJ$T
z-4hZNE?v6x;o0o`L)Wj1Z`-!*!-o$JpP!xm`1AREWlhbKYYa2&{(cFr`TzHO#?@7#
zHa0e(ZAPAxN~+YaXuXKo|M8f#MeVOGMSJGVoeN4Lx3*>{zqqi_!`Jud48!EEt@ocD
zb4u}?B%oDN6Wat@Pg+-3SMhG=^MK81zMxeXpsr6z$ritzU%r&Eu(BRJdzM$(y$^K0
zOWN64t<UFG^R@HIHf{a-zsls_x7+!kW6wd&6VMt=(2}>M-qVwwotf$N+fPYr4=cAA
zsL?P%h11j1^Qhd^T}v`AFXI&w5&|tHzPr0T+DgU<G_V7@ETX2SW=h<uj*b-tr~X<f
z+`D&gN7YxYRUs>zo;*ouX=#zrG0(kal9HMV>ZxB{70S%e($WH2fe^8$V&f&>jIuH_
z&?z4-E-ndQUtMKraBOB%G&dIq9aVH;p>y}wD?KZ0em`uNXUN#KMJM|t`_!77H*en9
zQK+oq>C`Qzt0T)N7`QU@-Ry+S+51FN{{H&vRk|^3ciGzq@87S#y!rh4{r|dpdU$#y
z44u+{{{OxI|Ka`r|8?s|Z?oC^p!3wkz18293=IV*sc71U3)=cFt13Em`t;%1`TJIy
z?AG=Be%{69Y3$DRjE2Xbo}OO#_g87Ne~h!UGw4jvr~g;3T*+Wk`{4QYEt$b5HuYS&
z67uEU-QPFad_vqnhdc1g+a2i?R{!vB_j@LWRbgwRmhE1iGCTJ)sP)GuYt<6Bw`yVK
z=VzdjV$-Hg1#fN`f|7=Zr)Q&z=ZgyqnRk`EOnO<++2N(DrjT}_@A-uN_6557`uZs;
zDWJs(pe-OpPq{#MfvgNtom*sPW(F!iZ#=gal)Sd9G&^QbMc~i<DxN>y*Z=>0CC*O2
z^VHAmO0n`^Uow4WnRw>!`qp}Hnr?K`(^FH$I(j-*yh&h6YFo2LhryuY@?u$4Q*CW-
zGc&Ujr%pAkUAy++SI|O_*Gh|;PHy7yob=$)qb9drsl-P|I$zw`xq0c~s%1Bdd!PEu
zFkmcwea*F|#s+lsy0m#7$f0s_a-bvis=mHzoo8DeW#yx^D5#&aLsCXYhT*^ijvcoq
z&fc0TBO?Rae%bu`<hgTUrzDF$Kl9DGzfX4GzI|$^1*eL?zP9%A>}iuGB{h0n0p%y{
z{nMZR)j4TZ_ND_o#&-U*lS^b|B<Rd`tI}6Xu0Lu$bC~4_&#h(Qx0XfxjNMtp`tIGk
z4I4KGo_hA|8R)<`EBRA%=E&UHRmy$u-aWVNn`?i6OZr~z2b%gnfA7=N)4qMzosStZ
z8iMW!&<bDIqwL=I;w<QLqNb*%DgW77SsghVL1HGNmNh>NK7IOBa?0-H_3Ppd&zNNe
z1U`HzTOb44iTvi~W?fmgm4}kUOl<O6kE2kZf4|?qIRE~>Q?=5fa>4?}pe=JRFD>=(
z_YdFo=<e2RaZne*_)+QYZMhHMz3Z#|{OqNQ!slman<q~e-c|NCt3>)%S~w#kBL^Q}
zom+K~+0I?NKFtsn>W{qmVTY(33dKA7w79IW0K@&7&%E#M>}>w>rG(+YGT+%B&fEWI
zV7OQLTz1Ef9iSU<BDUwr9y)Yr%ESxF$;pBe5-Tpt@7cT87IX&j_Ip*_wpCw1$4qS~
ze;>CdVk6V#<^JwRyF_(2c7wvrMU_cd0Ce1{SNQx%Dv5{NcyDY-WM+8KYksexyPF%-
z!)R$?0nN9A7B1!F=7NgU8#f}J{^ei$@NoO#YuCg;dnrL9uk!NpXJ#6+=kNc!?bUBL
zkl`Vd3>X<fC+TlK#md6+g2yW3)B?w5gNzFc9<{iF6+AmJjj!QFW=B`orV@^&d?@~h
zgvP{MDhCtGy?dri5i!ZVC35oQ$r{O?j*gBMERX(1eMGEL#JVsBw0efn;+au3Lm=pu
z2!PJi+gY5xW7n=lPOV%ouB;U9le5j*@aE;^<rhKXJ9Zp6e3<#-#fv*CKQB8a2-*%I
zI92D`G`<B}JUl%eSBI?)SnS3NI!kTm&YhqkOVAB0FD@uvSsCoEIeXr`zOc1XpxSqS
z-7ifeV`I>O-5iTTCUEU*%5M!?kTunRkFeS0cKJFEcXxLcH8se5hh6Qj1NZOC3#<7Y
zxV1HV>ZZ%fe4WF>#4cUB6tFhRl)=Hng99{<f3#aX`NM;QVmH~9K)^d$SjQN2YUSJ8
z+ruaEGpvZ(ss(CA9XWdR;lXD1Q(wNmzOHOz6XR*eupoAK8E6s7EYs|e-r1n!x<Vz9
zDQQ_dzdUGyJb%~X#mb<SL&EBQ44~POp4ZpcFOJ-tR+Z}P>^yPStY2r6RY6v2t(?&?
zV`bjmT^>F@Edow6r%j&Rykv<A!>sRi3=d9C)n;JO*Vo@$3TkjFD>GYKT57rYfHUbQ
z14hHk9fHaQ-`+%mR?&gZa|g{OnBS{l4qqR~%8-(p`tnb9diwJ1eAU&}bB#c{T%Pg@
z3mEfCoAH1`=G0W}iwhi?S=iVf-Pu_TTJXbg;O0$9hAC-L{PK2JqTS2Nw)shdT)F5|
z+uj7xLswP?f4o=y{^668lR<|eG_&y<6h3lcXxP8szTv;=-b-djPrW}iRlA_PyuG)#
z_vG6nM~)o0dNnjXDkMZ?(V|83j^zn}me5Ip8l6+M!(aLAuKvERb&;mE%EbRm*|fG?
zxDXJtyNp*{FD77FWmVN8-`QrM`F_8fRbO8nJbs+ry8NBVj&+Da2UK1!D+Hygz=t2}
z*8aG`vRVQ(Yz$h5s;1_aUX_xf^61f{DdLwI6Rxj|wW$84Q}yM=!m=6Quux^2oiH==
z{=Qn!B2%lfHyr%@{4zEb0-(|3)B5{+%I{UWFMrd*DZC-?u2oBGYvPj=6HlBu<C2r3
z!^F&daesaN={F3ZBpAqffaQqK)m5ROdr^{-lPAuc$!TwI4_fvGIxQykZr74mS673!
zc!5?>f=)|~j*f0=Z&x-m+jii)Jt*;XoH9~VNLv-TS#4Y1-K?^o4Gj%WQ`MD}l$5mi
zfADdDY!O`9#yQFuQGwv0_~+;I`JnTTYiephOOYEI7<}ehfmR1tJp~OXpPOra_~=nk
zPrakB@7USd=DH%LptKTrGBNihgIBlrbUj5C6&BEh^6$6XL2aLAcK)PGOFTV1JrB0=
zN;5FLeEAa8g}QU+&WD%F=PMZ-3xgJOcM7W)yttqk5&d}rICuR#x;xQWH+Gi@=y=eB
z&Fq2e<7^o~2SQAoJ-a*g^t6ZX?(PO1VYnsp@`R~VTeoa630WP+%OD^m)C4-8;^U*E
zpao>A<;|e9eqzxLmeW2nXU=43&<b7k!e@QdR;`ps&}}6Q34ecm<>2QBmA#-7VgLO7
zyD{yo)K?}oa878P(J(_(M~BCW161I-_sbn!>OFl!)z_>u^X=u8-TPXao0;|E_rZ(W
z)9r0-N~We)nI?ifAvjegrzv%r-(0K2S65cfu&cE)%e}?IFhPY=Sl#c)6wP1;hVXSU
zj0_c(l`nPQKYiNT)5G)R$&&-ej=AkU&<S$5mw=i=>x0*?yFv5GcXyY!gO1+Gx!II?
zc^Rk|E@7C|a!GpH+_`h_ZTj}@8$-jIH9BevprR%u&ydmZ@~Y6)pat`wDD~n!zIy$>
zt~+;PK+ER?0|N^R3K$q{YJLRd|9tZ%haun;sGt;_>h9xk#w7oq4GTN_Yt!|wuC0{@
zoz#5gO32c3o!sCxf7Zq9WGXK&-<)Nil#~QIN#9skON%QoF!1ONW^j=8Ow|qt^-HWS
zzq_|Ldtt^YJ~f53U1e{j?%cimb=mVLPaHTJkN3+TzI|JIs&@FXiOTL@PRUx8a5y?T
zrsZaTdvlY6k58}c4A>JTZE_6Dmn>P3d3l*w*NU73{fM+r-@m(OWod!-i_Lwh9k!-n
z>QqrF^E@7gj-DQum>8K$moHB>+Fkbc(9O;17Z<zphbBW}GBbiXX&LB1!rR+&L3`Ie
zUW?8@c=#~$Z1enM-`?IfOgSL{T6D?)Iv-?B^me|)#6-||f|0SYptN-NfddX--rv8!
z$=VU*u9U<zjE2Fh!`6PdoxdM+Z4zk1Wl~a7!Mi(_pdz^V`MJ<jq3h#hIXO8&)9avH
z2yWbn0G(xfyifMc-!4$pp0d5xAmX$+?W};fczD9jh6V=EP<bmzs-we8{8|GzJ|<?(
z7<mjYS0<z4XFf}oFORn30VOIWtz|(I8$i>Q=WV~s+}f7g?b<D-Xl#7>k|L<=5eyWZ
z(#7)RP%HNg!(=uiBO`;13ksmv+*|ea&@|oX3A1Nszx)6W*%LlXl^9ost(5{D>%U~_
z(uX%TCWCIN1f2!d(ZNwsS^4no_WN!@L6epqRsmVPh-pd}i^_h`;_G8QlAu1SkdV*?
z(1Pi?*5dkmJ}~|H`}gBX_4yBeeSJNRZ!*{t9*t5?4u+KUbkMmpKi+IUzoGJTT0~^z
zLhtE%88<d4f_h|8TeCpDESQ#g0a-!<iY%Z5ZtDL20`21kUDo&X^z@H6)8{|DwA8z!
zyZi93udl&desXV#EOzhTR&oNA7+t=wWP7tG^v#$du_kV>)b(|-pi_|V)qM5^Z3}m7
zW~)lwyJwF<#fJnJclT(8phZDSrlLQ-hI_j^x_l|wn#pXEne<YlV+F%fCB~ose!mAT
zXi>DXl6rf4d%KFK+Ue~%H$mrt1!-C>3VL?Ka6xIdg1PvHjT=i$TwMI!{rD16S!FC3
zO!9JGW^|lr37XgdYESw3`K3+&{`G6?`t|QOb9b!Rva5)dgP;A?6~=FGnYljhRZ`Nr
z`0sD{=K@t58H*B*6;W3&2?|aWT>40MLOZ|wqZ=EO-JZ+a*U4<pzaM6(v?%DsLFNU~
z<_h}!H}=dt$u2lm_uRQz+0l9Ho<)6s(704tNlDqul}SigxOt&-yVP9!oErvvzTd0X
za&rke_2emI{vO6TR!e^}$F7|PQd{+FRp{lI$A0rz7A-ctZl|PG=;g|^srq|f$?I!p
zU;OFl-~e5OBOw^5X`INkA(44Qk!t(}l|`F$EhHi~GDYl{n_+SB(nW!Zj7yamcNRWo
z(+XXcQnIhTot<4?#pqePe8WuR#`E^qO}rJ9v|h(mvUc>c+Wi$tSZ?B?>bJ6j^X}=>
z+$&eEbbAik23+*?6v&lce|~8j<VbvYHv7Pez-fJL9UiuZh6iqLK5$`S!}|5hy5p3z
z1if5&Pk@T>R&Md6%gcPLK7(#0bL|#;xb=G6W``6P)f+R757hr#{B!Z|tKklecf*AR
zr)o}OsrkG0nUsX&%AIOLft>+bSq`8xY>pg1{&?&4IOl`}1u3%}4hD<DM=YS}L{M!%
z&*tW#K%uD$=J`yqyP59YJ21;s=6!R=DRpV-v!)+f#Z9gsS)y{_@Zn2=f>W!PE@C@!
z^(trv3pATIVe;hW(%08OC$XJ4dGa9WG@K<%R6^E7T)dPhG?j;6J}b-a-CgEKj~MFA
zJ671Nju5=D$+g(G=B?>j(@7mVt3s|cFf>e_EUe}~@60SdrQjBZHlB+-hrYgE-!OkZ
zDC#!Z*xNsNKEH8WuIc+0owze;D_z7ww6YvJPWW72#&~*KW>szFze;a+XP1zoJs+B$
z-P%#2X;8as>0(8#>t4RR9%W@_pbhg&mMnSj?(Xgr)ky`4o|85dJapoAbq`@$6USTg
zL(w3WOF%UBWkyGb*Sr-P5BlvJ`0W^8U16;HvZ2Jq<!R7L4+p)N9Svr=w;ufY`FTae
zMx}T6_Hvh(m*3f0%)WEy&J`gmh33qit1GKE@u`dr$CuaaU*4|%dDzRvrLFeILN(6L
zKDMkY3{hLarunVRX!`o^_j}M*Tn`_gBR4mvE4CbXb#?Ve(DIXKXJ!@@6dd^W_BP10
z)H&A6B}=qbSRytwyq(qA;WJU??AA?@#%H&F{!{7g?h^8DN*7DY_jh+C43k)#oShX<
z&zjIG0NNY&`~Cj@vbVPqMc5-3e|qsQR>4N5ppellhvCf|9a%xaz?13e%Xg+(S!b_X
zSpGiBN@-Ei(hwB|emR?l_3QN+7C5zXO;F)1{~W6WT1NNg{{H(;7X>3vJU%|_+v(~0
z*}G<}2|0O5P*8BH+*IxCb#tfdG1^vVty}4`q$_A*!<7|*%;4JzBch`hM{UjW@bL+G
zHs7k0>)W?))42LQYMmJJ_w&BF$NT4taKQR`pjg(beRcJ6jC1*0rhAp{#ks1B($|IT
zX}Py8H#+Ce4#CN4zDx{X-rtuuPCEm>Twwb2_SDnU6s@do9SRg$`Qo&G<Im5gZ)e=z
zwiQ%HEe+pYc3@BCEW;_UuCcoHZ7FeaNsU|S;c%>1y8UoF|Ko>;+d)Cp!YN$v^HVCQ
z>IWU7c6YZq=ssfacF!e!XJ<E--)BA6!^p<F32fs12L}(_+&n8gdu`OL?@k>rW@?)H
zZctKIPW=8qe#6%7+mAP~au@vmmJ7O?yjxt~t(8l3UZo5(8&8AIX}0<`Yu147=30t%
z$l{rC|HM-}wwU<Ze_JnpYOXeHEx=y3GcyuFY4TK`zdh@&Qs%0!Onq`752i+Su4k<L
z@PM(Vrp6%ej>Vq;|9*@0K0RxG-=&>TwqzNnqCw6LgDYzyh2!Gl7#hBODFN-^%*)FI
z-C8$s=FHB>&1nyhbP8LPybxHk*85N5@<}f~rJfDmS@qRq@!Zo(AN^z$41B3@`oO2B
z4zI7x%FYcEdhj;)<%*6|ON8px-@JIik)54wVQCp@C1R3)Pv-1w^XoIq9vol<-O5+{
z>r3D=-`Qrok&%%e-rmkzv#v5QC@Ly~M##nV<9ZG@v#*vipUU|CJ+q+Ttbeb~bD3mh
zbwMfe=Hn+zGM`CFty;<J>2bkFdC~G!AvR9EJv~QumA+Q+N_a0~|L=!#N~Bvz$P}Yo
ztZjJj7SE-Tar4qzK5tHM++kriQCX>K+Md0p*Kb{3-e`XBLQHe#i<Md*O%)g}tPa=j
zkuYS+y|v}w1V!fuuU~Vsv$O9lI(YEls$*Y&d`w;wyIV|u-w&m%D=QcoKv%)9i{Iau
zc6Qdwsr<aWN3X4o4(*+Is-f_)$yT}9W{i!Epb~t=8*VP9u(g+Zrr6cl7!)sCs;s0H
z463EX#FqWpmw(@G?@uQVk4Y_e?!<^LX4;l}yDfTq-b<7Hm-cn6tCd%=N0}^Gb<}g|
zjqmrFH>WWcKVyj99c3k@wCIzvjm(cf#R_U{D}voG2a5^XiY`@RjNFnjajCOG#?da(
z4O_P!eZ7AFI-k?0PBAeoJ-+VGmS)wo)Ko*pg-vg7vGzzT{8{;27F=5X>ycTR`Om7x
z;=%LflEy6^U28*B6#oADy19g->fQbQ{l2r!7J?>Zi;IJoWzTN21@#+dzIl@~*T}`~
z?>}n+NzN_#>m^G9CV|V7pUR#G!s8k1|FV|6=aaEn@v?J5M;E9_QN7s4D?MTA)TNjI
zaQ%qCy!uaaQwDT^pY>WD(%{XMbKUPk+V1XHxboNfxY)~VGw+%S3r6;BHQkWL`{5Ax
ztYsTKJsp;MP3!9l*;Mq@Yfa3~p!8dJcbDsm_}&cEdY8k-Cliq%2`Sq}7hU}E@whx_
z4_!wG$K03q?%i7vv(pH)0Brl2h0g5)A|fFVcBP-6xAqtJ*AOS0niY~MAMV$i`fix#
z;nLZ0f+;4pQ8&8b*;&);FMd=Ua0t%Us^+m}@bmO^yu8d;S0wOefR@mNySvMss}^y3
z<>l$Ei`m(9=T1z>x|qnTg-&_*_H@SXE?ek7-|kek>`!bn1>K-K{lwI#8aXjEvoW4L
zd0_p1rhWUS^>uV~Y^kYj?3HfZU~rcErT6qrl{$ffQ@dE6{Q3ErZTa&jPafRP-w#@o
zv7z|6-_=KvTeC!qii^L_iqG_&VZiwM+FIv`2nn}7na(q3e2yGDret6s@b~xka9uyG
z)Mb9Gb$<jS_DW@3Jap+I=y2_*Eez9i7^}WA{rTI-Zx1Rcmj<nr=(qqnLv+vAYtf*|
z#(Pz-b+2yQoph9|qN-}q+==%LR)w$cYiVJ*vMyHo@$r85oE)7)hYy2pkoi{U+O{@=
z(PtJ*%_q(ai`$njR#Z|-uKISx#^Bcr<p-VR%pE6emM&sjqR`mb7_lv9X4dWBZ@1s)
z(h(LAIN->B(q{i(HXaEE7k6f1wFoOIB_*vBM>=P{t7PY4m{;xhxyed!=Gu_!4H<9m
z>=f?n>zl^cyyC;Zf6^6SLw&dYE5E<d)=)4oQ^9jqW0JG8>GcoA&vj%416KxVWjQSK
znVD4ZYp!+qhVu7uOO`B2xU-{B%iT4^r~F;UuG^)htk>43_iMShEbW{tyCJdp8Dr4(
zb+c~0=@QkI6%AYoitK*-e>WbKn46nlSry9t`T2R!28}DcT`Me#iW;uRv#*O|6%uCT
z=l6DZadByV`jGL+(acE`<ZMl1zkZ7KU&J)Ei$z6W|NJFI0b5%qhWx#(Z*DTbxwG=6
zXKP1?*YaJZrdw~wS~KPE=RGs~__Hl8tQx}a8w93mZBEa=_x;B^Yv1}$E-oRvwq`Eg
zxvc)Tu0eVqsDbkl)QT;B?&o80(fY!XPT`H!-}6M3m9!T1N;iJL$9$~!@|Sr|E-tBu
z8yFAV*m&T@#RJQHr}ee1aM2d!JOMfxyZ+zL<1hYv`BGBz>7+WSRm!5N>G1yEf%<>U
zKC{z6mCuXJ*><uOZ%q}nxIBD8m5*xJN)LxVdHX)l28X%wLPAZ+$NN@F2nH%HUCt~h
z=+Mk|;L_5D7_EI(E-oPwn|O4h*{-Z#zw;TWXQ#ACXl2M9R@<5%0-&vgmj4envx98q
z`2TP9Pv?}}wJWDRpJD3ak~)3y;%8C+|KHgCe#e?q|M(xwFWwv&$im9P95^v3kTI}W
z!J<eZZ_dgm8fWL;O71%qXlHrZNcX#c@na|5VD4h21uJ)&1~P6}($IMP=id&wyq&B)
zmGi4>?yVPJFL!>&&wI~zT3r7*FMDOkqkHq`tSNDEv2xcJngHsb-QJeF^WEfB$;8A&
zP#<xNjA)=;N(w{mFNW}S47<x1`}({<5v+Rlq^HmM>Ze^Rn2n64^mTYFf8okxlyrnc
z#<Gaz?(XvAH#etGoHwuU%a@Wlmc?u!k&Vg64U&#<EIq7pD)SIaAZP9!j*3c6SzbZG
z%<k#p8xosWFx&kNd705s(iOLc`O^0M_?X>gyr3z&hzN<0|F7-m&z#BWImw{%Q_7e3
z_v0UGbjEEi(KN`H+mIo6VclF%exA5(Yrg)nr?QqoYkr!fbX*b?e95xc!-3(!=kxaN
z_xII;t|$R5x@HL2niUGVC^U3+Sg3{7qU(RYNq_iwJn7)Uhli*3wRLn%F-l@7cqwF%
zBk|xJ-<F(}B`z*qmuIuE2ylW<8Joh_{Nn7n`Ruz&g;zg1H-9~-fwW``D|bWob(gyZ
zZzMi^yY1!fvP5IA1T(0AcC=gk`Of-}kB)*?ONc2eojSa?+95#T!7Hs_|7N`k?elkW
zQ9XDiq=$7)Ok~OFUG?!VJ32&`igTV=;yHOk{r`V9-{oy81SYHdUrTav+3DfSYE{ZK
z&tANlFE?ijsJJX??d@G5J<nde)HQ3>B|$+=H+`WApew+0Z<(C`>pR=bH6&z;n6gq;
zuMYFVg-K6u&$VvcelN&MNlEFF>AH1``Z_up?S431k#%wN&=;DpWQod*eG9d_y_Py9
zB`pH!J1ZXFp%?!Ddws+F`QTt)_vth9wKa^_*0b;X6ZA5pLu8>ir^nY<S2yP0-)Hmv
z|EJUX1?A<}lU!VGu8C&%nax)7cdJB#hl|S>wY$5S<7-ugCX~LO(%0tk`il~yq(y;3
z+53CF8<USeIyqVW)m#60HV0=ICVx1szaO+dhs({w?e&Mmi#>8ur&^bFbZGql_gOyS
z07GEAjOC&d6PKu^;=x})SDG&OpMR{0l^fKv0qtC85RjMeKXb+h)HakbPU|@oIO*cf
zGG88*n(wg*Cxsj)fy3@gt4b$p_Wz&mS5%MpGsj%->1l~mQJAEX`1I6N&<Q=;^6svR
z>)4Qdyzj-Wt=hB9^ZP*Kc-O9lJ@h!4VN<8GEJ!C_?#3=r1M_fjc$z<ZwumqDiigj`
zx;GL5>*jzuj23%695%7AuqY@fIQ)Gtt{;~GI(+}iO5wVnPsNuMd0Ag-+hSsP?eV|b
zkH=QkRDyZ~O0}u!>~HSzn&fD_nzpx^(b#zDVkND>Dnm9E>&R&v>;Kn*_R$|a$Y@*j
z<-o?|;}>?9=Pz|uPF6U5;KITK^Xr*zZ)3c=T3c37Q1IpMS63T17(BS3m~g|u@4Ppg
ze}0t1qQ#3rOA&K!Y+x*ZcL%h7<K|}dHh%d&(DL!5|J9e;EQ=eSo^A|X-H>*63MfbL
zwC=b6@VGxIXtI=PL*e5oeH|-~yeMHR`unRC)PHz=ZSCQy+Tjo0yy*c=3Z6gD51M+v
zzApCSia_P1hgFg{=FXn+GV7Acg8F)JrRZ#&z)<j%%OH^{VC^i4IesY(C1#A7S5^or
zDJv)D=lAE`-37V{4|LNZ!-HE}v$@>e-PEn5UR-uRbJs3+7C0F$oMjiQqFDQ@Lod?K
z&dc2;<V}egqh;YE7SLjz%FoXh*8cvMAHz9!h0EPttR*iPDk?kIg=_7VwsLiG`Q@~f
zDQrF4EOYTv{}h!6j~11LxE%FLpT7jO57ffa65R4DDLDe_&)={AFKfh}HqlH~y_@U6
z9hn;&nKx_#=O3ezu&_nv42^|L1By0X5)>5Gp6lxXTGIyVW!>7E4O)2y8n)e%agpiV
zxpOnDO0`rx9~~&xij<UeSRZ%b>uZM!iw6%Dl(@LKr0B@Vy522*D|KP__7#&mcv{Y>
zDMW&N0y=o%&(F`Gm1R}m-gMgk`@y_PC;7&PL<yr5jzfnJ?_}c;-Cy{)!M3`=cXmV6
zR&adv_`bchi0{^3X$kwdml+*hz30>wZvOo7BjMhj${nSz#Xu{lK<mz>&GSIV0KIwp
zb}C;#&&fwe4>YkJSmf$3+sw<|*~R79Dqrb@hfWFqY96%9Pw8u05%D6FX_D*GpoOWY
zr$ubd5`A}XZ}->N*B85Xi+Q!XwmrHY-#AgZF>vvbrT`Zg7q4GGk^}X7B@|z2DA_Kb
z&goG$ZQ8UK7Z<aG7UP#_q;Q;@%k1QIM0)woVsN?ZF~i*dim#&Lf&2B^vb=#4U(_-=
zh2*@u$J*4yU~Zo9x9Y$VmzSXNMiyb=K=YkD8Q<RSw(9oqH)K>~YM3IjAld!FQEtQ6
z9y5)=wbYW8K9UL7^#a37U#Yy(Q?fn6(xPE3%)!fgZjPY&w^LK6f{IU#a!_OB4(Fe*
z(^u>^GM?JkvEsmt27%gtJ7+vAxNCLbdN`;V&~a}~U2VeKsD#IE54!b1A#q?v!-T@<
z&1^FanP-@Czqk_#R`zMRw|HQ=?|i*iMoP9xt4bJLx-3gLBuqFYEGBT<2ntU8H`9P|
zM@{6F)X-H7zO%Jt1q1hOU~TESe0BArql+_xH@;Y)q6x~kfd@S%Ma<dYDIB;wcJHbZ
z7nh%hSXz3{FLqDjI?N^7pc~ClTnsKMv}B5l6JCcMIOlgIw_jdcR`BBfBqpb0PoH`(
z$PRz-(An_ry0vRTLluIey=!$hRGWWz(757p+`bSirA6i-mCn-|7dkgCc5lqR-H>$^
zTr94r2wBaRbDvMfj)zBD?Ujv^>`|7Mp5x~C8MMO~PERjhASWm&Id7S-tL*j~OM~id
zmo5rC?cAI&vGB$w)*t8V8#IF(_SJ%8bVbUZ|NCZ0zP{}5y4qv9o|n7J6y|7$6CdW<
zXy%=;t!9d^TM4Q@T~uG6)^C)vJ;J?xg~rmwil-VmIV88uHg9~8uprJh;i;G3c5fG#
zHvgqUrEznu9NPH~tc?cMu^?sZmisp@P&jZYi091aEg_&{Sn2JCO+uyfTU%FfPX?9q
z9=)6#lFPo7Tzsl!7Ur`(dgCH+cw|m*Zk{pk`!U`?^<~RJg(ZWULduF`eXQT!GMnYI
zWnBvaHNllqudLzBxyAD5m-dH~>R#^7EDf9-l8fH&V;0k8+V@{B<8Bl<=BIqUSG_1M
zW=Fu4UKg&%CVNsMzP({|>t#CD!)W&>0Ia0s@P&m*Rre+;H?9h8G|QdR*U=*p?Qo)C
zj;;2u9ozEHv(D(~=!i2<=ew{@RzQepj`i{qlawPf8YU#}D0V;dDR8UShtKC5Iy=Fx
ze<>y`obb0QFnn9S{wqHvRs%-Gzu*5@Uf8&J#@*!WdJ9T2!O7#2?^5sXT}HaTSLSMJ
zdbzu}b#Qtx9zAjdbc9q{S(!%YDv_+~>w4es`|bC52IxE}-`Qpw!OM8wym@nBeZ0L*
z#RrFl9lSAVY8UqJ&+xjpmAjxY5FEyLpFL7~b*A{ai|nkKD@$BbQY<+3GJuMe2M-b|
zs;iS<U0JCSyUPT0wc73N`K$XoyStq)F9RJi-yOL*4RnF*=JfN&Kuc^TB{_qG<31&s
zRvK+6)fNzAtEkonwF@WKefY?DZ7uVz62@gdjA`dWtdxwDYNXCBU$O+We@b4~s^q}4
zv$H?j<UKnx6ExTQ^73-fW)ud{0b8IW%0gF%C0<zIc#G@ihqY0xpjJr8^_7!Bb%)@~
z>3(w?^7k<w>u0yIamnVGD7Jw+%x-P;c0SPL>zV(16+xFwr9_@9jERVlXliP@a*~|^
zRE*2X$=%qJIr-Wn(~9~(j69M|JaRf>vMNeSi<*8v5l*<E5IEn>MMIWXl2c9LXUT&D
zjG)_2udR*#@a2ok%S%fWlareh54Sx$+AYq|@Z(3to!#a9`}Xa#Joo+c=ff+5mv52X
zmbj}X(nQicTTLuvk_sda{U@>L#POKqa+SQ(c@?op=opL2d{B>j%NCQ8H#Y=xZ*Oyr
zh>&38lj#7RN!QG8$H1VXqQby1XU?3WIq#o5X>sqDQ`FV<U0o<Wr=9=kl}0|k1JiUH
zX3m79By(fqz~xpJ9hz_DZG)_oj0$9!CxLF80)^>?h0ccc|7sXO>*zi_I?4^Y@Dg-X
zdC))esdHOfUHd-k>gqBwXliO2Ja#UAxTi)l%5I(wqu(5cQ&WS%3FUA?Hg`)qGp}^l
zuCn`f2kykYTyer-Bdf~%HEY%|FxXaqyK>{+?(+N-n|eSUss59%CQXga%35{oc>v?d
zO%k6A|5$9OwSMq!9jKCYX<M2V8hBjJTJ6=Hm}9$yMA^4BJP8KvIsEsh(!#<bBWHcY
zMkR&^A0HoowJtm|cH<^*4-bc*pPqic`pYowjKmb(=&<DI^>KSIfX?@9TmOIGKdFel
zQ9hD4c5DQv3&~~2dK*Qx8}`*UN|`ov3Qz6x2z-5g{qbL4Uz=`IRa4Uv>3x{MG|Au2
z&hEzkdizI@9tG^JD*gKH$BzmICBc;;D}}T|Ry3?wu|np#yPMmhyt})8{)+wl{CvCl
z^(_^tpVJa9sXXY`Z@9M?TsofE@bx8g%uc47Z;}CftuCyceMwMLZM*mZLqo$4A3p|a
zC>=ha7x7a>NQfzN+QyonMZItB|9%k$-RJ)PJwF4eNqXq;VL@^6<#x{wG%|xm>71OL
zwn%Muv?yeHaot}<E_k^XI42eU_*1+fT>rt}?~NxsmM&IgJ}fq&W>xfdJ-Z(dm_f(r
zWoBlA7FxaE|G#f-^!7)f6WA9oR%Upx`MllXX}ZxDKsOahnR0o0dd@IT=X-T!Wiul)
zo5pX|FK2tD9SSx?JgoUy1TLeO=uT4Em2~u2*RCH&I1gM61x2<2qh`ZW@9BrGUKQ02
zUw7o&+uILcz3Kw3**?-C_$oOjCPqL|uyJGZanMN+2Il7O0RaNf&dfY~b3NCim%$61
z*%yRp8Y;Z2-`oLO*fcF7s>^DJBeS9H`-#d8y}g$LCo(251u>|pse#rQE%TZA>DXh?
zSyONB?hf}3lw9zoWPv;XgKN5m!tSkH;D$lU9IX(6%b)lC*1ND-eM7N&!l~o#T2g(C
zig%SxA6OB1Ab)?S>%@C?(crd5&u#1X9afe1?GnyNfP(G-OH0Y=MXm=P9X-Hr&!8I(
z4q-vYMSgL6B!0ZJ4h$C-WxRdCqx{W1-Fs_#q}h4o)_|+HDR(C*9(a2Cz>AAX7Y~)a
zozmCwMI_q6!~D<J=`W(CWO&ZZS_)A&`~SaiAEC>O*$bZaT)H@6UJ{ejDQ^#f&Dj+d
z4B6M2)YQO!aM@{S$arn7Ft-RBYp2zm6%8{QCTyB#FTPf8lTO+-jZiPJ<6roCO=T*3
z%VcEaBKz;p&P#$5=Or>ZojQ3&M0-u#4u!mwbLSwY{e1XFXW4Vxdbt<Z{ja=q`FVt;
zWy<H9=?Avu9{BauVQ&?<Iq1R~xPIP>!-o$wcut<u*Y<@&SRm-wr>98@x9{vsGLQy!
zH|9S`NH`~B*m$N()XUwus_F8FN#A5`IBLF3{2^%zsVP@z9P1Ih{JHLjV!%=^0f7Z2
zE=wNYU~;-u`cmk^R&4<Z4xJbdk4fO7DKHS!3Nlb|xbktjo~EqeOA9rHCAR!>3~z5U
zPt{_aZN`|E266w<gJ*oMyzT2-w66Yx!^;kjc0)$Rw-q0mUfhwa_!V+yR^YZZ5EBcv
z!uf9OjlS|Vc2~!)`5NLr4lACVG%~0#DEOqZA-Vm9yqAlMOWSfU(S+Y+3PwT_MlLqY
z0((Ix5A2y~-1z^ueZtqQ1%b-oUd*CNU*7FLaBAv-S*8cV<27Y@Un-nya#hVf%$9IU
zB;l6H0(X9}I;GqfSGji-vCgoOToKlLo?%HNC&$z|$;TTQnH%=i9_0l!X?u=O(NyAm
zd0)PulvT<s#7gOs;F~5_)yMPe8~S7$e|>FCJ#BGUPf&2;#)&E)c>?zDt5Dlf==`$7
z!`*;!;iKs@I9OO2&CCvL%WY_F1^aZOkdDrg%?>XwJ-Vr282GZogB_%B>NK{J_k2f=
zcfTxq`xe{)?wFF2ufOd1-dbr6PR2`@r}lM-9Ajze>epdbc4ylEpD*VwS4EW;s7)&P
zlF8ryV@|<`1r=crzU*=9<>U~ZpTD1VnvS4-V<RMm>{{Z={h(d`$oWI9Uhd9Zk8~nJ
zY|7p;9qVIN^I>4)gEXlwxi+&gPS<BnKF09wokKgHm%9sB0yl@~-Ite>_Pswhw{iXc
zFe@q007dHNw9d$lRbPe93#63HXb8A?<al?a?!CI`HD&9PCMqc@Eiw;S*|0VH=#B||
zvPU+vNQ&C&20gT`R?Cx_#Pa42XGAopVJ9f~vN>q!k<H8uEbQIsTt-qp4l4?bj2i4}
zUF^?&C^&G^6P#H*=7+3OIW{3~5673c+$m{VvVxhM!U9@z{{L~mV`7%W@ajq+#8vL4
zue;Ja=UN}#%qq#MrVvtK|Ci;=Oy)E5`M2bQhl5;}oNsDssQlb;b8}<r=_!3}TN*BZ
z2s-%YK0lANTAf=@0N5NO7G-y)>3WRL?M$JoI!v8UBy41D`Skt}>w;+W1*zWfzJQ=4
zlc!L(FdG|F?C!2Y#gq**8Up^^*{?s#Lpz+!ERV(KW(8=``Oy<sL~d;1EGQI=-6L-k
zV5Ov`=;QF>{?cV#u56PhAMFV@GlS%nwiP*%AJ<Hr!&CB#WlzPz5|<FB84VX^pENQk
zP!N#SO<Pe=8VatujZ*UR4)n=7giNVmo0z&(c~R0amX>=tw@g42(pOhEvhjjDn;kul
zhua?AOulQC@T+9$;)O}a`u<GWKEIxA-w&o4mcksIkYF=Xv#nwXU(YmIo%!l&=Dk&#
zvVx+*Y6??!E4w#b2v|^GpYX^9l3kL&+)O|4^z@^dN3M!ie2)cLd_XT^%H^-GlOBX0
zZaZ*k>72aj9UUE06g?00+cVYGJ-YeiS1DK-ON)tZrP8u}+Tna0+{`*U;QmZU&&*cV
z4Y{*tC~9kX*sPuqm(1j}e#uhCyStgo-!ttlXD%y)gpA5O&qIeBt;>(>xU$7$sq!MF
z1SY2@9cKA@9v7F6Y5e^B;P#kO)lxU!gx6sYzTa;YRJO2PFPF^ZbZAfI0e|~WL;16_
z!L3)pKtD#tB%c$PmLB-?)63nt^+@Cf51ntub$e#K)iMDMbar%fd=it=N;@WH&bK0r
zmxC|NN=Zv$TZ6>z84?T1^A}`?CmiE}1jw&)<8-FjT^(B;lai+NwQ+TDb{Ouim40!7
zaYbbC8nabNg$hbaO12Av7_+Z2yx*fdFZI!*DSaJW4gPbEX<0`8IXG(;lXW@MwQCS>
zPN~0k?UCfMBQ7?VRn)a*1)mBF3nWjO$oS<YbIfkFWm}3%ApNLCO~pk*=Z(VGb)?Vk
z;aRG@s3w6asYlI6pni?CIn&l`s7D`GR(7V(`cUxTvj5b+woi?m9UCJzx%lk-{dT*<
z?QM{9A*AH(Ek<rJhT`X4npK|53#H<cH|5J4)a;1Zlm4Rs;!7<%Ww(x(mG=K63d-4|
zwgy=#onnr5=sEiHv%~jy2ddw9iV1+$CpjC}+g;eTwc?p&eca0o4gMTPL$>el7~kGz
zUbpT*8*kDxZ6h!JIEM7|Oo4%mA_N2_Cv}N3#>X4(xp>IwWrl~~vSp7Vmlq~0XmD86
z#fb1+(yS3OEm^tJVY#1!7ngt_;}p%sB`!-;Gcym|ukTJ#e*OH6<i@*4SXKluPEc98
zSVc)oZWGU){rYN-M#jRAC)L)*|GlzCbK7LW4I2fSFG-sSoOfX7XPP$6%iZ;+0i$8c
zCZ3iyrse+KPadz`KBce2%cIJw;NzmSO@+U<WPA)*dBn<h-@Zi_KYn#}-8|*>az$rH
zSGZSK#l=ab@-{^*KVGUU7zc0ZxUzQj%qbOBn}Qn-7L|%-xBU3cEMLR0Ci?jHJgb)a
zvbHzf`Yy&#o}G30`^(GS`KXF<aL>lm^K#}`vD(z@y<2OV)zP&zT>s<!U8RjdOQ-bB
z>R9n4GO}~}Jzl9jx4ylZS)n+&biYjjL&TnlJ&QMQV|#V=vX7UI&5>?tBZj)V1ONZd
zm*9W&P;p+jhq?iyVMz9MrpwD1<Lh}#UN0*#3GtXYGimRMHz(bE&a6Hfw>n&0H0}Ah
zNBh6N>YSFYq$K1uRp>CIlzH9Ow=aV~K2m$VZ&&s98FS?ryr#3&)R;`3v?}!Hr<nZG
z*K9RkML*n3Uv%lsW%rjI91aUvU14hhd>lUMDk(8BGBR3j^Pbd_e7x`A`SbpJ-#$9p
z4H{zuoq<zaT)ZX!z8q-7(cZm#%bIL!BqS^XzHEQkWK-oltx?+CWODC|3<bl$H|v93
zLt28Db^NcYW`24~Q_4tiV%(;Ug0{tf{>JB4|B2Z1ptDcx`~3|D8<(&!OwpZftXWu`
z9JI*%-)H%Yr4O3<U9PWD^PSRn;)Nlj=7Nih-49>C9-bV%HS6k?|JHhXe7>{ITGy`C
z1#MznvP1=R$msKPbDdLCRD8U5ZGO2mJL$mH-S4|x*(=Mhcy8jkvv2Lp92Zx{e!0%S
zQ?<Lf@(!_ljF*=`aL(`MjLr_8nQL_~ENcC6^3#Wht6NmGxFYt;{dgw5aeYnY*_I>M
z#S01(x2~S7;;G~J<GlT%(+562UKH6aa+qaF!z`1`CcW5QPj=Sh?;NX&-+6pIX;-YZ
zHE0m&&YmZA;-Vf-PUljTv!!C(-3?vRudE0^+1A{=>7$U~#1($g+r{i7-`%_F;}y0>
zz_z9#{4zuO|H7gmwX&+-ojWtGW!(tyIbZ!{#R)4<K_3S$MN3P`u&^)#L&K9B^>uW3
z9335BJz-{u+LFP@&(EK8Ys<<fLKC)_Ts;5rU+u)3)Ai$x_P77Ly0?1$F8|cffR7)O
z;#;{EIsN)@IDBgGQm<3Tyj@&=K7YWlC0o62(~;xZ`x1UVWDk^Oek3>FR`KJ<hnqyY
zG~?rVeq`PVurdDoiTin47>Do#CN5rH*PtLFA0M9!TQWhn0Z7}~*}?kX`tIVo3?Dv!
zK74O)wYQo^BwHIF>%Tw3H}=Q#Y*JcVvA5Dri)%-z_nXsM^WNY8A9^%+r_t=^ogF;B
zOT8QlHvC|8*;?~@TH0NM`h71}FmyzI+&oFJZ1dGkudcd!d#nySup#k4zx}49Fb3fX
zHAd#<?%VV3g7!h*xPANapP!#Wy(!Sa?2Fy|L1#C3cz7@{SUkOS?-jh$&YO00ZtnS+
z%sTOWYvOoKvcvYw_s#O3*TL9zQ+rDNKf&KQIVQ{ZJ!NoJQhKcFsdTd9wXVYI>)Uj?
zt(2>|f7bukPJ6->=W8<Q>)}o}uL=A2M<}EnU@$EIyQ9#{9n_?H61>>0_ri)m<*)mj
zo0>o;h*eZpf_6&Y*qqKE6coh3ASNbed2aik`&@H0H8qdUZfI%koViZTm+9A6?S1D>
zi#$DN1)I3JGKR0~s9oya-KCiQ>h>B<Da(8tLBXf4lNid&KMD&eZF_2V_1Binq_;v3
zu7<phwEy~wG4+(jcUc~<|NoNG)^G~DsNUPT`I4ZdsG5Q+cz*894Z+*na+`HdZ}@fp
z(k1=4Jtqotva+-oR`hF%1qM3Y-<Q-lH}i7W`{Ld+pP$d_4smtu3_Y3}sgo7DUhcxS
zx2s>~+uAh5wEY(l6cp|7Qk~TE>Z&WZ6+=qOlbD>c-(?$5?N;f$dV90FYU%4PR?(FL
z%lyUj#eaVhynEyS^`-N?<b)?Et%})c6t}OYGyVL$mAm6oOOKw?Q&ePpdV2c#@Yly@
z7$yg7%ZcpG-COn5Y5khr!U|T?+1Q!m;ts3|t@%68f91*p@%5SOHh=tn|B>#LR@Q*!
zd;$UtJ@Wc_Z}Ru^PWf`?q>GD7q>qow-Ot}{ANc*gJNM<w1FhV%JWnq3J<u(_=-2wl
zU}MdXpP2q#|JZcTWvS5J>Ie6kHEJPfB7nIT3c(sVDlwyK@nq@yj{V)y#mTIp}^
z?sljD&(<$3C~$CaU@*(Mv7m6y^5yEFjiLAJe(Rn;-__aK*x%2;Xz}8QZ{N=SuJ7x`
zG|No1S;%kBgE?y}D}D-DREK?Tt9>_T$Hdt)JEcp$Ntd0SHHW9UqvOlr)6*ZZMt!q-
z_a@R%bJ<GGfYsB|WW#rxr9VG^`R?w9`SWK?`~D&E;`Lv=pZ@M~t9-QkZcWvP2abJH
z&;0-OS|56+XL$bWTU)h3mtySLVevI&4?CaC0np0dlj`$DY$x>aT=c8{yKBbbStr>U
z3J=@X$9;CIdDgSTarTToy9CpH>!WvX%lN3Eq;$#s*|SC61$(OwJUEyXowdrtX%pMd
z`8J$uVl?Gs&zvpZS5f_3c3%av!n~!24mTd_J!*XI+uJwUv4?IjP4d@`+R`v}s_4Di
z@3A3kA_VLI|1}3Kl#Gaoc<|!lV$hCxh6mr?-Ub~7eRESP=pa`WRn^3Eb1W-fuiY*n
zDf#o5Uc}E+E8P3p_Wkxdll=Eywd%*v(m1y^iNzCbZ(7tw-KhxGx|lQfFI)8TK8e$g
zE-ooKK|v<F<=kC6-^I+FxoCFS-CeUvmn~l|XkY#RQ+LFL$|oL%`9*R!n1V`j7*8%g
zH`hA(?yk}q_Vsr2>}tEXM70(~Z_k@yRjPI8?%k`$ckbAsprpi<e7x`Ikxt>nyu7{z
z3lz@GG+zE<bG&XGPtEVOKh{rFk)Js;sV3r^bh=NEgau#6&CfNpjdHeeyvKe|pBAj7
zq_j9ORrT84>>D4hZhMk)_GEb7Z{Iz)S&lT6<>#MWvZSh1Su*R^GTsdtf*(E}PdYYp
zq4U%}P&dV;eUIVde^12~G&m&eWFj^=%w+JMq4RuBpMjbiPbFhR)>RYnyiec6Ugrx4
z3I<+OQF}J0?%yrbjq80R&$<e4UN+zS#@2Oiy-fT5+nv9smcyuNwXNYrbMf;dOCILl
zVkmyDCF{F!LaKsU*k?C!S>0n-nUe(V<Ks8(@eJFPn7uj!v<#eWbx!1h%HofcpMFdZ
zG*9o<yt&DBO)XD{tI+026W7RxhckP6Jv%FWGu)#?B0qZbvP*)JGHMFD-o1;x`03$0
zzBTddr^_0rN_KQ#_Ay(usG*(zXq~ut;bSTDcUNyuy?*Ssu%O_?X*X{%=|*?W*Ws7j
z<K?kvQA6qLBVF$p64IB?obclp)46k1Gp5X#(UHDR&G*O*fpZp7Z)P;S*fa$+6J<E{
z^19fn&3`hTnx382)be@~*zn``+IV>h!$+Z;9==$Sv&Y57Ws3c`Z;Qf%zRmM2f0uFX
z_vvYj#_3(Pe0-LZh4!xaetL?aaq`cfjB~9R+4+myW+xqCaS2aNWBU8MduIy2ER$Av
znAM_~yZh&FmJW@Xb0a-pKhLC@egCePA*+_O=R0+DbZGeZ^(|VckR8et$@b{6>3sfu
z|KqmIV^|cbQtkBUF>}a@1u=grSH(!)*eU$sJG+x}<NSKFNf&>p_H%Zun`ISRvhB{U
z&_AWG8r=Jfw&mQ}qxtRTtuBN6_5ZK(K3%(gMvj1>pro{?kZ0TG%`Z+cb}V`F%=c`1
z$IjyT-oGWKS7m}9J!YPwzy3~n!0LGUh}~i@ZiV`=o7L{RBq+(Erm#!HCT2@)-Dlnl
zJ2xvSs;G5IuGn;6V_m%X@0^??y^7hdqT}Nic!{ckF5{kJ4YFnR>WXRXE<r_qf321i
z2@E{2Ci1}L<w^T@?@sgX+`e5xXMJhxu8u~%`E^>8CPZx&;W)IxfbpX1<>k!G>|KF3
zqbAImQ~YZ0>FG!J1uZ=yUvs=~eICQrRU26+b#!!exlKA!vM+RX*YsHpFB<E<u6kyC
za(X<|`FWjx<>Y30dhV@?iu#}Zr{cjS)r1QQ8|9=$Ij=KbRD4yr^!4@j?+*_*?5y1Q
zzwi0^qmP_#?hcCGp>WSBBC5;&$(#BcrdcnZ9*G9c@G2M?9oby}ZOM}>nhK_&Du(&{
z*ZzDHw7f4OVB?LwHJn>An`gYp_+e9fs??#YL04hdi`n^Isy{t^MU!uA{(o?uz5c%m
zXU`s$U$Ni*!%6i={NLu5GXziL01cBUs;NDad^)$hq3-XIZC^@0`b_>SnSPjUqVkLx
z9ZRh@^{apUB%~kJedJC|&)K!%??dekwM6XlUM+IcdH?s*(unm2zvUm@-290D%9W5m
z4^6YaWc|85%hY>GBWQ2ViX*AEw<{_c<!cnSJ^iM$CGG6$^o6&!nnrtNUDZ0WdBR%V
z4FwNZU;G-P;p6ZnATRQNeEriizcw%1zczNa#HKn>k4-i$Pw&{;s%mEEwvOD$y^fo9
z&6{t*xF%xZO=*?tph@fRDd_2`{)^?26&8@4e!s3i^78#@5&PXiT5XdvHLnSM`CPsA
z?a$(mkIY{0)KX%K*yPgFzI7ARt7}gyZcIAH;==y+uJ*DyeO*Gq5BJ%9cK-e@YTnPr
zj*gBK4iVAac`tjV_TKvTt7pOfe_T3ID&JDia?F}Kp}_IWqQ^QBRw~(IIto(n-rd_}
z_#L#bZ=&F-YTqpq40dx^S99E8TGY%Z$HK%krLPHOUd2>h?Rm#H*-y5uQc1i0>qqj_
zM@B3$etzs5mx8w4d3>Lu&u@~mqD16ZKPO}#X(q2QxYw$52{gdmT=x50F6hSeIoXXJ
z9X(T3JXZuPWQy5Q$NG{(O(A6h=;HU8#_6E*lfBxVTwIn2@7c4*AoG$+OG}G}ESIFE
znnFqeNYOmo>P5M?w}D2yV`5_y4~e#Pbe!-=O-WHOGZWJeU*~duo-Jd8Cx>PiCx_&m
zna1fL)i<_gi-&0O6)Pzzt%~1ox2xo3(}D#GHQ#QguLxeww<>h?G4YZm9CmXjI_|9f
zZFX*sW%Gp#0c!FtE-p7$glM(2wSg{`23>8Tq@*-q;>3j|5h*P{THM&4{`>ozfgwCR
zyj59IN$HYpUS1x9i;<A5Y_GI=o`w{^q`I0yO2DSH-AR*;1O)^ABEK$Px>WVj<;#N7
z(yI$xQhFdez9bcki^1oQy)-z%VF<dMNy*4*l1d>Dhh`Hehv3u>&>h=LgA^?+B3=e`
zoNxdw5Yw8<<=n<|@y5OF`SB}4w2mA<{`~jmn3$Lc4<82JQOr7_9jBChoTcT&ffXUE
znEtMb+`Q~a(nJ-`Nh(usJP$s3Y3U{wgOyiPBRLODQuY4#^~SZe(auYQ5|8)E9{u)P
z)l=)knf9ZF`Jh8Vy_Pz?zP9%MkLn6}l}krT9Xfc<h{xAVOuqf+=jV@KUIzC_nQ}cn
zHTCk1@a=hbg*q39tPI*w@Q}%Cx?Zf(e1BE{@KZuB_Z@6zfBbxY{jp7{r$1bDmw#Fr
zlar$Zx^*x;Jsos6@r}Cwb1aJuQcsCkSXqhPP8VZ0H#d*jQJ{GIKG(#J$xKcu3CsNE
z&Pu+0YN|HaQaM|x{JmeNT}#q@`HnYaQ^`sl#`S4CmG3@&|DK<ppMQ>3Dc5rU`R9x!
zW4Gl<rbOy}+d4}|M&`kT2LU<rr^o*fQ}gxlsgPGW#kxB|P>|uk_3NN}hdq3Kk3Km$
zIdE@PX+%Us!jlsd85-RC<r1&0iOjgS$8yfxxrvF1jPCC4C9kjX8X6i}+Uc)<XY}}3
zZ?o4@Cm$c4Yipy~<KyE&yC0ldxn5jfKfg*>{8B!@oDJx1kq0kda%yX9PtlK;%e}P)
zw7ak`IXF1jF#Vhi=wgy>+qQw`jqlvOdvR-axQtzm1Ow>gro+dMu}xA@w6Td<{QCNy
z%FkQ=e$^0fX6J9yIsN11^7%z~=g*wk8M->macNNEg9DA=&K>AbKF}#sbLPxRNK9lD
z7Z?BX_O|$Rz1UOV-`8E1n9#~Cz9{YNtPNYXv~+iKgAT-6vSdk&y}F^H;QgA<yiCl@
z4<9{Z;^yYIu&`KA{QO);PtO&LvY(%x8kn26t9ahnU%%gN=AT!q*E8&?<neHFVrt`)
z<uW%nkBEvobnqbK-Cd=JUtV5bEVH}jXVHoftv`Q0pSLJ@zyMlCW|VqrLCnq~(B<<_
zPf!0`ySAf*G1{Snqv6H!IWuQ=E?A%tv#&-HbeQ3rH*c<VTg|g7<pSN73DWB~*J@$T
z%}qa^&#&K5{M@gly*>H=zrPummU!-~>sJ9EpwoSKcllz-eNaaxD!Xqedm9Bh4X~rD
zD`|eFZQ0LHsVr=4N{Wh%pz~O`xDH)j?!U44xgThPJNx=N*YopiuO4r2X;HATk-2y8
z9tSUPYild(qeqWG`;|ev_kCxXI5xBKegN&%TfKf?Q+qpm)%SO6?f%~_zaRQ2`%?C=
z@9+CPC#xkw56RWh)iwS0{^n-(!otFe_j|uz`92l2s;c<e8Ak^PhCO@t2CfRx1YM9l
z+dSW`qQU~SF{}Ju<@5OZ^0>!3pzT|KJ{;yRC@VV#y8PPM*cf!!&culmJ-oe}r%s)E
z?Bnxua}Qrx8T|15`~C+H5~gT}^KIO?@y5<#b<oLRphJ@k3<MY+Jbv7~GI)8?tu2`a
z<>l`C>;7Jue|b&hX7gj!e}9#l<lT|@{_ZYlr|iY${_>!mETEmT@9ys2So^ySw7Jf5
z-{Wn$w>Kmo=L3y29d6@23|hV*C)c-Pg~lBF`gyT?J?c*^F<!KIaVkSmNy(N=3t#BQ
z9lo=3=gyN_Q@#3nd(VEBh>wqtoTReTv6=1D<>mgxZ|eX2*!btk$;mRe*T?VIyM6oi
z>AJc)soRq$Pfqom^n_2=DkDeUJa3MU=cJtceEs;nRiVl8$NOYcr+TTrzq@<8LVW)g
z4g1uS$B(<485>VNeE6{GHqZg>-@bgAa(bGswz`_ysZBbkIXO8usdyGSI5@1E|MkU1
z<*)zGt@fGt?#0E$?Q4QoKDoL&{P_)W85y0`AzGF?IzD^f-rHOKX><DdBAM!6Uov-w
ztPCnDDA;i9+v@Q3rzWYKY-Z=b1}<?Umj<0IEG&GL+s-FDN!4?bt*!0Vr@qrtElo{B
zzkU8ZS=DpW>hSgJK3aBmc1oq{ojyC;Jk{OZ{pRV@-pSQfRl9UNC*4^Uy84cu`@D~A
zY;32rrh3h@t=@L*+u?Tp(`ssJMMt|tpH7(~^7UKl>1ih?shoWF?AfP<&g~}WB<<_=
zfSmsG^YiCYhhJ}cbyCte?L_hOb5qUp?#x*K`Gu%mwuo%BLkGiypT!RzxxW1$=kI^u
zR@CqP=KPaXKt-8G@G_si+RD9=tAmzuH8nSH%(|*&Q~60HKL3G==b;u(;ef4Kp}lXL
z*?13~n`<p7DcNb3drRb<x5k^FpP%no;Sl-h%a@j|d{dS#cJEhGQ)64WaAC&%eS44D
zMMp>b%rs(szyH4-=oq|xwZB(=pE-N>^Iw(Qwrx|DT0e>B<2K`z69PH6ws3;Z6U-0<
z9kmYXw0DVWXO)~@6R^-}-TdI?ezHG)|9*U+k$FPu&H&v-pq<|;o}dapCnx91ZCxE5
z@NMp)tHUhMove!~o2nh|HeD}vL&`~^J3EV)do<roPEP*#`~CjIckjw3AMd+*<NA)m
z<d&9}gvZBvZ|tkx?dGkhuxplS?yZpI+Y=O>4bsoek%>LhQ6ecUpyXh~`r}8&|L<<@
z%=hly`oA|HbO^J}_rL#szmM3GAqYD5zWVz+(3+CH{*zRW96kDUH+O@--u2x@Pfsx{
zxUT&<Zcl~b_4i?yKtl{`nNwfh*qF@FQ2qTK=pM5(8~=ZMdwW8vq_|!T1A~Q?m6C!2
z!~VbDq-$zxL8p#_PCKfrtJ|<)!-cFjx6(Sc&pmL!0kpZ<y-()gQt#=Y<7^m|Bt887
z-7_*Ye0+RN&fVNutUgUIwrj==37eW90xw^_d{sM1<>AG}?i)65)-KRl?l+fdLUs9y
zw6n9GT5|k5Z~y<up;qp~M@Kk8D{%DV_g(2w*V9|)!RhgBM^U$;fq{UuS<Zo#!OM$O
z6xyopt2{o;(!wFIo@MdksXzBx9l3Q&YN^-MQ>FKBZOsOqQ8Gm{m~Cy;R;Q_2p#iHx
zG(9G@#Ml27J>D;WyjxuV)8E}i-XT7|M|C~thd=xJ<%`P}rqq+c?x!vtZs%w0`1h~+
z+nbYLB|Ilhm@{Y1zwhf}cY`7}@$s=<P-Le>wr$;N3R?Mjb91_Mj&{ewdu43AQZ1dr
z>Pa^@rOvReHfsoPUct^QwWRRPot?#?`=)Q)ya`&MSx{QqI(4e3iszwgYoiO_-Py@>
zH+++~j}J>jO!{#nW8>3XV{-59k=$GLwQ1{C)2OXktmWn9Q?x?4w5D=_Mr0lyZs(Rg
z|GLo9((>sp`|RJ7)%{Jjc~45&@ZHzQ&c~sH;i9}F`#m}Bx4w5})coi1NJ~r4v8#=;
zZ7?|vI)3iYkB^i1{PiQEqhGstw*;M<rpxU)X+zRct}pNJ+s8kTh>lJ^)*}hpzYe-^
zx1)mtbh8}j@;)agCeTGYhXS<^2jp8XT)1$CRjC$euxS1Me^P>if}olYbivn--Md%!
zd;9wz-;#Ox!J9Wd!OQ&)-n$p~xR_7YDrAWT$U=r4)!+4EVq#3bEnT`4bm^bvqzRhA
z%MP46#bsz{xFT%rtYiCTo9C}ePu1?*@$Syf$*=#lw6OF(?%)wz{!n#M{nUvQ8)uv6
zFUq^S%W_ixmoFtUwpAeuPk3B6$+;o0Nk{zG!;M8xPrbOkeSOu&UA4czt-NoMUV8W8
z?6|wrOwT<&*vx)oTdwpp-Doz?Nku-c)!*O6s;H_i^qj0_IjMii5|z^MrH4*c2~N0u
z<-DMv;E6M5&TRbq>+9<kp{v9Co+uj|8()8-yqY`I@l?v*s;>+k>*9*^UUz^h;7KZy
zU%PpXYJ40zUc}FwF#~j@Q2qbE+jn*IDu_M&b-w;zGpOk;F7r~PzP=u`9`Eg~tv5C#
zI<GrE$EwupF7MNGbFFvs-SYSGIkGwZd_rE{IzJm7UERdg)YjSN`P1%*FG!lWRqe!`
zoyDNq4|JAg@Nz$?<0)%@7k+x;X;c3%X0NTTF7M%Xe*I4~+AdrOII*ec<>lq8PfIg^
zwijJm;<<C)<5#b`KzB2|ySrQJ_Hv(@lU_$jPf|H}?b<en)Js>diZU`X?kIaJC8ig%
zVk77pg&SKkh2Ok=dvRm(@tS(IiTe`8b(Bs`H%va3@aM<J51&3cP1lQE5VR7sSm?%$
zh%MRI`KrIa3$=2&BnWCYoSvq8ad&zCnYq^DUteE$pJ|i|+K>ur0Z-S91)Yr%xGg6V
z)MWr|iP&59HDGm^u7$Pr<CBxsLH!~jVPVjjtflhh3>g;}v4Zv_<lWu1u<Y$E15;C1
zkl_m)nOXN6?p;^<`dUG8F=$3d#q&@DBeTiBdKQ=02Rj6n4a(oefOdr)Jb19;<0Do+
zIh&5<{__vrzrWw(?uJ=rxm^pL+mlXCQe6?bS#4M8YqsFvU{FIwR6ES&XqRa59OJYz
z5};j~phF3-uZvC0%IdQJ|3^4{U5sNppR9nK+`Pwg#B`$)USC`L;>t>4(9Lnd!NH(C
zsJ+tW=Zv>4^`5>c^YStY>oT2h@9uKT%gY}*b}Z@iG+hH@<HM^$R~M}boD`kSIdNYS
zH^;<@C(fHp%eP8<b7Nz~rWDRMZ{Kb#dg=w*6EJVyypEopBe(PS&wc(}wB*;BLvx&O
zea-*>KV)T)>)wJ?H#fF_@BjbfzhD2~R!Ld;;lqcGqS|3AcJHXDs(SSKynTCbFE5{r
zMZ?d}&y$~@oBQF*7tkTGptEIr&&Ta5(cD$~x((zn<FqpeuB;3$d~#x<(C>9270qnC
zLH}*{+x@UPy(V(=hqLDQ4_vw=1lsiqIwZoaR|<S4R$<}Et!ti^_MgbT{r!G@KP$J`
zf}ES1B6gK%f=&lmus{KH;(1zHn&ml3vmA+O`tf}3?(S=%wsL{4Sh#cdE~wD2sH}YY
zOLpJych+Ct+!Q|EFCRYr<eR(uin_N)ZOsCm@&j7;-z}~WI#c_`_I!EJF=oo{eG77L
zZ~OVH?thtYN6Azb<E7UmAuIJR&U|U|RdBtcpkUxVNJ|7%!074ead2`5TIuXPl-|22
znHys~0CH}P%Mw!`2ha+j4v%a|KUi?$H3M+(S4nAYB9oH~2t*xbY2op@=Hw$ODaqvy
z8X2hG^vYc?#=|R#$%$>LQ!5uk!;Kpe&<?nH-W>*p8#iufNOMS<g8JT|9`Vzsty8tb
zm5hypUlw$9bd<QIrKy1~#W6QG2X)36qMbY>mwB-}gxpc{oz*hWwpvL;gJa>sg$2dM
z&7GZ{ml6d91vQnFm6^@+?i>JhKtWq{etdZN>aCcV7?->Iu@2Bs!-NR}OO`KBesrW0
z?0Fd_C8fC!o;_=Oc({FW;^DRlsgf^WzAPv!3$xNOa`JIFq44$HUFp2MJWw8FIB@Hh
zl$L0<jF8~Oj}uim&CJYR<%R3V?K!Zw`g=lpI;c0C+N`|z2+I<Vv~zPhHG`Ke2wNLv
zQT(ju*gjBneR**abdB+uvu8nvke``t&j0P(H!j!g8Yz>EhAmr6=Gaz?ojiGRLh8)g
z*v^g<8mj8*%9fUr3<+6TT<-4fENpCt&Ykmnc;bYFA*1G!9Xl*){(il#5xc8o-S1gu
zxm?lF(Pw6va)VYu+E$yLn`_;E;)KVHS)Vp#H+Ob&{`vd&;p4~6EiEidmM(Rimb&z?
zl9EzTkJnPCnMSD(Uc5N*Trh~oJ5I6o(@AyE-M*JDU;g;vp>uDoar!wIFE1{?`F3mH
zMMv(hs|9UWUmdpg&;rNiDI7N!tc~9<x3}u+snYnDmzIKh<+|=me%~uOtC7DakM($V
zm<60#lKVy^o}-;l)+r)FLQE%O!s~w<HW;w5vS#wU$-20R)yBpKbklThYi7u`_3`UV
zII7yOuPLaw%hiWWdEB1SAQF(4rUu$Zd$5^(aoX8gmfwyaKmPdM-s%ruzn<NA{``6V
z<$iN7xj_zjv+9i9!1bbCzV5);+2(~G9vqDMt`1v&{HD052y}JqglW^*jEy%7^@0zK
z@etq0x`Y9AcHepX|1!bL{X+dyzweqdMTFr*X`;Nhw|8$cI7{^KMmumNT$-uYQp3O?
za@o_xF=UD7hRvIU*DK7D*4EZ$Na}lged)4gSu&q@tvLcZ566}F>I)S$H6=AQx0eQA
zxYkEL{IE+@28HsuRxA~-AucC8p=MXrS1r)259q%6j_z(|2FSs<OO`HGG&2(er9aeV
z#Bb_KA0OjoaJav(*87>agoV7k{8Hx&)_c-g55Ep8K&8y{?l?R?);pEYS>Mx0-0j5l
zjt&kMHZ~@Qb=jUClUf27yCvS+Q(1sCN!NokNq7DJ#|H<Q|NMHrzM#1H^yaDHYjCGb
zo;0aR+C1;U-tYGwGhJ>y!lE)?&aS3I*}d<{4ZF-sDk|#gpu4<5-P^OXObv5x7_5oh
z%r;#wHpu^I^@9V9pp@l3UGM0FgUwrI?La39P4r7(3UXm&WUTn{LGj!i%f<IVLzETO
z)r)g(Zjvy~5}7n<l0o`88PIm<j-H+;JQ1McC}oQUcbMS>70<`__4M>QIyxM-W?eNX
zeB^RwrZKytqa&!$oD8~JR>>$U2VRyT6@j`HKR>D3{rwVbVQqc*)-5SsX|p5${{H@4
znwONM)W##()FrCDsO04(4j!H(D}$GNStLCb{WfcD++HcIsanVTJ3Bew-QB&s^~BV#
zZ*CfcuQ<QGP4~^4Hw%JRZb&;jt7d+F&(pOU*Uv<pjt&VC0qt#z+M2bn<mDyMnTq{#
zwgpd5O|6-m-}975<5sEhwM}LJ{#1evdd|AK3bZFeD`W*jeSLjPd;9a(HOac!t~>LN
zMO`a0E`H{-B18*ReY~zn*3EA{Sn9B4LH+-K9NgT8K@-Ajqf9|n&D&dB56jp8X)M2A
zYkvN7N~GKFvbUf!Bta(*mz9-8?5QvWjpWRjArZ1RDzy8)({8TnvkZo6>A$|bEGRC%
zd?RjM%ub=62y@Vp5|@|zx36BU{p#A<Zctr#|G89lrS>UP=AO-a{(j&8|LDcV?icq~
zmxB&+0$pOVB|~t}-o2n&ZqMg))|K<J*MR-V+_QPj(PPJyEG;FO*?1h5dQJWC=Vx)w
z-Cd#w4<7vS@wmKU#)Sno=S`n&3Y+qpO(H$AIyNRoX5YVG*;Djlt>TYM7guVF%=Eg_
zDXgC2Iq6B|=VvRoFI~3m)YVm?lcmk`{v0bd&%2}HJxQg#wRPq8yZh_ow{6`z_4k*T
z!AXYNYc;OtTwN8q$!qDP#fukvTlviW^69Df&v$osZ_+tk6cRE;$8*x2s;{qV=4P*f
zM%(R;$;Y2;*<#Y08@?`PqOPv))9?4||MxzZ%C?;RbWyB%vTpXylV{FMd3|lI_Wr-$
zZs*&an{hfS?^E^Nh-)o!wpAx|qqj|&ZJw`peO>JD`2O@tZHcmjT8YuyavE2zTzTx4
zyqK7nghhb@=pMhgP+Rlp?RlW%-L1;st+|($pWi=Wg20;_8<{gRGgYN_2Xz&l-Q?}(
z=a!b1=GnS(@|iPd0yZWcb%Huq$2wUnpI6$fW#!5==Blk(R|7Vu`Nr%hcnEebb645j
zO~%VMO_TPV6tJ@>)u!^(liuT!#gV6d>()n?9gDgKI$nFj1_Pal4GQ<4Z`dQsBRoN<
z?AMpfii(OS8HYZ9{v7=N{OOg!%OAXbJ2!dv{Q2_p?CbkZPuCaE-(a;!<2sMP*#&23
zn}eENYu2v4xG~v1W^dKjBaci)jz!(;>U#Qi>hALQ|6Z>;pp$O-^!YBvW3ksj27r#8
z`t|iSXnq+q_%Ka3df!KxX<HA)njhG4ItsP;|7q9%bZvqK+maSl6_o=gPih`7Q&(3%
zv8m_Bk00jqUcY}o|MgDLmfQFR_I$O0Q?k=?b9*mb2w-7hIdJS4+r^6)LFdj&ndkM)
zm?81?)R!rfH%{BWD50`C_3f>#pmV-@B#rM?$ef<J>`$C|vVJz`%>QY6v0MxRD?>or
z9K>{^z8ou#xW-j}w`cE~zaI|s@2LE&#wTYJvEXyWJ+3xR4^D4S&qmOGl(*BSO?&mX
z)iQkAQ}cPN&vzA_-Btd+@7>+qtN+W&$n;!W8x1-kc7|oKnwWlE%pXQ+j%3|@*Mpzb
zV)j%B9&YD%e|)U>;`;dg=5nWtzIi#Wo4s-RR?t;m)!*NB^!FcM<~w`B#EFgK`f+=p
zft9S6-7jZ*==uEmzLmkt7uEdy#A-GD>Dq)FtgBlhBO{;wIzDgKtX22oXB4Y?Rz2SN
zYmLTr9rIpkbGK$T-cPn)H*Pe#_9^U6&)PkA_g0I)y}iAC?OI*Xq0*j{?p4^Fo*8iA
z^)%^Zt!z0tISw8k7tl8L;^%%Us;Y_>7C(BQZ&)MxZr9Ue5vOh~@tmBHp5FfOaJ%@A
zm}%Ra5}ASy&6zW2!h{JElB=(6^O~wP^~BC!YgG1?KRq?|<BN-nm5!&K=-IkwDoe6v
zzW;nX&<({OzPt?n1KPh`Qu60|al|>U^1GjsbW-1zg2u8>PFBCaqmAoyl;6*td6kA=
zHkEyS6$-kb7IbRhqeqVfq@}y1&GQsYO+_;^Gdp^Fk3K%$|Ji2#)3pgR8m4U6Rr>nT
zr>Cbq{QR!nsQdGy&|^}|uF}{0KHixzV}{J_etCPo?Ck7{iVBhHJ-ZfYT-VXRzCPZ)
zl}ofs+od^W&C|6i63&&?A0M$cH8mA{eHHrV-d<_btScw3S00Ny$5nmzQxZ?Au6g9$
zNFib2&o;+yfXag6yPuL?r0VMF@omycesW?W=<IsXP1xn-<)9Oer|ZQw?cQy@%zys8
z=RbAwSr4-;X(@eujrZltm!Jai>TS^4pU~A|3lk5wy*e0E{_YNF^a(WaRrv1i?s7pf
zv1Ru_7u4n4+{AkD;6Vdp<IDHNFD_aXvzeV=?#Yimw$<MhtgNJRZ*SB6`QR(d>F9$S
z_w1cCb7tp+2?8MdqqpS*o)7qLHO<%;Qop>rzkhymbXApA*!nnLZEfwZk9u{oXLS|b
z?b*8K>Z(xg+uL#vgGQihf0uprKRe45w2kKN?d{2+36F?-UCk<|H?1iVXj|vrFLz^K
zt+iXf+}gOS)>>Lz?EG>rkB{{-GBC689GGRAUGVrAFQ`Iia7n$o#8VhFHy~l2Cj;sO
zR(wnX9W$@)KhNdstE*H0M5(UYaQ)2T!^~e_U2R^!UjNiIUC@=spiM*P&YkP%=`ne+
z-8^#N|9{msm7iD&3kyM=Nzlw<)z?>y44@XIQR=A;d-g8d0IG~{zqq(~Bjm2YY@V>Q
zmzMj>GwfNvcb&%dvk|AGrt8ImQlLrpHJ)?l&VfcnudR)4_nxi?YAAu)A`H{EUKf43
zOENq1w6}u;1E}d^oPKV__KkZ)nN`E=P0aM;_DoRsnV}HAD#UZ!rcINiO)?bzf4iM8
zb(}}Sz`@MWaAI<DvgtPa%1<tGHWeFU7CN!+EO~jUC?R2ijIi+HvmZnQZ)BULo)Wov
z`*t*_0@KyeNijD!ul(`h;itXT-?#h~6?y#ZncC`*RWbFO)`^~;z3fn&ak6gy+1cjj
zKUb{`(fV4blMaot+&eo0^X~52`sT?KmD#grpDrsaD|&rx?b8n*3VI($Tr+|B<nyz$
zsrvf*y}3I1uE!y3oFTKtN+5vJ1q3&Wx4_OQ&P2>DliHW;==c}v;=&T5CCVpjwWLJj
z=%a4^eGOhmwI+0UaPIjnB$#>g)z#GtgH{R%3opL6t^9o)Xf}<@O%*f|6S23dblq<i
z&r3xnPM@Ehefacg=$iSOo|BU9w|AVl0h+lDI`Eo*fAzOF2e)Ki7U_C66?6#3tcD-I
ze=Do1vYwq~db!HSN2H^JhZ{6GE~{|5sIw+7D=TYb?X;acD>v4P2?|Q;gS^DW&C7fA
z(a~;32Cb=}VM#SLwO4;P=_DU)VqFosT5QdlH4k3A=;*im#o<5C=Hd?-b8~YRcJ}1^
z`)Wa(K-a8U^Wky7Jre`yENn5|r~?NNGESQ|4K(~RO($|uVck@%&=245*FWyJ{}*sv
zP(p%3;k}{4Z1eoK_3QQ5tX&(p(1{gvfJ8@s|8<MB0}YIzow1J|Kfbs!Sbd6SFq^1$
z*pVL}A9K&GSQ(<#BV#FKUG~P|?X9henVFqYTeCoylAbMm^YC!{)$5C^zP@^uJ!$sV
z?CY!c_s^Xx3%Xwdv@!O3{GJNIYuBzx7$i9K-3JZ%bPB7Bz4|R6C@CDglvzQ^s7${0
zi{PWjk3r`?tcl*Pcij2s1V!f!X=kNsJ{)8RWs845pUdk+ZQ%f&fiY3p{mRKM70(?N
zAC;=Uzw70<|0Ces#^cy4Wy-+dJx#}PZPZrKeLXW~%}TnyE>^-UM<ONi*oB48j0~W)
zfuNSMQR=CLPft!xNR{N{<NJUA-riQ1t6K{h{=d&OIj{04V~WMAC1QMulCDoX*L>vd
zG&;RY(=aE&N+}{~QJSxBNx+&D3l4N^=wIOx%=NPBxag|B#K72Xia_w1GaH&sU+?{q
zak=cx=d<h8_HX~s&z5Gt`Pt3vx4YZ7UW+TTchhfOwPJ-sTAG@eUJOU~(WdG8@r$->
z5djswhuirdK7OqH|9sx|!wCin7Z<r&)cvsl9ZM#x?&q-l^1%!fzZW}A&CM4tTgIj~
zne*Pgdm7r>&QVcPyLRt>`1&<BL&cvThCFP{CQ`gz-Q9*pMoo(rDP@>Qwb!I=mSkmR
zy|E#YdDEs%7w+Hh-?Yi-$noQ+A9wcj9BE``-;jP@4%8)7Rb@SV`0$AnCl1W9ELJ;e
zWTmvo(_cVvVsL0k2**?})pz&z>mLVQdLE)R)%o_bXKA3D<JPPRIkGlv^#m2qm|Z2F
zeVJRM76z?6vNij<%%_?k9}X%`n=?nJ#A>dRn5d|#kg%}qt$TYagI9!T6~4ZvtJor-
ztgNi0px|(3u620LpC5sF;qmeMDxN~FuC7Ho%F50wLbMXiW{X-rKRa7pOef+&!TO6r
zLP9NZ>%)(zc!I9tUbK00aNpK#+qgU@9eH_q`O_P(uC8uvZ*Q;o_^1^$ySFl6_0_KD
zcduSmy^^(cLxfJ(Z=1?bE<1MY=m5>Z8ScKTmz11*6m-4%fddVod*2I6OGWwRY$VEc
zTwGLGrY8sr`X+^jhJrQ)Pt%QVYiwlvDJ(0?tFNzrXGfv)MLQK07Ee!4zRG2ObG7R0
ze}B_uV`CE#7iXV+wr%0Uh4SyN@2&oR;rey<$tRcGkOrM>=HlkIXy3kj5A7GHTO}nY
zCvLu3_v2o?Zu~x(sI_7HuD$;H;p4}N#kby;MXU`2^=^&R&P=%N>f&-B!9b%ZG(6my
zqp|e$HO+aO-4-8Q6S-OBeB>mRkdTlEcXyZnc)fmqKwMnko;@}$?(Y5-Ixa5tLV5?4
zlx7{Vva<T{?CfmA={%m3TK4U;bJbB%NjYPqtE;P_qjTi$?s7vLn>8m4Ei5?l^WV$A
z3J(t#SMhYJsMv8hf8xZ68JU@ZNABFY^WgdO<sCnN{d)BE^>zD_`}wS_td>XioxOSg
ze*gFP_nnttw%pR)(c$3i++6eb&(Go;Z@(Gd_L}r$1Eal(iAZZ}>xq*m8{^iy*VX-N
zIqvB4WqBf_lZ#2`&9`MNEG!Qm9qmreyYcP&_wbnAyLVe}*?2R@!^`W?o12>hgMvUS
zc|cc0?As@|bm`I!x8Lqx^FO|J_sW0a&t+a)_8!mM?z}W;;l_=HXPCM6?%5-g6uv&L
zRmJng%a;?=y*oNGYIGbrI@B89&n*=c6Kh+xOid?lPsGNaSMFD?UVV6Zx&MmQtAll1
zv$C>&9set#YhfYb#Ia@jc4b9H$B)s~)z(eT&FWXK&p&zpzWlp)?_RukQSjsK%gf8V
zUT@f7z@fO{YS!^NtdeW@?2+;H^);A%7IgGtkDIby{JuYzfBCt5$v<YR;Nmj5Vfk|P
zhW~HQ|G4(+>+6p{KR-9TzwXP^)8agQe0{TjzxFw}-B`A{xp~Ek6%w1}7stQ1o;G8K
zM23mfl<CuxQ&L>|%uP%dtX<2isj2z+*^jFsTB1B`%vsslqU$f^W@k5F%?jP(>+9R<
zwe;7!%rEQX_D<SgURHK0|KO>!XOC`7J`Osg64YzE&ePKIBEHR7!Nq0&!567cpP!#^
z7htk3d(&_*;lLEl;0g2Q_3hYUA<^p=p(AEf^@Rg8HQU|IJ<qnfZPhBRDO0B^nwoyy
zfA96{`k<ho1QRJk6O)$W=jRj^6d3mI-OKk^LtnrBW=@;m@@BWij!8+2oabN7GVPPG
zY&vts=g85cpsBwXZ{NB`MoKz4J8!K1{%-!4CI8HSe}C_uoU9D$7fn9dGI63HXl^4g
zFfb!Gcj|4>)n5+|HlH|s+BrRac}-mB*)-?eT-_cw<xQJ6GcwGaIrGDZ4-?Mk-P~2G
zy=wL9joH`rKz#@UgAdz#rgpse&&n;JwCH}qm!RcWu7td~vr~BL)Ty5$6<)r4$<Sb&
zeojG6jV=BByrcVSe=i7Iz3^(*#>~rVI+2@PCKj(z4SD$R;fi(Z-0JG=ii(N^BqTT{
zpJcJOw+G$OcyZf;1q^rZ-nFRuqS4mYCLk}bFRrk-IRD-r$$9hUZHUl0arSKM^5yE+
z*2QvnA61&q_-XIQK5_Z=cfTLAvRZfjb>h~jiF4=n9y;W7W{#!s(W6ISynGobqQJ1B
z{C%8@i_3x~OI+p^a&d7v6%`qUt&L)3u&Df`l6QY!@2pu;N0<Hj{QSK0`s>Qt+T5k3
zrT;U0TwLn4JbhBO%YlZz1TV4_@IyxBEqS>itMrPb#K5P?yF4)gWeCukzs!gR0g&>E
zmp3pdDuDnvTY`qc{0=jL$K^Waw6T(9&`;@KW)_Nj`1N@h7#J8lUHx3vIVCg!00((%
AB>(^b
literal 0
HcmV?d00001
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103135Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103135Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..1d66943e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103135Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
@@ -0,0 +1,235 @@
+ Result for Multiview classification with Mumbo
+
+Average accuracy :
+ -On Train : 73.9644970414
+ -On Test : 48.5714285714
+ -On Validation : 51.6853932584Dataset info :
+ -Dataset name : Fake
+ -Labels : Methyl, MiRNA_, RNASeq, Clinic
+ -Views : View0 of shape (300, 8), View1 of shape (300, 10), View2 of shape (300, 5), View3 of shape (300, 13)
+ -5 folds
+ - Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
+
+
+
+Mumbo configuration :
+ -Used 1 core(s)
+ -Iterations : min 1, max 10, threshold 0.01
+ -Weak Classifiers : DecisionTree with depth 3, sub-sampled at 1.0 on View0
+ -DecisionTree with depth 3, sub-sampled at 1.0 on View1
+ -DecisionTree with depth 3, sub-sampled at 1.0 on View2
+ -DecisionTree with depth 3, sub-sampled at 1.0 on View3
+
+
+
+Computation time on 1 cores :
+ Database extraction time : 0:00:00
+ Learn Prediction
+ Fold 1 0:00:01 0:00:00
+ Fold 2 0:00:02 0:00:00
+ Fold 3 0:00:03 0:00:00
+ Fold 4 0:00:05 0:00:00
+ Fold 5 0:00:06 0:00:00
+ Total 0:00:19 0:00:02
+ So a total classification time of 0:00:07.
+
+
+
+Mean average accuracies and stats for each fold :
+ - Fold 0, used 5
+ - On View0 :
+ - Mean average Accuracy : 0.266272189349
+ - Percentage of time chosen : 0.6
+ - On View1 :
+ - Mean average Accuracy : 0.255621301775
+ - Percentage of time chosen : 0.0
+ - On View2 :
+ - Mean average Accuracy : 0.261538461538
+ - Percentage of time chosen : 0.1
+ - On View3 :
+ - Mean average Accuracy : 0.26449704142
+ - Percentage of time chosen : 0.3
+ - Fold 1, used 5
+ - On View0 :
+ - Mean average Accuracy : 0.250887573964
+ - Percentage of time chosen : 0.6
+ - On View1 :
+ - Mean average Accuracy : 0.268639053254
+ - Percentage of time chosen : 0.4
+ - On View2 :
+ - Mean average Accuracy : 0.241420118343
+ - Percentage of time chosen : 0.0
+ - On View3 :
+ - Mean average Accuracy : 0.256804733728
+ - Percentage of time chosen : 0.0
+ - Fold 2, used 5
+ - On View0 :
+ - Mean average Accuracy : 0.250295857988
+ - Percentage of time chosen : 0.7
+ - On View1 :
+ - Mean average Accuracy : 0.231952662722
+ - Percentage of time chosen : 0.0
+ - On View2 :
+ - Mean average Accuracy : 0.255029585799
+ - Percentage of time chosen : 0.0
+ - On View3 :
+ - Mean average Accuracy : 0.286390532544
+ - Percentage of time chosen : 0.3
+ - Fold 3, used 5
+ - On View0 :
+ - Mean average Accuracy : 0.234911242604
+ - Percentage of time chosen : 0.7
+ - On View1 :
+ - Mean average Accuracy : 0.245562130178
+ - Percentage of time chosen : 0.0
+ - On View2 :
+ - Mean average Accuracy : 0.249704142012
+ - Percentage of time chosen : 0.0
+ - On View3 :
+ - Mean average Accuracy : 0.26449704142
+ - Percentage of time chosen : 0.3
+ - Fold 4, used 5
+ - On View0 :
+ - Mean average Accuracy : 0.246153846154
+ - Percentage of time chosen : 0.6
+ - On View1 :
+ - Mean average Accuracy : 0.234911242604
+ - Percentage of time chosen : 0.0
+ - On View2 :
+ - Mean average Accuracy : 0.236094674556
+ - Percentage of time chosen : 0.0
+ - On View3 :
+ - Mean average Accuracy : 0.259763313609
+ - Percentage of time chosen : 0.4
+
+ For each iteration :
+ - Iteration 1
+ Fold 1
+ Accuracy on train : 47.9289940828
+ Accuracy on test : 0.0
+ Accuracy on validation : 47.191011236
+ Selected View : View3
+ Fold 2
+ Accuracy on train : 47.9289940828
+ Accuracy on test : 0.0
+ Accuracy on validation : 47.191011236
+ Selected View : View1
+ Fold 3
+ Accuracy on train : 47.9289940828
+ Accuracy on test : 0.0
+ Accuracy on validation : 47.191011236
+ Selected View : View0
+ Fold 4
+ Accuracy on train : 47.9289940828
+ Accuracy on test : 0.0
+ Accuracy on validation : 47.191011236
+ Selected View : View0
+ Fold 5
+ Accuracy on train : 47.9289940828
+ Accuracy on test : 0.0
+ Accuracy on validation : 47.191011236
+ Selected View : View3
+ - Iteration 2
+ Fold 1
+ Accuracy on train : 75.1479289941
+ Accuracy on test : 0.0
+ Accuracy on validation : 49.4382022472
+ Selected View : View3
+ Fold 2
+ Accuracy on train : 72.7810650888
+ Accuracy on test : 0.0
+ Accuracy on validation : 47.191011236
+ Selected View : View1
+ Fold 3
+ Accuracy on train : 75.1479289941
+ Accuracy on test : 0.0
+ Accuracy on validation : 62.9213483146
+ Selected View : View3
+ Fold 4
+ Accuracy on train : 75.1479289941
+ Accuracy on test : 0.0
+ Accuracy on validation : 49.4382022472
+ Selected View : View3
+ Fold 5
+ Accuracy on train : 71.5976331361
+ Accuracy on test : 0.0
+ Accuracy on validation : 49.4382022472
+ Selected View : View3
+ - Iteration 3
+ Fold 1
+ Accuracy on train : 75.1479289941
+ Accuracy on test : 0.0
+ Accuracy on validation : 49.4382022472
+ Selected View : View3
+ Fold 2
+ Accuracy on train : 72.7810650888
+ Accuracy on test : 0.0
+ Accuracy on validation : 47.191011236
+ Selected View : View1
+ Fold 3
+ Accuracy on train : 75.1479289941
+ Accuracy on test : 0.0
+ Accuracy on validation : 62.9213483146
+ Selected View : View3
+ Fold 4
+ Accuracy on train : 75.1479289941
+ Accuracy on test : 0.0
+ Accuracy on validation : 49.4382022472
+ Selected View : View3
+ Fold 5
+ Accuracy on train : 71.5976331361
+ Accuracy on test : 0.0
+ Accuracy on validation : 49.4382022472
+ Selected View : View3
+ - Iteration 4
+ Fold 1
+ Accuracy on train : 75.1479289941
+ Accuracy on test : 0.0
+ Accuracy on validation : 49.4382022472
+ Selected View : View2
+ Fold 2
+ Accuracy on train : 72.7810650888
+ Accuracy on test : 0.0
+ Accuracy on validation : 47.191011236
+ Selected View : View1
+ Fold 3
+ Accuracy on train : 75.1479289941
+ Accuracy on test : 0.0
+ Accuracy on validation : 62.9213483146
+ Selected View : View3
+ Fold 4
+ Accuracy on train : 75.1479289941
+ Accuracy on test : 0.0
+ Accuracy on validation : 49.4382022472
+ Selected View : View3
+ Fold 5
+ Accuracy on train : 71.5976331361
+ Accuracy on test : 0.0
+ Accuracy on validation : 49.4382022472
+ Selected View : View3
+ - Iteration 5
+ Fold 1
+ Accuracy on train : 47.9289940828
+ Accuracy on test : 0.0
+ Accuracy on validation : 47.191011236
+ Selected View : View0
+ Fold 2
+ Accuracy on train : 47.9289940828
+ Accuracy on test : 0.0
+ Accuracy on validation : 47.191011236
+ Selected View : View0
+ Fold 3
+ Accuracy on train : 47.9289940828
+ Accuracy on test : 0.0
+ Accuracy on validation : 47.191011236
+ Selected View : View0
+ Fold 4
+ Accuracy on train : 47.9289940828
+ Accuracy on test : 0.0
+ Accuracy on validation : 47.191011236
+ Selected View : View0
+ Fold 5
+ Accuracy on train : 47.9289940828
+ Accuracy on test : 0.0
+ Accuracy on validation : 47.191011236
+ Selected View : View0
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103409-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-103409-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 00000000..df4e9476
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103409-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,1580 @@
+2016-09-06 10:34:09,127 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-06 10:34:09,127 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.0001380625 Gbytes /!\
+2016-09-06 10:34:14,141 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-06 10:34:14,144 INFO: Start: Finding all available mono- & multiview algorithms
+2016-09-06 10:34:14,196 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:14,196 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:14,197 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:34:14,197 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:34:14,197 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:34:14,197 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:34:14,197 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:34:14,197 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:34:14,197 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:34:14,197 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:34:14,198 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:34:14,198 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:34:14,198 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:34:14,198 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:34:14,227 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:34:14,227 DEBUG: Start: Training
+2016-09-06 10:34:14,228 DEBUG: Info: Time for Training: 0.0323169231415[s]
+2016-09-06 10:34:14,228 DEBUG: Done: Training
+2016-09-06 10:34:14,228 DEBUG: Start: Predicting
+2016-09-06 10:34:14,231 DEBUG: Done: Predicting
+2016-09-06 10:34:14,231 DEBUG: Start: Getting Results
+2016-09-06 10:34:14,232 DEBUG: Done: Getting Results
+2016-09-06 10:34:14,233 INFO: Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 0.652380952381
+accuracy_score on test : 0.411111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.652380952381
+ - Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:14,233 INFO: Done: Result Analysis
+2016-09-06 10:34:14,245 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:34:14,245 DEBUG: Start: Training
+2016-09-06 10:34:14,249 DEBUG: Info: Time for Training: 0.0530989170074[s]
+2016-09-06 10:34:14,249 DEBUG: Done: Training
+2016-09-06 10:34:14,249 DEBUG: Start: Predicting
+2016-09-06 10:34:14,252 DEBUG: Done: Predicting
+2016-09-06 10:34:14,252 DEBUG: Start: Getting Results
+2016-09-06 10:34:14,254 DEBUG: Done: Getting Results
+2016-09-06 10:34:14,254 INFO: Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.566666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:14,254 INFO: Done: Result Analysis
+2016-09-06 10:34:14,344 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:14,344 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:14,344 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:34:14,344 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:34:14,345 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:34:14,345 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:34:14,345 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:34:14,345 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:34:14,345 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:34:14,345 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:34:14,345 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:34:14,345 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:34:14,345 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:34:14,345 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:34:14,378 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:34:14,379 DEBUG: Start: Training
+2016-09-06 10:34:14,379 DEBUG: Info: Time for Training: 0.0353238582611[s]
+2016-09-06 10:34:14,379 DEBUG: Done: Training
+2016-09-06 10:34:14,379 DEBUG: Start: Predicting
+2016-09-06 10:34:14,386 DEBUG: Done: Predicting
+2016-09-06 10:34:14,386 DEBUG: Start: Getting Results
+2016-09-06 10:34:14,387 DEBUG: Done: Getting Results
+2016-09-06 10:34:14,387 INFO: Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.57619047619
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 34
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.57619047619
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:14,388 INFO: Done: Result Analysis
+2016-09-06 10:34:14,487 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:34:14,487 DEBUG: Start: Training
+2016-09-06 10:34:14,505 DEBUG: Info: Time for Training: 0.160764932632[s]
+2016-09-06 10:34:14,505 DEBUG: Done: Training
+2016-09-06 10:34:14,505 DEBUG: Start: Predicting
+2016-09-06 10:34:14,509 DEBUG: Done: Predicting
+2016-09-06 10:34:14,509 DEBUG: Start: Getting Results
+2016-09-06 10:34:14,510 DEBUG: Done: Getting Results
+2016-09-06 10:34:14,510 INFO: Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.62380952381
+accuracy_score on test : 0.411111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 7, max_depth : 2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.62380952381
+ - Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:14,510 INFO: Done: Result Analysis
+2016-09-06 10:34:14,599 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:14,599 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:14,599 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:34:14,599 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:34:14,599 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:34:14,599 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:34:14,600 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:34:14,600 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:34:14,600 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:34:14,600 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:34:14,601 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:34:14,601 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:34:14,601 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:34:14,601 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:34:14,648 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:34:14,648 DEBUG: Start: Training
+2016-09-06 10:34:14,649 DEBUG: Info: Time for Training: 0.0514440536499[s]
+2016-09-06 10:34:14,649 DEBUG: Done: Training
+2016-09-06 10:34:14,649 DEBUG: Start: Predicting
+2016-09-06 10:34:14,651 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:34:14,651 DEBUG: Start: Training
+2016-09-06 10:34:14,653 DEBUG: Done: Predicting
+2016-09-06 10:34:14,654 DEBUG: Start: Getting Results
+2016-09-06 10:34:14,655 DEBUG: Done: Getting Results
+2016-09-06 10:34:14,655 INFO: Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.557142857143
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.557142857143
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:14,656 INFO: Done: Result Analysis
+2016-09-06 10:34:14,670 DEBUG: Info: Time for Training: 0.0719361305237[s]
+2016-09-06 10:34:14,670 DEBUG: Done: Training
+2016-09-06 10:34:14,670 DEBUG: Start: Predicting
+2016-09-06 10:34:14,673 DEBUG: Done: Predicting
+2016-09-06 10:34:14,673 DEBUG: Start: Getting Results
+2016-09-06 10:34:14,675 DEBUG: Done: Getting Results
+2016-09-06 10:34:14,675 INFO: Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.471428571429
+accuracy_score on test : 0.622222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2978
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.471428571429
+ - Score on test : 0.622222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:14,675 INFO: Done: Result Analysis
+2016-09-06 10:34:14,747 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:14,748 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:34:14,748 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:34:14,748 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:34:14,748 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:14,749 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:34:14,749 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:34:14,749 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:34:14,749 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:34:14,749 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:34:14,750 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:34:14,750 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:34:14,750 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:34:14,750 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:34:14,803 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:34:14,803 DEBUG: Start: Training
+2016-09-06 10:34:14,803 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:34:14,804 DEBUG: Start: Training
+2016-09-06 10:34:14,821 DEBUG: Info: Time for Training: 0.0726821422577[s]
+2016-09-06 10:34:14,821 DEBUG: Done: Training
+2016-09-06 10:34:14,821 DEBUG: Start: Predicting
+2016-09-06 10:34:14,821 DEBUG: Info: Time for Training: 0.0746259689331[s]
+2016-09-06 10:34:14,822 DEBUG: Done: Training
+2016-09-06 10:34:14,822 DEBUG: Start: Predicting
+2016-09-06 10:34:14,825 DEBUG: Done: Predicting
+2016-09-06 10:34:14,825 DEBUG: Start: Getting Results
+2016-09-06 10:34:14,826 DEBUG: Done: Predicting
+2016-09-06 10:34:14,826 DEBUG: Start: Getting Results
+2016-09-06 10:34:14,826 DEBUG: Done: Getting Results
+2016-09-06 10:34:14,827 INFO: Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 0.990476190476
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2978
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.990476190476
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:14,827 INFO: Done: Result Analysis
+2016-09-06 10:34:14,827 DEBUG: Done: Getting Results
+2016-09-06 10:34:14,827 INFO: Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.411111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2978
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:14,828 INFO: Done: Result Analysis
+2016-09-06 10:34:14,892 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:14,893 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:34:14,893 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:14,893 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:34:14,893 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:34:14,893 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:34:14,894 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
+2016-09-06 10:34:14,894 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
+2016-09-06 10:34:14,894 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
+2016-09-06 10:34:14,894 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:34:14,894 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
+2016-09-06 10:34:14,894 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:34:14,894 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:34:14,895 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:34:14,927 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:34:14,927 DEBUG: Start: Training
+2016-09-06 10:34:14,928 DEBUG: Info: Time for Training: 0.036386013031[s]
+2016-09-06 10:34:14,928 DEBUG: Done: Training
+2016-09-06 10:34:14,928 DEBUG: Start: Predicting
+2016-09-06 10:34:14,931 DEBUG: Done: Predicting
+2016-09-06 10:34:14,932 DEBUG: Start: Getting Results
+2016-09-06 10:34:14,933 DEBUG: Done: Getting Results
+2016-09-06 10:34:14,933 INFO: Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 0.6
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 15)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.6
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:14,933 INFO: Done: Result Analysis
+2016-09-06 10:34:14,948 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:34:14,948 DEBUG: Start: Training
+2016-09-06 10:34:14,952 DEBUG: Info: Time for Training: 0.0607821941376[s]
+2016-09-06 10:34:14,953 DEBUG: Done: Training
+2016-09-06 10:34:14,953 DEBUG: Start: Predicting
+2016-09-06 10:34:14,955 DEBUG: Done: Predicting
+2016-09-06 10:34:14,956 DEBUG: Start: Getting Results
+2016-09-06 10:34:14,957 DEBUG: Done: Getting Results
+2016-09-06 10:34:14,957 INFO: Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 15)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:14,958 INFO: Done: Result Analysis
+2016-09-06 10:34:15,039 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:15,039 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:15,039 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:34:15,039 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:34:15,040 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:34:15,040 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:34:15,040 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
+2016-09-06 10:34:15,040 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
+2016-09-06 10:34:15,040 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
+2016-09-06 10:34:15,040 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
+2016-09-06 10:34:15,040 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:34:15,040 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:34:15,040 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:34:15,040 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:34:15,073 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:34:15,073 DEBUG: Start: Training
+2016-09-06 10:34:15,074 DEBUG: Info: Time for Training: 0.0351400375366[s]
+2016-09-06 10:34:15,074 DEBUG: Done: Training
+2016-09-06 10:34:15,074 DEBUG: Start: Predicting
+2016-09-06 10:34:15,081 DEBUG: Done: Predicting
+2016-09-06 10:34:15,081 DEBUG: Start: Getting Results
+2016-09-06 10:34:15,083 DEBUG: Done: Getting Results
+2016-09-06 10:34:15,083 INFO: Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 15)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 34
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.533333333333
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:15,083 INFO: Done: Result Analysis
+2016-09-06 10:34:15,182 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:34:15,182 DEBUG: Start: Training
+2016-09-06 10:34:15,200 DEBUG: Info: Time for Training: 0.161078929901[s]
+2016-09-06 10:34:15,200 DEBUG: Done: Training
+2016-09-06 10:34:15,200 DEBUG: Start: Predicting
+2016-09-06 10:34:15,204 DEBUG: Done: Predicting
+2016-09-06 10:34:15,204 DEBUG: Start: Getting Results
+2016-09-06 10:34:15,205 DEBUG: Done: Getting Results
+2016-09-06 10:34:15,205 INFO: Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.685714285714
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 15)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 7, max_depth : 2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.685714285714
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:15,205 INFO: Done: Result Analysis
+2016-09-06 10:34:15,291 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:15,291 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:15,292 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:34:15,292 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:34:15,292 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:34:15,292 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:34:15,293 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
+2016-09-06 10:34:15,293 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
+2016-09-06 10:34:15,294 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
+2016-09-06 10:34:15,294 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
+2016-09-06 10:34:15,294 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:34:15,294 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:34:15,294 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:34:15,294 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:34:15,362 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:34:15,362 DEBUG: Start: Training
+2016-09-06 10:34:15,363 DEBUG: Info: Time for Training: 0.07275390625[s]
+2016-09-06 10:34:15,363 DEBUG: Done: Training
+2016-09-06 10:34:15,363 DEBUG: Start: Predicting
+2016-09-06 10:34:15,369 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:34:15,369 DEBUG: Start: Training
+2016-09-06 10:34:15,378 DEBUG: Done: Predicting
+2016-09-06 10:34:15,378 DEBUG: Start: Getting Results
+2016-09-06 10:34:15,380 DEBUG: Done: Getting Results
+2016-09-06 10:34:15,380 INFO: Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.633333333333
+accuracy_score on test : 0.566666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 15)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.633333333333
+ - Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:15,381 INFO: Done: Result Analysis
+2016-09-06 10:34:15,396 DEBUG: Info: Time for Training: 0.105423927307[s]
+2016-09-06 10:34:15,396 DEBUG: Done: Training
+2016-09-06 10:34:15,396 DEBUG: Start: Predicting
+2016-09-06 10:34:15,399 DEBUG: Done: Predicting
+2016-09-06 10:34:15,399 DEBUG: Start: Getting Results
+2016-09-06 10:34:15,401 DEBUG: Done: Getting Results
+2016-09-06 10:34:15,401 INFO: Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.5
+accuracy_score on test : 0.388888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 15)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2978
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.5
+ - Score on test : 0.388888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:15,401 INFO: Done: Result Analysis
+2016-09-06 10:34:15,536 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:15,536 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:15,536 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:34:15,536 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:34:15,536 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:34:15,536 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:34:15,537 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
+2016-09-06 10:34:15,537 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
+2016-09-06 10:34:15,537 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
+2016-09-06 10:34:15,537 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
+2016-09-06 10:34:15,537 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:34:15,537 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:34:15,537 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:34:15,537 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:34:15,607 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:34:15,607 DEBUG: Start: Training
+2016-09-06 10:34:15,616 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:34:15,616 DEBUG: Start: Training
+2016-09-06 10:34:15,632 DEBUG: Info: Time for Training: 0.0972080230713[s]
+2016-09-06 10:34:15,632 DEBUG: Done: Training
+2016-09-06 10:34:15,632 DEBUG: Start: Predicting
+2016-09-06 10:34:15,641 DEBUG: Done: Predicting
+2016-09-06 10:34:15,641 DEBUG: Start: Getting Results
+2016-09-06 10:34:15,643 DEBUG: Done: Getting Results
+2016-09-06 10:34:15,643 INFO: Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.577777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 15)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2978
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:15,643 DEBUG: Info: Time for Training: 0.10800909996[s]
+2016-09-06 10:34:15,643 DEBUG: Done: Training
+2016-09-06 10:34:15,643 INFO: Done: Result Analysis
+2016-09-06 10:34:15,643 DEBUG: Start: Predicting
+2016-09-06 10:34:15,647 DEBUG: Done: Predicting
+2016-09-06 10:34:15,648 DEBUG: Start: Getting Results
+2016-09-06 10:34:15,649 DEBUG: Done: Getting Results
+2016-09-06 10:34:15,649 INFO: Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 15)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2978
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:15,649 INFO: Done: Result Analysis
+2016-09-06 10:34:15,791 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:15,791 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:15,792 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:34:15,792 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:34:15,792 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:34:15,792 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:34:15,793 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:34:15,793 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:34:15,793 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:34:15,793 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:34:15,793 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:34:15,793 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:34:15,793 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:34:15,793 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:34:15,840 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:34:15,840 DEBUG: Start: Training
+2016-09-06 10:34:15,841 DEBUG: Info: Time for Training: 0.0506250858307[s]
+2016-09-06 10:34:15,842 DEBUG: Done: Training
+2016-09-06 10:34:15,842 DEBUG: Start: Predicting
+2016-09-06 10:34:15,845 DEBUG: Done: Predicting
+2016-09-06 10:34:15,846 DEBUG: Start: Getting Results
+2016-09-06 10:34:15,848 DEBUG: Done: Getting Results
+2016-09-06 10:34:15,848 INFO: Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 0.590476190476
+accuracy_score on test : 0.544444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.590476190476
+ - Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:15,848 INFO: Done: Result Analysis
+2016-09-06 10:34:15,867 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:34:15,867 DEBUG: Start: Training
+2016-09-06 10:34:15,873 DEBUG: Info: Time for Training: 0.081845998764[s]
+2016-09-06 10:34:15,873 DEBUG: Done: Training
+2016-09-06 10:34:15,873 DEBUG: Start: Predicting
+2016-09-06 10:34:15,876 DEBUG: Done: Predicting
+2016-09-06 10:34:15,876 DEBUG: Start: Getting Results
+2016-09-06 10:34:15,877 DEBUG: Done: Getting Results
+2016-09-06 10:34:15,878 INFO: Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.611111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.611111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:15,878 INFO: Done: Result Analysis
+2016-09-06 10:34:15,935 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:15,935 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:15,936 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:34:15,936 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:34:15,936 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:34:15,936 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:34:15,937 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:34:15,937 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:34:15,937 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:34:15,937 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:34:15,937 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:34:15,937 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:34:15,937 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:34:15,937 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:34:15,986 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:34:15,986 DEBUG: Start: Training
+2016-09-06 10:34:15,987 DEBUG: Info: Time for Training: 0.0520849227905[s]
+2016-09-06 10:34:15,987 DEBUG: Done: Training
+2016-09-06 10:34:15,987 DEBUG: Start: Predicting
+2016-09-06 10:34:15,997 DEBUG: Done: Predicting
+2016-09-06 10:34:15,997 DEBUG: Start: Getting Results
+2016-09-06 10:34:16,000 DEBUG: Done: Getting Results
+2016-09-06 10:34:16,000 INFO: Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.547619047619
+accuracy_score on test : 0.566666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 34
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.547619047619
+ - Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:16,000 INFO: Done: Result Analysis
+2016-09-06 10:34:16,100 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:34:16,100 DEBUG: Start: Training
+2016-09-06 10:34:16,117 DEBUG: Info: Time for Training: 0.182558059692[s]
+2016-09-06 10:34:16,117 DEBUG: Done: Training
+2016-09-06 10:34:16,117 DEBUG: Start: Predicting
+2016-09-06 10:34:16,121 DEBUG: Done: Predicting
+2016-09-06 10:34:16,121 DEBUG: Start: Getting Results
+2016-09-06 10:34:16,123 DEBUG: Done: Getting Results
+2016-09-06 10:34:16,123 INFO: Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.680952380952
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 7, max_depth : 2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.680952380952
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:16,123 INFO: Done: Result Analysis
+2016-09-06 10:34:16,184 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:16,184 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:16,185 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:34:16,185 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:34:16,185 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:34:16,185 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:34:16,185 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:34:16,185 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:34:16,185 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:34:16,186 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:34:16,186 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:34:16,186 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:34:16,186 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:34:16,186 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:34:16,231 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:34:16,232 DEBUG: Start: Training
+2016-09-06 10:34:16,232 DEBUG: Info: Time for Training: 0.0485727787018[s]
+2016-09-06 10:34:16,232 DEBUG: Done: Training
+2016-09-06 10:34:16,233 DEBUG: Start: Predicting
+2016-09-06 10:34:16,239 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:34:16,239 DEBUG: Start: Training
+2016-09-06 10:34:16,248 DEBUG: Done: Predicting
+2016-09-06 10:34:16,248 DEBUG: Start: Getting Results
+2016-09-06 10:34:16,250 DEBUG: Done: Getting Results
+2016-09-06 10:34:16,250 INFO: Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.614285714286
+accuracy_score on test : 0.577777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.614285714286
+ - Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:16,250 INFO: Done: Result Analysis
+2016-09-06 10:34:16,262 DEBUG: Info: Time for Training: 0.0779819488525[s]
+2016-09-06 10:34:16,262 DEBUG: Done: Training
+2016-09-06 10:34:16,262 DEBUG: Start: Predicting
+2016-09-06 10:34:16,265 DEBUG: Done: Predicting
+2016-09-06 10:34:16,266 DEBUG: Start: Getting Results
+2016-09-06 10:34:16,267 DEBUG: Done: Getting Results
+2016-09-06 10:34:16,267 INFO: Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.528571428571
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2978
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.528571428571
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:16,267 INFO: Done: Result Analysis
+2016-09-06 10:34:16,341 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:16,341 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:16,342 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:34:16,342 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:34:16,342 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:34:16,342 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:34:16,343 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:34:16,343 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:34:16,343 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:34:16,343 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:34:16,343 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:34:16,343 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:34:16,344 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:34:16,344 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:34:16,409 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:34:16,410 DEBUG: Start: Training
+2016-09-06 10:34:16,413 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:34:16,413 DEBUG: Start: Training
+2016-09-06 10:34:16,431 DEBUG: Info: Time for Training: 0.0899150371552[s]
+2016-09-06 10:34:16,431 DEBUG: Done: Training
+2016-09-06 10:34:16,431 DEBUG: Start: Predicting
+2016-09-06 10:34:16,431 DEBUG: Info: Time for Training: 0.0902900695801[s]
+2016-09-06 10:34:16,431 DEBUG: Done: Training
+2016-09-06 10:34:16,431 DEBUG: Start: Predicting
+2016-09-06 10:34:16,435 DEBUG: Done: Predicting
+2016-09-06 10:34:16,436 DEBUG: Start: Getting Results
+2016-09-06 10:34:16,437 DEBUG: Done: Getting Results
+2016-09-06 10:34:16,437 DEBUG: Done: Predicting
+2016-09-06 10:34:16,437 INFO: Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2978
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:16,437 DEBUG: Start: Getting Results
+2016-09-06 10:34:16,437 INFO: Done: Result Analysis
+2016-09-06 10:34:16,439 DEBUG: Done: Getting Results
+2016-09-06 10:34:16,439 INFO: Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.588888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2978
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:16,439 INFO: Done: Result Analysis
+2016-09-06 10:34:16,585 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:16,585 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:16,586 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:34:16,586 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:34:16,586 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:34:16,586 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:34:16,586 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:34:16,586 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:34:16,586 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:34:16,586 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:34:16,586 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:34:16,586 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:34:16,587 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:34:16,587 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:34:16,617 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:34:16,617 DEBUG: Start: Training
+2016-09-06 10:34:16,618 DEBUG: Info: Time for Training: 0.0332248210907[s]
+2016-09-06 10:34:16,618 DEBUG: Done: Training
+2016-09-06 10:34:16,618 DEBUG: Start: Predicting
+2016-09-06 10:34:16,621 DEBUG: Done: Predicting
+2016-09-06 10:34:16,621 DEBUG: Start: Getting Results
+2016-09-06 10:34:16,622 DEBUG: Done: Getting Results
+2016-09-06 10:34:16,622 INFO: Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 0.590476190476
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.590476190476
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:16,623 INFO: Done: Result Analysis
+2016-09-06 10:34:16,636 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:34:16,636 DEBUG: Start: Training
+2016-09-06 10:34:16,640 DEBUG: Info: Time for Training: 0.054888010025[s]
+2016-09-06 10:34:16,640 DEBUG: Done: Training
+2016-09-06 10:34:16,640 DEBUG: Start: Predicting
+2016-09-06 10:34:16,643 DEBUG: Done: Predicting
+2016-09-06 10:34:16,643 DEBUG: Start: Getting Results
+2016-09-06 10:34:16,645 DEBUG: Done: Getting Results
+2016-09-06 10:34:16,645 INFO: Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:16,645 INFO: Done: Result Analysis
+2016-09-06 10:34:16,736 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:16,736 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:16,736 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:34:16,736 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:34:16,737 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:34:16,737 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:34:16,737 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:34:16,737 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:34:16,737 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:34:16,737 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:34:16,737 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:34:16,737 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:34:16,738 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:34:16,738 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:34:16,771 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:34:16,771 DEBUG: Start: Training
+2016-09-06 10:34:16,772 DEBUG: Info: Time for Training: 0.0359029769897[s]
+2016-09-06 10:34:16,772 DEBUG: Done: Training
+2016-09-06 10:34:16,772 DEBUG: Start: Predicting
+2016-09-06 10:34:16,779 DEBUG: Done: Predicting
+2016-09-06 10:34:16,779 DEBUG: Start: Getting Results
+2016-09-06 10:34:16,780 DEBUG: Done: Getting Results
+2016-09-06 10:34:16,780 INFO: Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.590476190476
+accuracy_score on test : 0.566666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 34
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.590476190476
+ - Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:16,780 INFO: Done: Result Analysis
+2016-09-06 10:34:16,879 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:34:16,879 DEBUG: Start: Training
+2016-09-06 10:34:16,896 DEBUG: Info: Time for Training: 0.160787820816[s]
+2016-09-06 10:34:16,897 DEBUG: Done: Training
+2016-09-06 10:34:16,897 DEBUG: Start: Predicting
+2016-09-06 10:34:16,900 DEBUG: Done: Predicting
+2016-09-06 10:34:16,900 DEBUG: Start: Getting Results
+2016-09-06 10:34:16,902 DEBUG: Done: Getting Results
+2016-09-06 10:34:16,902 INFO: Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.709523809524
+accuracy_score on test : 0.577777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 7, max_depth : 2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.709523809524
+ - Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:16,902 INFO: Done: Result Analysis
+2016-09-06 10:34:16,988 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:16,988 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:16,989 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:34:16,989 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:34:16,989 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:34:16,989 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:34:16,990 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:34:16,990 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:34:16,990 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:34:16,990 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:34:16,990 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:34:16,990 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:34:16,990 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:34:16,990 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:34:17,060 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:34:17,061 DEBUG: Start: Training
+2016-09-06 10:34:17,062 DEBUG: Info: Time for Training: 0.0743260383606[s]
+2016-09-06 10:34:17,062 DEBUG: Done: Training
+2016-09-06 10:34:17,062 DEBUG: Start: Predicting
+2016-09-06 10:34:17,066 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:34:17,066 DEBUG: Start: Training
+2016-09-06 10:34:17,076 DEBUG: Done: Predicting
+2016-09-06 10:34:17,077 DEBUG: Start: Getting Results
+2016-09-06 10:34:17,080 DEBUG: Done: Getting Results
+2016-09-06 10:34:17,080 INFO: Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.552380952381
+accuracy_score on test : 0.544444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.552380952381
+ - Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:17,081 INFO: Done: Result Analysis
+2016-09-06 10:34:17,094 DEBUG: Info: Time for Training: 0.106731176376[s]
+2016-09-06 10:34:17,094 DEBUG: Done: Training
+2016-09-06 10:34:17,094 DEBUG: Start: Predicting
+2016-09-06 10:34:17,097 DEBUG: Done: Predicting
+2016-09-06 10:34:17,097 DEBUG: Start: Getting Results
+2016-09-06 10:34:17,099 DEBUG: Done: Getting Results
+2016-09-06 10:34:17,099 INFO: Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.490476190476
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2978
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.490476190476
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:17,099 INFO: Done: Result Analysis
+2016-09-06 10:34:17,389 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:34:17,389 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-09-06 10:34:17,390 INFO: Info: Shape of View0 :(300, 9)
+2016-09-06 10:34:17,390 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:34:17,390 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-06 10:34:17,390 INFO: Info: Shape of View1 :(300, 15)
+2016-09-06 10:34:17,391 INFO: Info: Shape of View0 :(300, 9)
+2016-09-06 10:34:17,391 INFO: Info: Shape of View2 :(300, 17)
+2016-09-06 10:34:17,392 INFO: Info: Shape of View1 :(300, 15)
+2016-09-06 10:34:17,392 INFO: Info: Shape of View3 :(300, 10)
+2016-09-06 10:34:17,392 INFO: Done: Read Database Files
+2016-09-06 10:34:17,392 INFO: Start: Determine validation split for ratio 0.7
+2016-09-06 10:34:17,392 INFO: Info: Shape of View2 :(300, 17)
+2016-09-06 10:34:17,393 INFO: Info: Shape of View3 :(300, 10)
+2016-09-06 10:34:17,393 INFO: Done: Read Database Files
+2016-09-06 10:34:17,393 INFO: Start: Determine validation split for ratio 0.7
+2016-09-06 10:34:17,397 INFO: Done: Determine validation split
+2016-09-06 10:34:17,398 INFO: Start: Determine 5 folds
+2016-09-06 10:34:17,398 INFO: Done: Determine validation split
+2016-09-06 10:34:17,398 INFO: Start: Determine 5 folds
+2016-09-06 10:34:17,407 INFO: Info: Length of Learning Sets: 169
+2016-09-06 10:34:17,408 INFO: Info: Length of Testing Sets: 42
+2016-09-06 10:34:17,408 INFO: Info: Length of Validation Set: 89
+2016-09-06 10:34:17,408 INFO: Done: Determine folds
+2016-09-06 10:34:17,408 INFO: Start: Learning with Mumbo and 5 folds
+2016-09-06 10:34:17,408 INFO: Info: Length of Learning Sets: 169
+2016-09-06 10:34:17,408 INFO: Start: Classification
+2016-09-06 10:34:17,408 INFO: Info: Length of Testing Sets: 42
+2016-09-06 10:34:17,408 INFO: Start: Fold number 1
+2016-09-06 10:34:17,408 INFO: Info: Length of Validation Set: 89
+2016-09-06 10:34:17,408 INFO: Done: Determine folds
+2016-09-06 10:34:17,408 INFO: Start: Learning with Fusion and 5 folds
+2016-09-06 10:34:17,408 INFO: Start: Classification
+2016-09-06 10:34:17,408 INFO: Start: Fold number 1
+2016-09-06 10:34:17,439 DEBUG: Start: Iteration 1
+2016-09-06 10:34:17,447 DEBUG: View 0 : 0.526627218935
+2016-09-06 10:34:17,455 DEBUG: View 1 : 0.455621301775
+2016-09-06 10:34:17,463 DEBUG: View 2 : 0.473372781065
+2016-09-06 10:34:17,471 DEBUG: View 3 : 0.485207100592
+2016-09-06 10:34:17,504 DEBUG: Best view : View2
+2016-09-06 10:34:17,582 DEBUG: Start: Iteration 2
+2016-09-06 10:34:17,590 DEBUG: View 0 : 0.710059171598
+2016-09-06 10:34:17,598 DEBUG: View 1 : 0.763313609467
+2016-09-06 10:34:17,605 DEBUG: View 2 : 0.775147928994
+2016-09-06 10:34:17,613 DEBUG: View 3 : 0.715976331361
+2016-09-06 10:34:17,650 DEBUG: Best view : View2
+2016-09-06 10:34:17,793 DEBUG: Start: Iteration 3
+2016-09-06 10:34:17,807 DEBUG: View 0 : 0.710059171598
+2016-09-06 10:34:17,815 DEBUG: View 1 : 0.763313609467
+2016-09-06 10:34:17,824 DEBUG: View 2 : 0.775147928994
+2016-09-06 10:34:17,831 DEBUG: View 3 : 0.715976331361
+2016-09-06 10:34:17,875 DEBUG: Best view : View2
+2016-09-06 10:34:18,092 DEBUG: Start: Iteration 4
+2016-09-06 10:34:18,099 DEBUG: View 0 : 0.662721893491
+2016-09-06 10:34:18,107 DEBUG: View 1 : 0.662721893491
+2016-09-06 10:34:18,114 DEBUG: View 2 : 0.633136094675
+2016-09-06 10:34:18,121 DEBUG: View 3 : 0.680473372781
+2016-09-06 10:34:18,162 DEBUG: Best view : View3
+2016-09-06 10:34:18,430 INFO: Start: Classification
+2016-09-06 10:34:18,888 INFO: Done: Fold number 1
+2016-09-06 10:34:18,888 INFO: Start: Fold number 2
+2016-09-06 10:34:18,917 DEBUG: Start: Iteration 1
+2016-09-06 10:34:18,924 DEBUG: View 0 : 0.556213017751
+2016-09-06 10:34:18,931 DEBUG: View 1 : 0.603550295858
+2016-09-06 10:34:18,939 DEBUG: View 2 : 0.497041420118
+2016-09-06 10:34:18,946 DEBUG: View 3 : 0.491124260355
+2016-09-06 10:34:18,976 DEBUG: Best view : View1
+2016-09-06 10:34:19,051 DEBUG: Start: Iteration 2
+2016-09-06 10:34:19,059 DEBUG: View 0 : 0.715976331361
+2016-09-06 10:34:19,066 DEBUG: View 1 : 0.680473372781
+2016-09-06 10:34:19,073 DEBUG: View 2 : 0.710059171598
+2016-09-06 10:34:19,080 DEBUG: View 3 : 0.704142011834
+2016-09-06 10:34:19,116 DEBUG: Best view : View0
+2016-09-06 10:34:19,257 DEBUG: Start: Iteration 3
+2016-09-06 10:34:19,264 DEBUG: View 0 : 0.715976331361
+2016-09-06 10:34:19,272 DEBUG: View 1 : 0.680473372781
+2016-09-06 10:34:19,279 DEBUG: View 2 : 0.710059171598
+2016-09-06 10:34:19,287 DEBUG: View 3 : 0.704142011834
+2016-09-06 10:34:19,325 DEBUG: Best view : View0
+2016-09-06 10:34:19,529 INFO: Start: Classification
+2016-09-06 10:34:19,873 INFO: Done: Fold number 2
+2016-09-06 10:34:19,874 INFO: Start: Fold number 3
+2016-09-06 10:34:19,903 DEBUG: Start: Iteration 1
+2016-09-06 10:34:19,909 DEBUG: View 0 : 0.544378698225
+2016-09-06 10:34:19,916 DEBUG: View 1 : 0.544378698225
+2016-09-06 10:34:19,923 DEBUG: View 2 : 0.544378698225
+2016-09-06 10:34:19,930 DEBUG: View 3 : 0.544378698225
+2016-09-06 10:34:19,961 DEBUG: Best view : View0
+2016-09-06 10:34:20,036 DEBUG: Start: Iteration 2
+2016-09-06 10:34:20,043 DEBUG: View 0 : 0.692307692308
+2016-09-06 10:34:20,051 DEBUG: View 1 : 0.692307692308
+2016-09-06 10:34:20,058 DEBUG: View 2 : 0.769230769231
+2016-09-06 10:34:20,065 DEBUG: View 3 : 0.733727810651
+2016-09-06 10:34:20,101 DEBUG: Best view : View2
+2016-09-06 10:34:20,241 DEBUG: Start: Iteration 3
+2016-09-06 10:34:20,249 DEBUG: View 0 : 0.692307692308
+2016-09-06 10:34:20,256 DEBUG: View 1 : 0.692307692308
+2016-09-06 10:34:20,264 DEBUG: View 2 : 0.769230769231
+2016-09-06 10:34:20,271 DEBUG: View 3 : 0.733727810651
+2016-09-06 10:34:20,310 DEBUG: Best view : View2
+2016-09-06 10:34:20,513 INFO: Start: Classification
+2016-09-06 10:34:20,853 INFO: Done: Fold number 3
+2016-09-06 10:34:20,853 INFO: Start: Fold number 4
+2016-09-06 10:34:20,882 DEBUG: Start: Iteration 1
+2016-09-06 10:34:20,889 DEBUG: View 0 : 0.585798816568
+2016-09-06 10:34:20,896 DEBUG: View 1 : 0.502958579882
+2016-09-06 10:34:20,904 DEBUG: View 2 : 0.538461538462
+2016-09-06 10:34:20,911 DEBUG: View 3 : 0.544378698225
+2016-09-06 10:34:20,940 DEBUG: Best view : View3
+2016-09-06 10:34:21,016 DEBUG: Start: Iteration 2
+2016-09-06 10:34:21,023 DEBUG: View 0 : 0.692307692308
+2016-09-06 10:34:21,031 DEBUG: View 1 : 0.704142011834
+2016-09-06 10:34:21,038 DEBUG: View 2 : 0.650887573964
+2016-09-06 10:34:21,045 DEBUG: View 3 : 0.698224852071
+2016-09-06 10:34:21,081 DEBUG: Best view : View1
+2016-09-06 10:34:21,221 DEBUG: Start: Iteration 3
+2016-09-06 10:34:21,228 DEBUG: View 0 : 0.692307692308
+2016-09-06 10:34:21,235 DEBUG: View 1 : 0.704142011834
+2016-09-06 10:34:21,243 DEBUG: View 2 : 0.650887573964
+2016-09-06 10:34:21,250 DEBUG: View 3 : 0.698224852071
+2016-09-06 10:34:21,289 DEBUG: Best view : View1
+2016-09-06 10:34:21,493 INFO: Start: Classification
+2016-09-06 10:34:21,834 INFO: Done: Fold number 4
+2016-09-06 10:34:21,834 INFO: Start: Fold number 5
+2016-09-06 10:34:21,864 DEBUG: Start: Iteration 1
+2016-09-06 10:34:21,871 DEBUG: View 0 : 0.455621301775
+2016-09-06 10:34:21,877 DEBUG: View 1 : 0.455621301775
+2016-09-06 10:34:21,884 DEBUG: View 2 : 0.455621301775
+2016-09-06 10:34:21,891 DEBUG: View 3 : 0.455621301775
+2016-09-06 10:34:21,891 WARNING: WARNING: All bad for iteration 0
+2016-09-06 10:34:21,920 DEBUG: Best view : View0
+2016-09-06 10:34:21,995 DEBUG: Start: Iteration 2
+2016-09-06 10:34:22,002 DEBUG: View 0 : 0.680473372781
+2016-09-06 10:34:22,010 DEBUG: View 1 : 0.733727810651
+2016-09-06 10:34:22,017 DEBUG: View 2 : 0.745562130178
+2016-09-06 10:34:22,024 DEBUG: View 3 : 0.710059171598
+2016-09-06 10:34:22,060 DEBUG: Best view : View2
+2016-09-06 10:34:22,200 DEBUG: Start: Iteration 3
+2016-09-06 10:34:22,206 DEBUG: View 0 : 0.680473372781
+2016-09-06 10:34:22,214 DEBUG: View 1 : 0.733727810651
+2016-09-06 10:34:22,221 DEBUG: View 2 : 0.745562130178
+2016-09-06 10:34:22,228 DEBUG: View 3 : 0.710059171598
+2016-09-06 10:34:22,267 DEBUG: Best view : View2
+2016-09-06 10:34:22,470 INFO: Start: Classification
+2016-09-06 10:34:22,812 INFO: Done: Fold number 5
+2016-09-06 10:34:22,812 INFO: Done: Classification
+2016-09-06 10:34:22,812 INFO: Info: Time for Classification: 5[s]
+2016-09-06 10:34:22,812 INFO: Start: Result Analysis for Mumbo
+2016-09-06 10:34:24,767 INFO: Result for Multiview classification with Mumbo
+
+Average accuracy :
+ -On Train : 74.201183432
+ -On Test : 49.5238095238
+ -On Validation : 52.808988764Dataset info :
+ -Dataset name : Fake
+ -Labels : Methyl, MiRNA_, RNASeq, Clinic
+ -Views : View0 of shape (300, 9), View1 of shape (300, 15), View2 of shape (300, 17), View3 of shape (300, 10)
+ -5 folds
+ - Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
+
+
+
+Mumbo configuration :
+ -Used 1 core(s)
+ -Iterations : min 1, max 10, threshold 0.01
+ -Weak Classifiers : DecisionTree with depth 3, sub-sampled at 1.0 on View0
+ -DecisionTree with depth 3, sub-sampled at 1.0 on View1
+ -DecisionTree with depth 3, sub-sampled at 1.0 on View2
+ -DecisionTree with depth 3, sub-sampled at 1.0 on View3
+
+
+
+Computation time on 1 cores :
+ Database extraction time : 0:00:00
+ Learn Prediction
+ Fold 1 0:00:01 0:00:00
+ Fold 2 0:00:02 0:00:00
+ Fold 3 0:00:03 0:00:00
+ Fold 4 0:00:04 0:00:00
+ Fold 5 0:00:05 0:00:00
+ Total 0:00:15 0:00:01
+ So a total classification time of 0:00:05.
+
+
+
+Mean average accuracies and stats for each fold :
+ - Fold 0, used 5
+ - On View0 :
+ - Mean average Accuracy : 0.260946745562
+ - Percentage of time chosen : 0.6
+ - On View1 :
+ - Mean average Accuracy : 0.26449704142
+ - Percentage of time chosen : 0.0
+ - On View2 :
+ - Mean average Accuracy : 0.265680473373
+ - Percentage of time chosen : 0.3
+ - On View3 :
+ - Mean average Accuracy : 0.259763313609
+ - Percentage of time chosen : 0.1
+ - Fold 1, used 4
+ - On View0 :
+ - Mean average Accuracy : 0.198816568047
+ - Percentage of time chosen : 0.9
+ - On View1 :
+ - Mean average Accuracy : 0.196449704142
+ - Percentage of time chosen : 0.1
+ - On View2 :
+ - Mean average Accuracy : 0.191715976331
+ - Percentage of time chosen : 0.0
+ - On View3 :
+ - Mean average Accuracy : 0.189940828402
+ - Percentage of time chosen : 0.0
+ - Fold 2, used 4
+ - On View0 :
+ - Mean average Accuracy : 0.192899408284
+ - Percentage of time chosen : 0.8
+ - On View1 :
+ - Mean average Accuracy : 0.192899408284
+ - Percentage of time chosen : 0.0
+ - On View2 :
+ - Mean average Accuracy : 0.208284023669
+ - Percentage of time chosen : 0.2
+ - On View3 :
+ - Mean average Accuracy : 0.201183431953
+ - Percentage of time chosen : 0.0
+ - Fold 3, used 4
+ - On View0 :
+ - Mean average Accuracy : 0.197041420118
+ - Percentage of time chosen : 0.7
+ - On View1 :
+ - Mean average Accuracy : 0.191124260355
+ - Percentage of time chosen : 0.2
+ - On View2 :
+ - Mean average Accuracy : 0.184023668639
+ - Percentage of time chosen : 0.0
+ - On View3 :
+ - Mean average Accuracy : 0.194082840237
+ - Percentage of time chosen : 0.1
+ - Fold 4, used 4
+ - On View0 :
+ - Mean average Accuracy : 0.181656804734
+ - Percentage of time chosen : 0.8
+ - On View1 :
+ - Mean average Accuracy : 0.192307692308
+ - Percentage of time chosen : 0.0
+ - On View2 :
+ - Mean average Accuracy : 0.194674556213
+ - Percentage of time chosen : 0.2
+ - On View3 :
+ - Mean average Accuracy : 0.187573964497
+ - Percentage of time chosen : 0.0
+
+ For each iteration :
+ - Iteration 1
+ Fold 1
+ Accuracy on train : 54.4378698225
+ Accuracy on test : 0.0
+ Accuracy on validation : 53.9325842697
+ Selected View : View2
+ Fold 2
+ Accuracy on train : 54.4378698225
+ Accuracy on test : 0.0
+ Accuracy on validation : 53.9325842697
+ Selected View : View1
+ Fold 3
+ Accuracy on train : 54.4378698225
+ Accuracy on test : 0.0
+ Accuracy on validation : 53.9325842697
+ Selected View : View0
+ Fold 4
+ Accuracy on train : 54.4378698225
+ Accuracy on test : 0.0
+ Accuracy on validation : 53.9325842697
+ Selected View : View3
+ Fold 5
+ Accuracy on train : 54.4378698225
+ Accuracy on test : 0.0
+ Accuracy on validation : 53.9325842697
+ Selected View : View0
+ - Iteration 2
+ Fold 1
+ Accuracy on train : 77.5147928994
+ Accuracy on test : 0.0
+ Accuracy on validation : 50.5617977528
+ Selected View : View2
+ Fold 2
+ Accuracy on train : 71.5976331361
+ Accuracy on test : 0.0
+ Accuracy on validation : 57.3033707865
+ Selected View : View0
+ Fold 3
+ Accuracy on train : 76.9230769231
+ Accuracy on test : 0.0
+ Accuracy on validation : 52.808988764
+ Selected View : View2
+ Fold 4
+ Accuracy on train : 70.4142011834
+ Accuracy on test : 0.0
+ Accuracy on validation : 57.3033707865
+ Selected View : View1
+ Fold 5
+ Accuracy on train : 74.5562130178
+ Accuracy on test : 0.0
+ Accuracy on validation : 46.0674157303
+ Selected View : View2
+ - Iteration 3
+ Fold 1
+ Accuracy on train : 77.5147928994
+ Accuracy on test : 0.0
+ Accuracy on validation : 50.5617977528
+ Selected View : View2
+ Fold 2
+ Accuracy on train : 71.5976331361
+ Accuracy on test : 0.0
+ Accuracy on validation : 57.3033707865
+ Selected View : View0
+ Fold 3
+ Accuracy on train : 76.9230769231
+ Accuracy on test : 0.0
+ Accuracy on validation : 52.808988764
+ Selected View : View2
+ Fold 4
+ Accuracy on train : 70.4142011834
+ Accuracy on test : 0.0
+ Accuracy on validation : 57.3033707865
+ Selected View : View1
+ Fold 5
+ Accuracy on train : 74.5562130178
+ Accuracy on test : 0.0
+ Accuracy on validation : 46.0674157303
+ Selected View : View2
+ - Iteration 4
+ Fold 1
+ Accuracy on train : 77.5147928994
+ Accuracy on test : 0.0
+ Accuracy on validation : 50.5617977528
+ Selected View : View3
+ Fold 2
+ Accuracy on train : 54.4378698225
+ Accuracy on test : 0.0
+ Accuracy on validation : 53.9325842697
+ Selected View : View0
+ Fold 3
+ Accuracy on train : 54.4378698225
+ Accuracy on test : 0.0
+ Accuracy on validation : 53.9325842697
+ Selected View : View0
+ Fold 4
+ Accuracy on train : 54.4378698225
+ Accuracy on test : 0.0
+ Accuracy on validation : 53.9325842697
+ Selected View : View0
+ Fold 5
+ Accuracy on train : 54.4378698225
+ Accuracy on test : 0.0
+ Accuracy on validation : 53.9325842697
+ Selected View : View0
+ - Iteration 5
+ Fold 1
+ Accuracy on train : 54.4378698225
+ Accuracy on test : 0.0
+ Accuracy on validation : 53.9325842697
+ Selected View : View0
+2016-09-06 10:34:24,951 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..b21ed4f8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 15)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..77474e0f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 0.6
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 15)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.6
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..09eb9bd9
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.57619047619
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 34
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.57619047619
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..75aeeab8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.62380952381
+accuracy_score on test : 0.411111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 7, max_depth : 2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.62380952381
+ - Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..de64a87e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.557142857143
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.557142857143
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..8accfa21
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.471428571429
+accuracy_score on test : 0.622222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2978
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.471428571429
+ - Score on test : 0.622222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..8d98bcf9
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 0.990476190476
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2978
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.990476190476
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..ebca06a9
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.411111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 9)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2978
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..ca874bc8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.611111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.611111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..33f3d2e5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 0.590476190476
+accuracy_score on test : 0.544444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.590476190476
+ - Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..ef3258f8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.547619047619
+accuracy_score on test : 0.566666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 34
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.547619047619
+ - Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..42abfcbf
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.685714285714
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 15)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 7, max_depth : 2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.685714285714
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..098bc8e6
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.633333333333
+accuracy_score on test : 0.566666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 15)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.633333333333
+ - Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..f8a69440
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.5
+accuracy_score on test : 0.388888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 15)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2978
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.5
+ - Score on test : 0.388888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..85ebfdd4
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 15)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2978
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..d4cd5d9a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.577777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 15)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2978
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..e41e726a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..3c3a4f96
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 0.590476190476
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.590476190476
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..9bf78e66
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.590476190476
+accuracy_score on test : 0.566666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 34
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.590476190476
+ - Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..0b0dc718
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.709523809524
+accuracy_score on test : 0.577777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 7, max_depth : 2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.709523809524
+ - Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..fe4a10e8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.614285714286
+accuracy_score on test : 0.577777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.614285714286
+ - Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..eaf4be41
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.528571428571
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2978
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.528571428571
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..7a8b79bf
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2978
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..b1b7d9bc
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.588888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 17)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2978
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103417Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103417Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..dc3c519a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103417Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.552380952381
+accuracy_score on test : 0.544444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.552380952381
+ - Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103417Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103417Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..6bbe2181
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103417Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.490476190476
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 10)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2978
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.490476190476
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103424Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Results/20160906-103424Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
new file mode 100644
index 0000000000000000000000000000000000000000..6055a9f380f8d3c63daeba5147253811e3b5075a
GIT binary patch
literal 53433
zcmeAS@N?(olHy`uVBq!ia0y~yU{+vYV2a>iV_;yIRn}C%z`(##?Bp53!NI{%!;#X#
zz`(#+;1OBOz`&mf!i+2ImuE6CC@^@sIEGZrd2_ci=IqV?>>sZ0J=zm{!XUUa=*nI1
zYLD$lg%Vp%SRFEuHcpdJ<mr`sEn%W8xQQ?IO;?TYjEut_Q@RwZw_N)6@9)L+pWj|v
zo0t0N<J`}G+`oUeoL_wI`JD65cReq*doIH$C}`357=ol4Klr9MKsh_=`R_42gh*Jd
zyZC{P<9MMA81=T9J3z(zvQ73d9)gO;EPlu<05^*_zo7u4Ugmm61#=6`ShvH{3Q+Om
zrc$F;NPz<CPV}J*7X;>7l{$HPa^5a`c1Dt;)#=PE)794=pPy&jEv)Xh;k=}z<b{hD
zR~FlC%e~#U(7FBEPmB8c`jR&{1jWR}3`|X1w{A7Pwl=!`(xppQ$3yjujfHD}eOYPf
zlar(4)-M<P-XtkGS#kR5g#j8DmU@eC$-OPc)yl-x%A_5>ZcX`~^@rPdr8ktljbd=9
zsj>O?_V)2_Q>RRE&=66wx8Hy6V_SRs;iE^FI#y<T)Gb=PIC5t9(WEmo44ID}J<5Bk
zf7zF>Ukm^K`s(JndS6phlR?sv4lDWa`&F@ft4!zFR0h5MH`6#B<U^;08?J4#E_oqP
z_3aI(u&{7NW#!e^zpflPJzam{;>FERo}^5DJ9FmDjGLQO=h;@b{rvoVb<ASkr|;kM
zCnqPDyuT;QEv{FS`?2WGe!={Pf;-Vp3IYcX9AIeYlTEt4E$3!a+}^6plA4eZkv@6*
zIMs8%o-MUI)+gJ2<3_}tJ(a@M-`*UYXIuT@sCfJpZ9Rq;cXk?|n`axHEWypq{pQxz
z)%P+ZA|e#*?Bogy3nTVa82<YDx_!qEiz7#mCVqQ!Q!BA^#l4qTjvtq=?>TSxo5vy2
zIrII!y;{FtNBi^IZd>>>P(x%#%}=98j~*3#eHHrW@Avy0JUlr8b^orLJlnfF>#CMb
z)t42w{w{XwJ=DrAzF_(C@MIfHOUoHXsa$;Wc0Es?q-@E*FXug7Z*6g%$;WrQ-ygbt
zTl(O^gIc$potz8`9yn~tzRm}d4tMsOdwIG4;|B+uf4pA5zu@gH(?9=yzqcrTbtSuV
z_TzuOpzL-}Nm$*_B_Ke6LBZNuT3kP_W%usgUNfd@haWn2jBVPqX%Yqr4Dask?LKnE
zCFj-_&V>sX255-9v9DD+w8*tPAu|&cKi3>0cNVEG^P79>QO(CktR<zTAOHOPe7#Js
zt*!0Grc`dTygMCp=EyJ{I5}DU;fog?aeJ!{9y`Vs7#OHiQ&3>=q{uSGNOH}ZH3iSl
z$xe-%GhIJE>B@>gflikRr=PBgIXBOiduPnKM>qG?S}zIGOfizQv9Xyjb0+74HR;EX
z9%a3E@7@Ftl?|IVIaO6vt$qIR`Ez!Cef=FfcRH#FJ^c9ixQCzLu?9xwgxp--nLf)t
zJ+@R+WBdE-YxC5pqB^>|g%1uiY85_v@??Q?JKvgekA(pVhgvup8n$dPnK5hDrY+CU
z%xpe$#z!Y|6U)Sj69Y6vemt4%&oJfc`(354K}ISoE4Q??tgvlgv`EQkwi&OQn%ah~
zTi0G%!Oh8;czw#`$->pAWJN?+#KpxcDk=`#xg+!U*Vn`M>;K<<@W&+k8c$SI6vL@k
zTNf-?5NAK<@87?PX=%r**1WsFfBiOfH+OgT@9*!Y&)=SV``Eiq)9h<2dUtQxB4V6&
zreWt!%O^#aYu2m@2nuRCbjWGP?%jt^o#GPeWO;n7_wecI`iu+{Cr+Gj`soa_T&dk`
z>tlDfeSCb}S!v>dd-vq{<?YsN%(=QU#=idVSMi2hueUCeulvDx?b@{s8#Xlb_VVh*
z@8e;J*ioP;XIs_6Z~sSttF`H9?8N3~W~=gdG1<(2PE1r@^R96dTS+NDyPRE3$3o|J
zC#|VYT2qrwPt(nrzi*$Nm~Iq{wY9Z`q@<#Pf<vy%WHnzW504J7eND~HA1}JgGcwfF
z)NDvR%*K$KYMOpd=EjX14hsVg+`cV+^X5&{d)?df@27o#bZ>99Vcs2!J)h56S3I9v
zZczB>2%GJX2RrI*A6dLxdtqyKxR;NQ%0v&BnKNf@$-JyK%RFCBH)>1Az8{acm6W)6
zd6#MiFAK=b)D#rVy}xfR8=s7a%Mzi!4h>t|xhzbKvNjbHIy(B~?fsT5Tjt{O<<HOJ
zs`q=pzc|`0?&7kg>TA}o7mNElIx=2gTWeDCB4BIfoh_Ndx-mNf-tIf~a(edVWxTPw
zN;>oI@AKWZZQGQ|lbu~$G&MD^>?}^#($QIBoPI7~d*0m_4-dDyxP$}*IIyy^PMJ5)
z&&tZm#iiuK1IMEB^7M~ssi}g3fgvFxC!ZE6DebEIs#W^xN~e*rac9Si-S77;{`dFy
z=FeHTx9O&)qzDLJG{0Zte0iB~@X6J?is#$c+r51K`toFT|Bj9oD^_&q=;*w-wl><u
zWy<vF?$4e*6OotqKR?e_xz)+BU(QxZX_0%s+{AtR?5x*!y?Oig<)K#YFHcSiPdfc{
zO&$|7bLO!gNu9Ur_x<YX=rGH_=cDY_lW<&ITzs;F!h-pAzdAcQ*2V62TN}MSEZEp3
z`}#Unb8~YoP0fXe4mrKLyW4!{jvWGm7jJG(KY93Yb5~baN5_jZGmTZIdPUAUJKH?{
z`L3LsMp6a|4$YsPU8c;M<rTDYO4PO-&*Wo0i7&HXeSCcU%D&p_s;{p^1*gUux3;#1
z#KibGEu8S_)2E0CSy|a#<?rPL1z)~=dGX1~$&oTuRaGLw!ojNlyr=6;jL=D&fBL=X
zqW^K#E-w4_?b}lH)JsXJtD|GVGT+&krfP?GbY#4{vvbMn)!v$#nl3ITrLRIjNkuR-
zZky@V^RrB|SFOE%>eTvprA7Atel)u%3I1NQe7XDe_3`1=F|JirRbQ^h*N5)@|NH&^
z<ui@bH=VzE^X8Q;nZa8V+xg}FxW#l7loqk`%UxiXuW{J8aih-M$jxbjfj4ch>?+MJ
z^AA;<eDcMKiOMc6CGYN7zPhxO`?h)6n}}aOpU>~?$oTl^=$4wFMceWp9%}va^Z9(?
ziD}=H{{Q=%`TyVFH$R-)`6h1OT)Z{)U}4PJE%!^9J09;a`tslZV@*wsfQ(GfhYtmB
z?(7t{E_;)Zc<9iff}fvKHAJ`=QvEZttBUTdi8QXLs$ycu$;~bN{VkWlVYXTBhllO*
zpj`Un&*$?S3m?1n-kZAaMt0Ek&sKGREP{fAFYYeS-{u<~EnWWZ&cTa|-5)-F+}tN?
z&7@JicahP`kgg?5RL;z?6y96?9aOBYYU%9ia{B-8@5PnD>I|v=3Db2V4_(^4b!%&5
zBjfMtzrVgp%{{!pvH8Nj+G+-epdg`1lO{cQ`t;}{T@8%`UteD@{QfTX&c0gdyu7@e
z`2iY7&dxT!xpQi9S=q6b!OKAv!k72=<vBSyJ-ob}TwGk{9@|n;UCqpJ=ImKUhJHEQ
zRg=O}QdExh%kwV`;8+;Y(J8F1WMU%XaC1jvOAAXxWaLI0``TYqdQTP<6f|tyXvlEj
z`t|MC!m~_H@-vjYzQ)VR$$8@R>E`X*%?}+uY*_Q7p!a;VyE}VZTib_^9~*;K9y!p+
z{PExK_p5srYPnCHcgKI8jU>Z`h0g31RaHtxMw?Ep?mKqun1+VNfhQ*?U)-J_&m&_Y
z;BeD+d(u%Z8S640RaMm!=gzq~Ix<?HyK+S&X`=)~LS|+sD3N{Th=`0_xkqiK=gn&&
z_RIa}uiF-<-QC&g=-|LG&$gN^H#b*BRdwNx9TK;0-O4)sV!oK5U}Nd)Ym5x{s^8m!
z>{n81s@>izY0Sp3VD;*5z1Up}y1Kl(cI^s%G}9zg2-MPHWM)&au$b{RE<{XBtgWeu
z>CMf}><*Fr9+fxuudAN;*y8L*Hjd+aQ)_B!76fQqSnSUK?eJ0=!NhlWcD@m9z6C0F
z-{kA+>Q0qUpI`HdlZ{`lN6xltLGtmw2_7ozpR;J~sQj!Jx38vC&bCTHO^r=YPfv??
z)vgz}wra17-`@vneq31@Ji|DhPe@q!wTS!Cw%%#erX6FRAH6N-<Uci~7q4C^>FKTW
z+Tq;J=cqMx)t;y@*`-09KR-Wz{C@xcV=pc)zPQkteU43~(Ep$3>pz^<-=8r>ztLeq
zk)PATfW^hDZe*w``OGrm>=x5q^>xRsx+T}t?RBHJG)$i^?&R$J`U=CvOP8E{e0a{y
zx9>MjKX>5PEvet%-ZpRDYRa%8=Jooxy{k;`tNYJ8ay`DDx6z>?aIstBxjB|Q>i$|S
zTC}L(*O$zi&u7go>i^j+TC_+&R<`%Wiwu+edoruT*B@);7LSOTy57)t{mxgXw&{Hh
zzI(VMe0^Nvkq*I*zP`2)E!VIxF{_dn4TlanU1O+}<>BEecz4J0o29NUuYApiMz_kf
ze<CjzNn4lcJUc(X|I3$>H~06;hp&qXl<w;Q)r()=-rj!7&i9=wGc$8aO3H@hOP4R-
zJj~Pg_*Gv&<HH@>eV(72D<~ka;M?AGe}8{a<18{VlA+<zqa+m-6$XZ=?RmZ8`f)2-
z*8f?&>`{7fa4;yn@7uTUjqJa+RTA}8RaSPtUMSc6`FI@EkiK*0j)0(G<LdDBn+&Gw
z#WFEeR8%A!?~|?g@PM&llU(bk@2^fj-c$Zwu3^)?4*B{&f(!-U-^EV-GI!3Lf<HeB
z#m+DDo&Dg=n>!B}uZzx{Idfs;<}@wdl~bmOq@<@GK6sGv<jIp-yc2%>sMu=Uc=Oh+
zN4K_ScXV|%?cHlTXYSmMX=kM*BqSE(-rg2^C_8BX)j+MOH}=(9dwF?%cy(1f=kBhl
zZ>5b=IGmiEB7O=9CM74|{P*HT#*$^rm>A6R@AcIE{bg!luc@i|hW+X4@bv;>V#lNo
zEM2<v#m&v?a`tsG$ugFfmK9%K2+GLFJb3<`U0PaNMMcFyLEylN6CCybeyTTYD*vu+
z?r`e-|C5u|Ir#b8H*YqMiH*IP!czR~%)!UU`zKDH&Mq!4t`omc=GU)Z9$sEa_V;F+
z=l8vN^Cs)~&WuI7HmzN;?AOYbaoVTOpLh54<dk-ayZ1R@o$qY3&{v!6*mxuwX3UUS
zvSbMZ1E@6|xY%ta@9Ge(kFVG7XVQ4B%FWHa`Z`aXm6a8!y}f78o`l@oUcJ~|D|ny3
zeAyYgI&9<nsek^|m|gRnTwQpiLy&`$Gx5If!Gi~{&XYb~xb-owKwq_Y$e)Qeb$=?p
zsccuu&(HUnVZi9;=QrW>(+Q`aS`<IyVMus!VPQvqf4F2z-0k3Fn!(E!ELr06Sp9rs
zW232ECoeB=YilcO)t48I6P4W$oj%RII&3Wy1E~FGVj>c}+^;o8uYKoE%h^fSE?yMm
z7T0T;JzF}(Nb=XWx2NkGrak}p>gwtZ+qb7*-rgr|&ZjidL$$WJxcJY9!~8n)UcAWo
zru@A0;<anX9v*Jrn0{XFM2b;|i_(u@zZNZ6!0`6g*25t2ijPTUeV?D755LWD@!GYv
zwQF^EmA-CM^PT0OH1WaH)6)aAre<9(Ei81*%hP*OWSMPnEo}eOWxli7n3<Wsytye{
z`}-Tn3%9Q?_m|g+-p13+&fgZJ*Y2Tm=-=PpH|wgatIy0b<yM-=0m|~ndZnf3%<t&n
zDEk`~73CBkFaP=3S!M>CiVqCu=i7(h=2)$vt=-L=G|fXrfUEVx$B&EwW^3Pkd3iZ8
zGqY3LJa2*TY%{IOtn2G!b#!$T)6?7a_x}-^Hf>tyqw^<DGz6_|@lk7bQEGHr*syxF
zwwlk3266qk182_g$jQlV^%IVr{OZ)UMYCQlUsko&!`;2z*X`o!aQ&%rwhTYMT=q9i
zJk*lKzR-WB5$msCzbbxwP@LkWTJ`PC$!}-p+t24!$iKVGwC?9qaULlX4mCBkia$RL
zS6@GMZGC+Ij~^8tOM`ao*s-CS?ZO``E%VAxDMyYS3wtTKuHw#~%Fj1g#Di9fl$Dk3
zD0?f#aQ2;RdU`sKtd)q;L=L5i9CC7U1*N5H4dZ2whx$A8{(Yo=yfDAl-|i>NT&vPU
z+wa%?Ubgwr-qxn3rh-RDI<+pp{WrPn{k>l6@^=a6=2-Sldp7%n+No)eE#BoGJl;@n
zXMaFini{Bk{O#@S)jDw}3iGAv*S0>kcvmm~o)Kx#$Pmv^&)&qgwzd~nR!%;(i#7e+
zoW=l+BWofzGcfG`|Ia$@{5;+TIc0LWxw#r5TqjbDPCPA=v8|dSyF2%`8Aq$rn>#xv
z=UNv%;g~aLPRJ!+Z|~-5)5P}uez*I2m7VBk^@$z{X=!aMgO_j0ba|b9YKo>s^|zef
zL^fWjmgwzySFL*+nc1Bd1~}+Vch(RA^=|a0n`bOreEs43{r}rGZ#Hh&lo|2&@88gC
zt{*;rJlMu7{YrFuUmxGQ`}^bH?=dy8_gXsXY{%8@`S-J~7gpT;^XJdKUybinRaFJJ
zT2Gumzy9;auPd(`r=NRpWo7WycmMx>zYj`T<@am1U)xmu<AdUzJ9iS2l9*P8biH`-
z!s_W#y=8uLyLJ{o54&lXaY12C{C+t@L&Kc5E!PuoY)Gv5|M&arSaX@<;QG9fsgZ$=
zjm@Iug~07?si(!JPMv9MW3!>?sn@rirLV7bbay*DIWbMsi|x9&*nRbygP@@vornz%
zPu&xruGHC{bCW4OJ)MP}egC&&t@HP%YKIp*JtbQ5>I!FBS=ot`Cs&rw+5hiX_FCS?
z!|nWwtG~bd>i77>L}ie%H8nOYOpO12Jmx=g{P^nJA1k5@f6rgEe0lqc6CTsn?cZ<D
z#myb>eTO$zdFjHRmIV(O9zA|6C@<eX(>Oip(h^VJQ|JHB`gP5}w$Hlumx<pzo5;8S
z<ZP=FUSC`L=Aeh~vokZB?P`B1SXxT%|MQ7^&)&UP{oMar`OLGCv@UxS@RM&({eL^q
zc!9jUd_-hq;_q*7*X!<;G)~*_o}=&at9Ek-i+8#Pg^yfVSXmhvHf`Qq`0<hJj@`RA
z|Gm8}_wa><&R6ALzj@Ph=T3}G^|zkf$tP0+G(;ly*X_NP?B(sfvGjG=n&|C(YooR%
zWq1A&-m-P;!}ss|`R#r*=-&JO;h}SnjHQs7nb`!dr4btv7`f{oALHGzd$+UJR428`
z8>e>6oiiswIrsCkvj*no?M@3DLbOu3rOMvknriFl@9W#z-OaruNK=H1_0ZwNky1_z
z1x`=bZ{NDrw5O-%irxb^7cKjYpy1$zt5<ig-}j44Oib*?mQ3NUt}X-g&NIU)u~V~+
zQn_++aug<?G)O+iv$c2HL}m9)rLv%rzqGTnHnx8L_2p%Ax48bMEsP1jzPuFRVA-&F
zbF<UJgzliY-DSMz&Yd$bH9dM_qVnoo1zDR40o&?tEiroQH@P_%6&bPdNHAn)XP-zh
zVqs%r`tg494_0n52fgWw-TUQoe%)w$w!HN9wKwmdpP%onvF3NZ_w)1f+gn>%UzgtA
zQ)%3=%GNNx<|FGg-RNU>%a<=-9KXK~R8P0@$ws|Rs(H$-I_-G;y~jTe^V@@(Yhq$z
z8#ZoCyspUb;`Qs~gH5a(wr_Wzt{1zZ@^cysE9=IuYIVQgZqGVhc7C3%&b+Sc38i0|
zuf(huRaH|vbn&9#v}w}>I$eJJ`~9AgVdKV)H}+H-Z_B^W*Xq>x^z`&_%Wc))-wAZO
zT)1>;mD#_d-{0OI-jaElf#KS<YZtCwJt|-SXQ5e+g9F2nBtsP;&SU-Z{dexfFr@Ac
zxp(iLgh__LnKNe|yn3~2Ti=>BYZfeC-2C`>|K{A=+j5)R+u3hz$!tDx!ec7C)$u;r
z=H_PRs;{qDqobpz>HEHOUGT<wyK_6=)@h8#3r!XJ9^YHBE_QcNQj$`qi_)nxXO{TR
zHoLf|@^i+;MXq0tipL8GUc9z8I`hSag)S~rAOi?*eoUAk5TrHrO>yOoCr?sdU0Eso
z^vRP84-dDWJbALS)w1e~#?4!|gaj}8+y8aBckf>5sn36ZfA`dzzWQ#sW&KmT-*1d(
z878v{3hvyw^J4wK&+<x2T6%hF<>lqK_Sf5sii=O~?BslVb91}w_b)Glm6TrH-mc%;
z-YzT{INL1u#ldFwH$V35u~AYoGBH_l<cN!s(xz?Omc`fqo!ZfncXyZT+uPfhTbI8R
z5S-|su;A+IaACoVz0&3@LRW`{1O+Xc6yNCJpdYs<VSaE>kcWc6g?D#%-`sYzTm15;
zr>Ac|Z{-%hls>=K&BbN1n(w06-DMkVh1Gl(m}XxK(VQM%_p^1G-&`*j7dbh(l>B`E
z<$iM)-MJHU>FU*`lO_pyd3q|w#>STI1C1Pf{v0Wje7sM!x*9yLFm=k53BA3%Z}0El
z?-j)_XS1N}?X8Pzqqlc-WE^heRT1HOd2erZNJxl_n3z~jd|aH|%-OSpYievjq2Zvg
zK-GKNhD{mQ*UA3<`~7}SeC?;;yfZTlmn>iI-qzMOWy+M0+#RoYWGn(y|2;oHe{;>B
zKcCO%{C+;a-tT_xcUeI}P}O~}{(o)NpC5*Tf<J%#x^!H=evMgOadGjknx96$=ggh!
z>*C@+&nEFUsQeV_ba}IR-aNUpGmYCjI`Z!A@jN@rba6uigObv&f`?3{@9xa(@L0U*
z?CI01H`ZmXx_+vtsA$E84HG&#K7Ib|sWmkyCr3wn^7?(hR^2Mn3y<E>&MzNydYW$N
zHePA7o^AQ}<5b_g-~Zn)Y2%Ed;^NK~Z}$ECa@jx9>e#VkZ|r0YlUV-#c-)_LIc9H_
zY3+}P?Qb@Bc5=G9xd{mdy11}>eRZ|F<J3OxH*elVRGmC|5<K9gr+4i|oKAS1{IvS-
z1v260HL?PI)rVjFpI`9n#}5Sy3yFshAKutmto~XzK;7C}nyZz`f4<$(%gg->e|>qW
zmAI??eV?Lpn}UJ@!?tbPw0<`=HQiXPx7fYEZT@`uxIGmMHzk$$T9v=+*|EdI*I)2J
z{dr@1d-;hICxY7bx2H{?-agwr|IxFvvn#&765X?B&yB*jx3;bpnjW<B$c>H34<9~k
z{P*{Fc;#Cyb93>+!ooK)y}i8;A3SI%etz!Y?c34}H})@Ewk+e)5>7driiYp^>-Qfk
z&bYQl(ydSCq+NS!>%s*K8f-V}X=ycqMiCTjY-BcW-1vs`^XcjO7Z<s5Z+-sh@AvzQ
zmn>n~o_Dut|9*SFxmK-*4msVpbt~+)vcuH00C}?<iCJd3TnsO+t`-jp4qh!&{O?a?
zL{!u!n_s_vJ-X~~&&r^ps;a1|$r%?Hr}M6<i794(oh)d?;D*;_H#fHpn>QccS^OL{
zZBq4odCbqPaxyYK>-YbQTDsj!7&M^0f4@CwaOKULn5R|i&n<9lzA-r`K0dxj(%3E6
zVrft(s2z8>jkkIAYHbxEP6kkaosp5T;@ca^EnBxvoHC{5(W9h{?CF26N7b#JF=NI8
z&&g_QK;XlJgUnB!KYu*8{2pUK@l7Vv>;6$uQCq)8MaIR+E%Tq>cjSl*N2?QPD#OFW
zqrqw6g%xMwws&-J^hlX@-Icz)-2eEF!pArMfd(?;jHIQdmn>PbqWAdm<LunxdPg>=
zp9f7%h=__VjNe}udPvr~jA!M_l?5dwDNk3dUE906{Qb4xaZfDPy#voz2-(@$1!#!8
zxVV_Tr>7^2C)(~96T>u}$fk+P?x5Iz!ydo0m0R3NYwDp37bfIZe|o~%Cu7;<JzX#H
z=B88yB@uD)<mcz+{`m2@KmK>+)ZHa7g+QZ6N=*(52j<uR>s0fdwW4Kb-QQhqg>Ucg
zmoF?V?C9z`)WFF6;cof;l_pUQtKJ_7|M%zJ?)RJaw>m96RUujWkTE3FK|s(f=SIW6
z+TUTE-Yy*6+`*dFrMt@Baxrl5@`C2vrg*8={dmYOA}YGE@%gJq>()H_nUR?pYB~Sb
zBSp|K$g13kY5VO$E$_~{*SOxlPR(ylN9N^ait6g@YooTVGOgJexj8Kn)Pd{iIWotx
zI3Y2y(KP#-Lv(cX)2r*J>&IV{@c;Sq#*G^fuC5NxxVVTFRD`UF+{|`;ef;s6#_0xS
zZz4bgC7{UY5L6bBmE~O-(zRxdPRWZ4jIFJ$YZ!xmN@@R(`RBZC$Br4M4y&|pf2z-3
zHNTmie^pIbdF_<n&t~Ue*jb$3($eCfAdqlkg5uU`-m#@`ZU`=2x)hX#sv9QEo5#n@
z+#J=v_NCq0msVN#_u1aLdv{~<alRwRk0(Dm(wTKwQ1H&(yI1#>MXuBPu5`Tc>*3j-
zWxh`XO|daCF$o9=1Z3anvN!ni=g)%Gt5=s@TX=4+b@Hhxnp*GoPVZMgdE&%@x3{-X
zoHnhk_V>4q-=D>|cm34X(gJ0-cXxNQ=jZ2d$+*bm=jW$YdFsrWBY%H?Pt3~ddUkg9
z>OIW=zq|~dy2IPsd*ZBFT&+%xk(<*V9_F`q@$u>LnlVi`+G%0H2F7oinwlMbed{jg
zguSXe5_<0$wDT=r@bl--HG)rHzU*vhU@-e&rmemDRvbfmdb&=;1_nJnJy5OC!NJZi
z*AldH&7^J9rin#u$q0N~(K_qK&6|pPdVRL8_x4m8pPoN+rsOQs>}8iePn$I9(6KEH
z2~TIvn$^`Uu7Bv_#f^?XEC2nml(VTgVE6w1e);$N|J!BP)cpN=J%=a2chcm^7q@1I
z*L=C?{^Qx~{2RM}9XodH$LI6*j8o#I<+j4s0+{aUm$hD2bZ4Tn`;D)nA|gjl{eE<`
zyQ9B9J()T0-X2b4V`I>aZ18fwqer{NC(fVWKWUQCi<d7MHKdI%2)sHa_9K7q^$>g0
ztJ$fMk&zd!UvGC%0FBe$e8T2%vzgcT^~Pmxd<;DDb~4iDc`N~8^Ze)8G&VOoPc86T
zDpdXLjbmJ#9D{;^fk0kfp48KYn>;70DH<3Eu=C5U*~a+j-MhZW$NN{?yjfvU=03Sv
zaD|my&*L4tKq~^O4{O<dl(w(4S+jQS>k=M@KR+J#M{G=Ly`?@gzW%RiQBlzi!-EG8
zhQ^r|78W|DrKt%C37MQKPD@McU7z#h*)vcCHZJ}C?(X5ApP%34KRwsFTuD{6^{u>J
zjm12>S}t8(-In(D<kQo1CwQoUhC~V;G@P;&7Z*>yw8S%~J|;$H_SEG`JKx;c$h^D!
zeVdP3^YrQBO-)T74zkOy(6ul1|73d8EdQR(i4>!V$Vf-6sfW&;<Ga2tmYHEq>~68B
zsHh1iQzm$*6qJ@8y}P^o>NHTrGuOKOMsQeqdb)_1*r79Ld~$DXPCxH<e_!p*1^K?|
zk1OBr{SIo!R#a3t=uLn8?(Xh4Tdi|*b7kymB&xr?Y4lNR_E2eBzh0k<i|fY6*4EYu
zr=Ln#6eyVG+&B;yxjMgY)22-Z6(17J?tWaq|6kYr`u}s^`g?ddEcct6)ti2HmZ?Ce
z3#bFLr@|0a_AngSl6kq{{XN?b7bQ@asjRF_>$s(b#evhOxoxYzxum73P1B2AW%kdq
z_}K=>7od6YL#^DP8F$xiv4fW`3GJ==>I7;+^sRg=wffJ_U7K|I!-M}`30Xf?H(~=r
zV`JlllPM7!5*klU{`>H7yRw?vvRjTX)cxnR?AvEo_v@wlpHHXt4GattnD-|qB|Uig
z^5mg)5gVBT0|N~V4I9_5*LQMuzPj&_NyY_*9!X<1P*2K3rQ+u&)oD7Bi_G3{Ei5f{
zjf;~5H98J&FcKCP2F2B6b$`$p&(57YPo>Dty3_sc-8<0eGYb==kFW2;SFfVh|0%k&
ze`Z64@Vv(seXAWkJvnEYWH#;DV`GwiZOz3?TQ+Y_ww3t5`ku?pBAMfRGxxBA)}XAO
z>Y$KuCERbzmM#CDOUpl%+MQ$B_xPU4J?2A&GMiIQ3dvX$IHW#U?VxZVfB)ZOY~IB(
zphYjHEbkc~?l3=b;lc!6xoex#{moLf8B$VG95h6dPEFCwiH?h#ckBC#-DPjN#Kos;
z-4@r6<JlO&vonUz*w`49(M_KnKYyP8<HwI3-QCIBO~;QOy}J72#@yRxpoJKq1sul4
z%`tk%A06$Mwt3v=v@n4=?CQxgXVzR!C@wvI`e~7d2p4Gm!`a#34yMz>18;6_el7d{
zHE2Zim4U7<@BV+kR-dY?t*z}%7h0`hXt>c)`-HiP{mPXqH*DFG!u_{b&NeFb!*BbO
z7cWlK)%me))t(9Smc5Ez4;pIpE9YkLp03xra;4^$EnBj5cXNyDIcQA<%_)LX{o!{0
z*K_23zrMJ*_>|YX*4EY+7Zx(Fi{0JED{YqW_SV+*s%twsKyx0BI%3W`V#)9B>|C!J
z*Z2GS4|Q?LhZgJ7+S=Mmo}Lm_QUcBNZOy#grWd=*#U+*d*Vc@SO?E#XFmJPn&dbx|
zY;~Gyp`@hbp*KDFc%Q7Mt?k_0`}^zdZ{ECladr6mj*b;emb5IrDxsw}&%@=*kB`Yx
zHWdcEayC0Ie!3747#Mg}Z%4$sPfTsSPp_;DPRYz%xyjexzyIH_*Xym0Pn|Hq;qLD8
z`sAOQIyx!WH3cu8nQ8p;wEq5_fI5?>4<0zU_sL{V+45@jBd4m;!24Be*ZTbX^V#3a
z+gsV){kYWOsnezfU0)ZgX=OF5ql1ZwDdWqFi>2rKmxZl~a8!2h3%R*1`?}tyO`EFT
zW*$9$JlM^RO;B)e)z_|^+}zCP=jN`rmF@c-f3(`c;vMhe|MlFUHl=&Nob7~(6Aykf
z&Ah}?{QTTe^ZPZyZ$COZI)3>2wRQV;b5KM1+y4Lmq~G4!nw;It(dx8qflPqe;@-uJ
zl|g+@X=!N}SJ#cT-D0{;ot>N@A$$A%*FXIVZReBSkbB$A#l=NIQ<IYcG<RrWZ4FvR
z02*D`UvJM{|Kx-qsI*g??6@#s!%Cx8C&f*hH#@q_uq;+PbM~y_^wXeWOA#?KC1qu1
zD=RCVdBW;`2M!%#GMdTb=jW&Mc60i9CmpfmV?B}+PNoQSAC=NwCjnUv!6R+P6SPv~
z<HwJnDF)DxcXBfG%8)MCZn2FYPM$m|VUWPEX6;(V$tMLQBvzQ+`+mP(-hZAAW5BkK
z_)7hAr>E<KhC(Jy5(13^9DNCDhb~#7q9MY?BX2k7=;reGaW`(>Jb2>7ies8{|Ni-N
zVQ+OgXh`V(zFMu{eX`bU?Ck7cUR~whwr!h5`8%10P3j+wX7bqC*$H&IY}m9ZX>*;a
zioNeFlf>(pg@uMi#l@!cJXbF|aKK^4?Agu>102*QZ~V39j<E9d(}n;3RDSz;q*Hjq
zrcF(qou0jWTmJp~XJBlcY@77`{eAX;Z8Mf{-D<idNVAPc^3W?oQ&Z7hyLQd6uea0D
z(E&}l#q6mNoL~PBw4P&0kY?VU9f8?5!@^?!GMHY^cC{{h)39}`sg91$f&h&hB1an=
z8$CQc4lJKv$JOf87`)ss)biS?Q(SFrZCm@Ie||o1e{oZ)x7qI-UtV4=d~kqq>ek3p
zPoF(YdUT|7jr+sh@AoDDmXVb;Og|^{<=tKBySvMetIw}lWVW~P@v(<5E-tS4_s8<K
zn2gLBvC1!>KOYW{uRZ#0ZNx?<Gc&UdTecio;MfeRKNx1+?KYa(vo`zEmds#}r9n%U
zFTWlkCv!aXLz}dN%=h(a85tg(ot-W&Q>ILDxx2f3xw`*6fxt-qj=nxVP+xlDM8UcC
z_4~Z|Pi#&<zan;b*_63+eZ$wqC^9oMZ+m~RnLQ;f&F${4(#3_3k6oOm8@*z|f`*O`
zT&pY&xARZ-Pyr1AC@D!<m+6$gy%n1Kk6+fxC3u-n;O_NB{2rDoL%e=}d%O7g`T3J$
z^p>;p%LxcxoUHDD;=qB1clY=EgSxOAlij`i{M3{dJ$jUM>fAXsC8dP{9{O>67X0{8
z@#^Mg_3rI8^_4pX1#N47Ot`f@|NOi|@9*rKeCEuVjumUl7lZmr$1^W4^L_H{*|JID
zrM3#Q&GVOqt&IxI%F??1?9CgW=jZ3ED=k{INGV8bs)(4Fo2#p9N@Akp_Po1FN{jaF
zu`x0?U;g9A53Q$0X=fyEZOs-3jaIB&sR<f`sH?M^Yf;$5#wT;)lAK`@i{Q$7t#!+n
zyKhcA3t7_<`1;z~mlqeihhF01=T~1D;#FB$>EdFNbw%Ubl0VnhMyI5wAAe<UX*tuX
z{GHFyqen#q1s$ChPB=H$+TF!v;lhO}d3pC9{1Ffo6%m=>qoyn?D_c}nmUcZYc1y;^
zCOP{$AD7hYK0kkkzU!QLGUd(6J<ZL`J9q93%*@n$`t<3<7`^4SzrTfc{)VpMxE357
z5a7_-+Unx+W}d22)UFcEvvVvbcbs~s-RR)p-Y=)B1j<QSS67L8SO5I<^vT<|zAj4&
zZ(a+rPfSd_dM!KEf4*I6{r!Elv#m;Bxg0%ubd8V6OVL%jcjnnvXIxm|sCE0+_I&^H
z_xIKcm3@12(<Jc_i;~i*lP48BU7XnYWD;IZiU0od=g*Xc1c&MR@nN%<Enlww_s{3^
zE-q6hPILr~-`?l_{QUfK$Wj+4C#Q^GUtYf1@!&y%lu-(YU}l+qSjqc)wxFdz8j6Yw
zZ*6*D;U@~N+H;<qm}p>Z-0Y!pNPT`y6KG=N&Gk!{4oy<^zA-s+)rTvE&(F<$aeu#k
z)YdH4jS)S4veu7|bP6+^x>f_4aVdLuhqKeAiC5YTv<&O$(W4fKH5VtI7S;TC*nZ*K
zwPSOw%R!UR6_u46L-#NDn;VsT&(_AqBuZo5qECP3+t<r&&%f{X|KDHGLd_5@MG>yT
z&(D14Se0`9{rx@t{rc?8%#Eflr~2PtTPyA5<z<k0Nu{NwWx<9G8@!?n4GkxFsO0d~
z=jZ3&*<U~3cBh${S=-X3stgCNT@%}$fB)JG85tRw3u~jz865ol_|DC<?FJ2?&zsTH
z!*gdxA+x8ar-zqU)AZ@$Pl_zxynP!O5U`*ure*VHW6+St>FN514;^Bv{q@B$Bt%3>
zN$J%VuHv6k`nz`RdU0tf_oYjhR<(5Y^c*=cQTgJY%3_99r8RGE7%o}13^Z6<v{NQ7
zF7Cp$YiZdtB&4M`vp#j}mjlf%t<X`6nE!_%RXgtZ@#Bvl9&QIMa+>;4WmbUJR0-=c
z9Z=(P$@1l!Kd(Of<KtsR0gelMDvLpFNI^lt&_gd?yf|>-f`FZ!-H)%=<3GN+X>5{t
ziN)b&g}S%5w}*;Q&h2fyot>R;c7&~sT3G-89|ME8xA&IZ+ta`+G#*}C8?CcOEj2p$
z?;N|@UAL;%>X@2}zI*php!=wVS&qb}O`Afh;{R)g_^$VKabb~ue{EB$_m3YxR(#pl
zBW23PaA8fPvB%O&S>j*kn1B~$FfrWNRjS>_D}8LAwH6mQ_u-S1)fK0oR-Ar%W7XHJ
zvfk@Y<7)o>v9z(V0Zmn}42e2^>A{YA{kP_G8T)>3zrDL$|45RdlCtu{hYuU;{{A|6
z=T403nHLuqe|+3;uViBr^YrSJ1q&3uHTQN*m_56D!2*SClQuXgH1ziJ=H%pjIH^8=
zgBz#d!J|iAQ$wE~n=o~1>y#-X9WF|sML0?mJJy=T2Kq8jKYf0G>FaBtBnwIamzVp4
znronna?6%2Cg;xYe!uVZziXE+ITaRebUPjzQ?hQ$77?d~0@L;5*BLn;n5rEP>cC%I
z9qYAJ2-NP_xX~~sCg#F2UujTdLrbe`?WDDxzvcy7DmCriZM`k;Zd7V~sr|34s@PTQ
zer8@;0v>DkxBn{=U-PjwZRWrF#Kgpo-d@*`5RsVJ*o{d?LAAWhty{OW*8ja?CI}k5
z*_wU*!Q;owjSda>_EsmKon_k5*?I8i=jVmb&PYZ?M<?IkR|^^$G|Ro!(k-qJ8dbKa
z_^{xVQr-JKpZiYzyuQ^&YkIEl*WlY5MJp>S6YuUS{qW%f1H+cg%gYW5yM&}ABrNz8
zb*Woif5QCv{paRdU$4q}x+gm-kC}}J)UTQ_V@3yP`9idxOUuvC&yx?eaE5-%tkO?Q
zPd|M3t}JLdZ;zyLmaTf<<A2@X8w&17hy8Q8aPy|5eElE8eSg2**80t$prF9;`T6<u
z`N_%2AD^5QzO%nx9+cCgqNIX?f<VhGY-)drY}~j}%m2&dNs|I%Vt8t6Yj^D2si>sH
z6uv$#_4n!H5gJpy-rU~KzjNnKg+*1ionh1eAMck>{`BNzL20RLznpDCdV2eoEha8*
zZkr4`J3BS>^x9_6miC)t(fH&^3Pb8+rlO*vkViG2o^Tcw7cUIa+L&|GNJ2_#Q=50C
zgtWAD%-*W0x%c;0n}7N8<%ZVf_0F@+avwZ+(2#n1+Q!`P@9(>(rKz1ca|V>^r?$m;
zd2z+<sR&e+tF5iw^ZVUy(EQ63FV#77<_L(1wf+A7-dSm42<QD>rP*6QW;m@3NqT>8
z?}^i=oij5v&GPS^`L=WS?#DkqKGy0z@F|;FBfG`L-JMxO`@CN4t_OR+-^&iDGcq!|
z(SLu^B%wFAxBEYxGk4CM2@@s+B+DE<dUQwaZ!;Yo9fRs`IXu#4Jx_O6e}9+bJCR#l
zFJ$J!%-`SMYG`UY*45dSy}iY{G>CJe$C0$g=*?-oMMXs)UR+eRu&_9A{ye{#nVE-&
zheKGH*sX23+_JK=B4T1;F9p}U>)da3b!x7w=~e%yRcl|~tNr+hm4}y?amrVbC(oX3
zs^wjIx@*mvH4~;!KYlLr@v&YBtCAJB+F87oPKs^cxpU`@jmhlQ6N1E}qM{`1>ui?!
z%w)>V&9$(yS_NM5!7FXH;iHnPtLvKQUX!a0)z#V8ty?Eyl+tl(hfv>R6VT>}?`)xU
zr_AgB)wsC3E8E%aJ5?txE?)BbT5s;{O{v^&{c>xIYCv)G>Q&d<+uI+%yu4h(IE_cz
z#5(nq$eUYRx!>O2-ac`npp&z+Vd<-o6Hkj~%$~jZrRMMN@B1MOewo6;!d_fj>b><R
zXm0x9>(|k5|E*Y|5fKrwApQKjg3{7fAGO2p?(Sw}$i2Po=tO1r4LLWBrnT=Ze!ghS
z77;f$Hx53&V>1ks6(*ldNKS5^ZJxhs%E`OC%O5^^bck(2_tB(ptGp{`@X1;wJU=)0
z!^e+_%m=ciW<<yDE_>?~6eOe~#Hk|0>DD9Bn0|hqV|281JHPy~dA8LGlTRuLbv^(&
zXL8!opw4B!vz`3?`Eze?YhAZaZ_b=KSK6kYerjN5))t|Y*1S(U-v6pC<M$6A4m>#6
ztT_F2Vsi3gqmvDtot$NFZZw{ptiHJ_a(|ueZGExskEiwbJLrfdUtJZ-!ONRlP$wiL
z1X|*~Glp-bPurU}IiT@WhseXAwM$#gq`JGh3=$8qcq|P%@w6yrZ<Xl$`hS)Un=+Hb
z*T?a;IyGLn5b(__u5zA_uWw>@Hup>)HgR$Bsg7Tsf<|phUIch74f0SC;%IgH@#9Cr
zg9D8QCMGSNot(eEykzF)=I%WgIrmiR?(FO9v=Xyc&7bvZ>!z*Q*ONXxI0$Np_ez_C
z#-%pY1~2z(-M_znYx9M_fB!CAzrO$UbbV0SV_;yAz&j1p^L}}0DZ?qX9#Dc?=0Be=
zI5^nF#U<hCsi|3?H)mW_5)eF+WSEni`|#=0Rz>GF2JoVh!bdJgjvY(-^yK8#Hc9h5
z8PM(syV_qHGPj?fXM6hF^)mf+aeJjWIXQ1^&zHBZJK7~``mFo$j{lFi1&d_D7w`Y~
zi(5`kPO88E!M}1>H@BpBcXoDkc4lrd(U05HF=2v0&W#NVFJ7t<h7P)D*6@I~4Sm0+
zC4cxxq0I5W?;qGAEeWw>0}aW5b_VSLZwvZ9O-oa=asPh%s&8*NJ6)Jsof<(y<<{2H
z>F4LQ#^|v#q@<;7np%}@oN+;6YAL(asS6jt6WBg#&7GZ`ZG5s_+~RsB8s`oiIFQA@
zYFhM;f`zvjcg>30QJ^^c|NhX`VK1(&m6rDSWp#SF-LLQO&tE&m!N(UibK}ZKUN1^b
zOI`$+CG6QJHEI6N;^$6E6E_sC+wtnJy{3^7Xn-th$`&b5TgJU#Ztds7uPd)_*|J3{
z{lu=?-)3{>%mLMa5s{HsCBic^HSPZWNS2V6K78Z|%WSjURl#wulCH0d1??W#^tWBU
z?m)YIUB{(MK^h`ld#b*^+PcKN?hj~jkA?`>jT<)<w6tDrJ#xBG=7-k{neW@A%=2V;
z<!ole?Ws7p?d|>j^DkYxbY<OzziJXvQlRzEH=Z-I^99`AmaD0z_Kf%0s=PUK=DYwc
zczCBDFZ;`P#hyzgN2gwRed_Y%$z|{E{5<w4_@mXn>hF46b8b$WXJ3DB!JiDLyIgPc
zm3_nI*RQerTJ`FR=B6!M)|CCfyxjlgpP!#gUR+ohbvp0#G~Flf-ks~aeEs_J+2;Az
zH0@_q{rsf5HUIv+ygNG<ZrYoEeqPC&8ym0Bi#wYA?b1^3D+`_5ug_yU{x|xgoIqbS
z_tIs{9&Nv0clg@c=!?5bv$xg$`0z02-M=EpzNW<atxk&5^yB%mv$G{6C0FVkxw<tw
zJZ5JRtHaGGQDwKD1{bA9AGO1WTDf0O4PRx@lk?)jLeLI~x<4P?8B&8+oSSPcZf0f{
zpdlh*nk6!Oow2pGbnr5t#t5CZ7`@}}^0h1ra=Ok(Zcby=n9X?d(j_6TRwkiNmd3_L
zhEvOK#%#}%Wl+%9=Ld~CM(DJ;C>?xsv^%nH&YU?Pu7<}iEPsFR#l5}O%lzlBGy5Ns
zsWE5YKD%e<=5l9eXPZq;_weAz$;nB`%;Z#8SO4?%dc4k`E>Z0p`y89u9^Ttq9eU~J
zjzZ<=HE&K$)fV7tebf2sP%F2ffWU!E`|AE$F)=ewoG_um-~Mk%s@0V@Z*qF%Y^8)c
zS<KALb`(GFyH(}s<<+!yt?q2G?KQHZ3vzgyHG`KOIChN9XeN(w+L;9Iua;I;k2asT
z1MPe9@$va^*8Dz$L-Xh0k5=c-oofrwaLLSEd27Sf$}cYjV`5_!r+O()K6yjr!qn=d
zLx&H8%4|DvF|k9(kFzUH<OmB3(|UbBq~!B6-y64YJL^q%)|z_g{(X6df>&2GZ`{1O
za!py>x#P##FE96R4_etWaiU;NZSBYB^XnJo-`{6oW_Ij)eEr!&VS2Z_erv5-37Wm`
z;^O9h`0gE_wzjs1icpWVd7tlpo7!I`DypiE3j<cnE!q*gyKKYOtyeQtf7(`m+aP-8
z_SWonL1niME4OJ|SAEgY(a}+ueDcAYH)obz>+9)hiP1a$<>h6?$tM*idR$re`_nn=
z_n^Hcj*gBww|{<qK5_D7W^;4%DX`6J@(dH^%;{ldW;<}})-2z{ycu=+VSCc%+t<gX
zE)48@EcJlbyuqR`IQjj(y(*JWuDTn)kzK{sc5X>Y$ql<k2Zz<+>(!JNNt@>dtPWcX
z8h`9KRiz^&EZn)`&B_1Y@7JI2tN!$)^VSwN9tqI?fUNJKJ?rlrY-T@s=gyptj%m8l
zZr;=NQh!g=iF7hfJENeqh*MZ?!+z~$`!g;o{d&}`-_enAVuIq9>hF1C%l>r!Y~d8n
zczbKBi;GG6IT_HHhoIn7NI!gU^>-n`!2A1ZGjD8242g>JVr6B$vNbz=SH;IgUUk2}
zz4d<h@S)c2FJHc7+}e`)<};Sv8l4>)Dk?3@{O6xL#Kz8kd4i(zswtsGMMg7c%@Pv4
zcxtNl$$R(a?fd&JT1jcs)~!oVPuJhP<^QkO`bt6fA1_$E*tz`uJyoSe`uqP(N;@|v
zQdL(;X=jYz?d|#MN{fyhaml*1MYB4{F7uMgt*zPO+}4SQSfVzk@p@lAd$!lh+dH!I
z-R<q`5B^kCirQOcI&;R1fYZ}-K|3L?wyi$LZ~te3P<>dK*lG8p^JZ+=U{F+8sJLj8
zoIs<4!}D`<m6aA<xDZfQ8)lY#jOW+K<MOMI{QvW_SW8z|b<w8RR}LI#kh7_9aB%^x
z2yu6J*V58j6uv$#@cO#g%p)CwAwfY-mX?-FR<Ax?#bTm+8?rWYjm>}fhLSL~Szov^
zh1R8?pZDgLs#4VUJXzm)Haop`u=C3W{QmY<Q&Y3Eqhnj{ZMUS2GnQW7k{R69-><H;
z=<Dn2mk%^DUtK0HFYiCssx;|-rhM%e@OqGrQ&rlnPKr;TJ`D^E6pW0BxUe>Qd&i15
zyAR&EGw0Fc$Ju+u^<#G(x%B(h>h&w4x9441=*(WmTPSnAV2|bb!vD>C_U>h5K&)F>
zu*qIupTGXkNA|Sy^UiMDntwkoH^#kBrqOBP1&iEO7iNQ6-LlqY8$OEdE`RUl=El~&
z{SnupSC`6*iVjUu_1=(jQpm;KeKBa&@*z+UL4Cv7+2)|x`w%gIY0DxN7AD4fwcle;
zq!=w(wk)eePFUS9rM|trU0G3)F)S=>!s(|!9(C(~`0_GXL|j~1M~6q<e_qP-9aUem
z?(D7J?o|UCa9ka}9<+2E)Dcur0Buq`aq{HBwb9#6^pe@x*+Em0pzSJG=lO?)iA|a`
z2{d!f(BPsJSXpjoV<W@J$f$E(Tt6;E^0r~!p9&5ZCeVWX<YPUKE-o(3pMu|gK5u{g
z>}>PGcXupx;`Zz~w>tOsty_=2->*MD$FjKK$A`o%85f-vKi*aTo{ycKo#DWXi;F>H
zHd^0V7(hd!v(0jw=FgW`Qc}9{_1lY!%AlFfq>U0@ONAmMBeiT3=FOXT;o3E~%gcNf
zg*qR+d&jph0F-|hx%bPJ>fL_zN?lu<`}+F$_1ogKwRCllc8O{$Oh2tS(F3$#HY$Ab
zfA!cM1&my+Otw{D7y`BlBt%6?$=O!1EO@iqOKmcv#_H#<uE|b1neyX7Gk?LC7lBWn
zKL_<(XP9ILO)bDO^wRM9`ugh?`pcFqS)rr0GwCSTl4Z+48(`w=eyYCid}OgM@rRXy
z%y%&@d7imbr!M_eHC63Yea>`Yu2u~_y)fy%e~pnll9Q4YggP0fn7V<+Q8cEWs#^O0
zX8QbtmoEzo3JT6J&zC!rwDHucJFi0GRCgvtW@Kir)a5RXg6!b%f$qK#>gzD5|5xKP
z!(ibxi@9^>7Ct#4cxPuZJ0Blk#m`Twd@>dc?(Xir;b+#p{~6LVze7MwEbM8ioo`>Q
z?9YiOQ(oNIsQmBq{Qn0IA9h}B!_csIuPtb0sGwlu-s<m%Aai0Du3ZcJnHX``>+@3<
zCPo$}#<X*DI$ylV;9y~DX=?+mSl?Uy-Oa^?<=VQ~?)i1UI3GTI_~Gl<qZ<+r7d$w?
z$j}g?73#UTNb<^3pDR;yOTSK@I(6yCRS#}&&v!2=F<G>DF=#|;P2ApH2Zdc$Bqt;^
z^z`t2TWo7<E6c{lR@%2*FV!sbl1k3aO|1U&?OIz~TW^Wp@8sCEYgb2icd)d3>Gws=
z&CR!rN(&1Qf_DGDzP{dBN9_8~KNfw#;3*y5l$4Z~hlkriopCMyPJS7ShA&^fXg$>a
zH{tq{K+r0pFYoR~r*2!R?my2&=dwn1k+g(F2WVNj!_CPLudWVXykG$X!wq2%1__%A
z15k@*T72nCb#?W{-qZDF*i;%lJ3G7G?Yo#dY%M^yxPBPt;t;Ky)(<NyD?uA7?%cT}
zRih`$D`~`{sHm8e!!D?3W;Si^<2GLD4}X3ZGdKhW3JM7eUp@Bt&6}S0`~T0=UGx0O
zlNQ%*v6Z<C7*pRbxW08d14BtkNkU#;pR9Em1H-zwy{kSwzI^$zFe4*l4v$+{*fg*6
zN58$j9T*hU^yg2_n_FAEdzY`suWQ((e(7o6-CeD#!`B}=bB2fE!|nY28#kJndU|_1
zpPr^`m~?~#w7S+j?~X%Von23F@5C81Iu0Fj5)l^<pQ)&;s>*ut;zfprySvK|A2`79
ze(!fV=z{knM~`lt*Y*8gHGlH)K34|^2GEpTnBCLfM$qc&TU)cgYB4YPwQ22&UB6bY
zjnh7L>XcJEpDbvIFeN=*Sw*Gg?d2_Uo@aloI0fo$#@GK91ubd$^+ogB+uQ9EmEFTQ
z)3dX?fBdL0dzXB?Pu9T5$mw{WZ1!3AR8X;#lA>~LO=R%XU7F|DLsoYjJH@53n{U(R
z&5T#-^u@%)R(&}Xv!j49GBPqt+FIuLRax++^eo6qgbmxawMFQ(?c8Yz8a^$1dn=SP
z`eSFTufK=(KUpCm(84806(Prk0S}(fuMeBu-^k1!_FE)SK_KDD3DEXl14Bc{f&v53
zjFz{zH^YJJ@%6o+{nfW_Nd>JG*|u%lo8v!Mg|7ba>8W?e(IgEKuA1L(w}(E8-(P2Y
zdj9<R@?mSELZ8}zR@|&z%eygxCptR1qN-|B<JMU=y4O;o=iAkSy7fGAHWEcUWt1j%
z_$piH-LdGAwHB-W^(8U*%skubu-r;ZYiscG1^4CM-Q4f)>|{<)Pv4s9Ki5k1-@kua
z&HGHZ*8VO7FNjR2dw*~5&3#+5uRD2rck})zEiDbL%sq4E3dpS!PNozT79QM?cvx!A
z{K(B|8#&pJ^+-1R&Ne%E;sl37q;v4y*QHbE+SN*liHS`({q)7<<^0#yL^eM=JNxjx
zdvab&h0@Z}UR+-<@8jzm+Bexl<;T<M@d{$y8?&yi%38km?%rzi(~JB2`OWg~a4>)t
z1shg>%h3?wdUI!IaB9}ATkAHSn!j56cGp8lj~mnqdvLT{95mqH-rjyH<ZjVUnPbO}
zP4H0pfBar;yKCXvdWQe!F3k1_bUBj1IWfW^q-SH|b(cL=fy%QI)@x?zY+SN3@bT3Z
znX_WJr`-_PeJt;d<5EfQ3nEvJ_;qwJPARIZf0O@wQ(hwDT-Kj;^V;3-SQbB<JM(Vp
zJ<IfShCc+V+}zk|YHKg9kGG#=UCwuPb$B@2$`j|$yB}`johr@7%*N9&f4=;@`hS&s
zzFhL2VU*f+?;|^dzP|pNxV=)fzrVQ#1PDyikMDc&BBObu__3>3MH3Sfca*)I75C@X
z<BZ(g-nrK0Nr&5bIk>o-_Evp0$hl$gG|eM%zU$iqthW{}R`;9Ju{L`9t1Uf3fgcOi
zN|cm>w&dItnyl`BOnd#FMYqiUe_dancySSE|3hHz>}d&|$=)s@QXeG+l$3&I{MI=&
zc}wnXv#Z-PIj31{b8%TJ812vj0v@I{Y%LutL_U80?CjvcAZ3=*As$~NxPJe?U6=Cr
zT7<2O={!AMU%Ybfj~^8-E-oU~yZvu9FfuEg-gJ)nAHSSU!{*JKBb_DU_g00@?C@Lt
z;K-b3RW*<Nm6Wus)D&D?mUgIkcJ%eRB_%28#qH^+{{HUenY8#2?XWctMMXsuuV-Ib
zG4WnT&@>fKAu02`o~Ng$Yp>6}v4OGl^)=J%zf-1&fHwX4>xd~SY1x6z>)Do`Q&whX
zWNcjeZb$vMo9PuVmrlR1qcC~Tr&HReUU*oSy>UoLP-tpyzPP}V8MG?p^fX=Pm>BT5
z$%6+8Z*FdOPq)s#zV7Pm_Qhdqqd?>6phbyoywY0h-{0QOuN%D$wAA^-C2xIA<=U^W
zuk*jYzMh>yCu$2vSy`EtGOw(C-Jed`>7cC;&(F<0aq=W1gNv){OJApSopYdtkDs2N
z=HTOV`~L3k)K9a`au1!>-`^9pH7oJinVAeGYwZ~s8LhsW<=>O3{{F5u>9><h$hwtQ
z@2vy{XFhrUT>aIRmC8*`O)f{fL|uP>d+Qn(H&4K+V{7*Hu7%F+U4Q=6D7FZ!4qq<@
zT1zb8)N$Z|gP^qZ>MN7y&C_cUP%<$I$+^Al?1OEaHf?fQ>NWMq+uPe)1)M+|zZMlf
zKIX)s$R}^7BW0GO(INn9hKZk@WhyKwDe1(aSoim9_`|nvb>(cURHo_2>#_06>9h!d
zno^4@KR-+Q{_d`lv2k!kRaMuWJ289Tu8Z94#=^?l3EFA%S0`%AiG(%GY&-(<>;LVP
zu&de8(bp$-^5jX7*0kf18)6P7+g5$aP;3!cxpL*9t5-ws>?m~RP;}`Q(>?O<@9$Ot
zClwVHj{5p~#TJ3^buof@d3kKfDoRS93q*9376qwUSxJ3;ecipT&aRDDx(!sjfEHg|
zTN^DXF23C6^Mv{H*O#z^wts=<s6KosD0y>3u(7eRHShSndvXsSK8)B~WqPbvx?NN|
z3^ck|_4M1Do6Yy@e)F!4+8Pv<zGm&(!ne0fx8&cKyS&^VWDO{-f);uH|Mz!7s-&W#
z;)YF|F0Edle}CVL+uQRu_MKhe*bEx<Q!p?H&<_hPlv><-`0!z7)9h<5US3>#_U`?7
z&ieg>)B5{eet&zrG2^0AkCf@Eh>t>o6Ya_lDk*8DZV^AfNk`enCg!D;YRiEqCntj{
z*ds?==Gaz?d3t&>n8a>gv}n<TCr?`b{r#PMZ%<`fZuWx%jiB|f_W%Dh&$TX>^5JO_
zcyVDNvyhO`l-&5;WxQLnu7Vb*y?OIyL)uv>le{|;pv`>z_J10f+4)}hY>2pUYist!
z4T;Ws=RbImkow6!*~!VN;PEluoEsY$Ykz$)wDwDW^Y-o3`)k*%nJ{}cH^Yzb`~UYA
zKR*Xr2W?&U#$d(_LBYU}8(2Z*g>}h`2}YYszQ4O`SpTo4rM>;|*|WSmckTpj&;{*D
zdwEHfg@pw)ixL(VcFO0es8)zWRn@MGznq<%6j~1a`1lyKn>b`mMBsX#<$iMwYJZi?
zu`cKP`uaLM!yL<EHs4t$j0-Yre||X3|MBB-`NLPQikg|3f##8}t&Qf*+pMCizyLZH
zM#0on)X&c^?Reyk!iqUlr;4`m%lGBo-SzPP|9|FMAuAT>2+Qo9*RjH7MuULhRHaEO
z54T>A18o5*Dk^&L{Q2^pdsnvS$1hpBbfNETvk5AkJ9q8`?Ir=8iIaLtBxYZYq?wsn
zK~Ygqln^NG6+S+ec(93ehH18#Rr$L$+s`&Kvp?GV{odtjkJ!~=Yoj7|m+69Bux5>p
zP1P3;UTL!<<@amZ88Ytgv)%Xene@`5i)(*>i`bSUDP@xJ!KJ@DY_?f$mrQyy8*jk&
zyx1%2<K?$zT|IPdZS+^S%~gN?H?#8}yRtI)<F)AgnL6$+F0O|=IXXK!ct8QM{eIo*
zEoy>-7bhPS6HroGbiCx{C05X0tZg|rlU@dNbj-8NVN_HCfvcd51Y!j73JZW}B?!Y<
z#<GZIlaBMWR2kc<E0-SH1c$AQ;f$P?n3|eeaOuE>3jrrK^=!+%9i_1AQ|_%TnWuIt
zUanZvappKn%c-S{-TSq~CvVbGHZ!|+Nik6Jpkl<;<H>$5Uvd(eoSt6l64hqN(92Qr
zJhUowwT`ra;LIg|*G&55Bl|mjZ|SNH*LEKNSnHJ{r=}3{E+suZ`SLQ~2`ZfD&Ycr*
zQn0d;vb40E#@E}y^W$N={DkS#+nd?>qpYSBJzv~!=M)zwS5#cgtM{Ds`+t$LH61VN
zXEX%t+?;w^EF~p{fdO>h2*ZK<_5XWit;-^ebd(kubuRuX92jWGxUk6m_FXLl1A)K4
zzaQt;-?QMem7t)YpslTs!-|-lrLV<6eIw6FAAY@F&&&{4|F@JwaYOofInV%M+V#>;
zPdq{UGfQ7xX<WTp`^}p-4_?3S-mt*{R5!eQ`O@wB-m0%!YQD2tj`d0(zI<7D+qP{h
zLRJbzZOv*04T`I(x~|{zt?}m}Ays>O`xBdb9Glr*>R$eqJ}p)9`nuS|i{1M-6g+e?
z$-c%jRV%bf=kyG_+Fe0*>gw#)<?oK%&fniVVS)hYY?*7<#OBSL2delCa&MVz?!O7r
znQ~I7<n1ldtE)nrAse{^*T>m{ocsIR+u&3GzFv<9?JCnbEn%7!vat01ot?oq*Uy+C
zvH4MDOj(&(+SysH3l=DVmYiL_EUX)~<-nz--rXhb=JxjT+TrV5A|fPeYHAYl^ZEV#
z{Xq>6ZZRE)va)R#OXc7D*T1}^3R*&DntiRMxtV!Y*xIh^an-!b{pK2JPrJQ6Kl#;_
zm7v`mHa0d1`T6~?uC9K(^?ID~&ABHp{LcOV?=J^8H|W#{&;o|dX}+MD`s?fC^-cP!
zzrRbow8WExgQH>bV&y%1_J9t1*nEEK%I7@uO4ej%X3nrERC;w~C9}J`d&tTlR$1$^
zBiGhOGm8A3$q)=WX5iuB_QUt@%lrBHtq54i^y=#B_VW9+;^FJ#On2Rzd@nt?r}vFX
z!U2X8n|hSp`xqGb<n7iNZ)n#!y&`aN+d-@M_xJbT+?>AHwOedO%ub^>Z{7rK%?btW
z+@EchYb4pfE${7j(2)6s)#3Uf>*HiW(H*&Yncq)talM3RXJ&$y(%011Cg$h!i;Ii@
z`29QiKm((jz4xua_iTJJ91fRHE&iLQ`y;r(#irQ(An33e(A-Yd*H;UjTDb~}ia=}W
zLA{{Q&(Diz{y#s*vbm#!BlZ5O(A8}xPI#!OtIuB3e&P{JprujyyBI~!NgK9p6En}d
zBjCg_andBElt{5nI^FvF|7^No1!@ksxU@`A@${IqWKHx3_em;-xwlLf8{W8eYgWv5
z-`QqXuVZ!=xoYk&dg?X#<IkTzIU}bXJ<!Pfb=um9jZQ1o_4NE$SXo~l4K#T7@9%Hv
z-eBp~%a<>=D16j%a)ywwuxqcBsn5I3I;S7Ky1M$5)!ypwVv|%j%gf6@et76S`Ro7R
z_y30_RepIf@#LnqJG)A~Sy)&kdi*D;T-;flzA^v4UFy$?%I-YZiod=J6_k+3D6*el
z|F5#}=cm->*X?a>T#?gUXPf07d3Ser>*UFk4_d8Rv*yU5R&L*Sm&@<hs?V7_xA)y-
z70;v-6BL#7_5Jhj9>1SIS<Tld%Kz!9sjfvun=V@D#qH5p6}C33<S!`C?a}An$Ic@=
zeYxaofBU~Gd-m+<S!ZAK<AYPO>Gd^{n_H$%oq93%=Iz_vp{v6rW4?R7D^3K>4=sJy
z#Kt4B;QP<oKg-k3&Z_z<Yf<1(QBg6YCVt<qSF0b*um5K#(O<SV^YXGz&ERD(t_Cl5
z<6ZjZ;o)}fzw@T4c>2sVa?Q7H=a;{x{r$(Uu&^+z`>e<MLLIfk*NJ@l_Ra14{ad%9
z&djsD{o)tv_y4cg?-w&OGh=&QSX3l*yie9Rihtr#Pzk1LU=VO(lg_Q}`RhMdb&Kh?
zsCX{&oUEpH{rvg!;<8pH0slDmd^p7Y=<|7dadp2r0-c?mkAA=3FaCc2|9v06e>b1e
z;4|MYHuC!2yRkJN4zfRbaIjfPQ`1w%t|sEleEWFNkldEs+hOT@mrrb9VPoUc*48#G
zc;K+H`g`8Q88bAt<=$5N_U7i|DfNH9UUz+aYwOV?ox(?t^-3RI8NA%3re@ELJ(Zg!
zOtYqZYQJ*&wD+C8)z=ej#P#Du^z`&jl~sRxqiL3RN8{1s$46gYUVao5flA8CuB}|6
zC3-Ko;y$d8-+%ALzk7SD!*ATW#T7a2=nTVTA315~qT*uL#csV*a!X%ca?QED%~wT5
z1=Jzx>XWqwB@hpfj*~1;*6;r(v~S-&r%5UgA3RWS>zC6t%e|!nvTUNV`;jXvgQvDE
zD0_SB(Z9dHt4cHUxBUJ4w{`aH*#*VL!o|<d2yWcCQOV3K?94pdXi&?fGx_$mT(<9-
zhue5h9X;{+yuH4dUW~@Kx3|@WgoTez(~WkyzAkp^)5phpFGufx@Oy3L=VwlnR4#7I
zjow)Iw`$^q2?~6&R!fQ*e|<WwFWvL_-{0Sdu3rz=yuLj@9(3*kNWr!B@%m0qPG9=g
zZe%$Ukn~R_d2jjqc`Kr}t_sRK(7*^<gQ29T=;-3^-u>+CZ0^WuN4@p;hCE_<@_POL
zuvZ^GeG=jp(-BziH&;mAf1b$Y<^JJz`e9*Vp!T|d?)<ZfZx;PJ)+_z!P%AemjW|tG
z5fl<q(u>)#;PdCx`ukt3DgN`L@ag7PZ{O;sot>px_V$)4$ZP9jce|vdED<U`KhKu;
z*y7%$j?HW!y&@tiVe4WvA3b_>WMlI2k~}3)4%o3{M@L5o$H)8s|AougR)wgjs&>}?
z{?^ILE!H7rnx%0&dSg=SxiTYT<D;$I;z!QRG@hC(XHnpgFK=vY%xxPMWnKO*WLx+`
zCsxDSUnPZ)j&K&fx}v!;;UH7s&Z5-Rzxn%q9((f7Jo}nYL}X;=g9iy4i=KK3%FFBb
z$=mB4>y=htvvzH(&gnzv&-*`l@<hUZr;FGTmX=e!OO~iC7XNs^=5z0+H_zwSn?)Ue
zKEHllv10D4yxrScU9}Aj10y0MIU}dJIyf}k`_u#)sha!W{w0e`ba+U}l#{#a{{JnX
zeER%+d;OT0m=>MWj}G(On?$HtSVZjHEG{dXyJg*$EhZD^&RzS*-rhd`=5)WgR+6##
z|Ni_u^>2=C^|h2eZ8cx7hM)SS?A~V*k#=H&qSf)U0Zf;+=f^90P6Aai7r8GVIpXs0
z%^RJRNHI|MyjS$Lb(@Okp|fXwV|JBzu0PYtE#9(v_38<!kw1R@0`<J+K3%zT<)Wmc
zT~5<d4NG1GH0#PZaex+$Jl$h+Pbv7rU$xVlBn*>U4qCPG$*!8>?d5eT+OF<TMe5Hv
zmc?JDJ%9GB%WtmLOPSk~R1{P@OV7+OJXPeZyYT1FpNp!$ziT}fHDh=AdoA7QtE)nV
zudk24t{wcLHtB_o{mNQ1_eAll%g*^aT3TA3dj9ijc)Vz2WF*^Z-N;QY&zt@i-b$UM
z(%C0#J?jtOzt`*cyHr)}Iyu9t?9Garyw9IMe?29#^yuj;8&91)DQIVB=k@91Wq<pZ
zQ+HN<U3Dqy#hW)e|9-vJ-)qGB-CjrMOhQwjLFTPprK?ZQ=+tTD<d`a*e7rAo)-q!w
zqmpS$moDYBwzgJ0y=jI`<)(vQKY#u_b@$GztHaH2xgL<n-L9*xy;><=!@gH+0-vmv
zh$dU|mg8F;o7t*Pb8SnK*mXHhBDZ_u)~#FPf|f1aD|~B3;9{lg`Yi&budj(tQsK<c
z&tEL=$}eYgVW;)a3Z|l`r$nEwd;I-=eST5(`@P@iO;GU+Ssmt^Kl59X-?UUvB3)ei
z=ehm=z*D)mws@w`{5B(WecatAchBFs6BD>L%Cz}pz`^gc`}_MZcJJ|?t|z%SqyEc_
zi3R25;+u3p>j4)R{`vWQzE!M{_oN09^K0$=@?7TT=8C5`W!&1b@?u2({e8MJGBRFM
z8boZ$rOoq9qSifdP4V&RIk!oD<N0S*L7XREU0vO(;(6%e#fuMGOC5b@n~84H>AoIc
zADchZjB(4hZDQBg#TwVU_gGYaTeGR2lasUR^!yMeAz9hAI<u|I*Il%_wkC4%l-cI_
z^X?VB{by}uC6!xx?!<|JfAd72S5Nogt>Kt^s&&sE8_;<0Bo##+9iDUN&RLzld*{xF
zQ`+ktI2ui}uSu*?*3{$#9p1wLI@qS5q{O89_oM&vMblM4r&NNL34bhq#`*I0_WX(0
zcb31G17$7HQhRZ|m;<fc;sG0zTp134_P|Y_zC5_@`MJ5L-Y<6V*ZcI1jaP~#a@th(
z-SX2XOaSc<cATWbBWJVY#Mk?~O1VL2WU6?+xVzi@&D*!U(~66VoZjEt+k0(u_I14{
zPoEyVcu{b!b-CHjvteta7#To|9`?+aQ}+~7QdaK02CCFj-?f7J#yYn*CbO$}9$LMA
zA6IsE_Ttir$VkwsNdis}j?34ZM1iImH*DN!xZ}+I`|&?@{{H*@-s<$D$B#dLzhBSH
z@ay~g{4I6z^718bZd`ovX^}?Wn!>`u9p&%k7&MxLC4zPqrIx(9veLyc>wNgpF42pN
z-T4<kZR3$#w7pO9=-u?Kx%c<UGF;eIn!RK9?%6uGSA}X%e(&u$>B64M;vc_$iD|aE
z_sc!(xBqwGy#4<*HT6%QKi58eex7agl3!g;)qJX+L2^0fOTyR36_l1<jeK4>NoApP
zJKu*dUrct~xp`Cay#0S0hST%Qm6Ac7qz&co<3OuR89p47&X>?xy!QiW+v3N={Pr(&
z{e65w>hf=HN}afE{-jBZVjhT;-d%q5j>?NaKR<)|#H+&Aa+Q~tUs)F`{q@yV=fA(c
z2JWw`1sz(&V6xagQ{Q6img8%xzP@tZ7r|g6zI?7#X_J7{%)R>-JUu;~nc>ZwH(gT$
z=31Bg{oD&`uAFXbX_1H(6BlP^IC16-sLgfH!;-arSJ~TDYsz0=({28Gbw%LeC$6)W
zFIWHb=g*gKiuU&MprMflP=$~pQ`9JUxaTM%L+Pt4hP!w!sJv&Gva@u{yek`ZyB-~j
zDtNNctygO18ql_dqdo8HZ%<O;Jb3V+n!ER^n4O!7`h0tPLhjUtMaRU*sCXWlXIsrx
z8-H%LIe){;?@M>ykDobn<_z0vv!<pd&=Ab?dDVQSudjvf+uzjOJayB`l`B<mdkSxP
zzQL_m>cfW*22tmZ9b@B_Hfs@B<nsLP-Mg|*9Nn-0Um&~c(&590U&x*bkFO2gIXgdN
z#qFm0IeYf*?cVZt(wg5}GW30XBDdv8HZ?bIOghSSXJ2jf%&$K_%CB9sMrZEV;NW0I
z&!En4k=5!yKud8xK0ZG6y1Z@Gk}X+ZUtDzFnH?dLnVEU&{Z#F6v&d_wPoI9V$-4ZV
z#(rsQx3<2>w6tXhe{prYO7{ymrORKsbZJWDjI+JYlT`M84hu-st=+Wdr}3wX*ViXo
zZF3F06Tz)^ZmL$OQKWm~Y3{|gde7!O)(TzqV(#PPqUH0iF8kXf^0YQH>qJ)ZzWwXs
z_I6c$eU<p?%1Y1x>Zi}2yTAPXs<L$HQpZUuo6qkqlTAK#s#4wPSjBIfi^ibl7HGyx
z#nUM(Yt_YfT&%hG_DF)(A~!cTa|uSdB_>~u-&Mk??B3@R7bmx8&mPdZFk3P&v(?qr
z6%-Z*UhD7b3fg7gx!gdxC?Y~)&z?P?(+FK$T^G7`i!n5;jo$v~{r>-YTfCcha>aFs
zO#RlsXZdn<K6$&GZ-oYC{!WF-niU%jQ%(qM-o5wcty^N*)z7p}ZmbQPx;=SOZGPFy
zORS(H?Yv^*6Ys}2EDQa8YO40BJs#`#ev@KiVhUIpr26!IgMib@*O@i-w(IuH1?Qy;
z#`%Zacu$-=cWw9W)E)sQemR>1&(6*U%?~{N+VnR!|K1+X`uh6K$rUws7hSq{mTAG|
z^qUjv|NUHk;;Zzgq@!F<o;^Es>z0&zzg*}q5&LsjVy*oB{Z-V}#k1#cU9)zrprmBy
z&f@1ukB{~K_;gwybXM<*6&eh)-o?JXy<I=-F6;Y6S-W3ICTH#3=I!d{#`fyU%HT)c
zJ%^GmPMfIWxqNll!TYTO6(5g^dyBYx+SKJ_UtgEIWsT+U-zK6xU*Fx`t(LsE`g>mI
zwx{pj^+j*bo4NJ;_Po27tdFNoZEt7KySJy)y<cwSr+s@$UJCimv0(I^WKjAl#AmkI
z*#yhr`|<vhws}w4`8;+v-^I;cu^V2@++OuHi(%F}|JT>nYJXWCwKa?3%kD(cH`*};
z-=d4JtO#^|c`x)zDdT~oM_qsZ{rFsm>(}r1`@2nM@75I8i(z13VPli>wQKlq`#+)M
z+l<)VWxVx&KDuvAzF2nn*Yo-H8wwx0flgrf{r!Eril>A@g2TqwYvcFt`}BLJak|em
z)2(|F54SNiwDZf)vXOh{<>_fyIWIUkxbW#IQ3i*-RbQuE&&|mR*q#^5p$MA%7djp&
z<p1i%s)c!Xcb$6w@#9Ce)6>5Exwbl---$y-MP<gGnV?k>sb*iAp6}RVXnymW-E{$R
z@#Q7LHM6$dk7w}VQwY5AvnEJa>{juNNt2R3SzDQ#i+6T*PEc7HGQWJ$q$DP%^INpT
z*J+d=JaqW*#oguao$3!AKCIYsV4-t+$-6u3Y;1?FUl(`c(23o3CF6gs^OnUzoQefc
zPE1tl-xjnn$yK${@GE=q-Cd>rYor^$DIE;yK6Q4s`P7TKJe=Iz!FBN)5*#-sSN{9?
zd}{mm#fuh!PLo<{U+yU+ke{E=!ouRf(by|({^;T1cDB>0X=zGUR<|;26B83pwVpcE
z%H1v7KTY7!gJO@Uz`(#$m34c*E|1=x$I$TPNy?l1`}ZH&C=w3pX-#8&>&xH_Iy$oU
z_qUaz(;R!H&BNv?{(86jeb2Pr#n1V^y}KK|C^z%`^}W^Lt<#I2PScCc`XV!3?bgJ{
zJ+)<9w!A<6<x9!Zub(otW;~hki!XXl#m19WTzx&^H|%CDvTb+bnEZQx;bXTY%a^}i
z<NQSj9Bfmy!?o&<RDXMudClrp(a)bht;CflsU#k1;bbsTPIYy2Q&Lwyo*?VQu{Y2C
z@$vq4ozntN3;gHX?Wp^^>!JjY?qxpTow>KomMmHFLU(`3OCh^I9}cUR9rwHa3bdwJ
z!OScyzcMUL?AN!q*`FLQmc6~TFmSP(fD>r2-lS4+{<LXno7O2hw>fY$CLiy6cv^oy
z3qwm=+oPS&=NXqz=aI53Qep7j-d_0ln52$z+8K$t>}TblzklB!x3|iXqw(wO>*BLo
zKx-ghUtez?nRcv4a!1Y2P4`~gw{+<*gRKSl|HnA(>?~G?$qs*C$N#?+X;<|n!@}Cy
znWGUjn*8W!x7zm4f4|?KEt9)^M^0`osG-84IAO|^B|SMI0daA1ZG5s>Uy7HASzBAX
zxVbrTfacaU-mh<J5s)y;iRjdGVmvEws;{q4z$xL*j=~*<kK5vAZcaa+^zF?}hK8)G
zs}@#&f2Vru+2tj&35zy7jQjol{rWoxB<<^L`ed!sHvQ=oR`0qRwI+1K)oT|xsv6XO
zUa*tT@Hxn60y^C(>G<Wn)#V{;qePuJ&djmA{2={Zzx}^}f5$AWtn4PH-|p~QyWI33
zM_^{a?y_7^KPPBu*Ui^Yo<Hwi6}lSKQUCkv>(^<eZ*Bz6?EKa$9tWN>vM7Ai;^E^X
zvb+4fn0(!jg&)3u7r(sB_wwrBll|=iuay=T3xm2xpiLqkUS2}&{PJSl;(8)$qqYj=
z-rjaKy8QjUy{A+{_U+r3baPWGsKX5Eh=O)-fx1eE?%$6OSr>D2O(zHU?2y-IW*S><
zE;>KU)ElH|j$Lh(PSh3;oBw}4J55Uk?bw`L`tQ%@^QUrNPEzUo@S$L1;bS*Ychhga
zo$j@DvD&}Bzt^8LcdqZTDO0B|J@aAh-FvsTX8XQtwk#?xo_h0^s;5xwt`fo9+j50D
zIXRPVZ_7R9b5c=JQ7zrc-`iVwYxec9N7;9GZH?HLGqa<&R}?fnbD)tKv>a6U?d|R2
zVPRpQrEpcHE#Q%_*8ThUgN7&a^726E(uiUVQJkKp%l19<^Ru&uPMz}dn4}^jBXi`$
zL}k#j4_7CrCX3QnQ*La@oLupK?{}B8vrJo6JV7g)tzO%&?9{(}`Eu*}_3Mjret&zb
z{q4<7Wg#J<Bfq}BcAc!|+oE&Y>iD%AE-jmM#H|DqJ(GOBy@l`HyXUoL;>3wb@9*se
z1v6+|VtRaC<cUo>aaAu>Q>%~8<;cId$n{j&s*sgRpwSXgsQJya(Ol*`TP-FgW{Rix
z_cu2`U-@=Zrq$PFN6E{e)SovtCcD<u>;Y{EJGy@Vzg6XW$;rvB&CSgdQX?axqrsh-
zBS(&O{P<A;+Ca3i_IKIGi|+EBzrMca_MCL&dVKxb?Y3{X@zn0O^Na3R@qD!PdYrIz
z`8&{lW>EMqN<Tl(X_Cs^_xjGIX(=fhhUR&9BHrBJA0M-?Ci3R-rpeRA%GQ*<y|uK)
zK4e9J<Cz(T&U0)kgXUP3dVvPSDk>{AU)TSByPfT|LHU2*+O!TYW;KN`XRPY}?D&*w
zTlD0F(pu1oEK+l2&9$|?zr4E}ePYv^KZccmj$e`QxVbr<J32ag@@pwChZX_Q4GeDE
zqZhmJPMkAG=g*%%Eh?U>ehw`c&aBGKsotd1-6^a-D>U87sj1@K&gVVvHh)W6y<vu1
zuhi1XBB|-=;=A~tfwD6P2ZzC~`JiO>`}=$InGGBZXKL;)eSNKUpLt58m~$J?#pwMH
zTn~T0Uk^&DyzBCFb90N9l_<8{*jc<B)HA3&lNt#cU3~Q6;bE)SVqclA-}vpzp$Hln
zQu)%h&A#Hp1E>4W&(5{PU5YqA&sKZSo;^OtcGdhWdiuZo-5tZ7w!vxV=jpClyS6v)
z^udFUHQ(=+vt6&9^gCw8A-z@dHWdn4S68Kev3^;&+;6Vdm)#D(B_sD%ZN2x))ze|p
zq)9F9?d=znWkf_&^6u^m&6o@7SYJF}a`1iAp}wgCPHUpJdU<B_)XF_SGc&p5?;;h2
zV|K<aOC5K<Z~A)P{{I8e6wq|N*ar_EGDc48{kGT7&(H1oxw#J?J!0bL=XY^)lbX9D
z<L0JRP~+#}g9i-t_4PGhuZH^<o1bX7x;k82Ili^MT|D#dhYtlyBmaH;4w^3k9T?=?
z&iAm_{GP+vS*8zPyx_35wg!#r-`!Puxc>j|_{DcCK~p>3;`*RSToJ$DE+#fMF*&*U
z)KqO{6B7~R^m8V^<YG1?FoKr6g{_S`c=f7iUtiyf;N^VFd}cQN_)!5GmSspNI-(zZ
zTJHNncKL+s>tbKr*r<Hx?%l-nboTi8c+knIzrVi+Ess32YIEY@wjCueg+TLJpo=8p
z;^H=J+}PON%`Ih;(eUta`{M(R%q!yd?mAhqWwM9Yq?V2jjvF^`&fF^B`tjR0(2@(g
zpHGBAQ;L0keFeqE&YRQDZm9T}RP+Du_YE62YL?h*hp+27+|Hj2+9!K`y?oZyRjlgj
z>NjrPT2%D(RK~qMmY_*<W%s^>lao~0UJLxcpSr<&x?V?jH|P{`(2huJYwL>2%7y9Y
z=YclQ9_x`zE}9=#`!#gQk|hcE_Edt7;#n24k|{bm+QQ0eQQFyA9NgT8|NQ(sanhuw
zYipxluZ^Gby>sOz{e3?`gOUxM!s?)<>)*b81I?S=+Lqg0`ubYp%S%hsa^HLIUG6(I
z_rd~3(A+$z`tAMt@9%G~pO4PiE;h})q@tpxmh|Gn!WFT*&17U{AHI3h16s!dS`}7Q
zwCS8ijqA<+NmF!R{qpnk`|<I(ykXUsj6FXdbr%#B8Ce`}3-j>c0G;9rT5j^<)>duM
zWLJ7RJ3Bi&C=Gy4Ts<?>c;cKnIYnVr#Vl-WN}8ISpe+vb=gWgO>^ywfxXgDps0=^6
zGI;r!S<TNb``aI#r0RWPQ>r)Uu8CW>q$a7n?D1O>dSheV<6iR#bLOm((N9lL2Zc-7
z+gqUBt3txUhJ}xg2)*6DbE81yB$XB}=J@GJ;p^i-(<>kD6rW$1c6OFU%?|_60oam~
zD}&$t`uduKi>qn&Y-!NKhn^muGjpxQ&)a^N@tbeQoBjHKZ^oau+wUhmIWZA*(3w@)
zn~vn;eTi3Bh0ZWe@4K{raz|I*%S%f^cB^<ES{c0D%SO92^YSuZY0$Lr!i5VhN?uG@
zdRUPev`r`V^t6N5uZK%6?ds_0T6X6=bI{hT&^x<Ixu<G}yVca#%rePL+HeOHN>it{
zE?J`T<HrvM2GH>?pj~B0yF^bJEWCO1X2t)%-v!0QK<AJ!Fo2FQ-?PUCbOzGlHeT(E
zvBrx&<^25gl)=O~$}amP`_#IdH*en9mMg8|dB|ITZ_3Mpj*e4nite*|-~PJPV77UF
z*sSiYx3*@p*Vot2omdMxS7*u6rHNNo1X{h`|MS^w&|wy3Z*P5l(xk5C-X~|<b>l{a
z$D}1aF|J=!W=fsZw6~X6cIye)b$43Ik94I)pVr;k-Z10wv0iD=E{=oN>l76gZ|tq!
z{^|dbBQ6ZH3_oyZU0ETR5(&y#zrMZwUBawZ<rcHMjF(^D?#R#2&p{W!FfptOS^3F_
z-~XiD?P<oz$9U%0)k=YOU|(M53mVQ&NlksYe108hf%k+76Bs-vfu=M|US2wS>7kI|
z)JPwPGYjUPU-+QnLDao__dLA4o&Egya&mGCK0ot~h>SdV`Eu~`IlaBSHa0dqeLwwN
z+O}q0<+{EuHu#^urx0ioO8wi1%FK{+@~1cNc=99#bU;<r`fT|Zxfd6)URxWT-gu&;
zW1g&!!<h+Fr?N7z=~r#pyd*6(6?6kkWMt%qty@ha`uFbL8ze3$>NhRbV-o196i@*Y
zyxh<A`MJ5eve$x-PR!OxKQ%?O;$f>eXelqKv0MKBo~xf9pN)+TXeHK{mzUYs@BKDw
z>0uQmt<N(WW*j_tkdcAsT*K<QJ0qiy9X!a`FK25M;a^s^E$G~YD_26^+}$l+S68QY
zT5zhkp`l^bdQ}yb2nC_inr}DLPrX0?scu@DtaX{o@jlsJz2}vRi`J~s0ogj&s`S$9
zk511LnUdIYuWRRquW16Es#RWIe&fasjnvPdK24Z4Yt`pvXHK3x7+?RlbyetUsk`Uy
z?XC8<d1ncp_OHEP`+e@opH(L;)D+TIL~Yg53SHH*V~54w)aCy3-2wt8eEPp&fdYpj
zsPlI@^vRi-#wX67zpnY)!rtDVVU~G`gTsNzw;PKR-rU#-I%{d^VU6I5GhOY+*S<iZ
zGV1>QOyBeG*XvWI(xS4$0>-WF?Vz2Fpe~D%$oqrM?4Y^MxsvN|Z_DK_FE7`L+Y_PA
z7`dmy5VV=;)#~+9ciXnz4s~!~Xlibre`%i0?3mct$}?RZ$JbO;-0A8=q1x|0?e6E~
zXjs4hpA_f>uCK4JGcw%Xo_}1v{tqJq=;D?Kj~+E?1}_7hIk<4)!c#skK0iC_oRF~K
zT*ZfP-@eT-OlG@R^;-AKySvh$a_{0|_l;$5qd=#1i)x2$Iie>73c9IWJ`N0LW}Ev<
zz6zcMUWXmMEr*d|&&OlZ1%-u<TeGefJUJl<+GY$|44spk3py_iq`vZ>bM})H6E|$#
zdNh50ZJVrh*@5fV#X*M;-p=2@_SZg;;eu1ud>j~-ELk!mR8K*{A^nnLFsP_F*vvj%
zMF+HcWl?%x&P#@ld7v84yFK99hZ&;${m~acB2m&SeTC+fZNCEAFud5US3q9=eEUJr
zf=4gKxb;(IqG78aeMT7`ro%T7x<HqM&9yFXTfJIaD`Z83-(0JQZ*Fd8JFTX!zPR}L
zxq`yN#`bo0C1vG_vu3R-I^qHXOS`u&W!BIZ5)uMc-=GG-<z>FkadC2Dx=}3`E(Cze
z^z-)r_XK_W_4PIM!iEnom(K?syaZa92s-KD*O$zQ=+7HKyEX!U9??!TuKjS3y`ZGT
zB_~HmD{Ku1188aVpTB<%Q%{NH+}RPRX<huxCnYr%wARkf&W@o$&36{)8mx$oNvv%=
zl1US56+kI}lS$b@qst#3AHTS-wp#TkLxWc6ss*L5uie;LtZrm%JoUPH{=J@cvAeZS
zvNM3%jQ{@q>*(tXGtC2OUUaF2vty>5T@432JNw@2va+%tUtCn4V_z@F09pokXJ;{c
zueA9!ZD}E)rXxp=ESJ*;<;K9wqbx^!^!I#V0$oSf5)gO#<jI38gO@X8%=^vIFxxzz
ziDA#)y<cB_`1Z{$DoP4;WI@LaP)=L%C6Os<S>R%~ho7FF22CJ<ib0TV`FlRH<=x%I
z%5dV;sY^evpF8Kbp0B#Pdae;D@Litr3JVzX%G=2VFY`ILtMoN!*p7vbO-WUiRao7R
zg~7twnz`YU@9T$$+p9`b($im0Q3QETE8l`EOc2zTcznG7cq_N~gIBM*c9p(PdUa(b
z1E>$c@c(ty(zjiq7tYQ$2W@YZmX@A;Thh8rCnh$wG_Jd&!@<Sn$mAQmAj6Cf?<#%0
zA@8o$-0PPvU)B{lyV+TYRX%Hh5;rI3!MC@!7k+&e`qWfkUw=#HWj4^ZAI<3HezHG)
z{Yv`(?=RbHFAom~NTJjJ;K76CQs$s=6qQ|n-K#HlgIB`gHeOI0`o_lO(l~y8{^RH7
zT2E!a%=qBJ!Di5PbXDKqtu31Y4vVF1yAx)<ys<HPhFz_dRoNR3etv%FVT`PCl}|-M
zm%>=3F87()G-HNDOIusgmlqdLoH%h{PvvI;35gCyX10hlc2Hb&crmIeq<#AMv2p8G
z(`RRAxA*k$Tv-vwygBXcp&J{M6;JQt^}M<&^zcMw_Yd!OzXxqM->`M7>-BZ9hWYpQ
zIL-eLPJLC=3>giB&(1OhEe6?qnw^D3WTg$rZ-JEu(nkx@;are{6A^X~c0ITrU++5K
zuJ*y}*WBvr>MSfQ3CDUQL77`~`kXmE=jK`;K6;c@PEM|)r^h8LYgOwaHBctgvR$Jq
z>ktXvi|jdBt)RI0@a_ElyyfNPpd|j|#p3=66DBn1MsH(a0F6RV(~UlK_AKxGx?h?`
zMn(qZ?_xk@$#VbsZCg$G!R@)J@q47qF0Tq*UGV>3EyxT|-<tt+KJJOrrw_lnx_aWg
zd3@pF;h-zCA|oR~M<@07^D}@BR|YMl4qFq^sOa1lQDhGGtWH<#hBH$pPGn?Q5wmmC
zx!0kqLK<~WZ`it(l_BTe9?96<WucS!89-g!KY#xU%F3>dI{?YE0-zRNTbmO{BWO4I
zo12?KQ>+^k53_+fmlC#BCNeTI$mMOcy^0D;XJ@Ba#~hHwE50O%>ll~5y25FkcILpE
z$juvSf0r>dsCdq>D%AoV8^Hj&=!xOL?c34$wfXt^OO`GL9ZhUzX7-BJ59ET7E(1nQ
zCVPAPEqQmPY^%Su?A~o%_V!lmOyl%LUQ@L|YqN}`rp=u@_uj5=-@Y+4tXZQ2+FIe_
z5|U@gXn6V8*Vl$QHw^y#dcEF@+x`2yyA!8RZwIZp7ZemMC@5fHu&MbGkpK71n;eFK
zQv#seKh@pG;mnqbk4Y@-?5|7Lzq+<o8nkcZ$Pt&+5*?&w`Q|A5V|}vR;o;$PuPZ4j
z2?z=XHrav-mPLyqw&#H^L|Fbgs;X*N>!Qjs14hHkZ*OmZ`0m}h?^S+&d@TYe&YpF>
zx+*kqWsvHTqeov(1>H)~)YNq5*0m;9ZUGsYm`5`@J32a6{NTCE;0Gyybwn<1Zd{x&
z^W^>e@|~TXpb^H>xcJ>=x@*>~xv;>InZco=!lLZmozB<S*K4Qp$=P(Ap02;R{QbRE
z$9%vcd20jf5g%bSp9Ax3t1m2a<=*r4TJ(nx9}e`%T7zcNd1S3bLRW<_GWg6iVl^~0
z<lyB!`r_i^i4!L_R(^h_XkZ|~%*M0fjUPCaA|;|7&Rp4)>OIG%Qm9wj{Me(T-G*gv
zB0ys$*VaTvY{{4yv@U9EmPOGMj;!nJdQVPP2QB;ujYfQXd;9ps_29&DCz;zr5VW@6
zy8NBamKX;IhO+nf)`G5Ma{-;tIg;D8sY;A9jnnzo#qZC1X#fhWjukDZT$qeXUxnCw
zzf+uXdz<c}MT;g(oS67>0z_KklnYbQ*H@uu=GjVTW@bv*R2ZxZS=nS)`zzu4y4Vw^
zPj4;(ZF+KXNm*tzu>o{8%%K)e4==An{`P-c%I{UOZ``;Mx_J3s-S6BvR;8ydC4&1$
zfr_VGn1TX88@Z~#<$!vt?tL<jr>E(HHiPW>^Xc@5@83bkJvLdFzl*T)0TqWzMh9+P
zVia*ND>K`ccel$l`&z>LdwVx*+63C+n3$anI{FQCwNJ#>EYYejFA`q{bb2kn6VuRX
zwQ^~Y>Vad&E(Hn*25JVT2?;2&e0zVtA7sMoYim!OIN@-(jrZgH|9{v)`_P`An##yv
zo`289#f9Zquk>-2mJ<`~|1s<;VbqJ+P@)nNcV!Ldni$S48Hq1FIyzQ}Xb5wjxUn%A
zRDgk2j)1P)Fic`mQdSP!o)^1!6Nqn=!l9(BtSg`ys9FAQj@r)+8yU^=+1|X>l@+vo
zT~yp?S=<n|w&COB#+R3W?o(FMdVb1<X;aEcp)W5kGEba15wtl%#nUKqpKG^RM^{&n
zrqv>&M;{UwRF^Ms<9+b^J!5{px4X;JquT2k=GU^-d}w^OcYBrTg$)arE>==f;?@-A
zJOL^yo;*n@tF*Vb|MByu?cRotQ?p)VJm@tyDPL%w%cK>up~NL*UPL6LuzFV1eI@0_
z2?7re^EaNH%=p^qV|qtN$GinbhFsSUxATKmrb))uy}M%xIz?a0%_XF*=AY$-<@^^`
zi*LvlpUl5$&abbH!OI&wCpTnXZkQg=Hfgf1tf-*i$~8VJ3dzU&K%1O<^gw4_AM2N2
zpXB0_>QJ}u*}Bg&3=gz$9#|FHuy*az#fv_jyCycnklCVAXh(hgWzpB4&zrsvS5ndv
z2c^ju7ZgigUpxEa(}oQOpyQ2?c64~{bniEbjeU0Za?G8xGnr4MK6>o;)YXGy!UP5$
z`E{O?Zf|E_7q_(}#KmPvlTo2-(f4<;6;)MIvF{!}Jb045bIZH?*KNM+E>zx-$o%1)
z^|Zc@69(??2@jnfJaBxl{k}=;mTCIzEgMQwTwK10OzmVja{IP)XJ@C|^y9~mFAiGj
z1quhRyL(nvUa-;QyRqH>^BwlU%<J29KV0^AI6v>egM$v+^A4<yp4QjZ(J^n5(ZmMO
z*~h1+=^p;}_V(9lxwp6R1_lPsDVgx%lIns;V+B)Di#nN%ySFY03eG%#HT3eYjkVSV
z&v*jX%Lxc4zg*GL@j_EW__2K5pO42a3LY?2eR<J%;DE!G)#3aM1^@n3&atf)V`OBU
zVU|0qb+O8(1@iSQyUMPfdAqUN{K4z&mmM8l>N-3Y^>PxHL6@7ZZ`c3ubb7<?-Ak2~
zl@_Tl@!8U_W7jSvGcz&JsqRHZMFPUY%`;|5JbCiuK{LPIftA6_K?~Bn+dWexZ*R}u
zwc*NYb{XqqPnu8J`TL)}W14*}Lrrf@y!?&L$1ep63SJZrQWFwjC@3mA1Ul3E-5tx6
zv@|7SW8vrL<{mySUmvr?ldE%{=KVVEFE18m{=KnVyr6h<iHnPWn@VPs{ki$qRTN@(
zF(|t+@XJM587V1gEi{_kAP~stl#w<`&XB3%vv0!Rsss1#T?!Pmt+cb7mDk!W&V0X?
zorNuHUZIlGBBRwlHq1;6prO(&nZiOsLKEiA>-%$DZS5o#&ePL$HwTosXq`HFQAO_b
z^z3KfXBapZZ=dlZQq^<T_nLM-hyVX(O}uxkpM9Bs`XgVL5VonEEKgotU7c}x8SlE7
zolRY$+Df*zvhUu#1FdKK^y$-smoGWf)6>1%J->YU`<eg3?(JzEcSBb<nBUt_;^OjZ
zWhl2r5z7pF`HKIwXFn^h4r4rca9UqaM@QEJpOS;K3=)|xUA`<RE!};3y1p|<Bd@gC
zgM-cNpi3!bWMn{9D#+lHyL-8}WHQUxuG;w_>67EriBqR8yQ*&|w_z*mEHl=$GYmz=
zy0Vgjf-_h8s3^4a$sSq}xL80)h$-?~i%v_!+_|!#E8fc9-by?(!%(bm(hJRBZ_OV(
zo!*#z-GtMloez}uH@&*boRr*na&lwlWs~{+Pg1<yU0qz#G=w=l($dua{Q1McAgUG8
zpyKJ_?;k$RJo_5Y@t*-owyPgKG7%4-v}ES{#fi?|?k*u#H*RIkW3~im;?FlDb_i6w
zk#tm2(uxHY|9h)S|NMMDp8<41(~loNKx+>o>pnhm?Q;6=2wMwq<FlhnmDt;x%q?w9
zrdb(!o3gHe0&=Op2gigN96a)TJ+j<eve{#HCV_Iri4LQQ4WRSKZ*R*D{%4<XfB|&r
zfpa_G(FX^cKYqO)udJhUrb$r9mQ_1EE3Gx}GMhz-hq0x!pyaa$3o`#&lyLm`TfV@N
zIpL<!*$G!&TtZe&=?vTen%4qt&wqHh9dxnD6wP2z^YqA(BL_~M;;Q}q?dYGMpLJ!`
zE>2ctF{m~(DAh73(b$k7m=LRGx5{PO^<}&T@8&F1O*tWu@H#Bvq|gE{QEzuQ7nlF*
zeYP}!Mo%6-e7IrrW@d&Pw{IuEytMSk@AvyB&YIOFsvWk#sg=v$whwf<+qBeX`>?RE
z9hIM#1({h|N`eM$y1Th;Y-|K1B|EQNxw2eNd1_&0<$>t@%t`&3mrac48vj(#n#Cn*
zvR3}~cINbR8FCT#cU%N#$^Y74!_8*SoC!K|Vu|Nu(AjX^$BdGWaFkT!d{I<n0d3Kn
zWtt7TuAQOa@2^tOj;6f4JkWiApvK$I;^&Y4{QPWD`^#iarK4wCN-E>uUzvKdrs-U~
zvsZP|BW`ZybMx8V`mUUrFD%Gd^Huc0yS$etIyz2m4XRgt^Wp_Zc6PRfrDdd*h)M1(
z5#QNnS1aEh=@dS3=8Vh!y1!GdgAU7BvqncnRrTQU<LnF<mUs$-?*7mYTXR4lF5=ST
zOP3n*?l!cr_)ed=E>>4o@Z#o!mxVj}*siT*zE>l>B3?dXm&l7tM=wnj6r38Jx`;ch
zqQW96DM`WW;o+9-Yik;HPIq*41jK=^_}W$Wc9n`e!-aLR)<1s#R<^RbC6pMHS@u@y
z!lKqRzGnHlginrNmUs$X*uH+5>5{tI2UEjMl+$c062Sgh8g!qDiJ|t_mxC(;7lRH=
z1GRWTi}DHzE@+F($gIhV0Ue;YqfmL4d4AuktE-tot85#s%ik?1e0;3xsk4*QhU)Km
zp~*{v-YuVRQl9kojmhCRzkWwsDJ@E$-p{|G+Wf=)|4fgMG5-FZ?KV66<|VM7v{RKB
zIeB?Q>+H+k%(z#i$fDxu6c;zIE0D4J+na;&^?#RM{_FKyVb{0H2~B(U?giaKGG`9x
z*76l%>QjU4|1gxi=ex3w_sy-NfmIz>6g+2L>;Lw)k&(IK+FH|-Cwe3qo=;X%3UZz4
z;jn1YA|2T#$L#CtTGy`Ct@`?E>7HqzsgYA1v8<Wu-e*poN@5DSmwSW3#Vt!svZs?V
zCPqhAFz{#4YO#VJ1{)F@)2{5iySp)THN({COPw9378^}$`11b#{Y#23em<XH59+#=
zyt^ZrmzQ_y^{sV@{PK2JqSdXfqnGnc4dxbOc)yQ(&&RfBi4oD!pwh%HGLrG}apt+!
z*?Al$xm-0L7!4|o4$O9Oaq;(^>EYnDST}l`g0{Bz^53E#_OA<Pea>=mSLJ85J9qDL
z=l+#I8p^s*d13CU1%GN14zVQst4X+|l5kDOw_HGJ)0(QU4L2elO!jYhdAU*6+GMRF
zBO|DM*2%fMt2APJ-rRH9FJHbC>w0!mvGbC}mMh6@yiy?<lAe=5Cr^kjy7=PKQf|L_
zHl4S(=WEBV-U8Z_x6D_1P5l12=U=T#UxCK*K}!-M(w&@`q)f9~e*CDoD!*d+@;48i
zed|B-N;l~5yWnHy;j;An%gYY`{~egBec;W_1J>^uie^d}OzH?Z^m^+gZDCzdsowPE
z#@t7H_SoFnQz=|oS-B!)<)l@gTb3+a2HLUo;`;jeai177+IZu(W{Iwg-@lIMX!VB&
zj-U;@kB{{R>-uS_Ub@^UXKS)JCif0UOMA4Hl9G{{ii@%Q`6-$QZfrcTE!QDL<iYcF
zP&%I{nfhqkxvARW6DCbs^tt%$Ez^{=v`5|g`x?5tx%uR5Rz%snzP{c)E^c1jckQ*H
z!`v=j6kND);g46V*MrV91<jQ-Ha1EaBs63d25A;NX1lOTbi#}klAk7@WDk7#{K10C
zu-EPK4O2uQW$%J7<q`@?TEeGXn38&WdwD_gEucN?`73WMO?`W7>xWODoOYMJ<<VOo
zwpI#sI>D{2*`SkZK<EGLDtp^i`}<qrm(`!1JfGi~cGhI`9C`b5JJ~u;&AJe9HpX^u
z6{B+-Lt<h><MZDg9V;?Uxrnbh#KOjw^!3%%Rk`n<oSfV`jgy1p!II{uH#>!Ab&I||
zl74knq?MAARv#CrFul8**||MSE+T%9#EZMP!5K0#br-8k_Wyr>3rb41lt{RMj@)o}
zE!~>UP=D@%U;s-Xi*gcYpbKlZj>vAMG9ANdM<z)mcc1+G=9tjmq+ZWAZeM2Iu+B<y
zcje`hSlns5h^Z^KqhU(%{~OaMPIO#ZoHpb6_G@ci*gZe9XY=N{mha!Fy}6@lX8HLO
zli!?#PmUo%C9haw_Hb;;;r#N7^~+1;D)Y_`jr%-{*i(Lec^P$jzFlqC<Kz9xT3TG7
zb-X1qq7%iur+102+f^=px}qpOb<w257X#O%T;Hnw;b!_G^OUby3u>!DtwAFVwuucs
zv(0?7ckbP53);AvdU{&oi3y6kc)K*LBiYn^g{rH2q*TBCdT?p3i)z10=RN-T_y>>s
z8~J1#7CJXperAY{Ub<LW$%un(Vna$=nwBh6;JZtgj_`VTa2S;9eK;z9Ct<>hC95L@
zZ*1p}*y!|Uwta;_#O`U`z5;?z8S59RYV|BpQPAM{@iRT)Z&kt_3pII{V@{JA5)U`n
z{}bT$`|+#me8UtGkXtWhvrTM}v8fOcR`WUW^~aG;VbHlwvTiOc3W|&$zKd(ey}ix;
zWRpIR;M9W9)lA3x81L6{*ZjAA@R-@fO-oi(aN<3q4wjT>XJ+mwe(tyKcye;GVevB`
zSvMEA_rFRX{QcgzyS(x7@uRE4T~hY=__(}x51A5Cv#2mR;a7>F%jAv@6A2flPoRSX
z0~fpPl*<P##faIl;Bd!@6|OEFPYz!Zv8a_=5gq=rqr)Tq$|}}5mdrjg6oe*xzb`&}
zl91rUS&k|SD_5?p`1$FnbpQT}k527^6EEJ{AJ4OURhTc&-lF1QD<!2%=4ojMuE%%F
zYfPHdQ1!K;m77s-g_8@*0S6%ge}DfsH#R!IHjJFMvG#Y_l{JyU#{(x`jNI&&<92P8
zXu%7C2M-pMxVS7iFJ&U&yvgpbh=jEm2M0sm-5@J1C8eX1E=-?(KA*2_V<Tf*{q4y7
z`hT5YzLYFkzWni_R_+%!H>>;2x9dG`|3BuX!OA6*QWKu}JXqY{aIm>C_clXzHmI-Z
zoRF$oW#1<!deZ&vt)$ORkzt?)`%#G_b_#!geLdVOZ4SEOGw1$3*;W7VKhQNc7M`R6
z+QAGe7!CynbxxV6SY{T#&8O%2Jd@7TZ-fH>{rt%&uGcYd!olW7S?h+W+KlPx;JCJI
z_HbZGcy(nZXqpIgIxcvg1(ZPbVs|aby1I(X-Cgxkx46UoeGVBbejNR@xPMAtM+eW$
zxw0FwL@zAu_HeSXZCvWz_#gpno@TSB1H;6E9%*yGmj)*!Y^qeARsZ?OzM<Ou!oucD
zf`Wmbf)X4tdq54&FRxUqoa_DwYD9;D%EppI3eOr`TwN1SPEy?|7r!Zm^W1#<^@lrF
zxa=<F&bTGAqs+JGcG@RL7uT~F1#fKO{P9%$!S?$u`6vGU^>TN0St1O|VRI}5+xcWq
zef=>}*&TEQzO0*z>b0MrlkWc8VzRL%?QolyyNiqJR29bBUmf=*Sd|{>mvwGu>g@D#
zcXJ6@!=uF5+0*0l`PtcG+k1N|i|1IE`?U)Oc9#4wxUh&dV53sOt0^~LH+QVq5xR!s
z&24s_c)co@S63MO<ioABloll=3vhZQB`Nv%7SCU|C}`z}H#dzz4mhX3pW*H8?tA^o
z#~VI9o!ZyY(bK8Yd9R;aoFP1XW7akCcm_>PunV5>C^4>#+iS%qV=>|NN6@KM@7}$$
z>gn*}iDY|rmf7xy!<ve%>HG=Fi%UXWQr6tLd-TrcoyA9wl*r2~7zKeE?p;$HRTMzS
zi+_7}w|isq@kcK&FMsuEx&M67VlvRUB<PR}Z3#umC1-p#s{NlH&m^qYG56frGgJDy
zI(p`IcE0%A(Ziyn+jy+Eas7U=;>#T!UJT6<ZRJl+OavXd0h*aS+AY2~>1Y>2!;T#m
zp!(}Dzx|OJhRGs*lX$$cv@Yzo-;mn<V`|}#f~CqzN|!=bixoWMF(_6uNN&3^Ipb~A
zf;3-HQqdA=e!)0Vg){rwnu8Ayw|_jTKL5bw%fj>K%>%WLK}F7**xg}^(_3Ej&a-2^
zwUxQ-Ez`E#<WG(+zfPa=`BFE>j#ovM@!q`yO{|O9r+1uikaXeq5Ua1R|MC0x>i_d*
z&FZ?lyL_?tbUl!-CmuRk^~G%C#-vR@@9sXZ(Ai;g8Ym@vDdUx52n;-Mr1QXz!bj{=
zCI~F>5(PQ#lzH<D#*=>2Qe~`4IGWjboqm3LdU18Q{v5kn&>2giRwkFqr|U8L&gzhx
zFj;+Ojljgjjhlq_%DeY5Y|m$oi#xC?G|7-xNr^ehfYT!-MFlh*EiNt&p2fSi*7<m!
zte~7+U-kEQphY@CRxX#))%`kJ4&RI8Nqr!ZrDPORSC_Q&-o3qzip~vwa~pnCfJ%i^
z#(Vu7KszQRjnfVsKFka{^yAvv=#K~4<r6@sTBJ(e-CgcJS<P3$bJC@Slk9G9<IT7$
zwIbX<=iSpsi%MKvTw0T}x!1&Ksx5POW12Vbz%pM@&Y7ZKHM_wi^AgL|)#1lMr?^bh
zjaJgp;n|*l|Ja?K#Sh=UU0YI-qEYvQ@x@Ky7q>)j?3dSwje6P9(b461yzju#Zim%j
zi`<u#=Rdfp402|b@?KvD(8jwRJ9j1??GpX*Zuk2OJ&(U?+NVUmdHeC_^8+&s51hAW
z783*69`Z#-#^wF-Hr@k`%!|xZ?%RQkI5qX2nu7hG56pAs&b_!bJ3M4X0OR2{-oqCc
zyDJuNlHKbYC1p^c0O}*w@2gnxW!p+&LDAlOdtLKA_Es%QPk9lbU>FF>4Mi7j@7@Gj
z!1(0k<Q;{N*+8q;gw_2Pq@JDzI&*A_X0U>%(DS+gzs^pFgatoT3l6hwylg2rapQ^z
z!5h29BQ`qKuw7fJ{A{0~;7h^1z7Cr}r$0<q^F0Jw=Ce8d{G#&r_bkfZNTfs_`|$9v
zVabbtmbSJcegUR;cZF9Uo@2@U=hx~VrV~0&-0+wbA^W=Do*_K^<IW#P#ToXicY650
zsAZZox4)lXOednjy8PXP1C7j}!DG;M1)wuBtgN(T<uqU3GJWv(`_Z5&$NQNX89}Lb
ziB5U>gBO7b?_wStmp`ia>iIcMP_3J&st_5XB^tFgt98Nz0nj}H(q=ghr>E&o<?Egz
zB&axvpPPH*are1aU^C>wHE+t28z(jtCMVo7F%0kP@K9u%*^rW+nb{e)w<_`ax>&Dv
zC$Ag3#Xo#!&$_~J=y2nysi4x!<NuXas%^{tX0e=^!)cPq^ylmJOM!wDCmQ`=Y3aGK
zD%9cmIfvP1pj>uy6WFf(XU@31KR(m=K(~03$F(L_hqPsvCJIiRn8@VR=02_Q<>jL)
zrx!Xmu8lr=fk{cJYWJ_NM<=X#c(}3fv5WpE`~MoUqJolwY6>Y4mo7IZ9&T6}+!(p}
z$Pq7LVNhBz(g_P&^!@RRzz5%Q51i(X*ctTafU`@;nHdce9#(v0dU1{S$4m8uvr-!`
zTYhg+RMN7DkAHB8bz{!Y3k#Fvmx6QFBB4G`4#~E5{>Ev#jmqu~7Xm=3Ku~nv+O;qK
za`3V)T6Co4Yp?XwzOD{012u&u|Ns4D2h~W0&OG87*Zn|Q!E0WhY-6|h(L-{zzZmvb
zG4{)W;$*6WkAug4SNCpPJ0qhba`L=V8nS|dfhV6nP^hvu%VFS?W3jPqto;ote7z*p
z6qfAglVge6!VnV+8uwil3JUEL7GK`!Ryn`ADr~Fl+{OT^t6W`FS%d{H`M<u-ENjJZ
zc6N7SxSt;=q*G$F!bE28T^FHP<+^82!v=$;i<K4$HE?pg6pv)v_gC!42j`DBwsLRS
z7zk>pJXY~s^!@Q6)(3OTkIYj{KF)OVB&Zf*>*3^h+26yH@Fn9x{Xg~@CY%v_qu$){
zc5ykiD#-PV+xgk-I+092elS4Nour1E!V>wQpd&LL%hxeX(_>9L^FU7C#pRdFQlY)_
zfq_ZNhmZ9(W?pXCVgf2znGdtHO!@wV^TM{Z5zTeK%N|Vj2Q?@<ih?(*6+G*Cv2vcJ
zaK<H;AFuU6k;{CDrDaO#^S&7wbsv=qei&TXx%rZypy0*ZGiG+)>o-ns5Z7nO&rkSQ
zV>o46#|zyV4HJs=;zah(QB`Gp_%LZdzmk%X%-7e<6DJ->pU)Uy$H~INV4e?ZqrS9M
zQ&`fo!C*n8@qvqhZ$3VJxDZr8h6o@M)5e(hU7|^$&PqxkbKm9O<=T>|92UsQEu4K;
zSa4$E=dYrtzkYkm%_FU5X3*0eY^9}i$=S!jWBb1UavXeYyUN+Wy=V8D9%iMaq@<;_
zZQG*n$?xMH{HZ)}IXEZD!(|Guu)w9>Gd>&lA3rhifOtHp7Cs9~VjW%1?R*F3*LU4}
zXII-0x3|H)ACx}6um}ri)#=AG{r$~c{G5qfyzBp}8(%s)yiC~HFaF+Gs9f-3!VBJ;
zyTf0u=<wkGpf{l_JZX^x`<~S9A3{>L;PTpIdev8<)pAwU%#Do)9vnRI`8lWr=1O96
zI{N&ct)Zr&64Q^b;fCLvJ34y$H*da?Q})y=;j-ESKiQ2OoPv_7Y6?F^L`7RcWs`-)
zf{>L#pheK2)v&$Adhz>u%HG~Oc<q`PA0Hp+G9)>>niT;@xONt~zWG@A->%@3%7gdo
zL1DZ^#Zzdnytp32`#sA0#E<o{PMSQmucxErOvp<Ho|BEt>;fVpEcyBQJ~NG2&CJX|
z<@URKdso}3o0*B7oo(K}cdzZUGc%iy9C0}_*IHa$KMpj(<m21B;MDm!f_L9MJ>4C@
z<6fmZC|7#Khpbfk#uK@PV}^m_mkH~m)y+&*loltjlpl6gHZ&AuX5(>K?l*Ts>gj1Q
z=VzN{i%pt58FaPVjvYG~7(k2PudR(%R#9PTX6L^adg9cLz0xo4M!)&EF<U%f+Zs?p
zoVYJ=rOSKw-DOD=Cmri;>=bVFoXlWsymYbhqNHPr5tUy)pSK5{le{_YEEB_?ijPd2
zrp3l>&%fVy;DAHvCq@R{=xr{&Ql=X!Kc~$-y1GMDyJ1~y<F~hs&FnL41OzY6zPg5U
zjvcShJf51*y&f)lv7ioyR+^8)rxza{9tMq}&b2NF?N5-FmcDWGX5x<z4?TQ+Ss9KT
zJ*udv$oO?@iL!al4S|anFYe-tHcye9VHNtO=J(6x2cDg6Sh^H6f;myqz*zXcV){7-
zP0dB@wf|}ql$~Fm=vXnsfbrzw?Ca|kb#-~?T9><lj+d+bT?X2O4LVfJ?$--t28Ws&
z8-@cnZ(1JzdhXo0ia$RLFI~D+B2~W8+n>FShjE!dd)j$1>$$UMfC_LyNhwexRY<~+
z>BlE;L(jj@d_jd%(g7Bg{z;Q2flgv?WM)r#c&HUL^aE;UCLV4}e0;1I6#tX{*Q@e#
zb9c*JkN#KR?@;6!_4e`o`bNFjhOOC0AKU*ITs=oXP*AjGsj5M~T)`WOg6Dh>o~pKe
z&yxJ)<I>j6=^+;x8Og{1TJRKjZ+m|HiA_D1m;0|ie^P4F)L1XCu4A7i_OSCeG%$Qr
z6_nCa6BH5@6m1pLJyNu{_IKCAy|UI!g@vH3;@Yx0;c4K}F42$I<LejJ{QM;K)X&e4
zp+Vh$-ir3PRNkYf^z7{9Qcq87jg$-fE@7O;<27AxZQ!-#{_<a5US7W7q4?U_=0{Jd
zo}bs5T2@;FF8I8pqN6YVzPPmehW(Mt!V#NX)@<=uqAYEebD)h^+T_$c+v;TjCaUbh
z6Kp<x{CIGobGv}FboZ;Pt5=_X{O`ZGq`JC#$eIYjr>CYK-dX&-C`MjTuyI%E>t`>w
zis{90<mbO%#JOjMs~eN76+>BB(o>=Ga!{*$(WEub><=EfKG=M|!LGJJ*4jns(Srpg
zDJd*l{xZmjin7Z8`w^uf(=^rn;K73vQYBZ1ufL`dzq3g7%$YL}UccsM09|?D<m?PO
z6w>bbhlhuERDIQIYHlu-DxUcJt@(lL;swRbeshyPIl8#)3f!i%A(44QrZ7?owW480
z!;`?fyGlVjtR7B}uXB8VZ|}yEmq9G-?4TpsKqFoO3!PXQ4zzFzgDzyTx3>o!FA3f)
z-`pIy+)tKA#v<URd7#AWYn^w`{{7RS?A}oH6da^Iz6Bdz@P7OzcHgs^o%#HHZCO#l
zr^3P$Y9jX4ST6IO-3Gd+T+)~gbZ9y#rAZp6tvC<5X*ME40(9!t$H&J(D<U{}c#bqO
zvwt%A@BIFRkwH42K|({tn>(CSw3dRVKU3DsnS1n+x^{S%oS?q`Mg=ZGNgmH@+vb{U
zDrNg5rlc&XJHvSL1cO^IQ{A7y93j(Et?4&am6ZN|Ja2zsfn$>6l#7dx=Dku)-RgNM
zx~hDCT7w1W+%4XIY`?w=+sbK&b)04E?CAL7IaMnu;zRj;(0J|XX`K%vd8Ins54SmN
z&HD4P$K$*K<H9*|_H0(=V%l+U?{Jp9S^_F!U2d%mIWWWU(eAACau41_8oIpR{r<>D
zqtu39UtNtRO`Wl0hlaHA#EHpFPP6jvi&fXCs4%qiGyBd0wN3;t-VR-@CNxo}U!M8-
zIYvQ2CC#5N)eT*^8MMN#yir{lFTY`XxLTf)@)4GnxSyZ>7t~fCIK#ui&wj5KG$hl}
z!L=+Wa)Gb(M}{Mdv^H*3l050<_3WJJ>{m<<Uti7g^xRuD$KNT$XGX(>f;TfH%AUUC
zdvjYoOj1!vNlR0Ov!GP8e_gC=WSUyR+gWKrn{uTm%;K6cqvNmJCjPshzOeXIzIF+T
z_!I7uBzgGX-$pUrBkb$v&IJ|4f}-|Qy}o?g|9f3T1uMg)OGi2yHm4tbd`2?iYu3ay
z5s{tqCcg3HRNpE5R9aXdd3(l03FF@f*^_G4?A!^~u6g{7&zdDq-}3TEtDXAySG=RI
z?yC2u^!^|JSdQG8`0Hyt@7=5m4rdG%7sVJd8iviBc_2KV@$>WU|L5lE)^UOsR`@RU
zVmv#m(|wwI{}Gk%YaT|g4$t;0I)BgmkmoxWsTmCti>K>1HZnK%N*^_9Rd&~s6%-Vl
zm{?fAaBG_=zk^TF{_5{nFRbn0c(DIJ>pXk$<Ez83`*>NLoZBNIERg*9kL886(glxN
zX8gXmc{$j{l6Ia<TA@Pyd3RkRuc>@rvoJdSJX7@ceY2j#?cVn6!rG{<Ak9qCI}JO#
znXO6~^6oP6$uQK^f?AawE1sOPJNfr8zw6D@(+?c&USH)IH~-1Jt&^+6f`YbcsqYN#
zJj`+=B4(eCnZ%hB41V)Oyg7vg1t;dsoSi-YiJa(HEzO&Iql+h1U)!*D&5;)Gr;{6#
znUZ+?<}z883du+O{@(pAQAtVZ5<e(RW_P<!Yv+&mEwZl`i`dFF!_-@*xbPx4EolV1
z&Aidc)xhcDxjkF_!tU)qCfwRNd3Aw+pr9m^ySri0!dUB%-`9A)yZL<bnR&b_YF&>_
zvoh3FOJ5gFvVN(nrl5NB)6)YV9wr&x`TtKpA#)`-`G*MHRNq<tUhkBVapUT6)AFM8
zv!3K-{mPnD9hMZdSxtTC<hTTFoz4`=8Ahx#EQNpkDNcCkwDA_3prGJX87<KV4;AHZ
zs_#s{fAa4YO~V>(W8n?kuH8AgH_`dP-MN$Rcs`%eFvTP@Gb!lyEYkzq?=w9<_OZFO
zqvJ%xjM>a~c8@-uR9~c{`SPmD_s!`?C%kFpKDu#J>QR|VZ>pyhOtKa=R#Q;*3=$HM
z;gPY?Xp8y!dO6t5OH8Dt7r1hNJTb}iq;2+#Yh9vAPgods?8@T1yDA>k<DPkArArc1
zP>_lWgRFJe)3WbxnWMIYTbD|g!f&bVyuWkt&dI-DPrkF6|L&^j>BZhF56ozo!eXAs
zkbApJZo=c^M}PP!f$klhqylR3KmO|ZE^@Dmpx}XLXOrf6UQ_x0#(BHN$+@38)*iVq
z*IK>IeEW8GzxiydLIOc8SS2maP5wpi@5fI`^({K@<#p)r(GR+jVw3O0-#-Zo6K*wy
zww(WU3)1~RavzyzyU2PfXpP*Cl=)Boy<EOXCwo$LSkR|mD&OCH_LMSV&<Y9AtGmSQ
z<G>T@!C_Fb;l<w@I|Bc_a&U2RS(1HAZKr+3hb;B)9UXJPzImilzW6Z9kqak`KAbf_
zu*CDg1jPfBRAur#U0hs#Ese2UP+R?xA<nnx{JzP*&F}A-_2l2X-InD=&sDw)b(gPe
zQ&D!#(b-X=`QhvJ15ZxMgoCx||5o`fXX^qgXr6XG^LuwweP?xLrkbj0c2V+Ep(f6b
zg&uxvzrKkU+lH+ZISgJC$1~N7b<g*C6)E$d)IB`3ddZU~o@YJZ+1br1_F5J4uzw?4
z2WN+3#UF_mS2#QR#DsS<F@tkj$BGv-XLnmFeto@v){}WNet<m2k?^}L;UH7tnS&2Q
zohE&Kpi;i~IE%~uqenV?o8;_R`s7%pOoPC|u5@?fR@R!@qsph7+1EdtG{=@(Cz|cd
z48^|CRZA>OP9JZdeCI2Vut4p%<MK(LrY&6>k-poF7gWV7DFwytEJ{+nz$bg;eyU`i
z^Y>QRT7Vt3(ZzNr&&*8fOmKLUI;r+W*j%eQPu3}@Dg1hMLU4h%_=2j^g!ge}>YycN
zPu(UpoSJ%6IeSvI+p*Q2?;<--F7r*A*E#bhh~s&(`hDS~-F*g(7v1;&<MNr!_Tx8m
z^!BcG?w}I!l(n$%#-gyZvko*cF48gm3eFOZkB>J#J?+ZxwK6D4{W~~QRBudRTGX%R
z$71(KAY!YR&oPkZMTa(TsF)g)a&p#_d8Q@Vx6Km%)f9pf(zhioT)mtfc2XuB5qoDz
zf)>387FOR@`QE{?k^T0UmyaackBUnP3)Gq<u}tO%8MQQH_7gc_WuZwI0ut_8r9L(;
zfbK-O=Q4@G-2CH;sHvPDiiV6Aoww(8-m{I4Hk9{T3Jw3qil>t{u9+h8(Zjd)>+^2$
zseR`@fK=Yyz5mx{l}?o-*S2NIO`9fj*A<i+y{9#9*)qrc->pfFcTRCt#eOr=vh#8H
za^cj}hRvHJrabiRzj0$u>i>VgpFKA-WzEUy(pzX~eA#y{XpQQ}$*Bsqdo%JLa8Fh^
zQS>j_++5s#OH@S1|FrrP{mq-1=ghfR7nhf6p3hYKOQiO$w6K~&)rs5DHe5=|&MJ!e
z-f>%Og@lTxEnT+n=T{w_F1?u>H$E&dR#Gy`yOsB^bS_Uy+B5q+<IQ{S?zw%VV`?sU
z(c}Niigk2K%J~yd?SAf*EIQ3VO-*U@_RZR}o_l+1M;Lv6!ddXU?A)iG9`pTifqdqh
zH$4KGcWQS}&-VLs#LJC7Y<usS>*MpO%sD@m^~P=Sx4re(=I?B@c*-s;P^D~aydWp?
zqlQkTnEX0c&x}WpL1ppjNt5pVsnQC)sA{}z+a2La9V-lsjE?k2-IUjl7t4*z%6d4d
z@ynM#`}L=4y*Gco)LZ<I+JE2cAH^$;Oma3YF`lGSSG#eOP_jO^w(;f%htJB{iE#RS
z+w|m#`Tlu3D=+eyKVI+ZGDUpToX^kpuE@Vr8W=Vq(KbIRZQAF=JiUF%Tkfv0wi0{W
zTmNl#{kLtxPx*xfj3RDUTXZ>9Rh?U+qTm1g{8oLA3DdqkyS{nbxtUW$OcrW}cEvZi
z+)D9X5&G-d?hUD~o|8=SZ*0AL<at?fewv!y?oZXViKn(q{k&}ci4BH~iofcYEpwP@
z^e9jyD46@2_2&nT5s8n#ellOnq2&3~KS4+52dm#v|J&=o@7hzSe`3Ncb3?<Ew|O^h
z+qA~jy6g9|Ke?%IYE_k%8Zs(ssfmd_diWyZ$MZ?44~}*>shX6(d!keQanT1u!Kc$#
zW**F+b^qJy-RtUlpL^B1xU_;+STdyKdPJ@g30Ua#aK8R)@l2)IfR#tiT36rc=-^0T
za!Q%v<72_o+|ki7<>0v#P}WeoBnerR?XtuenkhWeL1$Ql8Ykxr7{Pm9l;$QfIk|v<
z)nS$v@S4_=Hck%Enry+BJkXP#K?YTdh>5XH^_u!PF)693ql2TjSRebqz!Q*RF24?g
z`ut~KTwDw~;j4F3*40%j|I0{AgU+A?UG7l)?2KbllG3wtbGy66^_7&An3gSDQv`Ch
zQQRKsbD+~v7e;T-+bK7nK;L}@*t)8A=M7v6nwp%Fl9EyVpySXQ8yjCeVP=ThmeVO^
znw9YR+1XuO6G8s-&^Kf}x!kdt?clj{eAA{)<KW<MNJ&}pD<URFMo37gi02|xLmRKO
zl9g4|^o5{eQt+iL-@#ML#l^*-#ip`WC7`2(L5B^VnQhLm8?^<z+Hbj^?2+TgnHlEH
zom(99{>_`7<Nfl=y1Kk+X=%Io=0ofSb-Y1)vOFiNz1q*t#s)es@x!N2hkkr~3_8MA
zMO~elK}ScYC?-EMlas;2&o2!UHE!L`8@Lu`U0wC!&Q9Y#Ia@C9>DsIe9=^V33-i3Z
zxENNP2S=un+Z#sBC0<jt9zH!i{m1wF^;ezOZ)!_Q6_=Oq-;{d#*~;(9XJ!~`#O~VS
z7CGOp_QG=i`HMJbfgE=)>u?+IjSY#-ZZ|ilpBL#`_9THR$iqDEPQ%irsyR713CYRL
zMNdyXytA{|@*HTR)7@RApTErE7T0UpzP&uK-3t^8KNq}Zf3RYO#*ZIAG(-YV9#D*^
zT#~E)+5X=T=IU>69B*&Sy_yP|$H>UgNJ>t=n%d4Uf9&e&a6uWFHIpMi3-3F=h*mp%
zS`ofJZo{Tchkn1`&#$ho9<!^2Q#*WJOK&f4+WC2F?eY(`aNgKes{QBBAA_tb8Y*gP
zN%!_tg7#^!@yV>XBMsUOpmd4Z$Klh8?Ca}({CqxtW5GiwoBDq;PftyC?w7MQNIfO8
zWB2aW=Zi~AT_YkSKu0Eje0==yojWp(jg6oM65;zn2~lbBaTb++w;qW`L1nimFYkQ6
zSIz(X`+NOopaARW;NgGX0A7_pk>tDt3M{H*O2P&ihFUQ!dQ|fLU93&T2Zl{L+Ok}N
zf)kZgJe_tHJ$>-%)v6K=7ls)P0i0VaKc|6q=!)yb1XzKt`riBC>C@IuVRa=7i-?y2
zCr%hLE)+`t{Ol~~ti)T}^ZQRuR$pB7^pt_2VW5?cl9G~ZN=gc7LvZ%BHJ~0pV}mD$
zW)~;NREy1NXPth3dkZ?Rc16gXXD%)-R~9%jm%P5l3kq(7M5b3)RyJo{UG<UODv0I%
zxfL!utH0~%#q4NsY-Th3si35E%57;-;)Ml{pz)$>YopsYY%oYkNzsrMlT^I7FtGE}
zgM-Wr4o^=_742*3=<xWzW9Lprfs>%QxxBl(mS)(b%$d;;&^c-AEz#XmIy*XwPW`Ie
zvSo`)Rh3mzQj$ntM~@(+m{U&4%j;A3pQj<qCaDN1Io&)ashnd}bTwd9T$J?j{r>+>
zOM?_OG(27gbgXCrIVVI*6m&{z;Jx+n_A5fPjvPJu^yki)*x10uZoP~60-f_d3Q1b4
zDY&Ticr873J^Ajg(xYPCDxMQmR^FI5ziYbPR@Da2wRWey7^dk)f4f$EdrKzsRIk?9
z-DRRTUwcoQ@_s6R_oL04%F4`IQ@L_)ZK-_x`a|&q58>~Oidu=wd}mKvob&KdYsJ4m
zmTtXLtwyP*GGtaqZ_nF!Byd%TCTQ(B=#Y+S&zC=1uAkfa<^BXk=MSg#_a|Ik75d}J
zWd9;teKj?<c7FM`?rv_Y(pMoV>+SRJ+4#(}krdSm(b)7{hYPf#@%Q`v#ebp|jmmr+
zI=p6R1}}S}0lHXuo_&4a#l`NA=a%2Q7^!pAc26VNKM$^Il`pBUsjUrM?8fWj?*91C
z&(Ay67OxIl+oW^abzgY!&reSS_tpGVs@}J!Dtfkwq2a^l3KPX-np|BN8h(9!efZot
zK21%{877%RtHRgwadUI`$XE(7eE9J(`OHjXcF@Yf!otSZR#wnnTshk+1uZSD<DZ|J
z#iZTYQ5di?1a!Z}dHer8Afs;Gk^<%V|Ns78t=>5yvHHgc#bdqF?EL)vpwl^XZf*i?
zy*hO0P|=*(lO{D)etxzvZg16w?c3XXdwK8Ny?b$QbvbCK4a0}W{q~2>o#UINqG)0g
zlKkR&6DxPg-)|n>pi{o4rLKwD8T9(&nKM2uEiDd9gC0Kaw`T(l)INN@en04HxrcAw
z@Njc;@7T3#QT+b89Ys&Q=EeW^Ep`yoj{{xna`4h6A<%u$rdc9F!opka&TmdX|KaWS
z`$`H54C~gd+puAS189zVYj(Jci%Z6w-Bn++X3U=LJV`~ys>EZlZ{6p!<_!02*j4Q8
z<Ul*y?%cVPkeS)}>FMdk?)`EW_5W)6ia~c6PxWezulvagif+h}BZ13&Bw1KlAHKS}
zx^kViQtbgb0VO7dW18t{X-WnL0-#evZg0zd_~5~Udshsttfb=W|C;{!^XI}+Z}BO5
zu~Ps4eC98Ef3Nq#g#gf0`q^2gr!T+unAGzB-}n7Dc9-j0mA~t0Xkch*Z%_XF>+6n^
zmqO>}S|2yO%=YHh)zzR%x2DDhwCznoqT~7b`N`kk-OaeUNfmSn*uA~gtIzZE@wsJW
zXzba$*D&LPLPSJ_f|;4v@qYQ^6P4XBtcf%>$-2S;+E!(LzouErG;2Zr{e7=q->?5)
zt9nmuZ`y$dM$pOrpfh=Xetv%V(xsrsKlauB1`V)o%e}qIJ{)wGN9E^d2k+mPzjyDR
zM(8S$b+NnKK#L<yOhlIZ&2>6E%k=)QTRYEg{_$q>`3)s6gLdrL@nEvQUE|D|lA!%w
z9UUA?mM%Sb>Xg^xKi%T`NvEc0e)#$obVbvtDVm_Y0B`Q?mHz$h?cslae+vo-Ffe@h
z`0?Nr&0vGtUnM(s?_OO0|KF?UnHLuwm43Xp^0V43v)rzh7M3kpSGnYCJ}~Cp-`A@f
zy$!T|2edEy*0$W)k8R)G*vJgJPWyPDtYOWM0?_HV3l}c5D1RpdYK`!HRXVIV`Cdm)
z&lJWJ6DLmeYERk_(SB>^&YdT<rh4^tcAoq?=hxTQrzfeL?3FT|G~d2{-s2yW)%~9=
z_n-evV}Ir6Wp7@*&@j)r5wO@sQ`2*ris!p4D<`}C+)?;A#dA`MySw|(UteEK9k(if
zr?WaltMdQ9zdtLhmpE5udfVCAZJIfAri_%-s=$9gJ|^EheArnpcGs3aZ*Ce#PD{14
zvx}Ri;`#0S_v^o6`sMAz{#$N0bN#hAe0|&|ucec&uZx}iI3gfm!kVC!Pu||%zWTI~
zkkFI*|9{Po?|goK{&erDTB_`<teI2d_t(WvQ}L8DO6hQ`++X)MWvZ9z@ngqUodzA_
z?LA4Qx}svm?b$k~i@dzL?rcbOj+~^jv+C<Bsq>%T+%)broBs6M+uKF0T%sxZ`ue?}
zzI-VGDJXq??ba&g>lMsQOd#{l&$SL;{O8HZ$(uSmJKsEcqN1*@zWQ|d+9*};Nh-5v
z&pv&;U;g@zXQ%b|PYKnUx~Jfw(^|tldatXCo}HQb>Cey4Pkm>bJ$-3a)%snk^|+jX
z5<|sDrX4kr-`Br>$QTj*`d?|WqNmWaGc%c^qi@R`FZ!Lj)@!O3=)x`!4~}_uwX>GT
zKTz>J6dqp-I%_0r3cs9<#P0I<ZZ0k?x3*@7Z!llDL0muX!6v5MlZOv8U#q$>4Rp`k
ziWM3>QYIX-)@4^L((=Jo^JCD`op*P4gANn@YFJ!cy!L#0et!I#k6J5L>fV6XV9z$s
zKej4#wMiArySuyF|Nj1d_~c2?<cPGLMNhZbfi5<E@cMQ4;dXxKX-}EHcKrKt*?)ox
zC+Mc+xpU|4x^w6d)BT#yyiwcp=02YF=6P!P${<xxm414f?%}=F-?>b0+N!Csx%bPp
z?%r*EtWS1z;hN0L%RqO?FY}$fq4>F9?>r|59@Ff5dv?0rys*$2v`1XV`AEkXNnrsc
zhdZo4e!Tcss;tbIpMQ7%J86@Q3kuhh-TUQ^e!pMezd8N9lA<DGW@hHA<Vh-uW@cvR
zof&GjTq$4bHI?DOg7Rm3YkqFJ_C4&5lCtu{wd}bszr4K6(9qJtQuFugb<4i{d#lAa
z={$bF|34c8X!*&%fB!5hJ}A7pzP>;2?yiNEpPyyi*r2GRqf;Wad)?F{{c{@{7(k~L
zK79CaN6}L*(2c1KN|GM_{>K+MHYX${Eh;?w?(Xi5H9w17TwNbtT<i|o@F720g|lDY
zzOA|0d0%p8Umx3oT8ofwG2NoMEcK6$aO%YEk=XzLpS6&X(1%Y?y?ZN?l9RcoHl*}>
zNAoZ;GTzuxsJv+L;$jsA)vv{qd-y?N@<&BNa^;!VuLQHRv!`f<dcEHG_4ReoeITG+
z_OGw44P5FaIz=m#Ym&-C&@sO=jngO0nl<bB^-I%M%$U{Hui|`f=JmsenJ02>QRTPI
z^zN6lWl}iy`~B_h{;w@3sVHh{dj9=xTm9|9?EHNPm-)^%sQi?2ViQkxb~b2fa`E$X
zx*PW@sD4dO|M1|TgngY&*xIPpoyE_YDoi`8K0G)m_59p?dwI}3r7|)y7gmL8hpdZ{
z3=0dJpu%~3Tkheb-Qu6W?woXYbNYD(hu5zn($dnRZeN|I8+~E9zkEb=^y1Rj*9ywZ
z-B$)JT@bXg;Kzr=IX0D>4t(4rH+}Qw&DS1BKJ}k(cU5D$%E}{kd6PaSGC8qyeB`M5
z@OtB=dnRW<3nW2PrawPFzbfppIc9H_X!^N1n(sdFG;Q6wHSUXx#?q3PmnNui+W-9`
z%*G=zVR`nNwQE63SI^9`6fS>v$1x&8B5ZwJFX)=lj~_qY*j=7)=)SgR(yxui85a~l
z)77u9tv&qa=4Q~rQ}zFTvagNW+O&K3?q7Spd@1pnZzrp#r`OTf*S2`^;$xp>?CbUf
z6oRTq2GB0@9lLgA$#{EvAD*o4U!<Z4%0W}7iY{8TC}3@rX>aoO{QF_+PlbsT-rZg9
z|N7&P9~HfyJG_+i4tu-gPn|fi@%Hxo#d&vkSx)Nz@}&fHpy0w29``}JcQ@&X|9G}B
zXsH)yA$!&4UCiu!E9wi+N$oluU-dQmnoWN@zkJg1KG}$^S)wYQr#6bb{rmg-#2GU>
z($3C$>R|`E7u0zwm)7fchFov^_SRNKV`F3YpP-X1UcQ{E{PV<_GgoHSr0_00<v)8(
z_<A`8!z<p`HuayFsO+xbIVte}1V>eQVF9HoZDC<yP}O*8srM^IvxXZjukHW;<Oj99
zV+xjp*VNWNeEYT+lu{ob>y0|!+1D4w@4U18{k&_Qzt7LJJ-q+_Kknk<;?hTnnVFp0
z+S+&a)$aark3qC^jjiLe^Yi=XT9+TXcu_ERSII>S_3Zngy#MHExA?11Teg_=NEj}<
zP%H&G;N39!7*A<6Gw29fpE(wRuT7_Fhaa1&9S(|u-cKhdt7qTMcAlhi<M!?6t9`@P
z#Yq19_Yc&97t@bhQ>bHOBQx7PzishiWvlXcbDlp}F){(QNGpxf&Pde!d^#PpoPK-W
z-337_L3b|Cm>~h$Z1em3`*16lOTy9HavWEOt-Y{4KmN>odwE_dlZLfxb>H3F+x_B2
z2593h=vaHN>3Y4==6MS$KR;U$zFyA8*7o6}M@^vRo9E}*R(yLSspdClh1_F4(87Tu
z_x4tU&Ugo%lMcEL`sylCDbuVa53HA6-<o-uEiW&xqoboi#Z$tfV8OBf?haq%YX1Fv
zKEuA=4z%lg!2$))7KalPl|dbYn%Y`!Q=Zk8pPzviUvA8~X>?^(D7S6Zmxh%qHOt=J
z;Z#>wx3IK)x+Cq(48tFPKA#8e$t`(*Pj=a|Wfp~xSU}ApzqwY5CMG7wO?9KU9od$9
z8`K26vO1i9*|KFHK7V$;yv(<_H}~hKr{MGc`eeI7mC5nr?1qMhppN>PGiL;ZgeE2T
z3+3)rb!qFeRd8`r(mx%s@8z70$;Uy*trnD&9GPXB4LYK^qM|}UNr~zG-tTei&QHJp
z)R?=7_vN+Q|Ne)p407FDnCj-n_V0WBe{uVNKa`b}lmZsJ@&5U8+5eTIo}8Q<=y-!S
zZ{C2451p7D0=vuKx3#ykgSJAyyu3Wy=KH6or$NCw$D)wQIQ`s_DVo6oa&mDw@27I*
zeRy#2RsH$({&we$($C3&ZdKi|Wy_H+QSA-c*Y!*?FR`SbpVzv2_3CTerrl0&*|tf(
z{*Pd{n6A^yOG`l|f6lEfoQ;i*ppB+y&YUTVF|PSh0J^mkbZ4Ch<XpfVyLW?bfoPvT
zefqWU9}e@|F90nuEPmFr%QsE_+fv<oSyxvn8W{<JRzp5LJsor>o{U8S!(6LUr-}-T
zTif&JFaQ31o0a0F@JVN;MCH4<x*j}!+&zEVWfPY*&Pqy)?xfcGpPOST92OQ<P+IC*
zP_W@rqNK>ZY!SI?2k?jm1%rPfi&AorOqn=wVSx&0C|v#Z?}^gllP1k*2<Qx27h}ob
z;N#PCXrh3i;LG{Z+w&MfC-qO^ZRwE`7SIZeiH&{u^l7VU_B91#<KUMC9UUEC+|tt2
zuC0k=Ha9n)q8ly7Q0?R)xy*~*A>@jh-<*znd#jZV3<R3lctPh9`uO-v;p^_`@DP-g
zl-yDJS`2hWENB5W=-|ES(Gd|EGP07&Y6>BbSlHQ>ZEa;AAMbYu`TiluJ*?*D<~<UI
zOuf?PZ91nt{QcXzySpzXP83XHa#9VtzpwV;vuAC#)!!Hx)~sE-FsSf|qM)Fpyyv8Z
z^z`L&Wm9L(0=3F5EG!P3J?ne(!3hmRM$Hz`wT_>joc!?R%aK-Yag)B-eKnGc-TU1t
zDlE>NJ$rFuvO8!?WnW((muq&7lxbGWmMtcrU7VoPLUc~cy!LW&ansb)<OE$szz`f9
z3~GchGBOqv6a-ip8M*m5aA}yCiEU0l-`3mPdo3By%BaY<yAqO;nBwE(L7PME?Bp^t
zGbf}*zTD8!(IK)pXk|inb~l%()`z+lk;Bu%9pY;~vVzW2{PLy5rs|8w;~Q%tH&2)|
zr|0wY^Q(`CnPpsHSR1|F&BKF(jbCn!%L&z6ZM@Q;3VQAFoSdA1y;Y^M?o0mP3rU}1
zeJ%Rm#&cY4U#Hct+1!-4cjCIjfEq>_`#PE1+w+eva_!zCcYj~4H4`(lsao0AoSQ<X
zSyw<u+n0(i3%t1@vH6msgv_<bgB$LB>Msp5ShM|4@?24#*S#gZoE~<mX=$M2CMAqg
zII^y;n)<i`GA<jizwWQhanR+mFJ5HCwt!~(T$W6?V9}mGO)s|V_V)b8CnhRK^&f`~
z!panvl^uJ0yno`fX>7*Eo0EDcgEE1K{6=;ac{%&Ko|VDNL4CGYOW(vzpEHMtA<F#Q
z#w}Z}ShRzbow%`qT}58aZw}~~tFL^EZbd{!G74np?AKFOb$vYNda9~g{r|t^iO!~0
z=jYivhlEVIlqeyB=_B3xP46!MXf8Tv$ar$`>+9>2pPrfuS~M*nEzQj^AyraSQxkMT
zqmK{In>TN+pbpkr{dsk5Z8Sqe-QQoL=Vxm?tf{G)!guh&2BctrD*t*-LPY*g&}A1U
zxwk@I8t_a~DNb&&;pFB9UB%0gao5{vk_zZLJkVu(KR-Qv`2Kx=ue5pAoW~CzHi8C<
z9~^9sihum@aQlz9+wVVk_H0_VJ|}4KUsK)EQgVCVU8mdIa(5mqVFY==y<bkz#>U3|
z=aEj~iBqPu9PJiAeD|&_=(foZ4-aq5yK4oy0dB9z`-g|yFYYSM)`{OI<J`t`vGfOc
zoU*Es(?jv+@86FvE_UBh@UUsun|F72gO2=qd1<L3Vk8%IzMz1h;6YJPBJMbmQ7F1b
zNx^f{vHP*Hv7qKx*40&se|~%fjngtQGZ#KMz^En<GC$?Y4Q3}0!^P^boB((L7c_5S
z_2JV~??31N|KX2`iAl)K?LF2j{rJK{=gM8*Zf;JOuqx4*GiMHHc)Ov2!NuKOy!gl|
z?Too=V|R;bP1QPn_{b3!PEJm%j+n^*|9<N)^PAiC?d|Qu2M;o8P33xhZSCpLUyt3m
z#}%A@?AE<=bFJG2mEAyF9n8$kK==JseSOuMeSO{2msQC*=?)KHosGDC3$(uc!Gi=)
zhhnMMRPZS5>(|{EE?lsQ&0mwd*5GT}nq1%6k(<*Tmj)d?eOlZ9xpa25Hpe^WMa;}>
zJO{qLy=|CwMj~W&7_XwDBIwMaK3VIw@AvENufI--bZh66{qTH#{iD_E_cb*)Gf&fr
z1fAjoy5=J5>ME-m@m--8teB6?nFHEqaQ3Y4;ypeyjgCretgHC(K{4&jjK)(_wLz^-
zi?TNn^VXYQ`xZ9Cin(X=-oM}X|3A7ic=?6R>HZa!l@IUjt^V=v_j^#&?tcA$S<~!m
zXTC-y=YssmYjAFxn6Pm3k|ioUG8O`&+F?h!#q~Gj-Zo=lWlcQVB`RT4QLyg1RB^Pn
z&tAr3H?PHsOG|g(+?;;+*fBN#=hDU1+5$7Xu7WP(@SdciAF;t9cG{##O5v-+eDiK?
zc`3dA?v~8SI-ZmE)c!6jz1hsppHf|2{j-_hPGj4QsbMp$xH&l|hH6cfk(7+QllW@s
zbp7~K-{0S#o*Fsr{9J2w?@21_V|SbRUzf_R*7lhznO$vN`)kXbY15Vkmc6}Ydh^ny
zpno3@^WU#nyK$Rwq+ZNk$?WLMYR1OF>!P=>`*W}Q{oao@$$8ngo@~8$<62AH-l~&p
zqqa`^`{&P-TU)dDf34ZJ{m(;zHA&k_Ux&?@H7o1Q-^9ek#(w*M63hMPhrM65?(DYQ
z+n~j7C2wzq#y)%Tq62iF^sTMjnVFeKrFI8#1*hLsRaI3qHr~AB&=v2rw6ubvBCQKs
zkKF>Lv73^?)!BD;82b2{T3=Wly1FfDYu3Z}`~TNH-g@lTxrRemqs!D@7A$9A5D@ls
zaSS<j<=U2sDx6cbLJwVD?ti~xp5b=W+)q*YHzl*9t?T|+Y|Fpj=h`jy^rzIe?SB%P
zg3g?qZ{IIvn)T$xq3jzQ7XGTpTeNub!?$neE(TqVQStE+>$<qTQCkka>O6EsQel_l
z-#>p6($m?qv$H|_ldrFj*WXs*&6S*+-+J`hw^M(Af4{#=+u(MjU*$^k13PZt>K51k
z@ZzE}XfH14tj>Rbe_vb`y4pPM@V2lGRm?rR*P<2t&tKoT_o=<up)0bc#s)O4HpSZ7
z+Id>)k)ucVevFEbk6(M8$sr~tX5YU;iLOIeW^UbT3hD{e*Vo^;c@xx-`uzO7`{iZ6
zf})~J@6DW7WOVIYm_$zg`Ze+U<u-2IIK#5|ne_Q&>&sKFR~diX_U)*6d;;j;V1}t)
zt?%yce*F9W{`$wJIoYhM&6D%9y{GFPeS3R*;gb^+YhsUon`ZCh&=px!WCWV7n-U!r
zwW?qINmS&t`xR>ywSDG(eREUTw)$Jhe_IU=j@(;Y9J|GI4YIH4=tOQ>@`71Uv1jXE
zQMTK&3x0k|1+`6lXPdeH|M&NPh2HI(+CNu<>;oM-eSV&;ptyMZ+1cjG%F4_uSFX(8
z)0Vq7;cMEN+_xVe9|tvdm-)<W0!{3_j)Eq<wA9q4U*)U4yt;l>IIf$j^0N23)TTpM
zW}1QQSnSsOY0sC98$ls)rDyHlyZh_qWv$Cv_U^UamUENIbJD#EyW2A-+^@QPY{spq
zUMW)n0Rd3;llcAJT@N20mw<o^((4nWwIlXsChO$pK04A_P*8B7Ls0ost?upUe+G=2
zY3I(J>*()4A2=^GY-7^Vq@C9$h#vpFE@Y*UoPAx)iyK}>$+`JQ4@TU&{rUO%i|b;o
zLDO>k|NYX|3R&^tTK0`|Tw(b;4X17UwoA^o>cN|voAd8Dx8~|D-*Y88&#NkWcimqr
z(6L|>mEA#SuNWE{g4%;YK|vFyO>3JmL&B!=lgjniH}8FF=kzdK9lc!-bji??B`Uq2
zjML9KOj5bBHu`(-bvIYnryBOf&v@3XT??A0QR)wu7g=_$sQB|U-znPR>sC1FOa#Rc
zdvZqp%9SfYYem1lzP_>ieH`d8+R9z$ZrqM^e64GK?83DzC#BAPdUJDg%z49a+m`vp
zz0x&T-MjYav13ltQbE%ep{qh1r)q^hc=xUkR8EGki)q}w+dA#cjES$m=H&l8%(7(0
z*B2L=U%q@9u`#K2*PExOr*m*}IzB$udv)?o(6F3#_&Nn;W#`AYet&<T{QKM6SDQe+
z`q$Uif)4GTIdkT_dmVhD%g)K!)y#PO^V{3o?I%unY{|dB??<z_Ku-S8<KMn{rKPDg
zH8+3!{eFM)>uYOYiN1<EUbbu{xSn}?u$kTR)TPUpH<rDPiinI{X`Y`FeK}-CmGQN0
z&tAUll(jBPINm1<I`aQl;Oa14(3lSBBH`om^*jx4*J^ld(mCzL9(d{NtE=1~@fQ~t
zU)^0b-@2R+bX3*lWxk9I%<Ozec9p&^czsP5bkZclk?EnE)6N#WyrkMAZO+%%*B7xV
zh4bB=oz0*<S5;qMdF@-96nepGb>L#QS65fJ|N8oRammX|9X&mu4kGB-;f{_Dp}((v
zr-3?P{`2izb8>X1=|;DKM(*PFRxvb8RCYgf?b@`rZ%tX(8Yk!GFLv)g_ULH0poqv4
z!${N9?D91X4fC_tYx{tT_Se_eN`vOH#B`$$96ieV{oUQept&RM@O23Xn^+}GvqTuS
zZM`nK>|W2-wNH=Tzc1e}XM3oXTl`9DPQI%{=Ux6@->j@vf3AkdpA^*&o3eJznkR0(
zQYYE@<*rzyKRD0`8bp>67G50q@5{^J$`cb5i*^=0O_7$Cwp37X=<Djbl=`DfBV(@b
zJe$aG-@Z+I`}S=rH#hgqvuAzf43k>sOqj6X_4Wl4=H}*+OM^0NQ?82Mp1rK;+RTmH
z%<b*>$GrAl8nhBrgh66#Tjpi8oxZcpHf`R#IVUeq&)UM`MDDFE6P=x%P0uC6;&qy|
zy}kX-)2F=`_q<*E0nuc#Q~<Y~sIgQ8n?V<&TU%Qlmj*rj@$s=&dyvX9-`Q+ayW$&N
zT)ZAE*B6}F2WfF}1qKGb+VuDL_s6f-?+-K30-f9+vAb;Twd6@EFD;}Fo<GkY7Z>OH
zYtAGU&2M!b9X+z3xm(sfb)R?V-?#IbZx_2TBQA1NO6N6}BiFBspFDXo<Km*$bID1I
z99%*+f!4fSV&0@9e){vWB}+tZ7SEhLTl{9Rpy0%hNlZ>IOEx$*vn8IKq{_fB)r)ni
z7pt(a@Ylk~X`lnwDypguojS!OsuglTeSQsS+$PJWfA(x?&_(qR4ltTzTwn+b3j-as
z%aCw(mTAV#O{%A+>+^eidxM5sPEXUlykno&R4vdnSf8x*vOZ-61qO$A#SYtYZZ;vN
zDi62I*LCdJVbNO*y52cDI{L`bqYJ&K>uuP&wRQ4j;eCHTon}&x)0)bq?A~`|UF`0F
z-DSBEQBj93Ul!i~_gi#dvHv`qmFqy)W3PVgGW+Z6>#OhgH#al?`tq`QclrBO{qg&1
zB(Gh&CSg&K(0ARy)b!|&kB_;(fWonJ<t0V|LCM|zc0XAP3kxkuUI^6v`}thP{3+;Y
zpk1Y}KU{Q|PrR}s@W=D{^&kFzzYm(%<>2QJSJ$4T^6=Bs(=Tps*9Tp1ztFk;(cSX<
ztPDEQ+j!>9n+IC6;^XTJn*HaIx0BhV^Z2sAJu5@a_q*j1hDj_rxw(d!msCz{>iP8a
z^y<eIwHhHF9vpVRUMPbm_==yMIr#VYcTfb(u`1=dc=6(f&6|Vcr_Y?(xh?nhqQb|=
zK!aX<vQ{mu++qu&wq}8DFKT=RIw|MIzFO-xetEv==xCj|JrcH6Uk<#zy&ZIuHp7mJ
zk4m?;<$?|x1&!T;ZUHyTm6|hWPLa+1HIbV^>q9_e-p*}22b<aXH&lE~`tj@6s{8%z
z?d+g7^7?(hxNNJwI3y$}#O<j7UAP3gmZHkAr?11~{Ye*>CBce{ilB)tWo6~7smg9W
z6GF8@`-=VNSSWf=QpvxwBXIGb1C7j^R6IYm@k(Dw&CAOJspglnnen(XC@83C%GT`b
ze(S>4&T^Z%eEIS>@88dVd?)#MA7~nnkDGgPYUH%bmo7c|^z`)U(pOg|1_uXUeg5|D
z?(k_Up7YFdXYI-X6+B)`C#9dAwe;hhd-v`YHM8*+?YZ{)52TrzJ;ig9N_14zssDdI
z_wRahsFfS!lz#hvEA(DpTH2kHo2z?&Z*_QV+0Rd@H?Ld?S-dYfDJdm5IJolM980P7
z#V;?Z-aK>0XP#~Kwp}{L#=&{__UwG~=8cYh)fbO{Z#JJ#DK0Ly)Y5vj_I+zdN6Jj+
z4ILd{Zd_X%4VrHTos!fqXB%Yy>gsBCX=!O0+p3Vqe?TWthprB@oOk@tA*a93pP!!(
z>LX@cT*M066Mf{!k(%SB@9tQtsHi0T`0!Aw*x9P&1p_}n|C_tJ&5xU||6XE}e^16d
z|K6GJ+3A^gcbVR~bLUDusGSe$RiB?Zb7m(qJKuur>+3)_0nY#aZuk2|xwp4To!^>!
z+w8=qo_V&_Sywhso-Dj7eEqt>^OIB*Ra96!Jv~8(3WDkuPGPkLaeJ#+>-1e*j<rDi
zvo-trhc7RK*E+YUcse;bHMvDj)e7BW=U-7_0qP_gr=NRpv|C*2{Kq#pjc?q#^-O!+
zj2RLhlUhLSYl~+OA2xpYP|$1S?d@%0VUaQA=Iz^$Pfk|<S#|e~R_LlJi*<e%+f{sE
zIC=8qim<g&eV;*dPH*n-kN^Aq_4W12#>T;~cTQ4yI-}wJyLWvngO@)#t-s&p?X9hb
znU|I@?dMlgN(Qav^XN}aOIuX)^V5%~)8n`J-OvkPH|N;>wQJTC%>gxaKvT`{|HfB;
z|HSoQ>OkS2<e09FlTSBsTwEWq(MeBLC&jQUM<dB==MtBW6B#bO0d9d}Vq)uji&ozJ
z!JYCrY)b2zDR1sP)2W+zM&kH;i<-aZ-tUQDJ@fhRGsfpX->t36v#&exus}mm(J?VG
z@z$L?SB@NMnKy6VzBT{P$K|d1|H_Pc@AB_9wzelzj54dMtsk(qPMkP#{i(9DG7%Ax
zC8tk&zt7=hcu>nC!0>~2Pw916(421G_M)PqnrVT5h4rR;mzS3p?ToSWo_HtkyZ-mX
z@kvRGY~=d;`ujs8B3^uc^W#TEXJ@DUtJmpWeSKjS6+8U={o`$|t*k!%oHuEbQvdPA
zOO`BY*sSnxj^FY-dE0Z$X1lq$rEHDbx^3IH%v$yad&z1Ch6m;eX0uiQ-kJX~C^U4c
zoSYofymeoG{8%x2_U!W8b@MbUEH4)p7cXvYZEg7b!Tz54gg0+;jvhI3;$_LJfB)`9
z@7ua{>EXl9Q>RbgfBRUt@k}2rUEQm{e(gFRxU0Rr{np*PUsq;tj7Tw>d2dhezhA#@
z{r>sq&z)+nu=4Wl$BrN0x^d&eqeqXfm*imhlQ@Hsu_6D#ejg2OZEv}LcU@gwEnVHv
zf`SdJR<E8qf4=_et6ID7uKWA9cIuQV8YU(o>gwuIadFq4JXxY6cKZIm>!0&aoj#qq
zHLA3_diBnomPd~rd-dwot5Ewne#>*rW(NfaiwX-bUbJXYcr70%=fxK<R=j%sdg}c7
z>u1f9ij9rUEGh~*dGcgde*XU@=3n3Q@bP6;S4US?R%YepUHkiY@3Q5~r%s%>u(_Ez
zfA5ngDZKprtCuf#fByXWz29Ev&Yjz`Z=c@ktE>9@_&kGZYxgQCDeWoWZenVhnVRaF
zo13eprxz9zb7y;xD8oN(4h4n}ynm)!Me2xc-L@?&GxKD%xJPN}R+Y@OG`E{KZ_b)K
zH#9$gy_1vElqpk|+`1L@^x3mtTO>0$L!+W*g@uJ(xqf~5xpRI;j~-pQb7y2=U|?28
z#)p-+@84hF-Ob(G+xzm{w=h;#)_V+(?w7^J&OMX1`AymGUAuRun#~T4jMUtH_uXFR
zCu=|M3;!3lcl)td@$+WRTzU6yY-veJMnZza$B!SSq@*rAeY(`2Vd9c?>-3f`Uw-;w
zK}Gr;At9lVu&`xaU0j}*u3VY&?c294yLP?Wyt1yYE^g+`nOQkGQ%*nqwbYRD!2i$*
z&y{z7Wnf@nc))yvnTdgc;eY{)0EpkF*Z`soco<n27#JE7ITS!#9%lwd1_p)%2__B@
l_ppEih>~byAYSW?{R~SUSeO*cAKU}d>*?y}vd$@?2>^H9yHWrE
literal 0
HcmV?d00001
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103424Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103424Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..f514bd75
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103424Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
@@ -0,0 +1,215 @@
+ Result for Multiview classification with Mumbo
+
+Average accuracy :
+ -On Train : 74.201183432
+ -On Test : 49.5238095238
+ -On Validation : 52.808988764Dataset info :
+ -Dataset name : Fake
+ -Labels : Methyl, MiRNA_, RNASeq, Clinic
+ -Views : View0 of shape (300, 9), View1 of shape (300, 15), View2 of shape (300, 17), View3 of shape (300, 10)
+ -5 folds
+ - Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
+
+
+
+Mumbo configuration :
+ -Used 1 core(s)
+ -Iterations : min 1, max 10, threshold 0.01
+ -Weak Classifiers : DecisionTree with depth 3, sub-sampled at 1.0 on View0
+ -DecisionTree with depth 3, sub-sampled at 1.0 on View1
+ -DecisionTree with depth 3, sub-sampled at 1.0 on View2
+ -DecisionTree with depth 3, sub-sampled at 1.0 on View3
+
+
+
+Computation time on 1 cores :
+ Database extraction time : 0:00:00
+ Learn Prediction
+ Fold 1 0:00:01 0:00:00
+ Fold 2 0:00:02 0:00:00
+ Fold 3 0:00:03 0:00:00
+ Fold 4 0:00:04 0:00:00
+ Fold 5 0:00:05 0:00:00
+ Total 0:00:15 0:00:01
+ So a total classification time of 0:00:05.
+
+
+
+Mean average accuracies and stats for each fold :
+ - Fold 0, used 5
+ - On View0 :
+ - Mean average Accuracy : 0.260946745562
+ - Percentage of time chosen : 0.6
+ - On View1 :
+ - Mean average Accuracy : 0.26449704142
+ - Percentage of time chosen : 0.0
+ - On View2 :
+ - Mean average Accuracy : 0.265680473373
+ - Percentage of time chosen : 0.3
+ - On View3 :
+ - Mean average Accuracy : 0.259763313609
+ - Percentage of time chosen : 0.1
+ - Fold 1, used 4
+ - On View0 :
+ - Mean average Accuracy : 0.198816568047
+ - Percentage of time chosen : 0.9
+ - On View1 :
+ - Mean average Accuracy : 0.196449704142
+ - Percentage of time chosen : 0.1
+ - On View2 :
+ - Mean average Accuracy : 0.191715976331
+ - Percentage of time chosen : 0.0
+ - On View3 :
+ - Mean average Accuracy : 0.189940828402
+ - Percentage of time chosen : 0.0
+ - Fold 2, used 4
+ - On View0 :
+ - Mean average Accuracy : 0.192899408284
+ - Percentage of time chosen : 0.8
+ - On View1 :
+ - Mean average Accuracy : 0.192899408284
+ - Percentage of time chosen : 0.0
+ - On View2 :
+ - Mean average Accuracy : 0.208284023669
+ - Percentage of time chosen : 0.2
+ - On View3 :
+ - Mean average Accuracy : 0.201183431953
+ - Percentage of time chosen : 0.0
+ - Fold 3, used 4
+ - On View0 :
+ - Mean average Accuracy : 0.197041420118
+ - Percentage of time chosen : 0.7
+ - On View1 :
+ - Mean average Accuracy : 0.191124260355
+ - Percentage of time chosen : 0.2
+ - On View2 :
+ - Mean average Accuracy : 0.184023668639
+ - Percentage of time chosen : 0.0
+ - On View3 :
+ - Mean average Accuracy : 0.194082840237
+ - Percentage of time chosen : 0.1
+ - Fold 4, used 4
+ - On View0 :
+ - Mean average Accuracy : 0.181656804734
+ - Percentage of time chosen : 0.8
+ - On View1 :
+ - Mean average Accuracy : 0.192307692308
+ - Percentage of time chosen : 0.0
+ - On View2 :
+ - Mean average Accuracy : 0.194674556213
+ - Percentage of time chosen : 0.2
+ - On View3 :
+ - Mean average Accuracy : 0.187573964497
+ - Percentage of time chosen : 0.0
+
+ For each iteration :
+ - Iteration 1
+ Fold 1
+ Accuracy on train : 54.4378698225
+ Accuracy on test : 0.0
+ Accuracy on validation : 53.9325842697
+ Selected View : View2
+ Fold 2
+ Accuracy on train : 54.4378698225
+ Accuracy on test : 0.0
+ Accuracy on validation : 53.9325842697
+ Selected View : View1
+ Fold 3
+ Accuracy on train : 54.4378698225
+ Accuracy on test : 0.0
+ Accuracy on validation : 53.9325842697
+ Selected View : View0
+ Fold 4
+ Accuracy on train : 54.4378698225
+ Accuracy on test : 0.0
+ Accuracy on validation : 53.9325842697
+ Selected View : View3
+ Fold 5
+ Accuracy on train : 54.4378698225
+ Accuracy on test : 0.0
+ Accuracy on validation : 53.9325842697
+ Selected View : View0
+ - Iteration 2
+ Fold 1
+ Accuracy on train : 77.5147928994
+ Accuracy on test : 0.0
+ Accuracy on validation : 50.5617977528
+ Selected View : View2
+ Fold 2
+ Accuracy on train : 71.5976331361
+ Accuracy on test : 0.0
+ Accuracy on validation : 57.3033707865
+ Selected View : View0
+ Fold 3
+ Accuracy on train : 76.9230769231
+ Accuracy on test : 0.0
+ Accuracy on validation : 52.808988764
+ Selected View : View2
+ Fold 4
+ Accuracy on train : 70.4142011834
+ Accuracy on test : 0.0
+ Accuracy on validation : 57.3033707865
+ Selected View : View1
+ Fold 5
+ Accuracy on train : 74.5562130178
+ Accuracy on test : 0.0
+ Accuracy on validation : 46.0674157303
+ Selected View : View2
+ - Iteration 3
+ Fold 1
+ Accuracy on train : 77.5147928994
+ Accuracy on test : 0.0
+ Accuracy on validation : 50.5617977528
+ Selected View : View2
+ Fold 2
+ Accuracy on train : 71.5976331361
+ Accuracy on test : 0.0
+ Accuracy on validation : 57.3033707865
+ Selected View : View0
+ Fold 3
+ Accuracy on train : 76.9230769231
+ Accuracy on test : 0.0
+ Accuracy on validation : 52.808988764
+ Selected View : View2
+ Fold 4
+ Accuracy on train : 70.4142011834
+ Accuracy on test : 0.0
+ Accuracy on validation : 57.3033707865
+ Selected View : View1
+ Fold 5
+ Accuracy on train : 74.5562130178
+ Accuracy on test : 0.0
+ Accuracy on validation : 46.0674157303
+ Selected View : View2
+ - Iteration 4
+ Fold 1
+ Accuracy on train : 77.5147928994
+ Accuracy on test : 0.0
+ Accuracy on validation : 50.5617977528
+ Selected View : View3
+ Fold 2
+ Accuracy on train : 54.4378698225
+ Accuracy on test : 0.0
+ Accuracy on validation : 53.9325842697
+ Selected View : View0
+ Fold 3
+ Accuracy on train : 54.4378698225
+ Accuracy on test : 0.0
+ Accuracy on validation : 53.9325842697
+ Selected View : View0
+ Fold 4
+ Accuracy on train : 54.4378698225
+ Accuracy on test : 0.0
+ Accuracy on validation : 53.9325842697
+ Selected View : View0
+ Fold 5
+ Accuracy on train : 54.4378698225
+ Accuracy on test : 0.0
+ Accuracy on validation : 53.9325842697
+ Selected View : View0
+ - Iteration 5
+ Fold 1
+ Accuracy on train : 54.4378698225
+ Accuracy on test : 0.0
+ Accuracy on validation : 53.9325842697
+ Selected View : View0
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103742-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-103742-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 00000000..e5263038
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103742-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,1570 @@
+2016-09-06 10:37:42,137 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-06 10:37:42,137 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.00010290625 Gbytes /!\
+2016-09-06 10:37:47,151 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-06 10:37:47,154 INFO: Start: Finding all available mono- & multiview algorithms
+2016-09-06 10:37:47,209 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:47,210 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:37:47,210 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:37:47,211 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:37:47,211 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:47,211 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:37:47,211 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:37:47,211 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:37:47,211 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:37:47,211 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:37:47,212 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:37:47,213 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:37:47,213 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:37:47,213 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:37:47,247 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:37:47,247 DEBUG: Start: Training
+2016-09-06 10:37:47,248 DEBUG: Info: Time for Training: 0.0386531352997[s]
+2016-09-06 10:37:47,248 DEBUG: Done: Training
+2016-09-06 10:37:47,248 DEBUG: Start: Predicting
+2016-09-06 10:37:47,251 DEBUG: Done: Predicting
+2016-09-06 10:37:47,251 DEBUG: Start: Getting Results
+2016-09-06 10:37:47,252 DEBUG: Done: Getting Results
+2016-09-06 10:37:47,252 INFO: Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 0.633333333333
+accuracy_score on test : 0.577777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 4
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.633333333333
+ - Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:47,253 INFO: Done: Result Analysis
+2016-09-06 10:37:47,259 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:37:47,259 DEBUG: Start: Training
+2016-09-06 10:37:47,262 DEBUG: Info: Time for Training: 0.0534279346466[s]
+2016-09-06 10:37:47,262 DEBUG: Done: Training
+2016-09-06 10:37:47,262 DEBUG: Start: Predicting
+2016-09-06 10:37:47,265 DEBUG: Done: Predicting
+2016-09-06 10:37:47,265 DEBUG: Start: Getting Results
+2016-09-06 10:37:47,267 DEBUG: Done: Getting Results
+2016-09-06 10:37:47,267 INFO: Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.622222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.622222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:47,267 INFO: Done: Result Analysis
+2016-09-06 10:37:47,354 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:47,354 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:47,354 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:37:47,354 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:37:47,354 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:37:47,354 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:37:47,355 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:37:47,355 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:37:47,355 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:37:47,355 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:37:47,355 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:37:47,355 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:37:47,355 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:37:47,355 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:37:47,386 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:37:47,386 DEBUG: Start: Training
+2016-09-06 10:37:47,387 DEBUG: Info: Time for Training: 0.0330820083618[s]
+2016-09-06 10:37:47,387 DEBUG: Done: Training
+2016-09-06 10:37:47,387 DEBUG: Start: Predicting
+2016-09-06 10:37:47,391 DEBUG: Done: Predicting
+2016-09-06 10:37:47,391 DEBUG: Start: Getting Results
+2016-09-06 10:37:47,392 DEBUG: Done: Getting Results
+2016-09-06 10:37:47,392 INFO: Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.67619047619
+accuracy_score on test : 0.544444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 4
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.67619047619
+ - Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:47,392 INFO: Done: Result Analysis
+2016-09-06 10:37:47,513 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:37:47,513 DEBUG: Start: Training
+2016-09-06 10:37:47,534 DEBUG: Info: Time for Training: 0.180033922195[s]
+2016-09-06 10:37:47,534 DEBUG: Done: Training
+2016-09-06 10:37:47,534 DEBUG: Start: Predicting
+2016-09-06 10:37:47,538 DEBUG: Done: Predicting
+2016-09-06 10:37:47,538 DEBUG: Start: Getting Results
+2016-09-06 10:37:47,539 DEBUG: Done: Getting Results
+2016-09-06 10:37:47,539 INFO: Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.766666666667
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 8, max_depth : 4
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.766666666667
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:47,540 INFO: Done: Result Analysis
+2016-09-06 10:37:47,604 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:47,604 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:47,604 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:37:47,604 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:37:47,604 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:37:47,604 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:37:47,605 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:37:47,605 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:37:47,605 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:37:47,605 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:37:47,605 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:37:47,605 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:37:47,606 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:37:47,606 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:37:47,651 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:37:47,651 DEBUG: Start: Training
+2016-09-06 10:37:47,652 DEBUG: Info: Time for Training: 0.0492498874664[s]
+2016-09-06 10:37:47,652 DEBUG: Done: Training
+2016-09-06 10:37:47,652 DEBUG: Start: Predicting
+2016-09-06 10:37:47,662 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:37:47,662 DEBUG: Start: Training
+2016-09-06 10:37:47,663 DEBUG: Done: Predicting
+2016-09-06 10:37:47,664 DEBUG: Start: Getting Results
+2016-09-06 10:37:47,665 DEBUG: Done: Getting Results
+2016-09-06 10:37:47,666 INFO: Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.547619047619
+accuracy_score on test : 0.622222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.547619047619
+ - Score on test : 0.622222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:47,666 INFO: Done: Result Analysis
+2016-09-06 10:37:47,682 DEBUG: Info: Time for Training: 0.0788521766663[s]
+2016-09-06 10:37:47,682 DEBUG: Done: Training
+2016-09-06 10:37:47,682 DEBUG: Start: Predicting
+2016-09-06 10:37:47,685 DEBUG: Done: Predicting
+2016-09-06 10:37:47,685 DEBUG: Start: Getting Results
+2016-09-06 10:37:47,686 DEBUG: Done: Getting Results
+2016-09-06 10:37:47,686 INFO: Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4648
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.533333333333
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:47,687 INFO: Done: Result Analysis
+2016-09-06 10:37:47,750 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:47,750 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:37:47,750 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:37:47,751 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:37:47,751 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:47,751 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:37:47,751 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:37:47,751 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:37:47,751 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:37:47,751 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:37:47,752 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:37:47,752 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:37:47,752 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:37:47,752 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:37:47,803 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:37:47,803 DEBUG: Start: Training
+2016-09-06 10:37:47,807 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:37:47,807 DEBUG: Start: Training
+2016-09-06 10:37:47,822 DEBUG: Info: Time for Training: 0.0717940330505[s]
+2016-09-06 10:37:47,823 DEBUG: Done: Training
+2016-09-06 10:37:47,823 DEBUG: Start: Predicting
+2016-09-06 10:37:47,827 DEBUG: Info: Time for Training: 0.07728099823[s]
+2016-09-06 10:37:47,827 DEBUG: Done: Training
+2016-09-06 10:37:47,827 DEBUG: Start: Predicting
+2016-09-06 10:37:47,828 DEBUG: Done: Predicting
+2016-09-06 10:37:47,828 DEBUG: Start: Getting Results
+2016-09-06 10:37:47,829 DEBUG: Done: Getting Results
+2016-09-06 10:37:47,829 INFO: Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4648
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:47,830 INFO: Done: Result Analysis
+2016-09-06 10:37:47,831 DEBUG: Done: Predicting
+2016-09-06 10:37:47,831 DEBUG: Start: Getting Results
+2016-09-06 10:37:47,833 DEBUG: Done: Getting Results
+2016-09-06 10:37:47,833 INFO: Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.366666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4648
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.533333333333
+ - Score on test : 0.366666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:47,833 INFO: Done: Result Analysis
+2016-09-06 10:37:47,901 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:47,901 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:47,902 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:37:47,902 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:37:47,902 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:37:47,902 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:37:47,903 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
+2016-09-06 10:37:47,903 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
+2016-09-06 10:37:47,903 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
+2016-09-06 10:37:47,903 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
+2016-09-06 10:37:47,903 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:37:47,903 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:37:47,903 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:37:47,903 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:37:47,948 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:37:47,948 DEBUG: Start: Training
+2016-09-06 10:37:47,949 DEBUG: Info: Time for Training: 0.0490880012512[s]
+2016-09-06 10:37:47,950 DEBUG: Done: Training
+2016-09-06 10:37:47,950 DEBUG: Start: Predicting
+2016-09-06 10:37:47,953 DEBUG: Done: Predicting
+2016-09-06 10:37:47,953 DEBUG: Start: Getting Results
+2016-09-06 10:37:47,955 DEBUG: Done: Getting Results
+2016-09-06 10:37:47,955 INFO: Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 0.738095238095
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 12)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 4
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.738095238095
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:47,956 INFO: Done: Result Analysis
+2016-09-06 10:37:47,964 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:37:47,964 DEBUG: Start: Training
+2016-09-06 10:37:47,968 DEBUG: Info: Time for Training: 0.067459821701[s]
+2016-09-06 10:37:47,968 DEBUG: Done: Training
+2016-09-06 10:37:47,968 DEBUG: Start: Predicting
+2016-09-06 10:37:47,971 DEBUG: Done: Predicting
+2016-09-06 10:37:47,971 DEBUG: Start: Getting Results
+2016-09-06 10:37:47,973 DEBUG: Done: Getting Results
+2016-09-06 10:37:47,973 INFO: Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.433333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 12)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:47,973 INFO: Done: Result Analysis
+2016-09-06 10:37:48,050 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:48,050 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:48,050 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:37:48,050 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:37:48,050 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:37:48,050 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:37:48,051 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
+2016-09-06 10:37:48,051 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
+2016-09-06 10:37:48,051 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
+2016-09-06 10:37:48,051 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
+2016-09-06 10:37:48,051 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:37:48,051 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:37:48,051 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:37:48,051 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:37:48,081 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:37:48,081 DEBUG: Start: Training
+2016-09-06 10:37:48,082 DEBUG: Info: Time for Training: 0.0320069789886[s]
+2016-09-06 10:37:48,082 DEBUG: Done: Training
+2016-09-06 10:37:48,082 DEBUG: Start: Predicting
+2016-09-06 10:37:48,087 DEBUG: Done: Predicting
+2016-09-06 10:37:48,087 DEBUG: Start: Getting Results
+2016-09-06 10:37:48,088 DEBUG: Done: Getting Results
+2016-09-06 10:37:48,088 INFO: Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.657142857143
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 12)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 4
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.657142857143
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:48,088 INFO: Done: Result Analysis
+2016-09-06 10:37:48,209 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:37:48,209 DEBUG: Start: Training
+2016-09-06 10:37:48,230 DEBUG: Info: Time for Training: 0.180283069611[s]
+2016-09-06 10:37:48,230 DEBUG: Done: Training
+2016-09-06 10:37:48,230 DEBUG: Start: Predicting
+2016-09-06 10:37:48,234 DEBUG: Done: Predicting
+2016-09-06 10:37:48,234 DEBUG: Start: Getting Results
+2016-09-06 10:37:48,236 DEBUG: Done: Getting Results
+2016-09-06 10:37:48,236 INFO: Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.833333333333
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 12)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 8, max_depth : 4
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.833333333333
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:48,236 INFO: Done: Result Analysis
+2016-09-06 10:37:48,304 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:48,304 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:48,305 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:37:48,305 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:37:48,305 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:37:48,305 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:37:48,306 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
+2016-09-06 10:37:48,306 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
+2016-09-06 10:37:48,307 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
+2016-09-06 10:37:48,307 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
+2016-09-06 10:37:48,307 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:37:48,307 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:37:48,307 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:37:48,307 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:37:48,363 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:37:48,363 DEBUG: Start: Training
+2016-09-06 10:37:48,364 DEBUG: Info: Time for Training: 0.0610771179199[s]
+2016-09-06 10:37:48,364 DEBUG: Done: Training
+2016-09-06 10:37:48,364 DEBUG: Start: Predicting
+2016-09-06 10:37:48,371 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:37:48,371 DEBUG: Start: Training
+2016-09-06 10:37:48,389 DEBUG: Done: Predicting
+2016-09-06 10:37:48,390 DEBUG: Start: Getting Results
+2016-09-06 10:37:48,391 DEBUG: Done: Getting Results
+2016-09-06 10:37:48,391 DEBUG: Info: Time for Training: 0.088534116745[s]
+2016-09-06 10:37:48,391 INFO: Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.557142857143
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 12)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.557142857143
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:48,391 DEBUG: Done: Training
+2016-09-06 10:37:48,392 DEBUG: Start: Predicting
+2016-09-06 10:37:48,392 INFO: Done: Result Analysis
+2016-09-06 10:37:48,395 DEBUG: Done: Predicting
+2016-09-06 10:37:48,395 DEBUG: Start: Getting Results
+2016-09-06 10:37:48,397 DEBUG: Done: Getting Results
+2016-09-06 10:37:48,397 INFO: Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.604761904762
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 12)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4648
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.604761904762
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:48,397 INFO: Done: Result Analysis
+2016-09-06 10:37:48,548 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:48,548 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:48,548 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:37:48,548 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:37:48,548 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:37:48,548 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:37:48,549 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
+2016-09-06 10:37:48,549 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
+2016-09-06 10:37:48,549 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
+2016-09-06 10:37:48,549 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
+2016-09-06 10:37:48,549 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:37:48,549 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:37:48,550 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:37:48,550 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:37:48,600 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:37:48,600 DEBUG: Start: Training
+2016-09-06 10:37:48,603 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:37:48,603 DEBUG: Start: Training
+2016-09-06 10:37:48,612 DEBUG: Info: Time for Training: 0.0646460056305[s]
+2016-09-06 10:37:48,612 DEBUG: Done: Training
+2016-09-06 10:37:48,612 DEBUG: Start: Predicting
+2016-09-06 10:37:48,616 DEBUG: Done: Predicting
+2016-09-06 10:37:48,616 DEBUG: Start: Getting Results
+2016-09-06 10:37:48,617 DEBUG: Done: Getting Results
+2016-09-06 10:37:48,617 INFO: Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 12)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4648
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:48,618 INFO: Done: Result Analysis
+2016-09-06 10:37:48,622 DEBUG: Info: Time for Training: 0.0746550559998[s]
+2016-09-06 10:37:48,622 DEBUG: Done: Training
+2016-09-06 10:37:48,622 DEBUG: Start: Predicting
+2016-09-06 10:37:48,628 DEBUG: Done: Predicting
+2016-09-06 10:37:48,628 DEBUG: Start: Getting Results
+2016-09-06 10:37:48,629 DEBUG: Done: Getting Results
+2016-09-06 10:37:48,629 INFO: Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 12)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4648
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:48,629 INFO: Done: Result Analysis
+2016-09-06 10:37:48,696 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:48,696 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:37:48,696 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:37:48,696 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:48,696 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:37:48,697 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:37:48,697 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:37:48,697 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:37:48,697 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:37:48,697 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:37:48,697 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:37:48,698 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:37:48,698 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:37:48,698 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:37:48,734 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:37:48,735 DEBUG: Start: Training
+2016-09-06 10:37:48,736 DEBUG: Info: Time for Training: 0.0404579639435[s]
+2016-09-06 10:37:48,736 DEBUG: Done: Training
+2016-09-06 10:37:48,736 DEBUG: Start: Predicting
+2016-09-06 10:37:48,739 DEBUG: Done: Predicting
+2016-09-06 10:37:48,739 DEBUG: Start: Getting Results
+2016-09-06 10:37:48,740 DEBUG: Done: Getting Results
+2016-09-06 10:37:48,741 INFO: Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 0.690476190476
+accuracy_score on test : 0.6
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 4
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.690476190476
+ - Score on test : 0.6
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:48,741 INFO: Done: Result Analysis
+2016-09-06 10:37:48,750 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:37:48,750 DEBUG: Start: Training
+2016-09-06 10:37:48,754 DEBUG: Info: Time for Training: 0.059161901474[s]
+2016-09-06 10:37:48,754 DEBUG: Done: Training
+2016-09-06 10:37:48,754 DEBUG: Start: Predicting
+2016-09-06 10:37:48,757 DEBUG: Done: Predicting
+2016-09-06 10:37:48,757 DEBUG: Start: Getting Results
+2016-09-06 10:37:48,759 DEBUG: Done: Getting Results
+2016-09-06 10:37:48,759 INFO: Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.544444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:48,760 INFO: Done: Result Analysis
+2016-09-06 10:37:48,847 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:48,847 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:37:48,847 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:37:48,848 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:37:48,848 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:37:48,848 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:37:48,848 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:37:48,848 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:48,848 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:37:48,849 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:37:48,849 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:37:48,849 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:37:48,849 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:37:48,850 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:37:48,879 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:37:48,879 DEBUG: Start: Training
+2016-09-06 10:37:48,880 DEBUG: Info: Time for Training: 0.0334219932556[s]
+2016-09-06 10:37:48,880 DEBUG: Done: Training
+2016-09-06 10:37:48,880 DEBUG: Start: Predicting
+2016-09-06 10:37:48,885 DEBUG: Done: Predicting
+2016-09-06 10:37:48,885 DEBUG: Start: Getting Results
+2016-09-06 10:37:48,887 DEBUG: Done: Getting Results
+2016-09-06 10:37:48,887 INFO: Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.690476190476
+accuracy_score on test : 0.6
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 4
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.690476190476
+ - Score on test : 0.6
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:48,887 INFO: Done: Result Analysis
+2016-09-06 10:37:49,024 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:37:49,024 DEBUG: Start: Training
+2016-09-06 10:37:49,046 DEBUG: Info: Time for Training: 0.198237895966[s]
+2016-09-06 10:37:49,046 DEBUG: Done: Training
+2016-09-06 10:37:49,046 DEBUG: Start: Predicting
+2016-09-06 10:37:49,050 DEBUG: Done: Predicting
+2016-09-06 10:37:49,051 DEBUG: Start: Getting Results
+2016-09-06 10:37:49,053 DEBUG: Done: Getting Results
+2016-09-06 10:37:49,053 INFO: Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.809523809524
+accuracy_score on test : 0.577777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 8, max_depth : 4
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.809523809524
+ - Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:49,053 INFO: Done: Result Analysis
+2016-09-06 10:37:49,200 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:49,200 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:49,200 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:37:49,200 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:37:49,200 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:37:49,200 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:37:49,201 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:37:49,201 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:37:49,201 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:37:49,201 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:37:49,202 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:37:49,202 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:37:49,202 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:37:49,202 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:37:49,272 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:37:49,273 DEBUG: Start: Training
+2016-09-06 10:37:49,274 DEBUG: Info: Time for Training: 0.0749552249908[s]
+2016-09-06 10:37:49,274 DEBUG: Done: Training
+2016-09-06 10:37:49,274 DEBUG: Start: Predicting
+2016-09-06 10:37:49,279 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:37:49,279 DEBUG: Start: Training
+2016-09-06 10:37:49,293 DEBUG: Done: Predicting
+2016-09-06 10:37:49,293 DEBUG: Start: Getting Results
+2016-09-06 10:37:49,295 DEBUG: Done: Getting Results
+2016-09-06 10:37:49,295 INFO: Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.604761904762
+accuracy_score on test : 0.588888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.604761904762
+ - Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:49,295 INFO: Done: Result Analysis
+2016-09-06 10:37:49,299 DEBUG: Info: Time for Training: 0.0997180938721[s]
+2016-09-06 10:37:49,299 DEBUG: Done: Training
+2016-09-06 10:37:49,299 DEBUG: Start: Predicting
+2016-09-06 10:37:49,302 DEBUG: Done: Predicting
+2016-09-06 10:37:49,302 DEBUG: Start: Getting Results
+2016-09-06 10:37:49,304 DEBUG: Done: Getting Results
+2016-09-06 10:37:49,304 INFO: Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.580952380952
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4648
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.580952380952
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:49,304 INFO: Done: Result Analysis
+2016-09-06 10:37:49,450 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:49,450 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:49,450 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:37:49,450 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:37:49,450 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:37:49,450 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:37:49,451 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:37:49,451 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:37:49,451 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:37:49,451 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:37:49,452 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:37:49,452 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:37:49,452 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:37:49,452 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:37:49,501 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:37:49,501 DEBUG: Start: Training
+2016-09-06 10:37:49,501 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:37:49,502 DEBUG: Start: Training
+2016-09-06 10:37:49,515 DEBUG: Info: Time for Training: 0.0661950111389[s]
+2016-09-06 10:37:49,516 DEBUG: Done: Training
+2016-09-06 10:37:49,516 DEBUG: Start: Predicting
+2016-09-06 10:37:49,518 DEBUG: Info: Time for Training: 0.0691938400269[s]
+2016-09-06 10:37:49,519 DEBUG: Done: Training
+2016-09-06 10:37:49,519 DEBUG: Start: Predicting
+2016-09-06 10:37:49,520 DEBUG: Done: Predicting
+2016-09-06 10:37:49,520 DEBUG: Start: Getting Results
+2016-09-06 10:37:49,522 DEBUG: Done: Getting Results
+2016-09-06 10:37:49,522 INFO: Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4648
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:49,522 INFO: Done: Result Analysis
+2016-09-06 10:37:49,525 DEBUG: Done: Predicting
+2016-09-06 10:37:49,525 DEBUG: Start: Getting Results
+2016-09-06 10:37:49,526 DEBUG: Done: Getting Results
+2016-09-06 10:37:49,526 INFO: Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.566666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4648
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:49,526 INFO: Done: Result Analysis
+2016-09-06 10:37:49,597 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:49,598 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:37:49,598 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:37:49,598 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:49,598 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:37:49,598 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:37:49,598 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:37:49,598 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:37:49,599 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:37:49,599 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:37:49,599 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:37:49,599 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:37:49,599 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:37:49,599 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:37:49,629 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:37:49,629 DEBUG: Start: Training
+2016-09-06 10:37:49,629 DEBUG: Info: Time for Training: 0.0316710472107[s]
+2016-09-06 10:37:49,629 DEBUG: Done: Training
+2016-09-06 10:37:49,630 DEBUG: Start: Predicting
+2016-09-06 10:37:49,632 DEBUG: Done: Predicting
+2016-09-06 10:37:49,632 DEBUG: Start: Getting Results
+2016-09-06 10:37:49,634 DEBUG: Done: Getting Results
+2016-09-06 10:37:49,634 INFO: Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 0.742857142857
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 4
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.742857142857
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:49,634 INFO: Done: Result Analysis
+2016-09-06 10:37:49,645 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:37:49,645 DEBUG: Start: Training
+2016-09-06 10:37:49,648 DEBUG: Info: Time for Training: 0.0510141849518[s]
+2016-09-06 10:37:49,648 DEBUG: Done: Training
+2016-09-06 10:37:49,648 DEBUG: Start: Predicting
+2016-09-06 10:37:49,651 DEBUG: Done: Predicting
+2016-09-06 10:37:49,651 DEBUG: Start: Getting Results
+2016-09-06 10:37:49,653 DEBUG: Done: Getting Results
+2016-09-06 10:37:49,653 INFO: Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:49,653 INFO: Done: Result Analysis
+2016-09-06 10:37:49,750 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:49,750 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:49,751 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:37:49,751 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:37:49,751 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:37:49,751 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:37:49,751 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:37:49,751 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:37:49,752 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:37:49,752 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:37:49,752 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:37:49,752 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:37:49,752 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:37:49,752 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:37:49,784 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:37:49,784 DEBUG: Start: Training
+2016-09-06 10:37:49,785 DEBUG: Info: Time for Training: 0.0350430011749[s]
+2016-09-06 10:37:49,785 DEBUG: Done: Training
+2016-09-06 10:37:49,785 DEBUG: Start: Predicting
+2016-09-06 10:37:49,789 DEBUG: Done: Predicting
+2016-09-06 10:37:49,789 DEBUG: Start: Getting Results
+2016-09-06 10:37:49,790 DEBUG: Done: Getting Results
+2016-09-06 10:37:49,791 INFO: Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.709523809524
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 4
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.709523809524
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:49,791 INFO: Done: Result Analysis
+2016-09-06 10:37:49,912 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:37:49,912 DEBUG: Start: Training
+2016-09-06 10:37:49,932 DEBUG: Info: Time for Training: 0.182338953018[s]
+2016-09-06 10:37:49,932 DEBUG: Done: Training
+2016-09-06 10:37:49,932 DEBUG: Start: Predicting
+2016-09-06 10:37:49,936 DEBUG: Done: Predicting
+2016-09-06 10:37:49,936 DEBUG: Start: Getting Results
+2016-09-06 10:37:49,938 DEBUG: Done: Getting Results
+2016-09-06 10:37:49,938 INFO: Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.8
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 8, max_depth : 4
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.8
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:49,938 INFO: Done: Result Analysis
+2016-09-06 10:37:49,997 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:49,997 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:49,998 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:37:49,998 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:37:49,998 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:37:49,998 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:37:49,999 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:37:49,999 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:37:49,999 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:37:49,999 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:37:49,999 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:37:49,999 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:37:49,999 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:37:49,999 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:37:50,048 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:37:50,048 DEBUG: Start: Training
+2016-09-06 10:37:50,053 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:37:50,053 DEBUG: Start: Training
+2016-09-06 10:37:50,053 DEBUG: Info: Time for Training: 0.056587934494[s]
+2016-09-06 10:37:50,054 DEBUG: Done: Training
+2016-09-06 10:37:50,054 DEBUG: Start: Predicting
+2016-09-06 10:37:50,064 DEBUG: Info: Time for Training: 0.0674622058868[s]
+2016-09-06 10:37:50,064 DEBUG: Done: Training
+2016-09-06 10:37:50,065 DEBUG: Start: Predicting
+2016-09-06 10:37:50,068 DEBUG: Done: Predicting
+2016-09-06 10:37:50,068 DEBUG: Start: Getting Results
+2016-09-06 10:37:50,069 DEBUG: Done: Getting Results
+2016-09-06 10:37:50,069 INFO: Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.466666666667
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4648
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.466666666667
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:50,069 INFO: Done: Result Analysis
+2016-09-06 10:37:50,075 DEBUG: Done: Predicting
+2016-09-06 10:37:50,076 DEBUG: Start: Getting Results
+2016-09-06 10:37:50,079 DEBUG: Done: Getting Results
+2016-09-06 10:37:50,079 INFO: Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.585714285714
+accuracy_score on test : 0.588888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.585714285714
+ - Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:50,079 INFO: Done: Result Analysis
+2016-09-06 10:37:50,290 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:37:50,290 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-09-06 10:37:50,291 INFO: Info: Shape of View0 :(300, 5)
+2016-09-06 10:37:50,292 INFO: Info: Shape of View1 :(300, 12)
+2016-09-06 10:37:50,292 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:37:50,292 INFO: Info: Shape of View2 :(300, 14)
+2016-09-06 10:37:50,292 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-06 10:37:50,293 INFO: Info: Shape of View3 :(300, 5)
+2016-09-06 10:37:50,293 INFO: Done: Read Database Files
+2016-09-06 10:37:50,293 INFO: Start: Determine validation split for ratio 0.7
+2016-09-06 10:37:50,294 INFO: Info: Shape of View0 :(300, 5)
+2016-09-06 10:37:50,295 INFO: Info: Shape of View1 :(300, 12)
+2016-09-06 10:37:50,295 INFO: Info: Shape of View2 :(300, 14)
+2016-09-06 10:37:50,296 INFO: Info: Shape of View3 :(300, 5)
+2016-09-06 10:37:50,296 INFO: Done: Read Database Files
+2016-09-06 10:37:50,296 INFO: Start: Determine validation split for ratio 0.7
+2016-09-06 10:37:50,299 INFO: Done: Determine validation split
+2016-09-06 10:37:50,299 INFO: Start: Determine 5 folds
+2016-09-06 10:37:50,301 INFO: Done: Determine validation split
+2016-09-06 10:37:50,301 INFO: Start: Determine 5 folds
+2016-09-06 10:37:50,308 INFO: Info: Length of Learning Sets: 169
+2016-09-06 10:37:50,308 INFO: Info: Length of Testing Sets: 42
+2016-09-06 10:37:50,308 INFO: Info: Length of Validation Set: 89
+2016-09-06 10:37:50,308 INFO: Done: Determine folds
+2016-09-06 10:37:50,308 INFO: Start: Learning with Mumbo and 5 folds
+2016-09-06 10:37:50,308 INFO: Start: Classification
+2016-09-06 10:37:50,309 INFO: Start: Fold number 1
+2016-09-06 10:37:50,310 INFO: Info: Length of Learning Sets: 169
+2016-09-06 10:37:50,310 INFO: Info: Length of Testing Sets: 42
+2016-09-06 10:37:50,310 INFO: Info: Length of Validation Set: 89
+2016-09-06 10:37:50,310 INFO: Done: Determine folds
+2016-09-06 10:37:50,310 INFO: Start: Learning with Fusion and 5 folds
+2016-09-06 10:37:50,310 INFO: Start: Classification
+2016-09-06 10:37:50,310 INFO: Start: Fold number 1
+2016-09-06 10:37:50,340 DEBUG: Start: Iteration 1
+2016-09-06 10:37:50,348 DEBUG: View 0 : 0.431952662722
+2016-09-06 10:37:50,355 DEBUG: View 1 : 0.431952662722
+2016-09-06 10:37:50,363 DEBUG: View 2 : 0.431952662722
+2016-09-06 10:37:50,370 DEBUG: View 3 : 0.431952662722
+2016-09-06 10:37:50,371 WARNING: WARNING: All bad for iteration 0
+2016-09-06 10:37:50,403 DEBUG: Best view : View0
+2016-09-06 10:37:50,482 DEBUG: Start: Iteration 2
+2016-09-06 10:37:50,489 DEBUG: View 0 : 0.721893491124
+2016-09-06 10:37:50,497 DEBUG: View 1 : 0.710059171598
+2016-09-06 10:37:50,505 DEBUG: View 2 : 0.680473372781
+2016-09-06 10:37:50,513 DEBUG: View 3 : 0.739644970414
+2016-09-06 10:37:50,551 DEBUG: Best view : View3
+2016-09-06 10:37:50,697 DEBUG: Start: Iteration 3
+2016-09-06 10:37:50,712 DEBUG: View 0 : 0.721893491124
+2016-09-06 10:37:50,722 DEBUG: View 1 : 0.710059171598
+2016-09-06 10:37:50,730 DEBUG: View 2 : 0.680473372781
+2016-09-06 10:37:50,737 DEBUG: View 3 : 0.739644970414
+2016-09-06 10:37:50,776 DEBUG: Best view : View3
+2016-09-06 10:37:50,980 INFO: Start: Classification
+2016-09-06 10:37:51,321 INFO: Done: Fold number 1
+2016-09-06 10:37:51,321 INFO: Start: Fold number 2
+2016-09-06 10:37:51,351 DEBUG: Start: Iteration 1
+2016-09-06 10:37:51,358 DEBUG: View 0 : 0.431952662722
+2016-09-06 10:37:51,365 DEBUG: View 1 : 0.431952662722
+2016-09-06 10:37:51,372 DEBUG: View 2 : 0.431952662722
+2016-09-06 10:37:51,380 DEBUG: View 3 : 0.431952662722
+2016-09-06 10:37:51,380 WARNING: WARNING: All bad for iteration 0
+2016-09-06 10:37:51,410 DEBUG: Best view : View0
+2016-09-06 10:37:51,489 DEBUG: Start: Iteration 2
+2016-09-06 10:37:51,496 DEBUG: View 0 : 0.710059171598
+2016-09-06 10:37:51,504 DEBUG: View 1 : 0.644970414201
+2016-09-06 10:37:51,511 DEBUG: View 2 : 0.721893491124
+2016-09-06 10:37:51,518 DEBUG: View 3 : 0.674556213018
+2016-09-06 10:37:51,555 DEBUG: Best view : View2
+2016-09-06 10:37:51,694 DEBUG: Start: Iteration 3
+2016-09-06 10:37:51,701 DEBUG: View 0 : 0.710059171598
+2016-09-06 10:37:51,709 DEBUG: View 1 : 0.644970414201
+2016-09-06 10:37:51,716 DEBUG: View 2 : 0.721893491124
+2016-09-06 10:37:51,723 DEBUG: View 3 : 0.674556213018
+2016-09-06 10:37:51,762 DEBUG: Best view : View2
+2016-09-06 10:37:51,964 INFO: Start: Classification
+2016-09-06 10:37:52,305 INFO: Done: Fold number 2
+2016-09-06 10:37:52,305 INFO: Start: Fold number 3
+2016-09-06 10:37:52,333 DEBUG: Start: Iteration 1
+2016-09-06 10:37:52,340 DEBUG: View 0 : 0.414201183432
+2016-09-06 10:37:52,347 DEBUG: View 1 : 0.467455621302
+2016-09-06 10:37:52,355 DEBUG: View 2 : 0.461538461538
+2016-09-06 10:37:52,361 DEBUG: View 3 : 0.514792899408
+2016-09-06 10:37:52,391 DEBUG: Best view : View2
+2016-09-06 10:37:52,466 DEBUG: Start: Iteration 2
+2016-09-06 10:37:52,473 DEBUG: View 0 : 0.710059171598
+2016-09-06 10:37:52,481 DEBUG: View 1 : 0.721893491124
+2016-09-06 10:37:52,489 DEBUG: View 2 : 0.704142011834
+2016-09-06 10:37:52,495 DEBUG: View 3 : 0.733727810651
+2016-09-06 10:37:52,532 DEBUG: Best view : View3
+2016-09-06 10:37:52,672 DEBUG: Start: Iteration 3
+2016-09-06 10:37:52,679 DEBUG: View 0 : 0.710059171598
+2016-09-06 10:37:52,686 DEBUG: View 1 : 0.721893491124
+2016-09-06 10:37:52,694 DEBUG: View 2 : 0.704142011834
+2016-09-06 10:37:52,701 DEBUG: View 3 : 0.733727810651
+2016-09-06 10:37:52,739 DEBUG: Best view : View3
+2016-09-06 10:37:52,941 INFO: Start: Classification
+2016-09-06 10:37:53,279 INFO: Done: Fold number 3
+2016-09-06 10:37:53,279 INFO: Start: Fold number 4
+2016-09-06 10:37:53,308 DEBUG: Start: Iteration 1
+2016-09-06 10:37:53,315 DEBUG: View 0 : 0.568047337278
+2016-09-06 10:37:53,322 DEBUG: View 1 : 0.568047337278
+2016-09-06 10:37:53,328 DEBUG: View 2 : 0.568047337278
+2016-09-06 10:37:53,335 DEBUG: View 3 : 0.568047337278
+2016-09-06 10:37:53,365 DEBUG: Best view : View0
+2016-09-06 10:37:53,439 DEBUG: Start: Iteration 2
+2016-09-06 10:37:53,446 DEBUG: View 0 : 0.662721893491
+2016-09-06 10:37:53,454 DEBUG: View 1 : 0.715976331361
+2016-09-06 10:37:53,461 DEBUG: View 2 : 0.739644970414
+2016-09-06 10:37:53,468 DEBUG: View 3 : 0.692307692308
+2016-09-06 10:37:53,504 DEBUG: Best view : View2
+2016-09-06 10:37:53,643 DEBUG: Start: Iteration 3
+2016-09-06 10:37:53,650 DEBUG: View 0 : 0.662721893491
+2016-09-06 10:37:53,658 DEBUG: View 1 : 0.715976331361
+2016-09-06 10:37:53,665 DEBUG: View 2 : 0.739644970414
+2016-09-06 10:37:53,673 DEBUG: View 3 : 0.692307692308
+2016-09-06 10:37:53,711 DEBUG: Best view : View2
+2016-09-06 10:37:53,914 INFO: Start: Classification
+2016-09-06 10:37:54,254 INFO: Done: Fold number 4
+2016-09-06 10:37:54,254 INFO: Start: Fold number 5
+2016-09-06 10:37:54,282 DEBUG: Start: Iteration 1
+2016-09-06 10:37:54,289 DEBUG: View 0 : 0.431952662722
+2016-09-06 10:37:54,296 DEBUG: View 1 : 0.431952662722
+2016-09-06 10:37:54,303 DEBUG: View 2 : 0.431952662722
+2016-09-06 10:37:54,309 DEBUG: View 3 : 0.431952662722
+2016-09-06 10:37:54,310 WARNING: WARNING: All bad for iteration 0
+2016-09-06 10:37:54,340 DEBUG: Best view : View0
+2016-09-06 10:37:54,415 DEBUG: Start: Iteration 2
+2016-09-06 10:37:54,423 DEBUG: View 0 : 0.656804733728
+2016-09-06 10:37:54,430 DEBUG: View 1 : 0.733727810651
+2016-09-06 10:37:54,437 DEBUG: View 2 : 0.692307692308
+2016-09-06 10:37:54,444 DEBUG: View 3 : 0.710059171598
+2016-09-06 10:37:54,480 DEBUG: Best view : View1
+2016-09-06 10:37:54,620 DEBUG: Start: Iteration 3
+2016-09-06 10:37:54,627 DEBUG: View 0 : 0.656804733728
+2016-09-06 10:37:54,634 DEBUG: View 1 : 0.733727810651
+2016-09-06 10:37:54,642 DEBUG: View 2 : 0.692307692308
+2016-09-06 10:37:54,649 DEBUG: View 3 : 0.710059171598
+2016-09-06 10:37:54,688 DEBUG: Best view : View1
+2016-09-06 10:37:54,891 INFO: Start: Classification
+2016-09-06 10:37:55,231 INFO: Done: Fold number 5
+2016-09-06 10:37:55,231 INFO: Done: Classification
+2016-09-06 10:37:55,231 INFO: Info: Time for Classification: 4[s]
+2016-09-06 10:37:55,231 INFO: Start: Result Analysis for Mumbo
+2016-09-06 10:37:57,067 INFO: Result for Multiview classification with Mumbo
+
+Average accuracy :
+ -On Train : 73.2544378698
+ -On Test : 46.1904761905
+ -On Validation : 57.0786516854Dataset info :
+ -Dataset name : Fake
+ -Labels : Methyl, MiRNA_, RNASeq, Clinic
+ -Views : View0 of shape (300, 5), View1 of shape (300, 12), View2 of shape (300, 14), View3 of shape (300, 5)
+ -5 folds
+ - Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
+
+
+
+Mumbo configuration :
+ -Used 1 core(s)
+ -Iterations : min 1, max 10, threshold 0.01
+ -Weak Classifiers : DecisionTree with depth 3, sub-sampled at 1.0 on View0
+ -DecisionTree with depth 3, sub-sampled at 1.0 on View1
+ -DecisionTree with depth 3, sub-sampled at 1.0 on View2
+ -DecisionTree with depth 3, sub-sampled at 1.0 on View3
+
+
+
+Computation time on 1 cores :
+ Database extraction time : 0:00:00
+ Learn Prediction
+ Fold 1 0:00:00 0:00:00
+ Fold 2 0:00:01 0:00:00
+ Fold 3 0:00:02 0:00:00
+ Fold 4 0:00:03 0:00:00
+ Fold 5 0:00:04 0:00:00
+ Total 0:00:13 0:00:01
+ So a total classification time of 0:00:04.
+
+
+
+Mean average accuracies and stats for each fold :
+ - Fold 0, used 4
+ - On View0 :
+ - Mean average Accuracy : 0.187573964497
+ - Percentage of time chosen : 0.8
+ - On View1 :
+ - Mean average Accuracy : 0.185207100592
+ - Percentage of time chosen : 0.0
+ - On View2 :
+ - Mean average Accuracy : 0.179289940828
+ - Percentage of time chosen : 0.0
+ - On View3 :
+ - Mean average Accuracy : 0.191124260355
+ - Percentage of time chosen : 0.2
+ - Fold 1, used 4
+ - On View0 :
+ - Mean average Accuracy : 0.185207100592
+ - Percentage of time chosen : 0.8
+ - On View1 :
+ - Mean average Accuracy : 0.172189349112
+ - Percentage of time chosen : 0.0
+ - On View2 :
+ - Mean average Accuracy : 0.187573964497
+ - Percentage of time chosen : 0.2
+ - On View3 :
+ - Mean average Accuracy : 0.178106508876
+ - Percentage of time chosen : 0.0
+ - Fold 2, used 4
+ - On View0 :
+ - Mean average Accuracy : 0.183431952663
+ - Percentage of time chosen : 0.7
+ - On View1 :
+ - Mean average Accuracy : 0.191124260355
+ - Percentage of time chosen : 0.0
+ - On View2 :
+ - Mean average Accuracy : 0.186982248521
+ - Percentage of time chosen : 0.1
+ - On View3 :
+ - Mean average Accuracy : 0.198224852071
+ - Percentage of time chosen : 0.2
+ - Fold 3, used 4
+ - On View0 :
+ - Mean average Accuracy : 0.189349112426
+ - Percentage of time chosen : 0.8
+ - On View1 :
+ - Mean average Accuracy : 0.2
+ - Percentage of time chosen : 0.0
+ - On View2 :
+ - Mean average Accuracy : 0.204733727811
+ - Percentage of time chosen : 0.2
+ - On View3 :
+ - Mean average Accuracy : 0.195266272189
+ - Percentage of time chosen : 0.0
+ - Fold 4, used 4
+ - On View0 :
+ - Mean average Accuracy : 0.174556213018
+ - Percentage of time chosen : 0.8
+ - On View1 :
+ - Mean average Accuracy : 0.189940828402
+ - Percentage of time chosen : 0.2
+ - On View2 :
+ - Mean average Accuracy : 0.181656804734
+ - Percentage of time chosen : 0.0
+ - On View3 :
+ - Mean average Accuracy : 0.185207100592
+ - Percentage of time chosen : 0.0
+
+ For each iteration :
+ - Iteration 1
+ Fold 1
+ Accuracy on train : 56.8047337278
+ Accuracy on test : 0.0
+ Accuracy on validation : 57.3033707865
+ Selected View : View0
+ Fold 2
+ Accuracy on train : 56.8047337278
+ Accuracy on test : 0.0
+ Accuracy on validation : 57.3033707865
+ Selected View : View0
+ Fold 3
+ Accuracy on train : 56.8047337278
+ Accuracy on test : 0.0
+ Accuracy on validation : 57.3033707865
+ Selected View : View2
+ Fold 4
+ Accuracy on train : 56.8047337278
+ Accuracy on test : 0.0
+ Accuracy on validation : 57.3033707865
+ Selected View : View0
+ Fold 5
+ Accuracy on train : 56.8047337278
+ Accuracy on test : 0.0
+ Accuracy on validation : 57.3033707865
+ Selected View : View0
+ - Iteration 2
+ Fold 1
+ Accuracy on train : 73.9644970414
+ Accuracy on test : 0.0
+ Accuracy on validation : 66.2921348315
+ Selected View : View3
+ Fold 2
+ Accuracy on train : 72.1893491124
+ Accuracy on test : 0.0
+ Accuracy on validation : 48.3146067416
+ Selected View : View2
+ Fold 3
+ Accuracy on train : 73.3727810651
+ Accuracy on test : 0.0
+ Accuracy on validation : 58.4269662921
+ Selected View : View3
+ Fold 4
+ Accuracy on train : 73.9644970414
+ Accuracy on test : 0.0
+ Accuracy on validation : 64.0449438202
+ Selected View : View2
+ Fold 5
+ Accuracy on train : 73.3727810651
+ Accuracy on test : 0.0
+ Accuracy on validation : 51.6853932584
+ Selected View : View1
+ - Iteration 3
+ Fold 1
+ Accuracy on train : 73.9644970414
+ Accuracy on test : 0.0
+ Accuracy on validation : 66.2921348315
+ Selected View : View3
+ Fold 2
+ Accuracy on train : 72.1893491124
+ Accuracy on test : 0.0
+ Accuracy on validation : 48.3146067416
+ Selected View : View2
+ Fold 3
+ Accuracy on train : 73.3727810651
+ Accuracy on test : 0.0
+ Accuracy on validation : 58.4269662921
+ Selected View : View3
+ Fold 4
+ Accuracy on train : 73.3727810651
+ Accuracy on test : 0.0
+ Accuracy on validation : 60.6741573034
+ Selected View : View2
+ Fold 5
+ Accuracy on train : 73.3727810651
+ Accuracy on test : 0.0
+ Accuracy on validation : 51.6853932584
+ Selected View : View1
+ - Iteration 4
+ Fold 1
+ Accuracy on train : 56.8047337278
+ Accuracy on test : 0.0
+ Accuracy on validation : 57.3033707865
+ Selected View : View0
+ Fold 2
+ Accuracy on train : 56.8047337278
+ Accuracy on test : 0.0
+ Accuracy on validation : 57.3033707865
+ Selected View : View0
+ Fold 3
+ Accuracy on train : 56.8047337278
+ Accuracy on test : 0.0
+ Accuracy on validation : 57.3033707865
+ Selected View : View0
+ Fold 4
+ Accuracy on train : 56.8047337278
+ Accuracy on test : 0.0
+ Accuracy on validation : 57.3033707865
+ Selected View : View0
+ Fold 5
+ Accuracy on train : 56.8047337278
+ Accuracy on test : 0.0
+ Accuracy on validation : 57.3033707865
+ Selected View : View0
+2016-09-06 10:37:57,248 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..e15b39c3
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.433333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 12)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..d6b6f1ee
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 0.738095238095
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 12)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 4
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.738095238095
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..8033aa1c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.67619047619
+accuracy_score on test : 0.544444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 4
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.67619047619
+ - Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..594945a8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.766666666667
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 8, max_depth : 4
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.766666666667
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..63c6ae28
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.547619047619
+accuracy_score on test : 0.622222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.547619047619
+ - Score on test : 0.622222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..8b456925
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4648
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.533333333333
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..00b45eb3
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.366666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4648
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.533333333333
+ - Score on test : 0.366666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..c2d3d627
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4648
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..d49ccc57
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.544444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..96bde0ec
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 0.690476190476
+accuracy_score on test : 0.6
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 4
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.690476190476
+ - Score on test : 0.6
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..40e8b33c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.690476190476
+accuracy_score on test : 0.6
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 4
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.690476190476
+ - Score on test : 0.6
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..61507bca
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.833333333333
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 12)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 8, max_depth : 4
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.833333333333
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..f8a94e87
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.557142857143
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 12)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.557142857143
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..8ebd07f4
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.604761904762
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 12)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4648
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.604761904762
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..cb5ad814
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 12)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4648
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..e113ccef
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 12)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4648
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..d2a1e680
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..d8ec68e9
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 0.742857142857
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 4
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.742857142857
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..a85fe8bb
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.709523809524
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 4
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.709523809524
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..bd9b6ea7
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.8
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 8, max_depth : 4
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.8
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..0baeab05
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.604761904762
+accuracy_score on test : 0.588888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.604761904762
+ - Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..5d435b56
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.580952380952
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4648
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.580952380952
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..78a5329c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4648
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..8f3e16ae
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.566666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4648
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103750Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103750Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..c6c250a5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103750Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.585714285714
+accuracy_score on test : 0.588888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.585714285714
+ - Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103750Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103750Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..367caf75
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103750Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.466666666667
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 5)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4648
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.466666666667
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103757Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Results/20160906-103757Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
new file mode 100644
index 0000000000000000000000000000000000000000..f5bbc8bd0c286eee40e266058650c7eda94a4b4b
GIT binary patch
literal 52514
zcmeAS@N?(olHy`uVBq!ia0y~yU{+vYV2a>iV_;yIRn}C%z`(##?Bp53!NI{%!;#X#
zz`(#+;1OBOz`&mf!i+2ImuE6CC@^@sIEGZrd2_ci=IqV?>>sZ0J=zm{!XUUa=*nI1
zYLD$lg%Vp%SRFEuHcpdJ<mr`sEn%W8xQQ?IO;?TYjEut_Q@RwZw_N)6@9)L+pWj|v
zo0t0N<J`}G+`oUeoL_wI`JD65cReq*doIH$C}`357=ol4Klr9MKsh_=`R_42gh*Jd
zyZC{P<9MMA81=T9J3z(zvQ73d9)gO;EPlu<05^*_zo7u4Ugmm61#=6`ShvH{3Q+Om
zrc$F;NPz<CPV}J*7X;>7l{$HPa^5a`c1Dt;)#=PE)794=pPy&jEv)Xh;k=}z<b{hD
zR~FlC%e~#U(7FBEPmB8c`jR&{1jWR}3`|X1w{A7Pwl=!`(xppQ$3yjujfHD}eOYPf
zlar(4)-M<P-XtkGS#kR5g#j8DmU@eC$-OPc)yl-x%A_5>ZcX`~^@rPdr8ktljbd=9
zsj>O?_V)2_Q>RRE&=66wx8Hy6V_SRs;iE^FI#y<T)Gb=PIC5t9(WEmo44ID}J<5Bk
zf7zF>Ukm^K`s(JndS6phlR?sv4lDWa`&F@ft4!zFR0h5MH`6#B<U^;08?J4#E_oqP
z_3aI(u&{7NW#!e^zpflPJzam{;>FERo}^5DJ9FmDjGLQO=h;@b{rvoVb<ASkr|;kM
zCnqPDyuT;QEv{FS`?2WGe!={Pf;-Vp3IYcX9AIeYlTEt4E$3!a+}^6plA4eZkv@6*
zIMs8%o-MUI)+gJ2<3_}tJ(a@M-`*UYXIuT@sCfJpZ9Rq;cXk?|n`axHEWypq{pQxz
z)%P+ZA|e#*?Bogy3nTVa82<YDx_!qEiz7#mCVqQ!Q!BA^#l4qTjvtq=?>TSxo5vy2
zIrII!y;{FtNBi^IZd>>>P(x%#%}=98j~*3#eHHrW@Avy0JUlr8b^orLJlnfF>#CMb
z)t42w{w{XwJ=DrAzF_(C@MIfHOUoHXsa$;Wc0Es?q-@E*FXug7Z*6g%$;WrQ-ygbt
zTl(O^gIc$potz8`9yn~tzRm}d4tMsOdwIG4;|B+uf4pA5zu@gH(?9=yzqcrTbtSuV
z_TzuOpzL-}Nm$*_B_Ke6LBZNuT3kP_W%usgUNfd@haWn2jBVPqX%Yqr4Dask?LKnE
zCFj-_&V>sX255-9v9DD+w8*tPAu|&cKi3>0cNVEG^P79>QO(CktR<zTAOHOPe7#Js
zt*!0Grc`dTygMCp=EyJ{I5}DU;fog?aeJ!{9y`Vs7#OHiQ&3>=q{uSGNOH}ZH3iSl
z$xe-%GhIJE>B@>gflikRr=PBgIXBOiduPnKM>qG?S}zIGOfizQv9Xyjb0+74HR;EX
z9%a3E@7@Ftl?|IVIaO6vt$qIR`Ez!Cef=FfcRH#FJ^c9ixQCzLu?9xwgxp--nLf)t
zJ+@R+WBdE-YxC5pqB^>|g%1uiY85_v@??Q?JKvgekA(pVhgvup8n$dPnK5hDrY+CU
z%xpe$#z!Y|6U)Sj69Y6vemt4%&oJfc`(354K}ISoE4Q??tgvlgv`EQkwi&OQn%ah~
zTi0G%!Oh8;czw#`$->pAWJN?+#KpxcDk=`#xg+!U*Vn`M>;K<<@W&+k8c$SI6vL@k
zTNf-?5NAK<@87?PX=%r**1WsFfBiOfH+OgT@9*!Y&)=SV``Eiq)9h<2dUtQxB4V6&
zreWt!%O^#aYu2m@2nuRCbjWGP?%jt^o#GPeWO;n7_wecI`iu+{Cr+Gj`soa_T&dk`
z>tlDfeSCb}S!v>dd-vq{<?YsN%(=QU#=idVSMi2hueUCeulvDx?b@{s8#Xlb_VVh*
z@8e;J*ioP;XIs_6Z~sSttF`H9?8N3~W~=gdG1<(2PE1r@^R96dTS+NDyPRE3$3o|J
zC#|VYT2qrwPt(nrzi*$Nm~Iq{wY9Z`q@<#Pf<vy%WHnzW504J7eND~HA1}JgGcwfF
z)NDvR%*K$KYMOpd=EjX14hsVg+`cV+^X5&{d)?df@27o#bZ>99Vcs2!J)h56S3I9v
zZczB>2%GJX2RrI*A6dLxdtqyKxR;NQ%0v&BnKNf@$-JyK%RFCBH)>1Az8{acm6W)6
zd6#MiFAK=b)D#rVy}xfR8=s7a%Mzi!4h>t|xhzbKvNjbHIy(B~?fsT5Tjt{O<<HOJ
zs`q=pzc|`0?&7kg>TA}o7mNElIx=2gTWeDCB4BIfoh_Ndx-mNf-tIf~a(edVWxTPw
zN;>oI@AKWZZQGQ|lbu~$G&MD^>?}^#($QIBoPI7~d*0m_4-dDyxP$}*IIyy^PMJ5)
z&&tZm#iiuK1IMEB^7M~ssi}g3fgvFxC!ZE6DebEIs#W^xN~e*rac9Si-S77;{`dFy
z=FeHTx9O&)qzDLJG{0Zte0iB~@X6J?is#$c+r51K`toFT|Bj9oD^_&q=;*w-wl><u
zWy<vF?$4e*6OotqKR?e_xz)+BU(QxZX_0%s+{AtR?5x*!y?Oig<)K#YFHcSiPdfc{
zO&$|7bLO!gNu9Ur_x<YX=rGH_=cDY_lW<&ITzs;F!h-pAzdAcQ*2V62TN}MSEZEp3
z`}#Unb8~YoP0fXe4mrKLyW4!{jvWGm7jJG(KY93Yb5~baN5_jZGmTZIdPUAUJKH?{
z`L3LsMp6a|4$YsPU8c;M<rTDYO4PO-&*Wo0i7&HXeSCcU%D&p_s;{p^1*gUux3;#1
z#KibGEu8S_)2E0CSy|a#<?rPL1z)~=dGX1~$&oTuRaGLw!ojNlyr=6;jL=D&fBL=X
zqW^K#E-w4_?b}lH)JsXJtD|GVGT+&krfP?GbY#4{vvbMn)!v$#nl3ITrLRIjNkuR-
zZky@V^RrB|SFOE%>eTvprA7Atel)u%3I1NQe7XDe_3`1=F|JirRbQ^h*N5)@|NH&^
z<ui@bH=VzE^X8Q;nZa8V+xg}FxW#l7loqk`%UxiXuW{J8aih-M$jxbjfj4ch>?+MJ
z^AA;<eDcMKiOMc6CGYN7zPhxO`?h)6n}}aOpU>~?$oTl^=$4wFMceWp9%}va^Z9(?
ziD}=H{{Q=%`TyVFH$R-)`6h1OT)Z{)U}4PJE%!^9J09;a`tslZV@*wsfQ(GfhYtmB
z?(7t{E_;)Zc<9iff}fvKHAJ`=QvEZttBUTdi8QXLs$ycu$;~bN{VkWlVYXTBhllO*
zpj`Un&*$?S3m?1n-kZAaMt0Ek&sKGREP{fAFYYeS-{u<~EnWWZ&cTa|-5)-F+}tN?
z&7@JicahP`kgg?5RL;z?6y96?9aOBYYU%9ia{B-8@5PnD>I|v=3Db2V4_(^4b!%&5
zBjfMtzrVgp%{{!pvH8Nj+G+-epdg`1lO{cQ`t;}{T@8%`UteD@{QfTX&c0gdyu7@e
z`2iY7&dxT!xpQi9S=q6b!OKAv!k72=<vBSyJ-ob}TwGk{9@|n;UCqpJ=ImKUhJHEQ
zRg=O}QdExh%kwV`;8+;Y(J8F1WMU%XaC1jvOAAXxWaLI0``TYqdQTP<6f|tyXvlEj
z`t|MC!m~_H@-vjYzQ)VR$$8@R>E`X*%?}+uY*_Q7p!a;VyE}VZTib_^9~*;K9y!p+
z{PExK_p5srYPnCHcgKI8jU>Z`h0g31RaHtxMw?Ep?mKqun1+VNfhQ*?U)-J_&m&_Y
z;BeD+d(u%Z8S640RaMm!=gzq~Ix<?HyK+S&X`=)~LS|+sD3N{Th=`0_xkqiK=gn&&
z_RIa}uiF-<-QC&g=-|LG&$gN^H#b*BRdwNx9TK;0-O4)sV!oK5U}Nd)Ym5x{s^8m!
z>{n81s@>izY0Sp3VD;*5z1Up}y1Kl(cI^s%G}9zg2-MPHWM)&au$b{RE<{XBtgWeu
z>CMf}><*Fr9+fxuudAN;*y8L*Hjd+aQ)_B!76fQqSnSUK?eJ0=!NhlWcD@m9z6C0F
z-{kA+>Q0qUpI`HdlZ{`lN6xltLGtmw2_7ozpR;J~sQj!Jx38vC&bCTHO^r=YPfv??
z)vgz}wra17-`@vneq31@Ji|DhPe@q!wTS!Cw%%#erX6FRAH6N-<Uci~7q4C^>FKTW
z+Tq;J=cqMx)t;y@*`-09KR-Wz{C@xcV=pc)zPQkteU43~(Ep$3>pz^<-=8r>ztLeq
zk)PATfW^hDZe*w``OGrm>=x5q^>xRsx+T}t?RBHJG)$i^?&R$J`U=CvOP8E{e0a{y
zx9>MjKX>5PEvet%-ZpRDYRa%8=Jooxy{k;`tNYJ8ay`DDx6z>?aIstBxjB|Q>i$|S
zTC}L(*O$zi&u7go>i^j+TC_+&R<`%Wiwu+edoruT*B@);7LSOTy57)t{mxgXw&{Hh
zzI(VMe0^Nvkq*I*zP`2)E!VIxF{_dn4TlanU1O+}<>BEecz4J0o29NUuYApiMz_kf
ze<CjzNn4lcJUc(X|I3$>H~06;hp&qXl<w;Q)r()=-rj!7&i9=wGc$8aO3H@hOP4R-
zJj~Pg_*Gv&<HH@>eV(72D<~ka;M?AGe}8{a<18{VlA+<zqa+m-6$XZ=?RmZ8`f)2-
z*8f?&>`{7fa4;yn@7uTUjqJa+RTA}8RaSPtUMSc6`FI@EkiK*0j)0(G<LdDBn+&Gw
z#WFEeR8%A!?~|?g@PM&llU(bk@2^fj-c$Zwu3^)?4*B{&f(!-U-^EV-GI!3Lf<HeB
z#m+DDo&Dg=n>!B}uZzx{Idfs;<}@wdl~bmOq@<@GK6sGv<jIp-yc2%>sMu=Uc=Oh+
zN4K_ScXV|%?cHlTXYSmMX=kM*BqSE(-rg2^C_8BX)j+MOH}=(9dwF?%cy(1f=kBhl
zZ>5b=IGmiEB7O=9CM74|{P*HT#*$^rm>A6R@AcIE{bg!luc@i|hW+X4@bv;>V#lNo
zEM2<v#m&v?a`tsG$ugFfmK9%K2+GLFJb3<`U0PaNMMcFyLEylN6CCybeyTTYD*vu+
z?r`e-|C5u|Ir#b8H*YqMiH*IP!czR~%)!UU`zKDH&Mq!4t`omc=GU)Z9$sEa_V;F+
z=l8vN^Cs)~&WuI7HmzN;?AOYbaoVTOpLh54<dk-ayZ1R@o$qY3&{v!6*mxuwX3UUS
zvSbMZ1E@6|xY%ta@9Ge(kFVG7XVQ4B%FWHa`Z`aXm6a8!y}f78o`l@oUcJ~|D|ny3
zeAyYgI&9<nsek^|m|gRnTwQpiLy&`$Gx5If!Gi~{&XYb~xb-owKwq_Y$e)Qeb$=?p
zsccuu&(HUnVZi9;=QrW>(+Q`aS`<IyVMus!VPQvqf4F2z-0k3Fn!(E!ELr06Sp9rs
zW232ECoeB=YilcO)t48I6P4W$oj%RII&3Wy1E~FGVj>c}+^;o8uYKoE%h^fSE?yMm
z7T0T;JzF}(Nb=XWx2NkGrak}p>gwtZ+qb7*-rgr|&ZjidL$$WJxcJY9!~8n)UcAWo
zru@A0;<anX9v*Jrn0{XFM2b;|i_(u@zZNZ6!0`6g*25t2ijPTUeV?D755LWD@!GYv
zwQF^EmA-CM^PT0OH1WaH)6)aAre<9(Ei81*%hP*OWSMPnEo}eOWxli7n3<Wsytye{
z`}-Tn3%9Q?_m|g+-p13+&fgZJ*Y2Tm=-=PpH|wgatIy0b<yM-=0m|~ndZnf3%<t&n
zDEk`~73CBkFaP=3S!M>CiVqCu=i7(h=2)$vt=-L=G|fXrfUEVx$B&EwW^3Pkd3iZ8
zGqY3LJa2*TY%{IOtn2G!b#!$T)6?7a_x}-^Hf>tyqw^<DGz6_|@lk7bQEGHr*syxF
zwwlk3266qk182_g$jQlV^%IVr{OZ)UMYCQlUsko&!`;2z*X`o!aQ&%rwhTYMT=q9i
zJk*lKzR-WB5$msCzbbxwP@LkWTJ`PC$!}-p+t24!$iKVGwC?9qaULlX4mCBkia$RL
zS6@GMZGC+Ij~^8tOM`ao*s-CS?ZO``E%VAxDMyYS3wtTKuHw#~%Fj1g#Di9fl$Dk3
zD0?f#aQ2;RdU`sKtd)q;L=L5i9CC7U1*N5H4dZ2whx$A8{(Yo=yfDAl-|i>NT&vPU
z+wa%?Ubgwr-qxn3rh-RDI<+pp{WrPn{k>l6@^=a6=2-Sldp7%n+No)eE#BoGJl;@n
zXMaFini{Bk{O#@S)jDw}3iGAv*S0>kcvmm~o)Kx#$Pmv^&)&qgwzd~nR!%;(i#7e+
zoW=l+BWofzGcfG`|Ia$@{5;+TIc0LWxw#r5TqjbDPCPA=v8|dSyF2%`8Aq$rn>#xv
z=UNv%;g~aLPRJ!+Z|~-5)5P}uez*I2m7VBk^@$z{X=!aMgO_j0ba|b9YKo>s^|zef
zL^fWjmgwzySFL*+nc1Bd1~}+Vch(RA^=|a0n`bOreEs43{r}rGZ#Hh&lo|2&@88gC
zt{*;rJlMu7{YrFuUmxGQ`}^bH?=dy8_gXsXY{%8@`S-J~7gpT;^XJdKUybinRaFJJ
zT2Gumzy9;auPd(`r=NRpWo7WycmMx>zYj`T<@am1U)xmu<AdUzJ9iS2l9*P8biH`-
z!s_W#y=8uLyLJ{o54&lXaY12C{C+t@L&Kc5E!PuoY)Gv5|M&arSaX@<;QG9fsgZ$=
zjm@Iug~07?si(!JPMv9MW3!>?sn@rirLV7bbay*DIWbMsi|x9&*nRbygP@@vornz%
zPu&xruGHC{bCW4OJ)MP}egC&&t@HP%YKIp*JtbQ5>I!FBS=ot`Cs&rw+5hiX_FCS?
z!|nWwtG~bd>i77>L}ie%H8nOYOpO12Jmx=g{P^nJA1k5@f6rgEe0lqc6CTsn?cZ<D
z#myb>eTO$zdFjHRmIV(O9zA|6C@<eX(>Oip(h^VJQ|JHB`gP5}w$Hlumx<pzo5;8S
z<ZP=FUSC`L=Aeh~vokZB?P`B1SXxT%|MQ7^&)&UP{oMar`OLGCv@UxS@RM&({eL^q
zc!9jUd_-hq;_q*7*X!<;G)~*_o}=&at9Ek-i+8#Pg^yfVSXmhvHf`Qq`0<hJj@`RA
z|Gm8}_wa><&R6ALzj@Ph=T3}G^|zkf$tP0+G(;ly*X_NP?B(sfvGjG=n&|C(YooR%
zWq1A&-m-P;!}ss|`R#r*=-&JO;h}SnjHQs7nb`!dr4btv7`f{oALHGzd$+UJR428`
z8>e>6oiiswIrsCkvj*no?M@3DLbOu3rOMvknriFl@9W#z-OaruNK=H1_0ZwNky1_z
z1x`=bZ{NDrw5O-%irxb^7cKjYpy1$zt5<ig-}j44Oib*?mQ3NUt}X-g&NIU)u~V~+
zQn_++aug<?G)O+iv$c2HL}m9)rLv%rzqGTnHnx8L_2p%Ax48bMEsP1jzPuFRVA-&F
zbF<UJgzliY-DSMz&Yd$bH9dM_qVnoo1zDR40o&?tEiroQH@P_%6&bPdNHAn)XP-zh
zVqs%r`tg494_0n52fgWw-TUQoe%)w$w!HN9wKwmdpP%onvF3NZ_w)1f+gn>%UzgtA
zQ)%3=%GNNx<|FGg-RNU>%a<=-9KXK~R8P0@$ws|Rs(H$-I_-G;y~jTe^V@@(Yhq$z
z8#ZoCyspUb;`Qs~gH5a(wr_Wzt{1zZ@^cysE9=IuYIVQgZqGVhc7C3%&b+Sc38i0|
zuf(huRaH|vbn&9#v}w}>I$eJJ`~9AgVdKV)H}+H-Z_B^W*Xq>x^z`&_%Wc))-wAZO
zT)1>;mD#_d-{0OI-jaElf#KS<YZtCwJt|-SXQ5e+g9F2nBtsP;&SU-Z{dexfFr@Ac
zxp(iLgh__LnKNe|yn3~2Ti=>BYZfeC-2C`>|K{A=+j5)R+u3hz$!tDx!ec7C)$u;r
z=H_PRs;{qDqobpz>HEHOUGT<wyK_6=)@h8#3r!XJ9^YHBE_QcNQj$`qi_)nxXO{TR
zHoLf|@^i+;MXq0tipL8GUc9z8I`hSag)S~rAOi?*eoUAk5TrHrO>yOoCr?sdU0Eso
z^vRP84-dDWJbALS)w1e~#?4!|gaj}8+y8aBckf>5sn36ZfA`dzzWQ#sW&KmT-*1d(
z878v{3hvyw^J4wK&+<x2T6%hF<>lqK_Sf5sii=O~?BslVb91}w_b)Glm6TrH-mc%;
z-YzT{INL1u#ldFwH$V35u~AYoGBH_l<cN!s(xz?Omc`fqo!ZfncXyZT+uPfhTbI8R
z5S-|su;A+IaACoVz0&3@LRW`{1O+Xc6yNCJpdYs<VSaE>kcWc6g?D#%-`sYzTm15;
zr>Ac|Z{-%hls>=K&BbN1n(w06-DMkVh1Gl(m}XxK(VQM%_p^1G-&`*j7dbh(l>B`E
z<$iM)-MJHU>FU*`lO_pyd3q|w#>STI1C1Pf{v0Wje7sM!x*9yLFm=k53BA3%Z}0El
z?-j)_XS1N}?X8Pzqqlc-WE^heRT1HOd2erZNJxl_n3z~jd|aH|%-OSpYievjq2Zvg
zK-GKNhD{mQ*UA3<`~7}SeC?;;yfZTlmn>iI-qzMOWy+M0+#RoYWGn(y|2;oHe{;>B
zKcCO%{C+;a-tT_xcUeI}P}O~}{(o)NpC5*Tf<J%#x^!H=evMgOadGjknx96$=ggh!
z>*C@+&nEFUsQeV_ba}IR-aNUpGmYCjI`Z!A@jN@rba6uigObv&f`?3{@9xa(@L0U*
z?CI01H`ZmXx_+vtsA$E84HG&#K7Ib|sWmkyCr3wn^7?(hR^2Mn3y<E>&MzNydYW$N
zHePA7o^AQ}<5b_g-~Zn)Y2%Ed;^NK~Z}$ECa@jx9>e#VkZ|r0YlUV-#c-)_LIc9H_
zY3+}P?Qb@Bc5=G9xd{mdy11}>eRZ|F<J3OxH*elVRGmC|5<K9gr+4i|oKAS1{IvS-
z1v260HL?PI)rVjFpI`9n#}5Sy3yFshAKutmto~XzK;7C}nyZz`f4<$(%gg->e|>qW
zmAI??eV?Lpn}UJ@!?tbPw0<`=HQiXPx7fYEZT@`uxIGmMHzk$$T9v=+*|EdI*I)2J
z{dr@1d-;hICxY7bx2H{?-agwr|IxFvvn#&765X?B&yB*jx3;bpnjW<B$c>H34<9~k
z{P*{Fc;#Cyb93>+!ooK)y}i8;A3SI%etz!Y?c34}H})@Ewk+e)5>7driiYp^>-Qfk
z&bYQl(ydSCq+NS!>%s*K8f-V}X=ycqMiCTjY-BcW-1vs`^XcjO7Z<s5Z+-sh@AvzQ
zmn>n~o_Dut|9*SFxmK-*4msVpbt~+)vcuH00C}?<iCJd3TnsO+t`-jp4qh!&{O?a?
zL{!u!n_s_vJ-X~~&&r^ps;a1|$r%?Hr}M6<i794(oh)d?;D*;_H#fHpn>QccS^OL{
zZBq4odCbqPaxyYK>-YbQTDsj!7&M^0f4@CwaOKULn5R|i&n<9lzA-r`K0dxj(%3E6
zVrft(s2z8>jkkIAYHbxEP6kkaosp5T;@ca^EnBxvoHC{5(W9h{?CF26N7b#JF=NI8
z&&g_QK;XlJgUnB!KYu*8{2pUK@l7Vv>;6$uQCq)8MaIR+E%Tq>cjSl*N2?QPD#OFW
zqrqw6g%xMwws&-J^hlX@-Icz)-2eEF!pArMfd(?;jHIQdmn>PbqWAdm<LunxdPg>=
zp9f7%h=__VjNe}udPvr~jA!M_l?5dwDNk3dUE906{Qb4xaZfDPy#voz2-(@$1!#!8
zxVV_Tr>7^2C)(~96T>u}$fk+P?x5Iz!ydo0m0R3NYwDp37bfIZe|o~%Cu7;<JzX#H
z=B88yB@uD)<mcz+{`m2@KmK>+)ZHa7g+QZ6N=*(52j<uR>s0fdwW4Kb-QQhqg>Ucg
zmoF?V?C9z`)WFF6;cof;l_pUQtKJ_7|M%zJ?)RJaw>m96RUujWkTE3FK|s(f=SIW6
z+TUTE-Yy*6+`*dFrMt@Baxrl5@`C2vrg*8={dmYOA}YGE@%gJq>()H_nUR?pYB~Sb
zBSp|K$g13kY5VO$E$_~{*SOxlPR(ylN9N^ait6g@YooTVGOgJexj8Kn)Pd{iIWotx
zI3Y2y(KP#-Lv(cX)2r*J>&IV{@c;Sq#*G^fuC5NxxVVTFRD`UF+{|`;ef;s6#_0xS
zZz4bgC7{UY5L6bBmE~O-(zRxdPRWZ4jIFJ$YZ!xmN@@R(`RBZC$Br4M4y&|pf2z-3
zHNTmie^pIbdF_<n&t~Ue*jb$3($eCfAdqlkg5uU`-m#@`ZU`=2x)hX#sv9QEo5#n@
z+#J=v_NCq0msVN#_u1aLdv{~<alRwRk0(Dm(wTKwQ1H&(yI1#>MXuBPu5`Tc>*3j-
zWxh`XO|daCF$o9=1Z3anvN!ni=g)%Gt5=s@TX=4+b@Hhxnp*GoPVZMgdE&%@x3{-X
zoHnhk_V>4q-=D>|cm34X(gJ0-cXxNQ=jZ2d$+*bm=jW$YdFsrWBY%H?Pt3~ddUkg9
z>OIW=zq|~dy2IPsd*ZBFT&+%xk(<*V9_F`q@$u>LnlVi`+G%0H2F7oinwlMbed{jg
zguSXe5_<0$wDT=r@bl--HG)rHzU*vhU@-e&rmemDRvbfmdb&=;1_nJnJy5OC!NJZi
z*AldH&7^J9rin#u$q0N~(K_qK&6|pPdVRL8_x4m8pPoN+rsOQs>}8iePn$I9(6KEH
z2~TIvn$^`Uu7Bv_#f^?XEC2nml(VTgVE6w1e);$N|J!BP)cpN=J%=a2chcm^7q@1I
z*L=C?{^Qx~{2RM}9XodH$LI6*j8o#I<+j4s0+{aUm$hD2bZ4Tn`;D)nA|gjl{eE<`
zyQ9B9J()T0-X2b4V`I>aZ18fwqer{NC(fVWKWUQCi<d7MHKdI%2)sHa_9K7q^$>g0
ztJ$fMk&zd!UvGC%0FBe$e8T2%vzgcT^~Pmxd<;DDb~4iDc`N~8^Ze)8G&VOoPc86T
zDpdXLjbmJ#9D{;^fk0kfp48KYn>;70DH<3Eu=C5U*~a+j-MhZW$NN{?yjfvU=03Sv
zaD|my&*L4tKq~^O4{O<dl(w(4S+jQS>k=M@KR+J#M{G=Ly`?@gzW%RiQBlzi!-EG8
zhQ^r|78W|DrKt%C37MQKPD@McU7z#h*)vcCHZJ}C?(X5ApP%34KRwsFTuD{6^{u>J
zjm12>S}t8(-In(D<kQo1CwQoUhC~V;G@P;&7Z*>yw8S%~J|;$H_SEG`JKx;c$h^D!
zeVdP3^YrQBO-)T74zkOy(6ul1|73d8EdQR(i4>!V$Vf-6sfW&;<Ga2tmYHEq>~68B
zsHh1iQzm$*6qJ@8y}P^o>NHTrGuOKOMsQeqdb)_1*r79Ld~$DXPCxH<e_!p*1^K?|
zk1OBr{SIo!R#a3t=uLn8?(Xh4Tdi|*b7kymB&xr?Y4lNR_E2eBzh0k<i|fY6*4EYu
zr=Ln#6eyVG+&B;yxjMgY)22-Z6(17J?tWaq|6kYr`u}s^`g?ddEcct6)ti2HmZ?Ce
z3#bFLr@|0a_AngSl6kq{{XN?b7bQ@asjRF_>$s(b#evhOxoxYzxum73P1B2AW%kdq
z_}K=>7od6YL#^DP8F$xiv4fW`3GJ==>I7;+^sRg=wffJ_U7K|I!-M}`30Xf?H(~=r
zV`JlllPM7!5*klU{`>H7yRw?vvRjTX)cxnR?AvEo_v@wlpHHXt4GattnD-|qB|Uig
z^5mg)5gVBT0|N~V4I9_5*LQMuzPj&_NyY_*9!X<1P*2K3rQ+u&)oD7Bi_G3{Ei5f{
zjf;~5H98J&FcKCP2F2B6b$`$p&(57YPo>Dty3_sc-8<0eGYb==kFW2;SFfVh|0%k&
ze`Z64@Vv(seXAWkJvnEYWH#;DV`GwiZOz3?TQ+Y_ww3t5`ku?pBAMfRGxxBA)}XAO
z>Y$KuCERbzmM#CDOUpl%+MQ$B_xPU4J?2A&GMiIQ3dvX$IHW#U?VxZVfB)ZOY~IB(
zphYjHEbkc~?l3=b;lc!6xoex#{moLf8B$VG95h6dPEFCwiH?h#ckBC#-DPjN#Kos;
z-4@r6<JlO&vonUz*w`49(M_KnKYyP8<HwI3-QCIBO~;QOy}J72#@yRxpoJKq1sul4
z%`tk%A06$Mwt3v=v@n4=?CQxgXVzR!C@wvI`e~7d2p4Gm!`a#34yMz>18;6_el7d{
zHE2Zim4U7<@BV+kR-dY?t*z}%7h0`hXt>c)`-HiP{mPXqH*DFG!u_{b&NeFb!*BbO
z7cWlK)%me))t(9Smc5Ez4;pIpE9YkLp03xra;4^$EnBj5cXNyDIcQA<%_)LX{o!{0
z*K_23zrMJ*_>|YX*4EY+7Zx(Fi{0JED{YqW_SV+*s%twsKyx0BI%3W`V#)9B>|C!J
z*Z2GS4|Q?LhZgJ7+S=Mmo}Lm_QUcBNZOy#grWd=*#U+*d*Vc@SO?E#XFmJPn&dbx|
zY;~Gyp`@hbp*KDFc%Q7Mt?k_0`}^zdZ{ECladr6mj*b;emb5IrDxsw}&%@=*kB`Yx
zHWdcEayC0Ie!3747#Mg}Z%4$sPfTsSPp_;DPRYz%xyjexzyIH_*Xym0Pn|Hq;qLD8
z`sAOQIyx!WH3cu8nQ8p;wEq5_fI5?>4<0zU_sL{V+45@jBd4m;!24Be*ZTbX^V#3a
z+gsV){kYWOsnezfU0)ZgX=OF5ql1ZwDdWqFi>2rKmxZl~a8!2h3%R*1`?}tyO`EFT
zW*$9$JlM^RO;B)e)z_|^+}zCP=jN`rmF@c-f3(`c;vMhe|MlFUHl=&Nob7~(6Aykf
z&Ah}?{QTTe^ZPZyZ$COZI)3>2wRQV;b5KM1+y4Lmq~G4!nw;It(dx8qflPqe;@-uJ
zl|g+@X=!N}SJ#cT-D0{;ot>N@A$$A%*FXIVZReBSkbB$A#l=NIQ<IYcG<RrWZ4FvR
z02*D`UvJM{|Kx-qsI*g??6@#s!%Cx8C&f*hH#@q_uq;+PbM~y_^wXeWOA#?KC1qu1
zD=RCVdBW;`2M!%#GMdTb=jW&Mc60i9CmpfmV?B}+PNoQSAC=NwCjnUv!6R+P6SPv~
z<HwJnDF)DxcXBfG%8)MCZn2FYPM$m|VUWPEX6;(V$tMLQBvzQ+`+mP(-hZAAW5BkK
z_)7hAr>E<KhC(Jy5(13^9DNCDhb~#7q9MY?BX2k7=;reGaW`(>Jb2>7ies8{|Ni-N
zVQ+OgXh`V(zFMu{eX`bU?Ck7cUR~whwr!h5`8%10P3j+wX7bqC*$H&IY}m9ZX>*;a
zioNeFlf>(pg@uMi#l@!cJXbF|aKK^4?Agu>102*QZ~V39j<E9d(}n;3RDSz;q*Hjq
zrcF(qou0jWTmJp~XJBlcY@77`{eAX;Z8Mf{-D<idNVAPc^3W?oQ&Z7hyLQd6uea0D
z(E&}l#q6mNoL~PBw4P&0kY?VU9f8?5!@^?!GMHY^cC{{h)39}`sg91$f&h&hB1an=
z8$CQc4lJKv$JOf87`)ss)biS?Q(SFrZCm@Ie||o1e{oZ)x7qI-UtV4=d~kqq>ek3p
zPoF(YdUT|7jr+sh@AoDDmXVb;Og|^{<=tKBySvMetIw}lWVW~P@v(<5E-tS4_s8<K
zn2gLBvC1!>KOYW{uRZ#0ZNx?<Gc&UdTecio;MfeRKNx1+?KYa(vo`zEmds#}r9n%U
zFTWlkCv!aXLz}dN%=h(a85tg(ot-W&Q>ILDxx2f3xw`*6fxt-qj=nxVP+xlDM8UcC
z_4~Z|Pi#&<zan;b*_63+eZ$wqC^9oMZ+m~RnLQ;f&F${4(#3_3k6oOm8@*z|f`*O`
zT&pY&xARZ-Pyr1AC@D!<m+6$gy%n1Kk6+fxC3u-n;O_NB{2rDoL%e=}d%O7g`T3J$
z^p>;p%LxcxoUHDD;=qB1clY=EgSxOAlij`i{M3{dJ$jUM>fAXsC8dP{9{O>67X0{8
z@#^Mg_3rI8^_4pX1#N47Ot`f@|NOi|@9*rKeCEuVjumUl7lZmr$1^W4^L_H{*|JID
zrM3#Q&GVOqt&IxI%F??1?9CgW=jZ3ED=k{INGV8bs)(4Fo2#p9N@Akp_Po1FN{jaF
zu`x0?U;g9A53Q$0X=fyEZOs-3jaIB&sR<f`sH?M^Yf;$5#wT;)lAK`@i{Q$7t#!+n
zyKhcA3t7_<`1;z~mlqeihhF01=T~1D;#FB$>EdFNbw%Ubl0VnhMyI5wAAe<UX*tuX
z{GHFyqen#q1s$ChPB=H$+TF!v;lhO}d3pC9{1Ffo6%m=>qoyn?D_c}nmUcZYc1y;^
zCOP{$AD7hYK0kkkzU!QLGUd(6J<ZL`J9q93%*@n$`t<3<7`^4SzrTfc{)VpMxE357
z5a7_-+Unx+W}d22)UFcEvvVvbcbs~s-RR)p-Y=)B1j<QSS67L8SO5I<^vT<|zAj4&
zZ(a+rPfSd_dM!KEf4*I6{r!Elv#m;Bxg0%ubd8V6OVL%jcjnnvXIxm|sCE0+_I&^H
z_xIKcm3@12(<Jc_i;~i*lP48BU7XnYWD;IZiU0od=g*Xc1c&MR@nN%<Enlww_s{3^
zE-q6hPILr~-`?l_{QUfK$Wj+4C#Q^GUtYf1@!&y%lu-(YU}l+qSjqc)wxFdz8j6Yw
zZ*6*D;U@~N+H;<qm}p>Z-0Y!pNPT`y6KG=N&Gk!{4oy<^zA-s+)rTvE&(F<$aeu#k
z)YdH4jS)S4veu7|bP6+^x>f_4aVdLuhqKeAiC5YTv<&O$(W4fKH5VtI7S;TC*nZ*K
zwPSOw%R!UR6_u46L-#NDn;VsT&(_AqBuZo5qECP3+t<r&&%f{X|KDHGLd_5@MG>yT
z&(D14Se0`9{rx@t{rc?8%#Eflr~2PtTPyA5<z<k0Nu{NwWx<9G8@!?n4GkxFsO0d~
z=jZ3&*<U~3cBh${S=-X3stgCNT@%}$fB)JG85tRw3u~jz865ol_|DC<?FJ2?&zsTH
z!*gdxA+x8ar-zqU)AZ@$Pl_zxynP!O5U`*ure*VHW6+St>FN514;^Bv{q@B$Bt%3>
zN$J%VuHv6k`nz`RdU0tf_oYjhR<(5Y^c*=cQTgJY%3_99r8RGE7%o}13^Z6<v{NQ7
zF7Cp$YiZdtB&4M`vp#j}mjlf%t<X`6nE!_%RXgtZ@#Bvl9&QIMa+>;4WmbUJR0-=c
z9Z=(P$@1l!Kd(Of<KtsR0gelMDvLpFNI^lt&_gd?yf|>-f`FZ!-H)%=<3GN+X>5{t
ziN)b&g}S%5w}*;Q&h2fyot>R;c7&~sT3G-89|ME8xA&IZ+ta`+G#*}C8?CcOEj2p$
z?;N|@UAL;%>X@2}zI*php!=wVS&qb}O`Afh;{R)g_^$VKabb~ue{EB$_m3YxR(#pl
zBW23PaA8fPvB%O&S>j*kn1B~$FfrWNRjS>_D}8LAwH6mQ_u-S1)fK0oR-Ar%W7XHJ
zvfk@Y<7)o>v9z(V0Zmn}42e2^>A{YA{kP_G8T)>3zrDL$|45RdlCtu{hYuU;{{A|6
z=T403nHLuqe|+3;uViBr^YrSJ1q&3uHTQN*m_56D!2*SClQuXgH1ziJ=H%pjIH^8=
zgBz#d!J|iAQ$wE~n=o~1>y#-X9WF|sML0?mJJy=T2Kq8jKYf0G>FaBtBnwIamzVp4
znronna?6%2Cg;xYe!uVZziXE+ITaRebUPjzQ?hQ$77?d~0@L;5*BLn;n5rEP>cC%I
z9qYAJ2-NP_xX~~sCg#F2UujTdLrbe`?WDDxzvcy7DmCriZM`k;Zd7V~sr|34s@PTQ
zer8@;0v>DkxBn{=U-PjwZRWrF#Kgpo-d@*`5RsVJ*o{d?LAAWhty{OW*8ja?CI}k5
z*_wU*!Q;owjSda>_EsmKon_k5*?I8i=jVmb&PYZ?M<?IkR|^^$G|Ro!(k-qJ8dbKa
z_^{xVQr-JKpZiYzyuQ^&YkIEl*WlY5MJp>S6YuUS{qW%f1H+cg%gYW5yM&}ABrNz8
zb*Woif5QCv{paRdU$4q}x+gm-kC}}J)UTQ_V@3yP`9idxOUuvC&yx?eaE5-%tkO?Q
zPd|M3t}JLdZ;zyLmaTf<<A2@X8w&17hy8Q8aPy|5eElE8eSg2**80t$prF9;`T6<u
z`N_%2AD^5QzO%nx9+cCgqNIX?f<VhGY-)drY}~j}%m2&dNs|I%Vt8t6Yj^D2si>sH
z6uv$#_4n!H5gJpy-rU~KzjNnKg+*1ionh1eAMck>{`BNzL20RLznpDCdV2eoEha8*
zZkr4`J3BS>^x9_6miC)t(fH&^3Pb8+rlO*vkViG2o^Tcw7cUIa+L&|GNJ2_#Q=50C
zgtWAD%-*W0x%c;0n}7N8<%ZVf_0F@+avwZ+(2#n1+Q!`P@9(>(rKz1ca|V>^r?$m;
zd2z+<sR&e+tF5iw^ZVUy(EQ63FV#77<_L(1wf+A7-dSm42<QD>rP*6QW;m@3NqT>8
z?}^i=oij5v&GPS^`L=WS?#DkqKGy0z@F|;FBfG`L-JMxO`@CN4t_OR+-^&iDGcq!|
z(SLu^B%wFAxBEYxGk4CM2@@s+B+DE<dUQwaZ!;Yo9fRs`IXu#4Jx_O6e}9+bJCR#l
zFJ$J!%-`SMYG`UY*45dSy}iY{G>CJe$C0$g=*?-oMMXs)UR+eRu&_9A{ye{#nVE-&
zheKGH*sX23+_JK=B4T1;F9p}U>)da3b!x7w=~e%yRcl|~tNr+hm4}y?amrVbC(oX3
zs^wjIx@*mvH4~;!KYlLr@v&YBtCAJB+F87oPKs^cxpU`@jmhlQ6N1E}qM{`1>ui?!
z%w)>V&9$(yS_NM5!7FXH;iHnPtLvKQUX!a0)z#V8ty?Eyl+tl(hfv>R6VT>}?`)xU
zr_AgB)wsC3E8E%aJ5?txE?)BbT5s;{O{v^&{c>xIYCv)G>Q&d<+uI+%yu4h(IE_cz
z#5(nq$eUYRx!>O2-ac`npp&z+Vd<-o6Hkj~%$~jZrRMMN@B1MOewo6;!d_fj>b><R
zXm0x9>(|k5|E*Y|5fKrwApQKjg3{7fAGO2p?(Sw}$i2Po=tO1r4LLWBrnT=Ze!ghS
z77;f$Hx53&V>1ks6(*ldNKS5^ZJxhs%E`OC%O5^^bck(2_tB(ptGp{`@X1;wJU=)0
z!^e+_%m=ciW<<yDE_>?~6eOe~#Hk|0>DD9Bn0|hqV|281JHPy~dA8LGlTRuLbv^(&
zXL8!opw4B!vz`3?`Eze?YhAZaZ_b=KSK6kYerjN5))t|Y*1S(U-v6pC<M$6A4m>#6
ztT_F2Vsi3gqmvDtot$NFZZw{ptiHJ_a(|ueZGExskEiwbJLrfdUtJZ-!ONRlP$wiL
z1X|*~Glp-bPurU}IiT@WhseXAwM$#gq`JGh3=$8qcq|P%@w6yrZ<Xl$`hS)Un=+Hb
z*T?a;IyGLn5b(__u5zA_uWw>@Hup>)HgR$Bsg7Tsf<|phUIch74f0SC;%IgH@#9Cr
zg9D8QCMGSNot(eEykzF)=I%WgIrmiR?(FO9v=Xyc&7bvZ>!z*Q*ONXxI0$Np_ez_C
z#-%pY1~2z(-M_znYx9M_fB!CAzrO$UbbV0SV_;yAz&j1p^L}}0DZ?qX9#Dc?=0Be=
zI5^nF#U<hCsi|3?H)mW_5)eF+WSEni`|#=0Rz>GF2JoVh!bdJgjvY(-^yK8#Hc9h5
z8PM(syV_qHGPj?fXM6hF^)mf+aeJjWIXQ1^&zHBZJK7~``mFo$j{lFi1&d_D7w`Y~
zi(5`kPO88E!M}1>H@BpBcXoDkc4lrd(U05HF=2v0&W#NVFJ7t<h7P)D*6@I~4Sm0+
zC4cxxq0I5W?;qGAEeWw>0}aW5b_VSLZwvZ9O-oa=asPh%s&8*NJ6)Jsof<(y<<{2H
z>F4LQ#^|v#q@<;7np%}@oN+;6YAL(asS6jt6WBg#&7GZ`ZG5s_+~RsB8s`oiIFQA@
zYFhM;f`zvjcg>30QJ^^c|NhX`VK1(&m6rDSWp#SF-LLQO&tE&m!N(UibK}ZKUN1^b
zOI`$+CG6QJHEI6N;^$6E6E_sC+wtnJy{3^7Xn-th$`&b5TgJU#Ztds7uPd)_*|J3{
z{lu=?-)3{>%mLMa5s{HsCBic^HSPZWNS2V6K78Z|%WSjURl#wulCH0d1??W#^tWBU
z?m)YIUB{(MK^h`ld#b*^+PcKN?hj~jkA?`>jT<)<w6tDrJ#xBG=7-k{neW@A%=2V;
z<!ole?Ws7p?d|>j^DkYxbY<OzziJXvQlRzEH=Z-I^99`AmaD0z_Kf%0s=PUK=DYwc
zczCBDFZ;`P#hyzgN2gwRed_Y%$z|{E{5<w4_@mXn>hF46b8b$WXJ3DB!JiDLyIgPc
zm3_nI*RQerTJ`FR=B6!M)|CCfyxjlgpP!#gUR+ohbvp0#G~Flf-ks~aeEs_J+2;Az
zH0@_q{rsf5HUIv+ygNG<ZrYoEeqPC&8ym0Bi#wYA?b1^3D+`_5ug_yU{x|xgoIqbS
z_tIs{9&Nv0clg@c=!?5bv$xg$`0z02-M=EpzNW<atxk&5^yB%mv$G{6C0FVkxw<tw
zJZ5JRtHaGGQDwKD1{bA9AGO1WTDf0O4PRx@lk?)jLeLI~x<4P?8B&8+oSSPcZf0f{
zpdlh*nk6!Oow2pGbnr5t#t5CZ7`@}}^0h1ra=Ok(Zcby=n9X?d(j_6TRwkiNmd3_L
zhEvOK#%#}%Wl+%9=Ld~CM(DJ;C>?xsv^%nH&YU?Pu7<}iEPsFR#l5}O%lzlBGy5Ns
zsWE5YKD%e<=5l9eXPZq;_weAz$;nB`%;Z#8SO4?%dc4k`E>Z0p`y89u9^Ttq9eU~J
zjzZ<=HE&K$)fV7tebf2sP%F2ffWU!E`|AE$F)=ewoG_um-~Mk%s@0V@Z*qF%Y^8)c
zS<KALb`(GFyH(}s<<+!yt?q2G?KQHZ3vzgyHG`KOIChN9XeN(w+L;9Iua;I;k2asT
z1MPe9@$va^*8Dz$L-Xh0k5=c-oofrwaLLSEd27Sf$}cYjV`5_!r+O()K6yjr!qn=d
zLx&H8%4|DvF|k9(kFzUH<OmB3(|UbBq~!B6-y64YJL^q%)|z_g{(X6df>&2GZ`{1O
za!py>x#P##FE96R4_etWaiU;NZSBYB^XnJo-`{6oW_Ij)eEr!&VS2Z_erv5-37Wm`
z;^O9h`0gE_wzjs1icpWVd7tlpo7!I`DypiE3j<cnE!q*gyKKYOtyeQtf7(`m+aP-8
z_SWonL1niME4OJ|SAEgY(a}+ueDcAYH)obz>+9)hiP1a$<>h6?$tM*idR$re`_nn=
z_n^Hcj*gBww|{<qK5_D7W^;4%DX`6J@(dH^%;{ldW;<}})-2z{ycu=+VSCc%+t<gX
zE)48@EcJlbyuqR`IQjj(y(*JWuDTn)kzK{sc5X>Y$ql<k2Zz<+>(!JNNt@>dtPWcX
z8h`9KRiz^&EZn)`&B_1Y@7JI2tN!$)^VSwN9tqI?fUNJKJ?rlrY-T@s=gyptj%m8l
zZr;=NQh!g=iF7hfJENeqh*MZ?!+z~$`!g;o{d&}`-_enAVuIq9>hF1C%l>r!Y~d8n
zczbKBi;GG6IT_HHhoIn7NI!gU^>-n`!2A1ZGjD8242g>JVr6B$vNbz=SH;IgUUk2}
zz4d<h@S)c2FJHc7+}e`)<};Sv8l4>)Dk?3@{O6xL#Kz8kd4i(zswtsGMMg7c%@Pv4
zcxtNl$$R(a?fd&JT1jcs)~!oVPuJhP<^QkO`bt6fA1_$E*tz`uJyoSe`uqP(N;@|v
zQdL(;X=jYz?d|#MN{fyhaml*1MYB4{F7uMgt*zPO+}4SQSfVzk@p@lAd$!lh+dH!I
z-R<q`5B^kCirQOcI&;R1fYZ}-K|3L?wyi$LZ~te3P<>dK*lG8p^JZ+=U{F+8sJLj8
zoIs<4!}D`<m6aA<xDZfQ8)lY#jOW+K<MOMI{QvW_SW8z|b<w8RR}LI#kh7_9aB%^x
z2yu6J*V58j6uv$#@cO#g%p)CwAwfY-mX?-FR<Ax?#bTm+8?rWYjm>}fhLSL~Szov^
zh1R8?pZDgLs#4VUJXzm)Haop`u=C3W{QmY<Q&Y3Eqhnj{ZMUS2GnQW7k{R69-><H;
z=<Dn2mk%^DUtK0HFYiCssx;|-rhM%e@OqGrQ&rlnPKr;TJ`D^E6pW0BxUe>Qd&i15
zyAR&EGw0Fc$Ju+u^<#G(x%B(h>h&w4x9441=*(WmTPSnAV2|bb!vD>C_U>h5K&)F>
zu*qIupTGXkNA|Sy^UiMDntwkoH^#kBrqOBP1&iEO7iNQ6-LlqY8$OEdE`RUl=El~&
z{SnupSC`6*iVjUu_1=(jQpm;KeKBa&@*z+UL4Cv7+2)|x`w%gIY0DxN7AD4fwcle;
zq!=w(wk)eePFUS9rM|trU0G3)F)S=>!s(|!9(C(~`0_GXL|j~1M~6q<e_qP-9aUem
z?(D7J?o|UCa9ka}9<+2E)Dcur0Buq`aq{HBwb9#6^pe@x*+Em0pzSJG=lO?)iA|a`
z2{d!f(BPsJSXpjoV<W@J$f$E(Tt6;E^0r~!p9&5ZCeVWX<YPUKE-o(3pMu|gK5u{g
z>}>PGcXupx;`Zz~w>tOsty_=2->*MD$FjKK$A`o%85f-vKi*aTo{ycKo#DWXi;F>H
zHd^0V7(hd!v(0jw=FgW`Qc}9{_1lY!%AlFfq>U0@ONAmMBeiT3=FOXT;o3E~%gcNf
zg*qR+d&jph0F-|hx%bPJ>fL_zN?lu<`}+F$_1ogKwRCllc8O{$Oh2tS(F3$#HY$Ab
zfA!cM1&my+Otw{D7y`BlBt%6?$=O!1EO@iqOKmcv#_H#<uE|b1neyX7Gk?LC7lBWn
zKL_<(XP9ILO)bDO^wRM9`ugh?`pcFqS)rr0GwCSTl4Z+48(`w=eyYCid}OgM@rRXy
z%y%&@d7imbr!M_eHC63Yea>`Yu2u~_y)fy%e~pnll9Q4YggP0fn7V<+Q8cEWs#^O0
zX8QbtmoEzo3JT6J&zC!rwDHucJFi0GRCgvtW@Kir)a5RXg6!b%f$qK#>gzD5|5xKP
z!(ibxi@9^>7Ct#4cxPuZJ0Blk#m`Twd@>dc?(Xir;b+#p{~6LVze7MwEbM8ioo`>Q
z?9YiOQ(oNIsQmBq{Qn0IA9h}B!_csIuPtb0sGwlu-s<m%Aai0Du3ZcJnHX``>+@3<
zCPo$}#<X*DI$ylV;9y~DX=?+mSl?Uy-Oa^?<=VQ~?)i1UI3GTI_~Gl<qZ<+r7d$w?
z$j}g?73#UTNb<^3pDR;yOTSK@I(6yCRS#}&&v!2=F<G>DF=#|;P2ApH2Zdc$Bqt;^
z^z`t2TWo7<E6c{lR@%2*FV!sbl1k3aO|1U&?OIz~TW^Wp@8sCEYgb2icd)d3>Gws=
z&CR!rN(&1Qf_DGDzP{dBN9_8~KNfw#;3*y5l$4Z~hlkriopCMyPJS7ShA&^fXg$>a
zH{tq{K+r0pFYoR~r*2!R?my2&=dwn1k+g(F2WVNj!_CPLudWVXykG$X!wq2%1__%A
z15k@*T72nCb#?W{-qZDF*i;%lJ3G7G?Yo#dY%M^yxPBPt;t;Ky)(<NyD?uA7?%cT}
zRih`$D`~`{sHm8e!!D?3W;Si^<2GLD4}X3ZGdKhW3JM7eUp@Bt&6}S0`~T0=UGx0O
zlNQ%*v6Z<C7*pRbxW08d14BtkNkU#;pR9Em1H-zwy{kSwzI^$zFe4*l4v$+{*fg*6
zN58$j9T*hU^yg2_n_FAEdzY`suWQ((e(7o6-CeD#!`B}=bB2fE!|nY28#kJndU|_1
zpPr^`m~?~#w7S+j?~X%Von23F@5C81Iu0Fj5)l^<pQ)&;s>*ut;zfprySvK|A2`79
ze(!fV=z{knM~`lt*Y*8gHGlH)K34|^2GEpTnBCLfM$qc&TU)cgYB4YPwQ22&UB6bY
zjnh7L>XcJEpDbvIFeN=*Sw*Gg?d2_Uo@aloI0fo$#@GK91ubd$^+ogB+uQ9EmEFTQ
z)3dX?fBdL0dzXB?Pu9T5$mw{WZ1!3AR8X;#lA>~LO=R%XU7F|DLsoYjJH@53n{U(R
z&5T#-^u@%)R(&}Xv!j49GBPqt+FIuLRax++^eo6qgbmxawMFQ(?c8Yz8a^$1dn=SP
z`eSFTufK=(KUpCm(84806(Prk0S}(fuMeBu-^k1!_FE)SK_KDD3DEXl14Bc{f&v53
zjFz{zH^YJJ@%6o+{nfW_Nd>JG*|u%lo8v!Mg|7ba>8W?e(IgEKuA1L(w}(E8-(P2Y
zdj9<R@?mSELZ8}zR@|&z%eygxCptR1qN-|B<JMU=y4O;o=iAkSy7fGAHWEcUWt1j%
z_$piH-LdGAwHB-W^(8U*%skubu-r;ZYiscG1^4CM-Q4f)>|{<)Pv4s9Ki5k1-@kua
z&HGHZ*8VO7FNjR2dw*~5&3#+5uRD2rck})zEiDbL%sq4E3dpS!PNozT79QM?cvx!A
z{K(B|8#&pJ^+-1R&Ne%E;sl37q;v4y*QHbE+SN*liHS`({q)7<<^0#yL^eM=JNxjx
zdvab&h0@Z}UR+-<@8jzm+Bexl<;T<M@d{$y8?&yi%38km?%rzi(~JB2`OWg~a4>)t
z1shg>%h3?wdUI!IaB9}ATkAHSn!j56cGp8lj~mnqdvLT{95mqH-rjyH<ZjVUnPbO}
zP4H0pfBar;yKCXvdWQe!F3k22bUB)!EX2*>VKOb@y2eha9@`tHRg+g38g^x7U6JrK
zIa`r$vfx;sY_j>c1($kweK)$S@la7|2s-z^|L@zFYq~m%HZQn;{_}?p>-2Mbj5qH-
zWBvS$C7Zm{`I9F>3nSS0WI86R`!6bcd+U^f=+UD`Gp?<fS#%h*3hTq?&(6(kycZU^
za=W;@zm~0M`0(x9u}!I`4H6D8u&}a%=KK!NGR@{W7AF-R9`54e^5E0c(^Vg{`xkHB
zYAPfow4v&277Huu!otVLBuulW6d5nsa(+?S1E#kZE>2eSJ#?&Bx-`nI^TokJwGt(z
zpe>n~*@Bn(9OTyDv*4E5|F7%o6Aw1Au87>cEbI0b7H^qorA1vG=NkkBCmy<!9w@iW
zcedHeeZJlsj%-v_TGerb3ABgP#bwn)V+9u%(T@4^<(Zh6Kx2hApU)Uem}E@2l>gUJ
zCvp?Z^YiodKZBN*zPP{tKXd-~M9}uHl*nt92mV8LUu=$amWba~;yJU!Z}pQSbDr(0
zf8?*Eq-CuJHdw_oVsjdAP;fA4^!LurV)t~j-<!I+yPd15s%Bp2=H$F6?R#pHil<XX
zhQ_|%@2sD01r@J>i`~-J);~PNsuQ#0!@{FZE-w6ioE(CItc%}D&YnLXwD^0j^}G3X
zzciz^WPrwZ_Ix<Red>ir^tK$wgan1A=H`oAGJ`=CM7OxUTTqY?Xxq<&2MIa%_r<1L
zYlp45FuQ$m+}<jS%1<ia-rw&}J3C8r{rfvRg>!Fhas2%3ET{@dU9!HBnLX+1s!)ao
zR&KEa=g#@1FIni^&S$&j@uk(_`YI|a3qaL?fVA{#bI!RNEspm{Hh+G8UfIGz!nXQb
z$f@<w+xgDhewTT5b#?np<8-DATmG@Iu)Mf(dsV3Rn;RP+FMP`_cv0B*YUTCLjuR^O
z_VyPyBsvR<h^VXzTdQSN_C}+ttE)x8X-&jNryd#0AQ5qKZpD^}Z8<Yv+}pcbz^UWF
z0f&h*W~{g}dG1`@76H(NL(Z)&Cm(G4^y!n!)~u^XdZo=<1e`#tx0FmwLO2wU?5q9l
zvN`Q6Nbuk9_lr_bPg7D?cjr(%vVQ+Rt(uyeBYUgAA1S|ItKP;huh${~YLhPtT^*Km
zdt0uOxp{a+RaMuaLr#0&>c#JiVPR$MOglU4<zJnM4F?j|IJfbDX3lp?*j8<c*qqku
z;qNcLHfpPp^|?t;z2sgOK0d~&*mB~`8Ig7C);)T4b+r?RV$}9L-Lkj0R9gg;R8?Jn
z{Qk|Y*s`PI<D!D%Vq*zqLBW~rixUI|Lk+U8t#O>J<{J<b(-XMZ?cv<=dk63AEZ$i7
z*iA)MRr9=!h`6}+-A2&<&cesX6pf68=31A#B_t%QyuESzcK4JNm1(-sZJ^!AdU|}$
z?R;0Yw}VQ+Z@2R^Zf{$A$tpQFxA*jP{l&@0`)1hHT6J}IgHkGJd)n@@w@o^yIk>r-
zr%s(3R|}e6XkcWPdvOD_U;<R@F)=fLO)?c-Zg|VW#>OUKdtNN4cCoXwt9aaN{^9BL
z_(jRb`#>8|j`hj@cK9eHIMJ@`ppw$6ge~IdH|Z!F83nzxQf)afMKgH9tXW-Gu7u37
zC}hga%w#Z$-JF!1{P4+>mV?dg%4%xI*5vZaT7l*pemtLFKViaz1Or0>rv<^w{X9H9
z1Gm-w{FHiSbvXaNefvO5lRrH@Ehr_`#V2d^;JAGKk#~1@m#RhRWFGAj1r=8J{0j;;
z82y}Yn3$Njqw1?x*!sBMy1&0>$}Ttj_U+rL_hDgS8ag^G412y@@&;v(OP4NfNIfmq
z+$kw2Xe+U;K|nAxq5Au~(6njqZf;h$`~Ak)#m()|*|VTMix#D?L_k~8-rcceVPOGH
zqJ)Koo$`4ax+=u6tZduGUxyDJQfN64oxiu$Z?4tCtgEZ4Qg@ZSY<hWl`Qs-iCxg~}
zGd%e5@o~nzJ(di<uTTGezaKmVzHZ&R4I4K)*3{UT<=x3BnEf=9k+DI4-w&plGiMeQ
z6&YEdyVQ61@0vAh4643l?D_GiyW-{2=>bc<L{n!zc=6T6<>?`omW~xn6ID1t8xxzG
znYp;Q9z1`(yyu=t!U2Yq)YOHsyUQl1aJII#f+iB1nwmgcj6Xj=fB4ujHa|Z<FYiMu
z0vCg}E1j8b&c8YBtkd;%v0t}Ymb?f6ZOpFXUzvS%bvVCv*cwnLX5#ee?bYAkfi~WP
zYBoj&S?jVRhg!L(iuL!&S|@#cbTs4EmY2@9dQoq0ZPhNG<2&2Tb+KFTg(aTCpjebP
z&$}Xh_SL`t{PK1(pw*$->-VN@k`WY~%B!xVqNHR5I`Iay346*l6(yxA3vf|hGC%L`
zu7}g(>khW@O6$mS30_QY*Hv%<fhAIXO|Bq@%c?|JsSjeLwEg}4-FaH-iA_Ds?0gYc
zbB>>yr0T73+Gx$1H9US&5^{2Uk<%{jtF7jC`JOV{EO%B>-U$;qZKcnKj0@E-H8QhL
z>Gq7AHgU#`jF%HuOnM-&=G9|Eb)~9328;{UgJNQ27|yo#s(421Dp{GztfVw~%ly!w
z`74wE@7V4)^_o?={XYNBGlyAP=2`sw`7>gBo~(*z)8@^_EdrqGf{TkwM^;Ku^26hP
z`$a2Oa8!SLv#~_Oe-6Lh4+d9PR}CE<lO1!!@B9zGwNYu&|3oGy|2e$UW(w-+><pm8
zf*8Q<&YYW@j!sGx6b$u}IXl1OR2L_QZQ+)?Ynhmt4J$q**!=x+*=jwgTXk_U$Qc@-
z<=OA<?pE{^V&j!!VE}C^Y7qdn)bsA|%Pos_?~^$QnkwIr=)7asF3^h8ef#!xbaWhO
zV&$GNcW!RUUC_ezPft#QR+%qfuAY;d8@M*g6tr6K@-pAUt=!^YPG!r_(AN$8^!anM
z&S?p=oEi6wa_iF~rxpJElzL}(IsfP9=iN_F(*<cev8m_f<>lH>xw*I|OqkGcJ-%Kx
zF)^{Ayu5wwT3s7kTTuPvGu!O!o8+?h_iRB8$rl$Fvu9mh#adlmZDDN<TCXEvS)@|-
z?#{|7duxB2y?OIS;k41QKH2E3yPTYyr|OO#KR#RZ{NGQXKO1IW(_vv{J$Un`<h*(F
zGVbm&71NChS!TWG=+UDQn^HKHl#~KCr-6nS4`03vsvr;Usr)=){`~xsyT5;5o^M^w
zx7e-M>F%yl&>jvc)2s#g_xD*;eOVE?5wu7lc)6eJ=VxahK77bnU0r=;U95EN?{B6z
z=bpUqJNNp!*p7~lhW>tj(CUTld9k3ztfZvm(qo0s&&h%o#xgQ87L=5<Y~OBfRr)IB
z+fJjea}FNvGu77S1|4E^xQ!Qdh|#O7s}~nPKX+qCp>o3}d*OsrD}$GV3W6WMe;+=1
zk`q+;JU-U@@!9PBN0*j*yIrro@jIt$<HWgbJd#Y2(>B)suVXl{ul9Ed+qVg+lBKV%
z7)Csgum9V6b94IQ(%08MeEQThcdl&Kl@*QR@il_4udOwf?BAC6_WPB4d#gWwI<3FB
z?Cq@$TerI2-Bnt8YQ9~qlyUkwm+R|dAHIIgU0q$hW5<pI_wUO)xA7SMioDcUWnS_k
zfMHgc+}rZ(lk+DjKbmRHb#1BlbkGErN!}fa&{ZLglP3%R`TO_dtJUitU0E5Ndh7q=
z<Nb^4|NlF+@9L^h?!dr64}X92NdAdOITxv)xU;jkRmJnrty@tsd#ge@6pyrWi?`^U
zcAA#@@#*yVX;JQRar0i>-@ku?il@h<C2OKTxld9t%)Mo@*zm@!TeD)e$L=n(dL6T;
zB2aUG%1NQgAAkP*$r(BA=!J#OU#A^CdQ|hPimGbof&~hDExeA|R(wcUT$q*o^~)EL
zc7Ay?$>6N4RV#v*_noYrmKqtetHd*Mx&M4Sr%5Ug-@Vg&cW-a@rhn7psysWN-rHNP
zc3P_F@2^rp0f7VqJI_g=AUb+(uJzQXzrVjXk2t^7d%8<P!h(~3b1p1sw5b2L=it}F
zhYz>voCYlr05w4lojiFmK{hNbEa~5$pK9M<{(iq-{M@;7Zr?p8sernyfgvGFw!i!P
ze*4ME>e6eEpFZuqWchOG-p6NV8ZUC|m2$f-Z<aIT-0w|Kst;T!temp(-=ELt58b|f
z`^B%t?)`eEVZ~=>nR-V=MoQkDws-GdP`P!mRQ{RW^k;|p?L%JaWuBOzST+Ct@A_BQ
z)~*h}|L*SY*7o-HliyRnzq@<X{{Nq31G%?VmzVhpU%Ys+>g$!2!S0&7^Y8Bq+P_@Y
zQwSt>`{wri`EfgcU*EoI)1{w!24!o!qBf;;ZrHxveAoYL*RCafdvnw6y8h1p@%4YV
zZrHfdFrt6*<jJ6=5f`gEbb2{CYy+8?nLz=Pe7w(U{r0@OQ67_2W|`;b75#nmDCy$L
zVD+c{2QK^D>)QSQQ(XAtL*m3)v$R0V%|U0Aan;w?H*=o&^78W3(z|!>9<6@ASN+?Y
zo646iT>`ZWkEGA9RRguiZf(zBzxm!`4+oH4Q>RV^&6eNTmOI<S*H?75S+3CS?fK!S
z_@zuduB?xb_nBqlxh8gZn2c>zh>E&;_t)3gyEi5u*Q)o}yLa!=wb9#ojuk&UBl+;r
zqeVH}%irHidV6cDlBH#&jCGk$&HumOm9(_HLRJR3K5tt0g!TWb(A7Qf-mi_`-m-S>
z+6k$VJ@WSVo@_K+vSC9&OG}H!?N`^<&c3lFbFzo0r=YZXp2%G5a<NT1-5WL-Oy)fC
z_V)JH_3PJ9Q1P^|u;?g!e5|wd^|j6?Pf|en8#J%3s<<M4|31+9YhPYnh|Bo>`!~0_
zx%tCq&(xlsnW-!!EbN+;v}lHH^|lpJTUQAkU*<cT=iTL3S65H%p2)^4)$#N5^X|FU
z<)BvD#>B&H7Z<zpr-GK6e!Zsuvo8DUs;LuHJh$ZEk6RP9)oaO;B^`UKzrTuUu={?e
znC&sh(w6n>*9YYtXkbkG{p~HN!3$E@(%Sk`HaeNJW8slMiN~s*pOXz-?8dwF&ICp0
zBYm>gE?!<;pvH6t$oSOAEg2UV-QhfOJ-&YJm4|QN>OMO=TfI-tR;y3OQVBHCRTH;n
z&6*?Ia&ODsw3~hShU?d>tHY1Jyu2J#{<f-kD(UF>%(1JDdj5QReBH&!=NA?_pGv=a
z{J8s>S*G4OcXx#@S+=ZeZS?l86)QAUpWWMAE#2cT?R|NfFG#P3hKEk%CKpgy(9F*N
z>W<5;z17<b3JV4Q{{Alh|L^<yqm9h$E>TglZtN^xKEphJ9;j5<w0Fl2i@9%~JbTu4
zdAWc0OyhK|+n~|sdv?#BJ)2gwW{Z%J(3H2KD}!8p=G(=}*wsYH*j9y{nQiW0QCX?^
zT0dsTg(v^E=iCg6h>Yxf@E~Dh%1NP#bLQyq$y%wX`OVQ-vvzH(&gnzv&-;srh)CG)
zbP+qk(lSpXGc(go|B<*}%nBXr>Thc<T7mMzT<*-^^4nW=b)r_TTzTl!DX$ZobeNc!
zzD%1ATIoLbzx^8)m+0`EoHZwR)&2ilKKZo&Tr1U>n3xuw(~tgszi%F)W?>PrbMyAJ
zvr>yoQ&Uqr|NN<W`v2(Bqo?+jyu5VuMb-S;Z!<U9tNYC{kX)}AU;B0HrhhXGlX<2|
z8YZ=zD>R<cz$0Pc;4w+%)s>aWUk;g=nuc0fTXRQFJG%S*zSY}fsu${dPAVua7Jhzi
z?q%)bUtcmGzIvsV5-GNC-@Zi~HUy+srll<dB~4Jqkg>d!z}+t3<TKYQ)KdTZ_7hWT
z_MhCO6Ic5+w7C@2SJa$7ZQ8Q!f7Wil7c}ksOk?-_%EZKlFD@<ZKDSZVpDAcw#PNRl
z^*ZKPgrZ_%bgI6+iOgEFbZ6=7X_q#It`5_kIC0{_@MTYaAGu&z7x6o%?~>js-?_6V
zadB}?IbZz!-CftXxOoSQ?$`ZZdye)0-z}$AJVAXfv!AjxzrW>9oI5wyXxX{B*4p!L
zKYEn3*C;jfqfzM^E-o&Yv$IU6K6(6ne*HD=;+L0Hmu|gq^Je6oeYMf+eNO)E@8~d?
zkub$)7O07Dl&0hjYS#5|3ae@OUbeKfv^u?M)25cWbLUP-jr5smbo9Y5P}twSb8Bn1
z^joEbo^7|MO`RG#Q-6xQE4RmTzqzMGnhkF~zO^MYcyFp|v?0&kO#R+%M^D_k6{W9v
z`O-a3UQSNU;=fKD7gq!-D|$})@bRNzRp6KJ_v>HZnY+iHac9NHMMc*?w#(NU?A%*^
zuhQLPl1kRqRZF+~R2!<F-ULeF7p?4mJ`rAeXIJU#l4+U!k#BEp^}Y93M@L6wYt~hx
z89Y;K_Fr8SdHIFwy`9C+d*Zg--(Bv%WA|?E$Z4(t0TV7-tc%&X=(6fY8_pBW$Ev=+
ziv<-$DUo95?S8Mh7?FQxhoX#(%&DmjQ*6q!udg$WTKB{?rKV=jxlQdG&p)e*;5@M@
z^>mAh=b?)iFFt53^#o~~kQ&J&W#W-P(~NP;wryh5_2bRz-FqwwAGK_%*VNRkIxQc@
zv?=YZRO-z;JBxd-ZLIlOv^2)Yr{`YL+ke(pR#Lg8=T4ps{5Q|`#CN@kQa=Q&Yz4cz
zx<H3jcuZ>P=-{xhwtlTsUR->bTYnFOBFi-W_`D5$Teg^hX8joqik^5_SXs^5Tl?`p
z|4#2oEgwD<nB?C2a<KfFqNG8B!_4bDi=Xp>^41*7Vz%Y}^N+o}ygYDgRw%=Pna1e>
zAt6hG?GzOqf7agFQJ8w~-iE}(3a2-HO8EX4wBYX36Hkvxpb`7&JO91Dz8+MkPf+2k
z{q?1B@7}!!i~RijUR++@f3a#?-rcT6i<B&^tQKWoU#GVH=C<5uh67huhfn=}`LBwn
zQ%J~^i&kxXva8PQ-BA5KZ*FSzwwy*4Pta0<HEY-QUQ0|(Z3S&X<Y=5<_v>WF&U<^S
z4<9>r?152zef`tTUw*&e|60e}&+pj$`hT3DvHuMhe`Q}=(|K;LwRCKp7{63@P>>L4
z(3RoT1Ch3h%F2b_)AgQq1y=7{6}Gl3Zf}*Ou6fcCj=iNkl^1HmZa+Ian~~wX{ePP~
zcklX6%e}pA?aA*;RXj7Ut`e=OtvxMrcyIOh569)}AH2D_`PBFD!orQ4{v7EPPF=NL
zXUp?|Nh(hdmO01O{Y>4lYuBqa#rB?)F02ezuc)e$ih1|uO%EHd6vKu;mFh-qoE)}-
zpfy&~=6MVZag|R+n^U)l%Z0CtIoNOiZ^d>SQ`69&dwpk{rQZ72$}K*Hd(NhHzgNtd
z4LWLu>u?*dbFY-?gLm)xetmrn>Q{hz>7b2UptWfYX<yXevM!(YZN}AgvAfTRb2mJD
zw8eY6o`93a=IhhH+%3P)$nfUP8##@IR+XQYe5(HZ?5yuJUT$vVT{@tBE)!<Y=GNBM
z-aE0#>))3b7o8*D|N8ox?e%I<i?Z9q-{1eo&z~>fIu{liUb=LNfdRBuNU}X9>4-sc
zA%nx=Hr`%6W|NcF45@jtYuih6PCs%?*0uZ;;Nj76(#px%S>9mR|6K1$DlF{mvt@Ko
zo<8kud3>3hTh)cyuxQY-HqS%f-rn}EjX!qe2!p}jeJ0<3Ynz*kgZ2dm1PFi@l3WS)
zUl_R9?bP@1f`Sb}X2!;wYpgSyzR6r$8O$yxC+Bwh=8YQ*A~&ZAI8AxIXZP;i&K!v!
zKl`Qd2n$pxWwfvQ^5KI)75nDu@9$3Ct#Nj~JMGWoCyyToFMB&Z{jR%fbz;(~M@PE_
zWn@76=<n<(be{S3$3OYCd-mAO{c3D%tmqlE^IK%K`VY_=oQ2NqQ?9?iyL<VTtgo-G
zdhg7R5Q~hAJoP><E>7)qRduy=Y}w~$zE8`OkDs_;c>3&F(}?)YjTw&xonE|g4hx%R
z#JzcEp~s{p<@da_)-Ls*W?Jv|F8^;;#PMx9awgX%brx^Uy6ST+@YbhG8U5XspjqNq
zRnLFa@$WjHb-B*%)TezWrYT!3@5fKqi*<T@tQXXrnqyNbboA)a)L;LqW^CHDNx^f{
z&F8VZ`wXW(-6JfSyr*{F3&W4s<LeVYKRY`?g>&Z2nQptar);VFTcs1Rfq{>YPs=4k
za%pd9{kuDspylDNuCAcb@+F>=FRY8T{_*oCXjycmy_s3q%YQ0z2YXHk3JQW0oSkhh
zC@tN6ZEZ9&`1mgS|9>`L(c^ulRjC*hcFX?Lym|9(Y|o#6^Z3CVHk|xY-Y2_z1(n@q
z+<klY+O=<u+tSy4lDU2S>f|z?E$`2QMocFA+i8~9F0HL+oE7%ATU=l3v(neMx5XJ5
z8CQg@o%N~Qfn(v7uNwRIzx%#qZn;wOjD`!z`CngMJ#qg0_1(8qdjy#3|Nk`y&3-j9
zvumxN{wBZr?Jd(Ezkkn`J^c6ew5i|n#2Dt<oo@T{`MiDVy89D$?63fBK)rQKik(j;
z;Frk#^H*Z6{QdoxEMIPa!7e*8I$Bvviwm^3?AF$7Q2!0IfNkT(jSMDtcUzUc`QcDH
zeNXqQw~NwyRzAI@Cu(VFnQ>!7;!b@duWr|9w@E5<*F~w;i#t7-nLbZ5T;^%v|BY+o
z_wT#7>Din2i$jdAMQ_iW+4J?<+UTdZrtRKsy)J%#-Zi`U{dKYLWx~Vc<m5oB%-`MJ
zz5LYgPj_|}Th#n8Q1Lu;Wo0m^zu5fLr?%eK=aydJvw7ujtz7!G%MLC&es5FiX@-nl
zc57pHZn`o*YHJqbh4*cto7NYcyqSMCaJirC((3TY>#Pjo;^I%g|DL~D<K=n#|2mg*
z-YuJBS1ZLJAR&=q@{{4;_dm^wx01_#eoFoG@why{O!qeFYxe*DG;htm4%*KK8i`f$
zlrTy;!F>Jp5>Mf$-ya|EpRJRfRk=CsECWON`+KI9HfKs79%7yR+|t-sSX@7jg~256
zPDJJ1W5?KfrA(UynA-W}^~z<Qe%#QvR!Yh=YssH^bLQl1%Beh`|M*z1VvB>9SJ%ne
zoZIv62BkUcSN3U36;IB6Tg>a}>FH&D(B1R*U)BRp7=)J2J>PycX`yYqxL%CGPubXr
zh>j~)LOdpgEIe-=SZT<(@c6Bx-QrVsCrq3;@x|rk{wwV#PMj#<l<@xEUaRtV%*@Q7
zQ2|93G2N(?E&ul^-U{q&5xB9xzJ8{A^vbBMt0o<o@vC`f(bH4y*LoeQCnl_NeR^wa
z_Nf<UZ7wb@8vD=BHh<rkoOxkE<EIaGIXOC@?YLi_>-jT1di=P#p@D%zaYNbLs5^VB
zuP5xhb0=m^<mO`;wt|9!r&>?_`T1GiLQYq)<>2!PTauEKr+%)fe|^4Ne;)^fhrj>v
zb+Nlo$)0ZJ;^YjB65C?V$OGEqe{QbzRj$(uE?fwBD!J(W{{QzfcFs1>PdeTw`}Eed
zZ#8#!7PG&;zTP}4@8Tj?xATRri)1a&-AH%${=M(axpQGr`tnOBdG@V*zaf9$&toaJ
zs_w_MbLD+6&U035Ir%q!cbV>{&6`WFDOSmVgKd(k_Z0g_d#k^jWy|L7eDo-3ZnvQ4
zqz~V2=QCgEnIbAG+S=5_)LhD;_+^*u-mll9C!|U`aeR4wonKN?(k<Cwi;uMl=mffg
zhlg1AK7D>}?&06>_y51}?nY*`E$DzY6*aY2y5~=yKHR{_>|>{KYoRkc=l}(dM*aPN
zE<JIr2@9Kca+h0=1fwDgpPUVNuAf0bPOfkHysA@oVvRD+%rImyxhtpUHz#AmnI9h?
zm&%#!pTB><J*fWWP~4Dpbyd*h3EkrQp!p)T(`RNFGCw~*|9bV`H#axWFwM3$(lugW
zPypW%AjaU!^mTpxU+?svI|?5^c=Luwkp;98);Rs#j60x}RS88~=CY?|XLo~+a^`5<
zvc)7dW$`4?`BFtsJQmMa-U>R&Kue3ui9^P|E~c`6s!Gd&6@iOSl?rlYv;~xvl(Yyi
z39I`Zxw<<1>nYHb!shhzYz$Xcg@X2$>s&XJSAEMMyp;F5RmF#dr)=jyqw&q`{IhiG
ze|&hzo~3(D>t<GV28*r3<eH56{3gquFi7qH`|URK@ynaj{jaPDWOm{>Gsp7sgY<X(
z_WuI@9kZ~qvYV2AyF+V@yXirWK-Yl%b+w9~lUBs--F5S|h`4ySlxY@6<TTgGYQA5l
zmA<<ZIkWTI%jNUM=GXt*X;JfI188L7_O@K%_xu0v`|$m{_~m83mskH@+;10jt+cpU
z`2D`$>uzjL@AvTb7XJP1t?=jP=f!hxZxelcd;9h1^5cE7r&L1r?b`<$RtI&fLEFLi
z{QviRQP$N}hwk5xuc)k?X)2hQIXO$#zAh#`>|NyMv`%JrzK-PMeVwe_VjXO}QXtpO
zHp}(7R{8BsWa_pO&q*#RDN7=Dm#y{i^AkJXCo8NSwnm_tonP$SxpQjCr%s;^PWy2!
zFaKzlsM_~~GOewxr*hs-Qt8aQyQ|Z8wpr(tDIyy)FRP_4djcApFkYCRn%ep6>uc?g
zw*UWp20PTyFc8$+zPr1;8?=tJ<jsvhP#<@Cd|l-Fz7HP?6i;vZ@cp}ZUS8gzvuAxl
zCxa>(8V0JUsde4loZh`EbhTDt&W#NRAK0yp-yatd8OiB6sinEOS;9E2=f<AO&7cty
z_kKCi*VonxgErNyh~B<#f{G`olb`;#-SlH>S=qJ^U%!U-y=`D*KJxADZCB7p!`7^+
z8Ta<=oRAv%{D#!*j(}-TgVdFD{MSTAMjkqT+`ZFOOUvucjg8Kr;SJO5YaP$$Rr{UT
zq!YCzV`0vFNA*L|8xk5f&3$!sbvJ0R1Qfo??tP%MQ#i%N#Zxa`dV71j{ia$o_e^0y
zy{Ii7k#>oPSU}zRy1!q;LC0^tntS@l5toM#9w?+lih%~iE-Y|Vva^c=jVwJr-rs$8
zws|+d-46$o%u6mFlT`No`<1=-ZRYmW7r(#O-KtXY6srCGO_Y<96Ewn<b8CwyXbq3Y
zq$QvKrmi?QXO4{OvkMmjHfCSf18oydmG}0Gi@LS()~?dkpZ<cTiVTyF@qm^}6@GaU
z`0-}?e9rv*{M5hy|2(&6du>qu-?uicLyJjG;mZxHk{1&`rP`*QnPIf{_xt+)w_m*3
zQCV5J2sH3HE%j;t$-nl?{yChPXB!P3V7(r|q|_qt_;|nhuDe#HuR6YbDS7z(xw?v{
zRymVWOIlcN^6kl~kv_9bPOh;E4hlLF>~EV|GJmUh^wOgyWp5(pF43^J-+!gn;P}<m
z;jRu24Hy5u0gZ!g&7OXe#f9Un&&~7p|7%vh=Z&0pv_nw&%QgMViysd2+jFF+r#HWj
zj*gx^M>Cc~QAkLrW%=^uCzJI~Z`!bNqu}pvZ!bq~Un9Qb*u8uwj>2DGGF2CvZvh<^
zG5wdEQMuZePsger9pQZV_HC}wwfFme`~CcXcDA{1-uWpvH>ZPEQ7^t-R8SyrzyAMT
z$SCQuE#0s7|Nj?#Wp()Z7v-1!_Q}~sy((|~X1poqrqSN7rzbTmS-O;ypPzs6aSII%
zkC^>+d#_mCuY4}Mc>c>t_gR<9YAd#Y##|>|koawLZl>|^7r#<H9eO~a-lV>d8{8TI
zP5<28RSG)OEhW<I)^{sgTiMv%WuWu=Y;0@<L`73e9xXcBE&h0B`n<;GW@gYJ`ql7w
z+f#QHn5XGRFYBohm6VKJR;sM5JT>j-@B94UzI}_>TV?v~-QDi+xJuU2*VkGXFIIl@
z=1suvvRu$S;*a<B|FgGPpMP?4GH6z~N8Vn}ca}-x>ebpgIXMrWK5hN*p#W4EzNjv^
zx2G~<V-oAFZModx;o+bjXyW0vj=ny(q9UVtb-ywh3@jf_Uzz&78g$f1>FcnPcXuS;
zy?eJ|^XBID>-DR?zw3?NU6y!mj%C`_n`<JC=UA8Xfez=hDtW<>pPzr@#*G6<kFxsC
zHaq&_;^K|f-}A1li@lv<bIVgj)f05+cVc>a`n9?#$BrLoU%7JSj~9#kLGy_gR#u>?
zS>5PuN6hcnG|SihU^Fu`Tl#MK^y%WDHB~JwETChuzrMQKJbAKkPHrw}^GVp+sKiG{
zI&bW+w+AgQZZ7Tk|K8}v+GukXRaMXlmmfZVZk{_=cF*3upaaTftV%dQse7kCXh~@+
zx43|?a5JB*)q<+8uQqJm3JTCy*VcA-ba2#syP3YD;33nz`hS&szn=bNXBhfzX8OE?
zFE1}Q%)F!mny$HbPwwifQ0L#@-fk><>ZKF4<;0q|wrjp`S-A=1bI|EYg^yeoZ~XW5
zdc4<9_L<dg*`J=A^ziisosSr}zpfVK-<vlj&)a<F0i6-}<x9!jz#peJ$v@SMzV>zQ
zoH-vZ``ahp*-=>WY-aj{Cr?re`gqrF*kJJI(`kLs9Sfi%cW0SqgJ$>m`1n@D?luF>
zPR}$>SF*Le`((|RXB<2{E>TfZpn*0}ALZILF*!N8if=d5KfYW(|Iv$!i_^|Je*Sn|
z{_*Yn{jQ)rUk@KLW@l%6Oj=@WwJ<F6_^+wqagJ40yNdSAnL8IWclGP*>*Omd0zqC_
z;yL-$*89(nIi+|`642tQiEWJCRl-^N>dL`jf7{kwrLU9j@2iDuy`18=v!{m#G--GC
zEbrrEz0Q7qe9z9!?Or~wiVJkc;nuJJ-Aw+Sum9I<n0)NP)9LYU&(F;*{P4h0Cw7-e
z@v}35zsiLIZ|X*E;aKd}+oa-YQS~KbXZq508Mn4(xBvb9UD?J)ruzH4ttA#I2Tn{>
z-dOQ5>Bp~MT8l#j1p{x|eEi%1Y6e_iAKyNGy7;8YlR>LBo1cQ#fbQ92vt;Sgg=uGJ
zF*H0oJ9}~PazD_L%r?%m=g%MC{eB-P8LWxhE5%??^P@mSR5bBekEDcYR>;nGvjlBV
zg4X{q%u>{q-~OaoYu}$gH95Doa86QL=-Mqdjjz39#igo8yAv{Z@0*hL_t#gi;*DYL
ze6kPTzh7^>>3qNazm7F)bb4g1!_xoO|Nnje$M5(1H<rG>)-G;yQTWM$MrIEmpB5F*
zQzt@Q_$RNNIH#+-8#KYDbvN&{@gHNsi<94V<}(_C3R%z&?&k8C!-o!mj^udyf91-R
z3?|7Bri%y(F-1<BIBQlHBQu-Lt%goH;pzJE$L{VfFMM@HbI;Fbvl$v*U0rQ|?Qrm>
z^V^<+nt;dqWS#fd{r&J}^Lfys*viVvA3vYZ|M+(MeI->@)=4T0gO~fgxVCoo(!;7s
ztG3K&nBizW-}&Un16ylqYC!veo0^zF2Z`O<nhiS9V)^prewNbG(iIgI5;C9XcJO51
z-lnS;zi-W-_(>`?U$2JqzkT?*>jh`M&iO}|E?@q5kX`=8-gUJLwwJ!X7O}HvDXWsQ
zQdNxsqoFhCWGIO}FD{;&G;8{F&>7rMo<2Qz?b@{j+i%~#dBwVfSVc}#^b`VZ6kg&f
z>@(L&G<J8{){=;R5%+Z)=e&540h&F(vA5b>FK$mq^!B`k3l=cQ$;ol>^1AM?`+H$~
ze!O?Pr%T97Lq<c;d7BIpa~Q*|i#KoUX>MkIcXxOChIxPf)GVE&ICJJq&~CUtfByJP
zQ?3d+a{Tz~z0)R5N^11D0?JTY`|Z#Dj+|Kf?vCVK>vFSQbDp<$eERgM;Nv6Ll9!iU
z*B|Ge+0NM^xg~b}me`G6yUN~5-MM=gw5@Q8u9lWlTH3O6vo|eRzyR8~+bd<-la~(a
zxyXH=)6vn<A-5OQp?Okcn<67DU<^vnpn@kTXwtn+$;bN?H8eba)&~R#v<P^3d0l#`
zRrKdap@_Kn>nVFdOEnqJN?R!?Jb3t)!B#|A&F8?L%FjBoY?22*2N!<46B1)%E-HQA
z|7gj#H{kKuoq3BlB{L;0J8{ATl>a0pB~JzH0iEge^Yio4v^{x{)7d_rHNXEtMd5zU
zXI{|uw7u2ki*H{j+s(zn;ZRWULHU+&RlvG+>umHzCC>XFJz5O5DaXcMRC@PmdkZxM
z22gVdw9H#lQj($J>}>PL{r3MD81{TTCS6ci=vY!>0$Rl_C%5iQi=41<^ML~nb3Z<K
z`}QrU*|X={t!&T&PEcXT%+8l^v`ch`X|~v^u(es8u_{VRN=mB)W;8H#ban=|{hFe}
zDQ%W>pp93WfnoigPh2rEF#)T?bU{bv=kNQe2HHOx9UcAU#YJXAL&H;_<)f01^)#+s
zt84f7OE73b-oAbNKqs!m)&DKM`n?wv6)ThynV23NYUNIw%FV%XVP?sNDWJ94KR!Hs
z%GnK4@M5EKv=zgmQ;X{V|C`3w==0#`${iJXT6*(lclADRmkXctV9Rb%H!m+PCnu)~
zbLO0BKL^sKr4YA%s(iEqh~42~>NCn1&Vg_WbQCqHF}QlQ_NtJTO`whGM>>VsPA^-&
zd~x>mbp?fmjqUC1ixw@KFmvY0q9ZOKAlkikDf0@QprD`&%Y3Cli&HKy^K}jk6cp2m
zX!!7<K*qX^hgZrZ;8a>l$^zfnW(CE?hd~<(=FE{fGuK*tx!>GF8<USG<eok32QtoI
zEsk^c%e~+4H7{Nano)+#ZX9T21|3fQ;zb5%s$A2$_?gd>r%x4ib$Ly*udy(IHqC%8
zl8cFvaqE#toLH*>%5a-Z$_^S`Ug+F@VPmqp>Q8XZ^7!ZT`R-0mOhv`TQ?FlNAKx!$
zTcvT5ouTB#1;&5>{()wSR~3R*Gzo^P_&A&?`Tj1}!ouRk-ghrvbX0$T_wdTfV1|a(
z;p;(%b8_(VUe%Ts6KlJ2<%+wUwxFP(V4&+!mLop?_J2);goPDb0^+naG!9%`?9P}m
z?>9q3_Vsm)3^g@1UtWFq_|Y*eOpJ?*E2LuvD08iNl*p8{Y-95AM}K~PmN3ha03B2j
zyxi~M)bO~2UteEmWT>pHwEBPR=FOK^6Z7-e2e*NAz3@EDa>Qq`d%s&riAmVHm`=A|
zDbV?Z;(9R$mUvEPU^sW~9K(W=%e$(-uZxrY`SYh$Do9($JQEq#)jVluW;AZUSH->D
ze}3EQ)!I^~SuHDrmoqW^`TLjg&-&L>w`qrYUSAj6ynDBGPEL+bUizUH&Vtg?tCef(
z|NjH6WIlX@7ZlD$hfht_2DLTkUf;BNv#!Y5$Ie2k@>vUvxVgCx_ez@=yu76P)D(1{
zW%hNx)nRKdY2MzD==|f?FVF^Iw%1;so{k<K9Frzb7L<|s<MCKcNl9tZrpI5Ses1Ir
zvjnwtJUu;U7$i2Gdp&XDL=O*-068D_2HWay5B~i844SgKl_&%X`%sBDO{vS8+4+-x
zeR&Bwgww{x2Gmwn)YIeB-}8a#Ud3bHxtlItyx7s-?_N=1ajaLmea;-2BS(&a4tR8K
z<JowIPXKHXgO9_Rkj-hn5z*1gYHDnjmX<4mm-D5aoz==ItmZJSRC-d@)m5#3e}8{`
z*8KjFDVo6v+1cHYJ7bnzd2SC5V!3pXA=~p}b8c@t`{olPBV($TKPZ8JQF&ktTT}>I
z-tFSzvP5c>F){)n^~3A+`xnLUulw-zYwP66!c0s|pcCz%Jbn6dinqVNJ7~$<qeo2q
z{QNGiu8PLSmz@G3g^|p)X?zPd?I?Q6HOoAo&)nR6&%a-<3(Cun``iC*0rlxMH8pQ+
zNMvSEP*!Hny|txr_H1d;j{GN2o+P}xvlFza7__M-J6o;@lx3@mK4#tU@t&@C6tqeY
zw7ZN~+Kh#vN5YWF)zvj(TaM(W&6^*-desH$x9RKiXJ%%uh}^8ku%PnuvlmxZ3O{=E
z$ROzmhxa=+P==Z4x{-U@%%Y+q28Ot}IIH#H>*HiM=^VaxO^jhf-QOzEtfZ$M!vg1a
zz7OBOAD?BKeT5rR=rJWRB`y2#p@2itAm@ewXqXi=q5A6T>Wu{toqD9r`S|$wK<lgX
z?(ONka3Nst*TaVnO_((+tMNo9D3fXNl^rw+o~{=Q3PaFp^V#P4Yz!WgK*boS5I`z%
zuYHG?xCzP0i{l=EvZ9M9hwucW51&6D{(RoPA2d&W_pWU8_PnEKW*P_Xtttiegd}-S
zpF8K5A6s2r&EOChCnqcb4!<NrM#IaiLRTAPUD5dS>-BmsZuj%^Yy(3=TFUQLvj6@4
zy}h}anc=~^ySuAiKY#w5xgiKzU>{*Q;sa`*HZ?bY-4(v~$D?l0`p}@@;HeikBDKKJ
zX8oOEkjNAh8(SLp`u=|VU*F%y_t}D~wMCmEb{46It&3UtIjXE|TkE1qP<P~VueAAt
zckkAHuX1!`Y!T4V(Q)aOG7VT8WvZg8`f{pu*&B(brlvEuuI;J(EFdEj^KJ$x1YaDK
z+{HL&(xj#d69l+jzf?TXh)DbN{kwZsmKJD0aqji-H4%*3+S(?$w?r5OBqcjrT3Aw2
zQZ!P(y}1e6rm@(6zTK*0KA^A+xwwJ#h)@3hzh>L=@Aq9>8~u3e^*GQd)3%(OO|sTy
zpbcIt;`UlGC|FrZef;>5gP$L?6L-So$;~rnNU*T6C7qa{IPETIrL2+?cQSX!%v)PB
zo0Z-B6iiG+Kmi8YeZnoSmvC)OB<NJupmkwuqd>zeudc7}*9=|;I{(|Y>dS$1bFD3Z
ziGot4?Z+ETL2L^bF7)v5(AXN|;J~mg_x3U;kYin3rgD$uM(tE3#+k<Hd`3n_I<nv#
zEI2iIsfPpTN*2)Y$J^W6-E(quii(PKWWiOdplyTK#D<t%C7hs{{je~xHh%d&(30W!
z{dJ(@zq-1*bY$f~fi5_6Wsr)3Y4)`vxAXV+ZrNf2+9p@=xYrzXqEtr*$B!RB9(=tX
zfBfQNcU@UEu#TyVf>ab#P4n(ZY)(Ia>}a<*Xtg0|XBM}Z&Vl0dw(Rxw^)+8Ey6>p`
zylm-Va92u+Su2)_i2=0geQTB|XbBOhR}>ZoKI%luJg-O6IBh}P-l`iL5}Tn~7X1#H
z#p1%zIM=!ybnFah9=w5(x!~)o(3(#t)j<WTkdP3lw2IkNA*iURIK!fF(NbqnTSRHm
z?}%tChGL%?28^r2*B=Arw8xK|!{cj30|NsyuB@0C7b!0<4=OtY0|Ui6rhtm34xg1G
zoF}&5uj`K6TcxO_#U*Q9)}rF6c6xi>T`AC+SFY~hg!pWg2<HiqiBF!SlvUc>+yD6a
z({@(_*oYU6AzkSXpzCVq*Z;fu;?rbx|3~NT|A!cYN+2brDuJnsSW^zQaDomp6A~8Y
z)swfaGGSt3+EDQ^X{xfaQnxUZMG?!6TImT(+<z;d?yr;1I_Bo$lEOGuiSg#{a{XgH
zlF22NE-ox$dNC`KTwFpFRn(dD_i@TtB)s&{THiH8BJ|0Uw}stjFAH~cG0Dj4{`FH?
zr0%iQ!y)hPE>?ek|HY?mZEP%lDqNiW<dmp_l~lwwo{TFTH+G3~a9-Tf7V&8LjvGa{
z(`;TH{i5;xaL{oLZSEg0loOu$JeV48(#bD)Q8`RgNI+g*e#x?BQnB;q%<0Lxx@x6_
zpkU;Y`}NH8YnYB4VbIe%aH#dbhldTNOMhr7ud1l~%{#+JQo@L3hQ0icFTo3HtG(S_
zUHW%=6|P>KetsV424l1Qduz7mTwdl28s}Rdw^v6%QgG#>6&w+JBr+~IOm66r=l}DG
z+n~y1GXJ7aE5i9Bc8gimNNm{7ZdJmdq|~rET~}68(Ds<t(fD_u<vF0E1=h#y6_AkV
zIMyr8%mCWhDy;4Y+L&{BneXO;DK1|;-rVE$nZxntkFh}lgF&{~hbzI;`nq0x{B);y
z)<KzT>#lC;Fv(=<kz%d+q4?lw>dO}$FARe<qa0#lV?n!u%kS5Uv$C>+nx!>0He6g>
zAMRDZ2c2IETB^A8u*#zLmv15$xbrUvRzGl4(!+;kntu8x$1f{9gf1-af412?oo_=D
zYruZH4<C;^#LZi}Sb5QLP$V%NIC+v2v~S|%Wc7)&XLsMZ69ZasB476-ai*H4$|A0J
z*K{A;|9|aHqiwNUvHQ0RiVtpr{C;ur!E=6>U;Vh9f8fHx1J&;td8M=R7(~RQt*n%)
zctU2ev~<W7|NMF2&dyoZ>rPM4ez$9$E%%pqXDzimbPgV7{_&^yY;VH^frJYRXL}j=
zs(oFahG@=XWNi5Jr)Ev`c0Nr_P0$^ZA8X4KRXiWLw75*2C@?Fmulye4zaPvJ#%w<x
zFrWRy;*wIyE5-2odbUu1r*Px*`RrBSu9RH<^nH6tiAxA)Xcx<qOG~{$=hTBn&sK%5
zR<gF1e)sO(j^gKhU0q!dUcTf^PfrI8pruZov0|pjhvoB4l;7vvUiR{pql>8j?QI7x
zE<Ug-^uQF&S(X*YdKmB3&o}4kSmC4PD!yj>{W|HJH*b3Q_#FB9`MF}tfmc^ofBb&G
zKKa}n%YuS}1AVgAI<jhlGhaV)T~J@YAk|yJJly!w)n{jY>^iipBCqwhmA{*l#&lTK
z+vKiVa+1kijn0lU0h&S+_EvvSdUInVXfxcO|C_gPDS{5K1KB2PRdV3R$H%%ds)AFs
ztfP&Oq#tfO@bB+|^81V@PtMxO-m&7-hV9o&X8-vbes;6sWOZgn#sjae&dQtW;-VJP
z#iC+vE?!qx$Ize^y2@daN=kaV_UY(tIi1e!e32SfN?M_b3)8NBV&iSl-*+Kq#~l0h
zl8MJpow{^U@M80-Fy0@pv>#m2JbR+_@2|`#Gk*DPB`Gc;jiFsEOUmBf0+ovlpo@!4
zv#+@%B`w<Y=gdsw4coVG52<iT=@FRq+=_|0abt3$c-)0E33j=&((?F~s%%VhR=jlE
zS*{-tz_2B6t>nRX+wbehN(zQLOjTmcyuZ)ZEce!uKlLXjD1uI7aO;!le0aG1@$2>b
z-CSH+Sd~?jY}NglVs~dv;?vhx(B$msX3n~{r6k4W%Ze@8>@xQ2D$P`4<6>N4YXJ-c
zFYCTOYu>m<$J^b_Mbu-d1S@Dp19TSYyL)@Nt*xzH+}$6)y1KffzrTIzR8ck_iH7rb
zzt3Ds6cS{J*}qOwH2q`Jf;iiR$8Hafid#-+zc|@xTH^{0U+xooDrZ^NK6+##EcNgA
z{f4z`mny3$ajzE9RoI$+-7O_W<;|Nn3=E(Nuq_#apl$joX=zG&dVHa)LMopWf-a)l
zq$B-j<EBlAwq{?Svgzf^moIK@)sBgYdGPRI<BAm;DQRhTpCmeU)b#mhSO$Nd<6g7p
z*`dc*G#|wOPx|EeMWv{OWtrbqn-%w#^MAM&eZcs9#y<HU3#={*uRXNA;mNaShpt~2
zpRDeG>~{YC+=~&Q36+vpS3IAaDzbo18r@a;8g#%B!-R<w8_jZW9XNZISHAv_VNp?$
zLHWBF(D|z1X^zrX`!_DnwDs7+)-WtuY`Ru{{a&^`zx5ux%X@jEV};U(Z`XL7KK^=r
zV2S5})B4P7qZsGct(qA*w_~1#=s&L;7cUB4yLPRhsAyA(26ztVO>O+Nc7A!cygWTN
zKA9DJ%#em^{_o#k4cZvIGFbh{(W8k+Is{i8^Zjx{#%9IK`*WwT?D;Y&E$zv#ue!3L
zf}t+GQjF2tv!4B(X(5<#k?F;4@fo(#D}s+-x+plY3zSVyobWhu{J3+-0kzvtPfvHA
zmip!W{rJk<zrR37wzp5JVQ_%VZ1kIEUyD%kS=9b|`+bwYyKis1`se=v#)P|8YVs~2
zyZU4sr-+=jabV}maJx|TJNLmkSx_p{S}DTmvA^zb!RKebTe7e7ou00L{NdsDiIXNR
z`o4JEp+iofTN2hrZFPEkYbzr|`1-h`phb03Qmex4baX%$zGSs~e);g{=kjOgQXLoy
zzG!U7nfdI#SuQvmsD*{is%)tH>H2xji6+)r(h~7?D<zLj?~uzD-N!A)(9+(ny<gs@
zA|QXK0MjIug)3Hg99_`>I`#0|+uK%m*Kgrpls%ta^W^d4?2(a?8X6i6y}i9zg+f!K
z9T`8IHJ|m<`rSQNyB`cQ3>-gC&=kDbTvT+aXO?-<lC&vn3Ll$zo=~4(z`(uec*If<
zhot0WUD+l_?XWcsYuD<6*5N$ex@V6KLx9^GEl=ynByfjkl}yV8A3d|oNogvx6Aw2?
z8aKSU3a%slYHAu>yBo~!T_`#J>Z<qUqn|JPGcwz9b*v~_Ey8)?!NF!-*`|5*|9-O1
zHqUnh^=+R$Tej!jg9i)@JF360yJrO%k<!{SMZ~v!b?xs4zqt)tvm2NBf>MC(xmjkc
zWp5a^<zMHyeS43kL4g9OYW*KOw{yj()gqi8u3Hihvw?cfCHtBFUH`i3$sC0!$Z%El
z%C+*sYCa9CSFb*o{rC6x>y;PgzL<FO#Dy;><93C7E_lIjpOI#g9?qc@<b8eJtoEzP
z$)>TJA3Zj`Yq#8oF=WQ~ZjbDzr>27Dd(LIwym?cs>)A)e&Mu2BS8CgMrNgo$Jtu+M
zSfYzAzPPql8gwC#w0WLJ?CLF`>D+a()}U=E&%d_uN`o@!?y|Q=5$W)O^_~Y09=MBH
zPjvkMhxyFh)jtc1F4?>{EK)g8ncZ<>Qc;n~eQ!g<hRwzw9=0D?5%?!hsr%vAsGp%-
z{_pSYtya8S{(b9;6&%;s#ezmySB0)#_R0o41GG7vAC%pu*D%aVY@V(c+tt&<ld5qK
zI!Xp=%w`n|&0M;|<MJ)TQmumD-~PWdx_6z+-6cg(Pw&8&m$Q~U-uvBT^4z+gOVu=A
z-kZC2v&+<gr4nu5A06%9QSh+o-1(=cr-ND%`ulz`>FMc#Rug4?TobjG%hlDj>hAh2
zixw{i&BcEBP*C&rYWNJ>YO`NoUV^4bcqENnwpw(0Z7pB_jPZQpVH5tEWy_i6>(4xC
z?$DYSwbdkc)1=9bQl<?Z91EP;8xG$V4z-%9#AtZz*fF;Cd%sD6F3GwcSM9s??YfAK
zO!w~HdvRf5^TDTEv#uWc_xCqw!rEu15v!r0A!zqz*}FR@FRovaWKqsn@zM3He13KH
zgA;-YSGCT$`M8M2Z%&(a^NO%g!|Usf69f`Y2s}6?3d-$itFCZ%ge4>>@X1+Pb
zp{v8!zbfU{)>KenxV<eGG&h`ge_yYv_p}93TeDUj^+*XwQ~U7iwZr3M2To3&wdC;$
z!3C+_AhS~JJTH~(|MfHd!Rz&=&8a_rXYa~*`Xs{2N=fTz^27$vS*3k*1bR>G*j;Y=
z_~rHW>nraiKl6F;CUQZ3{I~gPi%y-sB_(0(X6%3Q$H!UhSEWrD{{4>sBVYYpR_y52
zZH#~BEmcmKqQs%4rPL|=CiV^6#^lbxPSayYKCSA!dqa4)AzN>t$uYT{h^9r04mL3z
z>x%8^Nb2n9@|yX+|MSj5W{uqPvWNd-ug9I5x#|47H*d}vFIO}Tefgq8gPZLlXYH*m
znKP%^SAJq~?~?%?dA;xFGih0O*A#{n$sd2qAG}`Qcq1a=LxN!wPxj-E6$%?S3KqBW
zNGh>ik+6}7*yK_Y^4#E&OVx1&p$RL4m#0O=t%)$4q8~5E$jB&RQLrGdS?N-_px}WO
zfesZrGFA!5@YMWRcqvd&Fw_6#r335tGaYX4?hLuTRakiCx!K1%Lpr~7II1YTeEHI=
z-66#y@4ndK7wh8rLsl}iv~<WPC@ng)D9!i5hs1>EehF_P7F3lUxGz7$F1BZZt4o)b
zK&?Xu$AQbs5B&J(aI`C_;_m!<vBSYeKNS`o@|e`XC)@D(xohznS!<X76Y}?q3a=NM
zn8c&RcyoWf{jq-e`A<J_3adSMaIo2`r^BOv$+9lK>7rT;-C~T%#~8xbGcmI<xVd?`
zySpsW5f(PAe7H6HfOvezJ|$5thSg!6oe`ZL&v}#>XBsB6{rmU(z0UOGM~{ML=|P71
z|M}RS5oJ(eaADWh8TXTKnH)If_3}hV$CuOW@(lOu_4ldIHevkro!!Q^@$c`ceLX8K
z7<RBcxwEr)MeuUJX{WQVu4>(!e*Tq#l2YqjdwsXE%*$*WwzB%o6*~NSn%2^i6c?9Y
zXRnIh*uZE}$h2V_ThvyexmWdL0<4S{IUQCInh?G|?%~(h*Wbtddvugr*3D(fgx}wf
z8ce>rs`D9(qvN8*sb^<-xx2ZfM7+Af*eA!jEsu3wjNsxFUOAn8>75>{d6XDe#_TlO
zmV0~JOQXnXf+8X<{dT`Rjt2_93@9=(sNR;beNX1(86h7Yxh@D+@5}dhnG!vTrR1g3
zKL6+E*|+5g3U50%gYm;LwTagxT$nyRogS}bV<U5SSLxxM#m`{}IM`N;fkZw&J`P&F
zttG1#_-}IR#gGGsnP=EY{&=&wKK+lWX{eQz(xRq=S3+vm$L(h0ky80}Y3FX{>}w2j
zxEeI>OS&*!+FhOxDj7i8_v@>xiHV7f+}zxtN$T6%^N%0t6b9XIzx1%msrZKv9q#UO
zSRc1&@sn2Wq$%3b(My$;m9%DbiHn;mPS<1n`ik-IuTHgf?R=WDl7Wp$0>Xk!paUl^
z>@LsGxxJ0oJns$z11OoxT9>)}{Pa|`uTMk%?heM9zgsh=zj>+otHLC6QAvo4ORM|O
zPm3(CZA?ybE6vY8aGZUHb@&wz;lOzkK1@s<Ey-VAT=Z&pTq5f~kHI*-i*xgvx2{*~
z-`{IErJ$wN*}*mM*H>4eO{v@;&YC+sJ+)}@uPK@h&CQoC3QFoU@7UDY)pcl7>gi1}
z`*Lm?&9N+YYZnxJx_F96!poqG9}909B|LI5RPvs0=VRB=F-6U@VgCG#X<zS_AIRU|
z`Tn(K@eu|^mnq3?g{8)chgf7R3KUL1W#^YWa->tZR6;P&ZbGVpg~W;=)*r957dW#U
zHve4W*|2o!rHg`s7jK_A<5Ill^>xPj`j4@1A1Vq7m4&FE5^VNxP*PUDxFT?|&UEvv
zD;&%H=U+>7aj7hM#&cm2>xF%`4<0eOxHIqn!!$)_B`Do~(c8A|z@wu{2?F~17eluB
z$v!wCxKvr$NP=x*gNmx^!5bE@-)U)0<?8J0wCd^5nEvjbsPoIJs;>X+?99KvcXzUO
zcAO~KvAg?Z!r?<l?)%Q_*g11uOrV*b5_5t8r$;N7sDO~rq^F;1e}7vTv$F_fMEg8@
z@yDmaS1`P}!};d}bHO*A4<C=u@tDwYVuEQN&m0@gSxK(0j8nBb)3)-<YspGZyl2$G
zvZVh1KhUxbw_d4Mb^m!QPS3Zg1YOq$T3H0zBi<w^l&WYO+mrV6ou1o{UyryAFJ2Xo
zXHZiEl@~5Q9hV9fn;kvU5qahM8bR5s&(AW)?F+H8Qrep=z<DA(zP5E=?eC<Ei(EHs
z-t7GH($c_PC7CyF-c0=X=;({PyUpGDWLB2UaGAv?&z5ynNI23oL%}WJ=fCO)&HPjP
zdOA9`96aQ7=5fhesShXBlV058w`VcS6=Loe5M*HXbWrg;^y=zr(6J_p&TR{do}O9}
zv(pH)uT#dRLI8Aq)Cx((pes|g9gg=oY))G=P1#1~#vaK8V@IV$hYsG8`|;8J!JkUQ
zONsq*Noik`k5B3A@z9sJ;?i<<mZ|gRw6j<0_g8)8vMzt;a=cG=WBz@+Jt?N@nlCRZ
zUp$|D)5vhv)iz#-@9*9`c614esI2Iyt8eG)RIHIUVc1nBTHG;3e|<@bOP67X)P(IP
z!KZ0}cKoY)O>y}C?r!0qABA6DUG;u>Va0~3zfuu90=9gQ-77W2E_Ms^yNk^y*#!kV
zr9NI>?s|X1<KwQI*90zZINm?Cug61>ZE}N)n_JSA6@dk1Wp1m()@~?!8wFYm3>tsl
zTm5}Q)>W;g&PxutO*<;kBVWtXBd06Wmwnx3c04!t(#6V3O0|oE7;ArZr0oh`#$f+n
zCoe`fMk6mpP*kaEZUg9S2GF6f?d|MY*Vpx~jo$ufvcKKIWxlf&P8)$1f>eEd<@hKz
z?b6oE%a6`@AG@n#Vd?pKtaZOQ3km|PjFglvrB9o7poR0m)zuC2=kI9zdeQyB`~902
zTZu|l&29jlV<M^*a^TWZZ_whfme$sX-`?JyVO_3gV{1E=ub*r6{{L)te?xlmzMi)~
zu&?&Oy}e0y%9WHBy(ubTVPZPMt5aTn(R@juvf*-$K*r_UIZvFNtPYyf5!DV$03D!m
zW5dErih+sKrm*<T(Wp~#a%vFOZg`Qgpfnp){wX<!trXgj-v1-l(~IfVHBsNH{Cgo*
zMoMch-eyU8d1+}uQIXSfzqt=yyy!UC%>MYoLgxvQww}ADOjEn{GHeCInfdxwyr7a<
zhC@R7ib9c#%aZd^+eD04u8(4su~c%qAZ0D~z=S!3;bkt9QOS#dKR=((-%<D1%B@GD
zaj|><qRP+D3d+ikJvlj9(NpMm-Gs8Y_xZ1^VC0cv*^<E+vri|@c>A_6P#9`;^z<;)
z*I%?x`BPyyy<@iYhr|31UR{?i3O<$I>+5h&BYK;Tn%|s`ZgG9kNf4k`BA>h+PvkVi
zk{1D>E9Va$JUEBB<<t42-3N}#gF3ioW{Yg!?J9M6c?nb%y0kh@R7zU1Cj0u4BijzQ
zcQ1>&y&V)q998oh_MAO^nj17seZT&H?}P~gTk`Ho%{I#gU2^y4&6_EF{XDTwOa>(y
z0b5tiIG+4E?7@q`1!cLQ&|P%s#7WMGokAAHYz3vPR;5g{%tVz9g(MA{y&UcZtPBBN
zddbAhT=@TAZO)AijAv(?hg<0wh3LmK-LF;uRk<dfzvlnm6|65WwF+|z2~Pa9Dok|l
zmGAFZ<!ptN4ZpqPy|Zs^Ns7zPqzRlSZl5`O_Tt{^a?rB#mjNd(oG{u@xcJ2j^FpNy
z+t;snCuhoKU>s~^rKF_fn|rI{)&x=Q24?mnCwd+qWAvY|B`X=&CsD)XG)4P(Kl9hu
z%(m4`S64AUKhNwvjUnfhyNk=OGdCi7j#PYIC83)AG3jF9-{tcgu7oUItb9sLO(A5*
zojsbz!q>;go9wl!5V*0Cc|}AZsJYXz>HICJ8D_jYid=c-d^#%bFjdQ;z`%Od<a^0X
zPOZ*KN*}&nU$pb;MCAjot}YUe-<$@HiYe84dXLO?jf6BJg);8P@yN={g1i}-#N@;p
zou#!QXXc7|al1mcOx;^0`r^8O&vri-mk@y~D-?gVvamCM{Mayk`j54{i``$I=rBrP
za$=pV!@Rq^d!ln2FLO<ei|N-ZE0bzUm6R4WxlM8@Ub8v<s7hP)w~oG7lhqhKJ-yuB
zmmFhh;hF~$Z?dy%SiE?}w79)e1!drLsPxv@jLq(UOwSvOBo>46brM;1N4d&E)b}1>
zY2oVoQIT+fVL?@?q1mrKS%-`jE2gO_DQV^8<TONXcHwTArrUV1`Dp%=dwbuk@asxq
za%y{jlsn-VPholbZ8Jlahd)0ztk+i;Qc+UUik15DHT=O9%>&oOGOqJw+>P2Ip%BC%
zEMOG#^5rA$tx2wiA`d4hF530;v_7M-Ft`l6q;Hzd)XvX*dpq;Sjf>1u4lq1;7rRt>
zu~Qo-hvYJ4_l8?r8|`Wvc9k}IPj9IF+>mq>T$y;(dkS4x*qoucp<<(ln^p+JzQ1A>
z|6*RgSTP}i$*Jw>tI!3-=?grC9hNeInu<A$r>2L4a-`DX#3ZFCo|xTgYKC)6y;YP}
zhcRYmdbzu^$_NV>@$}0xE4wpI)#~JAxxSX!Zw`Z$8K_)QQab$gAp3#u_nD{bch9c=
z`<r>*yaWIKdbzu?N(c*F%3mMft(9bMp77Mmu>bhe(+AY&cSwQeVozCzhbJ6nD=a>L
zD~c!m{yN!$hfS9z1~MMewz%ZKN$1wmuvIKQQmiGf)n>)_%JWA=XvoS63I-kwS>Bg%
zZ%^rJiGAC%LJe!)|NRZJibGi7l6_#H!{#)H<$ex3iw?{*KJe*jQjnvPlF}tTPo`ZZ
zjCye_&(4e2>fXI`WWsm-I1O1@Q3f9ekNP`1nZLZ%ZsV-^;NUam%W-*!saoJf8}ehu
ztge|?*2gll@rgL!3tz*rCRV%ckoqZAHH9U6($6=XoXl8WKCwh6n(fb5@e9j(FHHok
zB>MR1Koe`yiQl_Q|L={gcroG9#EFebOirgfy@e0|4PBw&_CUfe=1!`l5sQIY7&!Y@
zMT44`VHFk$X>1kM%=`8^`1F8uB{Dgk;@}lM{O9X?{a@8bjxcOaXErkfyUpc&(0;pu
zHxe5Pl?&d<1nk^2!~4lW=B3KY%m-OoO0?D3GVaS|Tx8M+VzH=`iP#^<^B**A)#f;<
z;oaS%?|;p)1T`O}CRr9YJV{x)SlRYCOH0Y)gUksx3<{?|ImDXqIW4d?diy$vkMd^D
zWL)gtt#;+#zXMA=8`|4FJZ=Qe-E@GZWy|ZU+6nJs3~TPM-`{1eJYBD|(pJUu<%^Dv
z4h{PoH#<9<^<x<J>}j~S_o(kNW%nt4JzoTbC&+Ylb%Ban&@oX<JOo2l2C;fi*Si{_
zuBOHYS^}|kt7%Sd?#1Q)@-{U;0v2|NDxF@m_*T0-)8k`|^Xs(U_4UZ<zMCchN(_(R
z@v*QnCMG_LTHq;cxc>T7Z7+AXsupM7rw$$-95uDI4`06QJULnY@TpT=phK?aT9;pW
zedOp-(AH*+ur(6T&dh9HvP1>6pzZnj`R*<*EIr(ZU-+r}cilYQFUwq0({OjUs}gAL
zxz+jcu}4)2kKGEDHwCFCya>4Ha{8R#%NHJrtq-y+>Qz)#7v|sJmvMie?Y5kolb%+A
z4nis_GCDWM5_AeFgTvaWtrs?>dV?m+4>U0D;)?v9lzephnKoXBtyu>enH@eq^Ku8B
zROS`1vLSA-3-5_Zst49Y9++i%;7I3z>G7LfT@;V0Mcn*yJAc2Tq9UVl`Z*ScJry6B
zHtD2C=xb<j*xA{=dcw>AT4hpRUY>JrPh|SXS641CSNgajBGAX{%V~Xw6qU*RN=iyh
zIyw#O_lx;%wExNS<C*luz}m}d7k#?9WYrXIibzRuNlQzEF46w*;X}jJsiHYKIR>Dk
z-m1QGF%*1%7YjQ1G41Ru$!+UncbhF*ytq_$TiMcOOpJ_+ntwhz`sno2SD_C!pJz~3
z2A5z;wMqH>e|{{UVZFpl6f_kUXL~VaHkYWH!p)MeuR@nBSpr&Jl5u;RZc|g!h5hyQ
zEG#S!HlMd+W;k;EI5WeXIdhD1-amQLa=c$&Syk0_d13b1eYJ~bUVT2FIrnzgxzzi0
z+!2wQvXX*=f-g7o+q0Ct6PYZuzmC@=L&5F%p1RnVCp_F|G^AYKoPHj3vVPs)Ux^P7
zwSta$7SoLa9Wa>u_*gHf`7-H${Uh1UKDSI}&7RHNP$k=3`cCG;uB{$cZ{9XuUhZ1$
zCoT@E{*{!RAHDtizVyh)$4Z8hrd$<&B3_>GI6b2wWpZmPE5ic!emSY9d#k=S>72Hx
z`?I5JuDS1~d*41i_3r&EG3WR9#)Hj`udX&qn>Xa%cB$vr*N2+96x>#wCIV@z&MMQ7
zQps{(;qXW!;%3OE6i?99wyM{Z3mWAG1q=+J{nW3Ph3C&o+N*14CwH`4yuGvYWcB>)
zCnqLqL~U8&7I}W2?ZwUM=kK)5uReJaG!OgcW<w|EhJ?n9vec|qzau(2I#wiX+QOpd
z!|?07c;|`#|JZe6xoW=4Uf8)gX|j0!jSY;_W;qu$ynTE^RHikxaC(?kR#rYd(8%1;
z-R<1UC3@v)`TKjU3PKYnsd^u}cTaA)|NL_sYF%7dR)w#hxAr;c3KJV!+t2DB?s)l&
zAAZ><&3<P$JCC$l+ga_nn43F5_CF{pVVS1SzV8>8gfZKTTcKYBXWGlJ2zGz|Fr|#y
zLQ9J)E{AcFdQQ_+-@3ZG38|9%|9t9p`+45}Kaa4muu0}67KRH;y~SDB*_HM5{Py}~
zWN565*~w&YZ$J0gob394oLAOze|g7yWesP{4uvec88rfef)n*ZR<XRf$=uSy;OY5j
z*OGMq2hV($Dle`%z@jpHRrvb8^LD>^USD7D-Y;hhT7Y=){CWP_X1RxMY)pRm;spl-
z=!zsAU0u-WsyeZ|L^h|NZ<{|~epTq|wz|K+G=D4Id;Y7maQVVO<v?a3F*X)f#{8>4
zKnoLJ`kbET5He*(vW||=8F`~JoexhsFHH=TGhjT~`1JJj<X2Z#f)3L=F;N+OYQV)s
zt~2avcU8%Xi;K6kwjP9Rb2+@H^7DhokDDh<n2_`Jf4~v@{|t-WyDzHy&0)B<j#Wvi
z;aKlfP*1Xh%UW64@c50Zs}KD6n4~9YZvLQMzM;uzucM;!$(g>-eY+|yFEcPQ3No*C
z2nssl(iFU$X?<MhOi%Y-A=!hV^$TAv-rarR$;kt&LX`x6{;OW#%s#g?<oeIAQH)2V
zCLHpZc9g|osy6fC_U?<S>*7S`o|y>_TRZJYA(VEpsc>=E1jhuXpc^jk%x7nJTeB|p
z?%rSjPx3^{w=&R5h`pg(wLTn|KX7mFfy3<%Yon4F=HA{uwXf%1Gp9$Qpg8-F-`$<e
zj~_SQ-F@_g(A8CtTpsw&L+HY?-W9x&8<{K$7*<3Cei5zw74q_h2Dgs`*RJR1UFG@9
z%M~mld|nlQ)48y2Zqnpmpg4G{ot&KTz~SPOnlBlKlXuOQ{&3mfA#2s8ix=w+7&TX1
zy4-lcVPeIZdAe#sd&+nvtU}IYfR;6fT!@HfzPy|{d|k)vwedBKAuE;g4i()|-FuK_
ziGo!{fXUWR-^GiojvQgQU&}6{y9zWO;^K1a`6H#gBW`Y8ey`u&X4Z}B*tdR(g_;8E
z$)~3m6<2<K*09qOQg;VzTLZ3Q1z*PXN-c^%u`V|0N1VR?f_VD}=VX^EYhRhsAi~lw
z#~5F$+IIEtYyF8OGS+NYRxgKGb83>xf#d8J<$gX%JJS0z7TIMAO_;Kzq@-0ySit$)
zD^Y{`eJ@f(B{<H^P@ENA`u-f>GtknNd`NTmOQ)<M(+q3z6#<Su?g#o_S*5NiVVJ_b
ziHC)iG5I*t_B_UB_U?)6WMroF^@vQF$G2o@=lct1<laXuTl=--=bU558dhc=Ywz^@
zH|6<tGdDM{`k;;uE=y_YgljqpCj=4>u_RnjNcfnPaL>kYiGHT6uz<6$Amg9+^#|tH
zcU_b%d&^|^%j4w=5!KVP#Fu%+WV}9H_44Je%Kf2Qg&XrzRQ}c8e_Z=|?@o>Lr}y>r
z9Qpm>SLw6<DN}k<&(CM}pZ6qZ_8$oc*QJ+y7fp`8y&}Tj&xJJ<J`$b1dRuN|*jksf
z9#>a+xrZ+>WHc1np3nUF7~|{f-4CBQIW<hvoj8wg$-Zmvxs!9(PoK7I_u&b1yPwR7
znKOIVv-|rtZH}vpc_Ds{`;vKQURvkL-IJ!3Pko*;d%1t{KHoY6qf76lmTo`o>%;Z_
z`Q(mI3t&Up7k4eGDt)l|d_(5tBOFmv-p^=wu~0Ae$kDhgtqHRx^<7avKk?Tcrj?ia
zJxrr}udG({$W>R=^(}Xfdj9?B+MPQe#+2-@{BUSl!Q>_N_S|A(UhbdkHtFnh{-n)#
z>ZO@}+>@NyQ>Of>yqhmnUF+Rvq-}1NR&M)ix=y!yRn<HB%DZvWzm4Yn_3-e2-%zsr
zn}>h5`>$EIJ>q4%zf8*XDA(yMkyv@X_M6X3|L%v$OpDs3%vk2ky_8t<kEiFbQ{tar
zmOZntwikXrW}ug}>T`GT)Gqr;E^Tg;{;d@?VA(Xs(DPzi@1=VA^YfnO#IIcW^YgB$
z>jI6;^f<)(+T<_KezI6$qDtZ43Af$jw%6bP*VFTFt?AX86$`Z9@`UDAuQ>OY_3F{T
z0k%f1tIB*=*p;-O@@Qq}cbOAwaT~T4z`pF1t(rnrM#|mCA9!AH&#~wK@=n*vYpT}L
zk_Z=<C}q$8aU#-9U%s3X7Kv5yJ$Z7L@27j)&#n&NwR=bCrzRuoeslAyUrhHkZL;^&
z;xO0qWxT?acZ>1yab{_I)_*Ix&dl#$@#yHr%FUNPN}8!D{K~jtVAx#g!V<80+NFyE
zf*0#ogfz^YSs~H1=9m7`WzX)ju6h_>Ae6c&Co<tMn_<rT6@d<?r#bxn<>j9GVMfD?
z#XF0SX7KzmQ&(D4laTTy|G%};saC7fBOFT{9hD>(+5~FKGNc~5JsUK>B{<P;qKaYA
zg==fQ+><`cXaE&Pf~nIN_Pkqo9Lk$`&VW%7R3a+PO=JSKHC<e+4zsj?%HocaHck%k
zYP**_kQJ;-mn30}&W(Mb+j`PR4T6Qygd8qz?q;v0r#=`M89BMQv>e-IXlR&mc^U7T
zwQCczv%BqTe?543xSh)tRJ^+^vG1A6ci;kO4WMcEwJRFM#l^w@?e+BddZkRAHYOc4
zNIxe7+W8Awie3NDMoCF2A?M6?@FqGg{W#v|0f*aoFYYXUt~34o982MoCr?@wKjWD*
zcP=Bt)~u^er>E%(N=S4tGPAwV3IRv(-ot7UJ3qX+X}o0V($rl#SljL4ir}@IFICkP
zET6o8&o3=4J!AIl!?$lsPn<aMRciVBd#+ASPHk#Y44|FbF7EEHjSz<4Ov%dXS{J(;
zba3c~&6^J&?G`u8zh`5TcSi!WA#-ciRRMW<eujdgqEj38PM$0*XJ6MdWs1m*88cp3
z)j*sGY7K#oZLj+JYUTfLA3r)y*NY9<niUFK*IoVXjbl-f5yOEqXLJsKd-m)ZL&K9N
zDUbvilChC9Ob$GsVpVcrp>w;!X(JsS9R`N=aeGf~cz1fbKBL24K2SsjCLWQRV51ef
zN+fQ7UGMYr^H<M~+nd?-b=!|06(ag^Hql$<mEHSXE-rF4JyrGXP3Jt@>Sz8JK$X{*
zIOzU6lT$*%!lGTv9wabLa=*SlzJ2FT%Qb7(B&4Q-P63`{S8H{+N>EU6Mcm%1ZQphl
zJanq4s(Mzw0%XP%=9zaH`ntNBrca;lBq+)QYGckkoAU2^e0}2eb+H+DcWvFZ#(TOR
z2OpnXOw62JIwmF};p<`==g*(-m!t{`qf6my7*8&HcXv1FlHsqfuY=Z%>&5J7m|y=-
zGAt|%bdYA%-j9!uC%?Y7HsjV7%{G4dK2R@nwmCm&qvig(zf-CiK~8b0YT)!RD}8l^
zbN}CO(qeis9n1aagBsMayUUI~IXO9SU5w?|Dm^_t4sPzlpi{W>?pVy2H4C&n=<~C)
zk6(m?3<-H*$ar#D;^8*X{`8ws-w*TKAGx=;daDd5s0AlJOq3S@IeKsx9LK!a9hUf(
zzq@m=nVnzZ^rlM-1q1~(ohGT=xPANZ(W9;}13DNC7#A{SeSUU!hGntZxp}s$OEg?u
zT&h4Pse_i>|M~N03STRirJ6#>gPePNByFp{INaWr3))>jMKhSq&CSiL-POfqNynBg
zCZKC2zrMN(8dGL4;8b)qWL(IU{_M<5P)8JW5$n{hyP!M{+AVZ$uJz&L$Jrkr?|0vt
zb@josXUC*VmT<()4QwoWeol6dZME3HfB!&pa~<~<1+9GW{(b-J>+6%>-rCB+&Fvf&
zHS1DhpyCm26RiL_n+gU7jo@WHhb9UL3ci#N4-aQhlI-m1IkGl-yGU<4m%5rl$bvnR
zZ@WTIxwyDU?)_P7Vr~vv(w~%^EZWz>r3}ettrJ$R)cp7Jx%|?@3!FN@wL_qyil@-;
zh6zeD8U!YW+0_4&(VEJ&Z{I$zb|)9sBVS)%Ul_E~Amf5U@27ivtDTnyJ$!d}_sx6L
zySusX+`aqR+J!grsCUQ>Lq<idgb=OJKDq4c>#j!WOj2>2q_Xzy{4L(|?YtQ#N7a5)
zW8~oAsCd6?ZPeB#uce3jWUW8F{i*7C>f?#_pC9i|nKq4Ws#j~++NiysD}UH}Eb08*
zARuZGyxi|>z_x;iPByi_L=Ly}A7_`ZS+H?U)z?>1or^stD(xtFDYRzInk}2^FWJ@p
z)S38C+M+<=+WPqZsoLS7y#<l$#HFRXFI)%!9qnEF``gcNe?C7udw7ZG<OeTa%*g+J
zhJW3<bvsI42DKHpuGo;w<P>tiXP%9v^G(q5&o3{7L1p_=@9Ebfb#CR|bGWtCf4=I2
z`+@H~{)0M^@7}%Z=<7Q+(>Q&Tk6ZY<7|zIPpKI4G6H@hB;L<7dbKCwmRrk{`ojB2P
zzu9GGg0PMf6T_7?k-}zXW)(j_se+~_xJ0!MoH@f2U;9;*;lanp$7dL)^UX5L<w{Ra
zpD}ah!HX9KV|SOihJ=K){Y||ZllJAs#ekI|9bH|A^7sF31D&^U>5|a5Z{I-s;i~k+
zjdJ+q?T);-xEOS1I5#)9h<=<5=vt15h={h*_<c2<m7kw2bnBG@ja{u-vnF6&jAhBo
zORV+(eyTHoE<9-I?d4VRbSfy=;C6XEE4SE-Urz(o*!W~PBBy=)eBM5NmyNBhY-3|%
zz{-#p_xIa_H@f`zc02##0!L;GOUr|2&hY5z>AkqS+gvYx-<-G4>%!$HfELdLE%h=m
zH*fFm=7yAIOMJdho-w22{r>-Q_wL;T?Ju1<Q_^dSMx)<cD@7$Gr{ufwbw62`En7B0
zg_E66=E4u3bF)mj5A6QkaOC*$$DhyJx36BUedg@hz(p>cC2wzurk|U0a>G0Q{ePII
zdbNIieO=kaM8tQtS?kuVrglFbF#q`V>(QyH+BfUgcYI;YcIe<}$XGsS-aI~edHI-~
zMXYP1x3{fbyS6Im_=^`AHNW3(2Nk+jWp6qbxpsp#eBRhutlrezT=?Jsqe;#UgXr_h
zo<gAW7T(<3D=lkP(y)EI`JuyyH|E{70^MP;+<$)B>`dmer>8_Ks;ial?BveQGHvef
z=bvX++cnp^95lrhu{}?=>dT9Tare)k=RbJx;D;|?TK4a^KXc}cfVg<OvU}fy2M3!o
zZfsC|b!BBUs3y3!CbFQs+<krA-Yd7SuaA%KobLG_w2%R`IRR9q{rUMhF)wf3#+vi<
zY(ax8udl7WdSAPpPxjEB%Fm#yX)3C!7J+h0)mJUh&YTAi5<nYt<!irOte$VZx=t*<
z<|8ZU;xr3u>*Ny?6b%dw4}N=lTToE2amo~th=_=cHTD1gRD#Zx2OZ+Fe!V^@TtVgF
z^K)}=Y)<F@_U<lsettef!}RImYX0+jGA}O!O}B&7rG2lod0qM*(4AP#Y`maD`Nedj
z9!&PPJNWkY_Km5h#Xtdhdwae+=z#b=$8v9POFZ5u`{T#se#4R%0dwr@<qjS^Xi@vi
zL@#d7iDhvq7AcoNZ5bm5OCzJ8mjOIt_Ya@hxog)ct*Kspy}f6P=FT$BJ~c_@Wa+Cb
z6X)C2&Px1g|NoD&edQ+==bydO<|)?J);r7I-jaHHexB`QRnJMcx96`9w5$4(5jjca
z=DBlz`^w(VI<|>V*6M`TRIhux%k%x><X#4p*?Gss#ck^B?39s_Srhp0$H(NGhYvgJ
zMQvFzXT}VT)0=dTA3uJ2lFG@mvrJEy#P`eFhyAx-eO}~c|GT@pPisx}irbv#+m^a-
z-@Z*=OD8oqHC;*N<mCMH`T2R{HPz3~NbdBQsI+PQ`t@!<w`N~Y@tpLek(oVf%J+A7
zqr+B)6oFb2Z%#~9-lXEWslC1Z*JriUoBn)y>J9S9CKb=3RxVMKZ<A-tSn+XY_2X}E
zZ*N+>cyY|utkB+1SFVIaPEy&KdV1Q-y|R(VUcY^-yE;Ux^4*=CQcvsu{al`tlA>a5
zVR7Q*$&;@>P1Ozuh1{}b%RWt1b~o83X`I%xCTQi8R&Mb$sl)Yqf6kp_nA~Pl|F5R<
z@-p9>xBC7a&F<Zk%*!Fju&09Y$dMQSDs62TyuI(*=ZmR$9-3oWoRFJ4H!(7-{MxB$
zy4>;c@hog?k4{cjzqyN9-BSp3CG)4Jr?aQ<OBgVKmJqnOu-w{`IeEAE!VTj3aSCpY
z=O?wdv+I0)u({;T4MP`K*Ms-(%gfjQ*?4SI($TKOOG`X|e7WquvG}<k=)|hMXRcf^
zx%^jDJIrVGN3E3#b#Flvy|c~pkDZ#TZJNdM?(Xh(&`o>?4>~T6T();_?Xur1!`4cH
zPGr4sA>hQOIjf~EhOQ2CoTLJ}5YWub>{XGmu`y_ep>FiHGaJs{e!X{X$jTtlCSxWh
zrh@nP_S%$|_4oE3UFh5nDocB$OudpLo7s3DJb&KqJKHSr_qVrFrmYPbS=UwsD)(-3
zY-UTmx2IBaR)^qAV>Jbrh8<iVK3)3z+0Bh<-n?D)_sUm<t`3`anoriM1$5lda=*Dw
zPEJfgK|xnet9Uxa#Kdfy%}|m0scyPfC>H}yRocB7GiLb2&)(+h=5{EU-_HAZzdSpG
zii*mE+xh#?ZuoO<uJwde$?1BrObnnil_EE%fi})%K@Qzr9JtsGbV|>eGiOSqcCQmX
zvR*bnKOfZme)#a=j+&oFT48HA98{QBu3Y)y`}g*V6DJ-svo3#k1aw1=sA#Kuznr3}
zY3N>`Nh&sVe<ZeT+h*CPYGozm@a*hDDf7HJsp>x#yZ0xZoTO?|`AJ1cNa(|_ui3p7
zj~+exG^JsWY<QYsMRhf3^t_|5@7#n2kt^?gwQ6QG2rxdHmXNZ<==^GT9bMhPB_0!f
zrmv0O&Ih`CX6shdsBJl&n^I4MO0o$moS-X0{pMIShOduH{k_x3JEW)gX!s<>^JiW^
zeAwv0YUeFi^(nII%L_q<i}&i!&$FGmPT5mvo^AEAf7RF5#e$CMFw4Ew;yGE(aaw9b
zR21m;%Nf??d5vpXHCM!2gYGQaQTTX6(Nizb{LX>>b62qQNG#a+?$6K9hG}OcZrr-H
z$hBKcBX}7P=-OUSA@KRk_Vsq|_qFrAeSBCNeB6&485*AaQoJ?wwAhz7H-)E7o0f5N
zlj^Cd+U-iNT@Fiw6irM->i_*rU-$TDevF@=-%P#w;D1j}3ilRyPU3p=T}{-^$DxDa
zqMRfDJ2~yQv1et}{N`{-OG|^+dEIUN(yJZ5?!l2x;ahy?6(XXdUb*_V1a*n&a(PYy
zt#A4A_O^NS?TDzTq+>mjGa!o+^Fhl=Kv&(Y3SBKAFW-N6cX_sDR;tLA^|JN#^`PQC
z`}(@0Z*FcD6ccNkZJrPIVfeZ^ReP^o3Gtb2#(VDExsJZRwz+fX9;@q<v7EHr0%Re>
zj{5(0F)=YF)0Qq-@?d`bzmpSMKv_skOsu1;t7-P^*~d2B-k!hOJ@s_|j(2x=Pk;I8
z$B&BMuN_=Ue22YV-cOx2t?l;q{KZvYUrA2tzj7r+#=6XB;R%oXSJp<0Z_;`G>(NF~
zzPr17d)2006DA0Jm3?mRTVAm|eE;5;&p&>6=nPtqryahoMa5IHZPDKP|8|omO}ell
z@i5Ox_n@FjyH*C}?cFIj;pT<&iHV6G{{H6!>K-5C4Otgs>HKNpym|97KYd)r8R};@
z*);14gTkz;lQYjK7kzl(=rJi|MSe=a!Q(6~9rsS3IKgr5+&R#)=v|$>3Th93J)50h
z@cNqW(<bG;mzH=6S5#Fc-Q87sV@F}~vez!|?yF}nytBJp|FYlj&d$z?e}61}e0{HW
zg1VYIIywcvzvaH&%Wz6O{~60n`+B==`S<y(t*t@F879v)|6B*!sG%RXCt&*WB})=+
zY)Gtpclp8l_xzF53~PUte0|Q#Flq8+!^A@^KHD{em+|Pu@9X*f{eAWn|5+xQmv@?}
zc!Gu>ioVR$30}rC%QU+U)T=l-Nmc4JsHIZ*Iqk;Ho1pvHZr)R$xG+&zN9okGiOTLt
zXJ#0#h}x=URsN1=s#oiV4F*S!9Rm#o+}K^d{*vOPc{ettaz|~+XpGximH6+^&lRDo
z#n!A{TloB(ENHLIr>CbudlI|Ebh}<(Uk@%|0vEGsg|FjDPfxe0{KT@m?5$H$l9HOw
zj0Jg*`54a5Hb4IE?(U0Qv%^7?xW|qi11*1fb!Fw^2inX2XI)(-8WS5UC@9G2IqAas
zc>CVR938isK{tthetzD4s#fTO*RQ)l3tJx?Y_9nAMRS%(=A`9m5xr8Thn~-`?`vpa
z2w4@v8N0izb>l`uQ1G&|vs+kLq})h*a$@3-Pp9=kd%Z!;-($y)SyX+|SQodq>)YGg
z$`%$Dhs{84d%ypGU!SZs=mr!6Gc&i#%X}{`aBS8|j^0;e2|6_+aIstB`+IvoeEQ^+
zlcTdO?=F|HurLc7TiT4pDw}T!DLEgN5?JJ<WPLbdU-sO<#cqlU3JhgsWuRm8%<}GV
zfOh-4xI8$_Z-1t!-cr`i*!=c{yuI&#Owo-Fvn@=Gijo3t#{To;aX;v$pWVBy!`4PE
z&3p9d;loDgt>_CEE(9IDR`%`==ybrGySqd|2Y}xA{^{vyP@u}#R*4v=ojLI2<YYlP
zxpiBwd+jt&IKc4r=b!uMp68#LY0Q4V{=e;+vu8IZ9p!p+V<Yp|S62_O3|{`=(W6N(
z({)$B?>KV`bU~|5)D{lcZn2}Eo}S*Ad0Fkpj~@yuDlBShYLeR|tx7aNN5ooKSt%J9
z2!Lk4b{0J?`1HgxWNnn`<?9<tUIu-6agkZwZ%#nH+Wow5QzQ4}<>hts^c;C`uvt)8
zxcTSj=gKN7ETFx|6%`h@w&l)Vd;R;q?=2qjo<_l0dj&;BS@rex_e%O*7WinZqNH@{
z*6jNyC5_V#96AKrknrL2=U^+H=0ng{2x!cLf}u{XDdxh4OP4Oyl;IQ<ocOQu^Y0U#
z+@7934l5isZr{$%aOBuAEr`_4($``P2aX+cyXmkZAeqUDwX3_E8+20r)z#t3wzjb^
z3pzSFzPMFYS%s~S;|&iF2X)05vYk96K|>W&?t`|q%h^^PIDVY{-@ktarKPPCCr-SS
zC@3hXsidpRyFKqNXwfEUIVF#@nNMt9TH3OLk`M{dNW<~-=lNHzTq$9mCj%a|Xz%Fo
z(BH9ZSJI6Qi7TSF>!n1xWo2p2nLBqXUpJScnnH-e*H>4$*R5N3V{5iJ!+}eegtSDX
zWrPF+A1Zqa<>cgCIlC@yZ&hnc3(JjLw+>ys8hZ1=ihv}hN$key=Z-A#oE)&TD0Pl)
zby#wwj}H%Mo!*BJ1v6&Ndi1#8{#X+$cS3sl@_;R^pgX}QOq$eG{QTU*o14=oq|VHZ
z?d(_)Q1$JN<UHGIHU<MTv$n;HmG|u3o0y%wx*!GQnUhO*?65d9&sI7tENrG;_LUWa
zU834;mc`E$G&MOvM|uDJ{2WvOBq#5CyDK3jrA5tm)`8Qfxj%mV=;7(vsB;=JCL=M`
zi#0nt+rrA~Q&o$|VgES2zwhh+JMXRf3R+w4-Y=J_GuyIQZPH}$NtC6xPepCbT9|rz
z8V3*0kp@QQDI7a5Y}&NRsg+Cg)1LXf(q;?N&(HfQAehPA-~aE?#*6VbX1{umw{vNk
zb)9cjcJEWr(BSA6(>+yE84w^)Q&VGNRc4xhPv+gbcc24g*Bx3?vn}y3+fwHR<qtlB
zHFqC(+yC)Lfa6gXm3GkSa#zFS6CWICToJM|s0}m~?&{>klyz;*%*4pIx3&t(%C7A;
z=mZVle-WCU@bm)csE^y*^B+Gr*gR9OzrUZI!NSrqHR2h3T?4C{np#xvWRMOI`HgHU
z?4VIu&ERDTpPrn2weZb9ZA(i@2BWz*j^DWxlPn7w%v*G*%~Zh>bZ^#;TenuF3!Lo%
zEk7_k_N{(z&Cj6Zx&Nys&xqMoqPc$IBrz2=HPBr{FAX%fZR~z^A9t(&c;kh+oc!M}
z&#ZdQrxf>ddYHxTF5|WT|HoKNKMr&vKSP7gY0v;iP*9M7v^2N0v~=m^)#kn%?}^FF
z_dhz?oqqGrt7~hc85-{Gt<HY#8ZfW2vQkr)X)j86yVu{{Rmu%ILduG#S>@y$3B~9`
zhYme>`<9ns<~dbGPa)7y0xP!|Xrv|M+8Rl-ygMs4_O-XOdrj2>9nzEjO9nI;B47W9
zF+BXbPPQjFd@N6%Jh`LvwOG{lytACkiaR+y%xtT^a6EkYQ1X<z&y0rj^Yao<PEyqf
zUnip#x~gTNb35qbr5!atH&toeyKo_3OU_Lp(5}D(jm)L8c7lR}f)nFFW5{P`nI2y3
z-e2(TO=RiKxz^<mUcT&H<l3F|<HJJ_FE3CpJYZKzri+V90gDbO2DG+VaD~}TQ1N`6
zk7J%*KusPzJiB5e8*DDe6ncP=v5!N^gRifz|M>NK{lhnJc)Y#6K@BB#KA98Owsm%P
zf-c~bk&yw7X6NVUySTe&TRu9Zy#09E*;%FmD?=pZ+}zweJUlKqK4HE8`T2Rn)Kem$
z%l5Wy+ZM1gBw}}2?zZjEcP@^tw6wFS-}3y+%gYjGITE0HTw7ZkG#Cmxt@P^Z@XcFo
z&r82Ob^7DO%Ehtb`f)Au=gXg(uHUcZ+I8UGJ<w^djs*n;+qOO5IoWs)y9@iTZ*RL7
zEKul?u@uV7%LAPUy1VRc!H*A#SJubdpMIMX=?3D1ZZ(j#7TdOM8|XZmS!TIiD^_Ss
z(T|U7U%x2f)Z2}WF8AlZyuAGI)vKY4|1|U4T}V38di-!ZKd4;<a@^I`;h;U6oA>IT
zmwkKs^hd^ue;KFC@7J~`AMaZjy*&@qzyWmxK|QQ_^X7rNCw9MHEY{inv~qE6rKKIi
zdFkA)-rl2|)6aui9aD57h4kb09C*L~KVN)&{F;c3Ow4RNCyJuyS?^Ss95&acGH9BL
zXWWK_#$9ioo}RvG_Uzer7C1JS=k3nCtQI**#Zp7VBlg>agUmZ4HYR;ipI@_R>kRJh
zJu<To9B_zS8gz2v#EG}~Heaav`)g~>|9`cS(^7wad3kx0isz?UrrDdeLOt>D=VIIA
z{qoa;gMv&>t&7{MHGB5#)AQ?oy|jio+a|w6-_|yEUEJPXe;&5W?<=;QXKi_0Eh1`L
z>FY2FY3b}Ue-qQv+W!6h{rJbn$FCx<8h`%%e*fW%7X{~9l?F|}dGjV{y^3|&8_;R~
zVafV0J{9HM`v#h7TE0A6`;61+BS%>H`1p3-n)bZ!+ta5%_VhmY|F<f7yIxPPskHfy
zojVr>E%n+_^70bgMfr~_7fVl4Ss1c1sN&O;lh)TC@0?uh<FMt;nVH4~<>l>Dr%rXN
ze0yu_)zh1WWMz5%{m=6}-I{&<Shu)-!P8Sy|L|^`aPss<MwhACmzVh_CMPp{dwYY<
zYrDQK_HxvoDYZ7`U*ylr-`!RJ|6g@_&a=wJbKc0vH$3mv1FdW8m$N<8D{a1^;Gxr-
zySv48qqbB$H?t`(>f`K~cJ%o1!zWK_I`5o3SvVyUbP8$lR^5j3Jn#3@rza<8W^&fo
z*Xu-Y<GH;(|2XI-@(ULNOfoJg+%{ETVrx_WMf$w_-Kgz(vX34;y0F;2zkJ>C&u)@8
zZS8FGOCHz%|1JOK?c0aXp0yb!A6roT{M?U+?eh0_@}8eNCt1Bj*W1spZRX6#wxZ(V
z;;*0X<Y=G%WW9aq_tU30zPz?pdROV|W&dtpzb+2iVhp-0r1+VSPV_dN|IEvB9`9Uy
zzvABuKOY~SHeTsAHeRU({`2kL$4r0T`|jzJA0PHy?479Wz9@Qo-j3?;dS?0edbVsa
z*;DoP)!f@3?wqu|){rr?wbgZ*{GI9g@vEjkPmA`PbZ@8b-uSpa-!v(cj0UUHS1HvU
zsh8yDS+})6_y4!M{=XgQBq<T?FrH}eO+W8r=0ESvd-~-^O51baf5qkH?c2ATSABoi
zJJ-5gNmG+^-@bkC@3cMlJ7>sv(MVlg{mAj-=^FLHVVhDs*XDS?U*7wDJ18mq`B6A8
zdGVfyJCkQr{ChHgx&Qn{zO&6h4ft<wZ?n6*AOD_r__JGiQE|<tL!aGt?<#xS6};SU
zZ}B=YP=aQ0ISpEB1?u?T+?+1;RNZe*gNkRd$0j!h2A&#E7srqlaeIGRZ}08uIu%g&
z=m=*-RMerncWv)K*7f;Rl(VVm@iE>Tw{JfW@0j9kQ_drNe*T}2udZr?&ZoJxH5;_S
zM=Na2jJ0no?!9o!>-P_<xR-H#_Osy5&d$hnhl|yp&uGYqyuL0Lbd;^AcG!y3pc%*m
z2OL1fFX+&{2GC)FpPruf@b<oZ<J-T#zd5+Lf~MQn{1AxVp4Ym5y}pl+Psw*Dt_6E!
zX1DXpr`@<e&$b$L!qT?f+gbJ%LC{zZtE#g4^XCt!9Su5o-gedf`j2}8-QxQA&r83p
z`tZQ9ZPS%2A!{Nxvx$j`z4BD5eD{Rkb6RTTO9if7@9yqS{{8K(gke%k)wZ{nm-~Yn
z4jC5~FfuHN-(MH8yG%Fl?ylBLOTC!|;zM|)%|Mr48dQDB0BtwAxheI>@Avybhw_H3
z2yi?(JyhYzw;MZl?_TWID+M|U4Ai$fal!+1Iv;2xprhl#-4!*T9{1aW#`Hh~&kha@
zpq;x?rdbRO`+mLBj);!_d?zp<ub=<C{98%GB$jD9kxcRN@x6Sf^Fb%@F#MRkc*-6b
z`FZBIuI?;Ox2gRl(kpG=wt2HLsEoc}_nQ}VP;e6~H|W%32AlFvy-vakVp{9t_D(w9
zC#yYOCo*W;h7A*5Us<XA{{H^><hgZ!f1Px5b1TZpS>yI|vbsNL81>Y*Z{IdeojUc+
z+qb&zuC6bQ{xIooKc04JiRY%}%a_Nji7>o*=8R8SY3bB<UTHOT6_ppiR7En@ty{Or
zYw4*kAD>lxxD)6H@j~XUEt%7fyDSY_dHr$a;vR_0w`N{e+Znn#Y}4e)lXJ4Nv~J(J
zb!u(Y)=ACH&8F8L@0@HO!3<lMVTY)~LG6(a59%(yxLDoV+UmG8=;4Wp%3kf0^q2X~
z<(k@M-{|7v^<cZc;KaVQk(=4Hrg8-a2EN*~xBB~|+xh!L4YVFEpI>)qmT9(8teWSf
zzQux|T6b0G>SwR*R6T><d=(HBEdwnU5&3Yx`@U`Uw}e|;GPztouTMQaEp=7{Xd>+7
z$&(p(c5FPCoV3WnC1e9=LCPiOO*+}B`}O1Zo!MSHbJnb9xA%2)toV?`<m9r%09wK_
zEC^b;AZTSmO3IQrz0*dZMYX4<YJ;Z5`}+DMj8Zt}*;Fpt2%78s@#~k8iVDlttgB6}
zt*l{dA{ZG!{peZd`F%pFUI*^nk(sI$>g3?iaO_*^>1jt>xy3;Zi&B|;_wF%lddIve
z^|Tmh=55-vX&bh0KfZi^UDxjN_gQN$uaCEnh>m{z?(S~Ts@^qg*FJpkpusTt*b}Rd
zK`TRkyjs0}L-KJx(D1~wXU{+z=l=b8+|Tp+-_PgsUwvQf)XMehyNdDaYion|%SA^=
zg9iD^-`xq+@9*qvT)I^C*Vot6A6oYG_8R8h*%7l>PHEAhBM~ndIy$&^D>}D<j)}7<
zc)(Ei`|Wlf@u$sfyoYXW&Hi{ZeSYGP4-a=#epUnBKnl9@?cu|RuToqmsT?_SWI_1)
zI0>5y12$f%mM&3kCI%)ZCQuIzR5Knsb_~?VYvYsUik$ZG=kxi@44@Wy;^DS~w{A%Z
ztNXR+oYn|j)MC~5->j93hX-_XX`5B)tAulNEI}i9pzsHs;A~|j1seQ*d+GA!i@Qp*
zL517r=jV?%GP7?;ILNeP=g!0r4-W3Ey5A>f+qGbU0_en(YuBztY)at-mDZqvyoilS
z3=N;3pI==1`Woo)oGqD`L7l^u!OIUFJg9j1ALzjIlatjKyZ6gUSQIG8*;F)u<_SF~
ztG&AZ_~FCGmzS3(zqzpybZ$ybP0fc#N4dYezRoXS`(<LB$zo-tOVvh#f-gf75*C0a
zCr+I3D80EM@o>siuc?W57rXa^rZ7NPfot${a!yqBoMfJJV}V|AY3bBZt*LwJ|NrCp
zUHS7<YTG8W+*>E|^75oor)q~!n{ApsE%EN*c7D)Y4Iel6<kZM%!9hV!gjBswTwN79
zIeu@|(zyKZ@9u)yW>;1QUrqh{>+9rDt*JJ}&wP4otE#F_ZOyuR>O;)`t6MUIcY00L
zl1iPVa`M@;XP?f_HouyhetzCmRnJNFe?GdGMjr2zP4)Kn{&{Y$^{Y)YW=Pz;aU<gK
z-@e}7slC0uPc4g|W!Ri=<rYs778b7j_U7iRO<%s0><nD&1}b6xytt_Rvs-`P1Ycj@
zrz=*h_`Xj}Noi85*+C_xOUcpO^NzC1*EBpkJNxm)#qO`%d!<YhQd3)<+xarr#Ba+1
z-Gr)ixbDoEGgtoC|N5dSXIs^BtXKN+v0iBpFRxRDG0XkuyE!>AfiC*HXuc$NciD#z
z9}c{`yW2SD^Zi+UZoN_ukM&Ah*1a>2+Lpr^9v=SnKIq!z-rnA-`7d9-<ly9V1nmxd
z^JY!l`lO>>pw%$P_JKymPi*SRySpoM&v`yRz7+urn~wd~@f2cWX8!o*rZK4V!N|x6
zS|FWuWyQqT*PUEKW^CXT6PzfwyXvdfJe$f%nUfVgCry|zVL@Qnww#+j--YGm`ZR-=
zfkwY8K0azaW+x;poS2(Cckzy-<YYxpp*wf)8tZ)i@WG*?Vu#zzojWaaa&t{jfkqcV
zlilv;e{Ty~>XoWdT|J*)&c>mr$mq_UJ4S25Hzv8hxw||3@As|Q*C))IH}B=HNh+x?
z8sgW*Sb{c{Cm-u^oT?RSQ26Lb%zi$lMTbDk>n6&9j_~XhQr%GZ_m_`#&h6W`v;X8K
zCQeM8D{Y>)z_nZK$A`oGprb9PXoYeuTeb|;$^d1xFJDU59{qpp_S{!n_ntp<Z~1pm
zfB$x`rHA&_{x+Js;KHd>TFy7+>;Eu%P6F+mD9z;*6x=HhTE=HP;cATd)6>)4Gcq(@
zU0=WcVvm-3+1p#K*Vo4<Utbq1<vY>I*?F_<@%pKn!EBp!9)EgzI^))smB%)np02;x
zXQt84E>ruPw{L^W+3@voXE*4mskuFV|LN&z=V_n|5!0f$KgBaLGIH?pf{w%lU2@JN
zXCrZYTkh%YH#$4GjJ`1{DqUiB?Gj;B_?5orEMz=tX5YFg)26i@IdbItyWhUP2XC9o
zc6N4h@bH{5>HE+3e)dezYK4Ob85tQFJv==Fr@z0uTYS1+Y*T;#`ReZrbG4>&t&QG(
z?AzPhrfrw@mb?^la&j`c_V&(B;}EUTX`3(F*Zqn3`+v51{wnu_pn-RT{ChT_G0WH2
z)?Plxt*E4x0IFn{H2gOc{`Bcnlg??-2rsDgl8}^KSpEH7#+@C8Z2WRPmc`E=oSA71
zx+v1e$EV=wDN)e4srNJ;$LHtf-v1eszwP74j|U$dY@Rq}O3PI3@I@}2LOaUe$F*62
z4s9)d=Cfnx&VvUJGJ<A#1vYQoXt?k9JL{TnH`75DfL5DTe}4xXL2&DlnCSmL<X+A1
zx7%-Q&zF}n%jsC?+^(dm%IZB`&-Ll4sfFL(M1HM$dTQ$79!X<^x<3_n_WZ1Wdsor>
zb)d}ghwoce#dW#4)O@qExy70@9h-d}0}~SuIf#g`FKaxQAi-UB+GT@etzuln2i`ZV
zcW$#uhwqW-i+T6?$l>|S`+q#F=X?JA%ic-bw{N$xwSD^IhsE7IbI(ot_s?%_ZGCxL
z*WKM+ve(Vh((>f7V{TbnZ*4!r^<lk$0>k};e=9CuyB20O*H2DPZdrcgn>TM%JlUEZ
zrFz}k4kzmA=>@F}*|cfXq`P<Tn#?{s)AoN!OIB&A>EeqT)2C0jw6*nJ8|J<H?zu;g
zlKA-e()R7$wd+)c&AGE@ebpv=Ub}vMIiu13@AvM-t-6}^>u=q;^XG%thHc)vcW&K&
z`&#BFi!1G;|G(MYpEfss{q@(Cm6e{`Z~Lyle)`g-pmpolfByH+PFTUSsH|+-)~#Fb
z+`H%Z-$GYkKfSovcx{-rXHj|i^y}BJ+t}Hii&?dQ|9=0yapxaCOq@4={{JkshW}Ul
z<KuU(WoKac!1}I%fq~&DhX4b^0trSY1_lvj2L^_QBo+<^hJYRhMh1p1K?Mc|2SX+n
h1_li`f{K6g-j@9}H$m!i9|HpegQu&X%Q~loCIBwyx48fS
literal 0
HcmV?d00001
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103757Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103757Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..65472fd4
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103757Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
@@ -0,0 +1,209 @@
+ Result for Multiview classification with Mumbo
+
+Average accuracy :
+ -On Train : 73.2544378698
+ -On Test : 46.1904761905
+ -On Validation : 57.0786516854Dataset info :
+ -Dataset name : Fake
+ -Labels : Methyl, MiRNA_, RNASeq, Clinic
+ -Views : View0 of shape (300, 5), View1 of shape (300, 12), View2 of shape (300, 14), View3 of shape (300, 5)
+ -5 folds
+ - Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
+
+
+
+Mumbo configuration :
+ -Used 1 core(s)
+ -Iterations : min 1, max 10, threshold 0.01
+ -Weak Classifiers : DecisionTree with depth 3, sub-sampled at 1.0 on View0
+ -DecisionTree with depth 3, sub-sampled at 1.0 on View1
+ -DecisionTree with depth 3, sub-sampled at 1.0 on View2
+ -DecisionTree with depth 3, sub-sampled at 1.0 on View3
+
+
+
+Computation time on 1 cores :
+ Database extraction time : 0:00:00
+ Learn Prediction
+ Fold 1 0:00:00 0:00:00
+ Fold 2 0:00:01 0:00:00
+ Fold 3 0:00:02 0:00:00
+ Fold 4 0:00:03 0:00:00
+ Fold 5 0:00:04 0:00:00
+ Total 0:00:13 0:00:01
+ So a total classification time of 0:00:04.
+
+
+
+Mean average accuracies and stats for each fold :
+ - Fold 0, used 4
+ - On View0 :
+ - Mean average Accuracy : 0.187573964497
+ - Percentage of time chosen : 0.8
+ - On View1 :
+ - Mean average Accuracy : 0.185207100592
+ - Percentage of time chosen : 0.0
+ - On View2 :
+ - Mean average Accuracy : 0.179289940828
+ - Percentage of time chosen : 0.0
+ - On View3 :
+ - Mean average Accuracy : 0.191124260355
+ - Percentage of time chosen : 0.2
+ - Fold 1, used 4
+ - On View0 :
+ - Mean average Accuracy : 0.185207100592
+ - Percentage of time chosen : 0.8
+ - On View1 :
+ - Mean average Accuracy : 0.172189349112
+ - Percentage of time chosen : 0.0
+ - On View2 :
+ - Mean average Accuracy : 0.187573964497
+ - Percentage of time chosen : 0.2
+ - On View3 :
+ - Mean average Accuracy : 0.178106508876
+ - Percentage of time chosen : 0.0
+ - Fold 2, used 4
+ - On View0 :
+ - Mean average Accuracy : 0.183431952663
+ - Percentage of time chosen : 0.7
+ - On View1 :
+ - Mean average Accuracy : 0.191124260355
+ - Percentage of time chosen : 0.0
+ - On View2 :
+ - Mean average Accuracy : 0.186982248521
+ - Percentage of time chosen : 0.1
+ - On View3 :
+ - Mean average Accuracy : 0.198224852071
+ - Percentage of time chosen : 0.2
+ - Fold 3, used 4
+ - On View0 :
+ - Mean average Accuracy : 0.189349112426
+ - Percentage of time chosen : 0.8
+ - On View1 :
+ - Mean average Accuracy : 0.2
+ - Percentage of time chosen : 0.0
+ - On View2 :
+ - Mean average Accuracy : 0.204733727811
+ - Percentage of time chosen : 0.2
+ - On View3 :
+ - Mean average Accuracy : 0.195266272189
+ - Percentage of time chosen : 0.0
+ - Fold 4, used 4
+ - On View0 :
+ - Mean average Accuracy : 0.174556213018
+ - Percentage of time chosen : 0.8
+ - On View1 :
+ - Mean average Accuracy : 0.189940828402
+ - Percentage of time chosen : 0.2
+ - On View2 :
+ - Mean average Accuracy : 0.181656804734
+ - Percentage of time chosen : 0.0
+ - On View3 :
+ - Mean average Accuracy : 0.185207100592
+ - Percentage of time chosen : 0.0
+
+ For each iteration :
+ - Iteration 1
+ Fold 1
+ Accuracy on train : 56.8047337278
+ Accuracy on test : 0.0
+ Accuracy on validation : 57.3033707865
+ Selected View : View0
+ Fold 2
+ Accuracy on train : 56.8047337278
+ Accuracy on test : 0.0
+ Accuracy on validation : 57.3033707865
+ Selected View : View0
+ Fold 3
+ Accuracy on train : 56.8047337278
+ Accuracy on test : 0.0
+ Accuracy on validation : 57.3033707865
+ Selected View : View2
+ Fold 4
+ Accuracy on train : 56.8047337278
+ Accuracy on test : 0.0
+ Accuracy on validation : 57.3033707865
+ Selected View : View0
+ Fold 5
+ Accuracy on train : 56.8047337278
+ Accuracy on test : 0.0
+ Accuracy on validation : 57.3033707865
+ Selected View : View0
+ - Iteration 2
+ Fold 1
+ Accuracy on train : 73.9644970414
+ Accuracy on test : 0.0
+ Accuracy on validation : 66.2921348315
+ Selected View : View3
+ Fold 2
+ Accuracy on train : 72.1893491124
+ Accuracy on test : 0.0
+ Accuracy on validation : 48.3146067416
+ Selected View : View2
+ Fold 3
+ Accuracy on train : 73.3727810651
+ Accuracy on test : 0.0
+ Accuracy on validation : 58.4269662921
+ Selected View : View3
+ Fold 4
+ Accuracy on train : 73.9644970414
+ Accuracy on test : 0.0
+ Accuracy on validation : 64.0449438202
+ Selected View : View2
+ Fold 5
+ Accuracy on train : 73.3727810651
+ Accuracy on test : 0.0
+ Accuracy on validation : 51.6853932584
+ Selected View : View1
+ - Iteration 3
+ Fold 1
+ Accuracy on train : 73.9644970414
+ Accuracy on test : 0.0
+ Accuracy on validation : 66.2921348315
+ Selected View : View3
+ Fold 2
+ Accuracy on train : 72.1893491124
+ Accuracy on test : 0.0
+ Accuracy on validation : 48.3146067416
+ Selected View : View2
+ Fold 3
+ Accuracy on train : 73.3727810651
+ Accuracy on test : 0.0
+ Accuracy on validation : 58.4269662921
+ Selected View : View3
+ Fold 4
+ Accuracy on train : 73.3727810651
+ Accuracy on test : 0.0
+ Accuracy on validation : 60.6741573034
+ Selected View : View2
+ Fold 5
+ Accuracy on train : 73.3727810651
+ Accuracy on test : 0.0
+ Accuracy on validation : 51.6853932584
+ Selected View : View1
+ - Iteration 4
+ Fold 1
+ Accuracy on train : 56.8047337278
+ Accuracy on test : 0.0
+ Accuracy on validation : 57.3033707865
+ Selected View : View0
+ Fold 2
+ Accuracy on train : 56.8047337278
+ Accuracy on test : 0.0
+ Accuracy on validation : 57.3033707865
+ Selected View : View0
+ Fold 3
+ Accuracy on train : 56.8047337278
+ Accuracy on test : 0.0
+ Accuracy on validation : 57.3033707865
+ Selected View : View0
+ Fold 4
+ Accuracy on train : 56.8047337278
+ Accuracy on test : 0.0
+ Accuracy on validation : 57.3033707865
+ Selected View : View0
+ Fold 5
+ Accuracy on train : 56.8047337278
+ Accuracy on test : 0.0
+ Accuracy on validation : 57.3033707865
+ Selected View : View0
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103929-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-103929-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 00000000..bc6f192d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103929-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,1250 @@
+2016-09-06 10:39:29,831 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-06 10:39:29,831 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.000170875 Gbytes /!\
+2016-09-06 10:39:34,843 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-06 10:39:34,844 INFO: Start: Finding all available mono- & multiview algorithms
+2016-09-06 10:39:34,896 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:34,896 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:39:34,896 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:39:34,897 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:34,897 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:39:34,897 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:39:34,897 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 10:39:34,898 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 10:39:34,898 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:39:34,898 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:39:34,898 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 10:39:34,898 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 10:39:34,899 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:39:34,899 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:39:34,942 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:39:34,943 DEBUG: Start: Training
+2016-09-06 10:39:34,946 DEBUG: Info: Time for Training: 0.0496671199799[s]
+2016-09-06 10:39:34,946 DEBUG: Done: Training
+2016-09-06 10:39:34,946 DEBUG: Start: Predicting
+2016-09-06 10:39:34,948 DEBUG: Done: Predicting
+2016-09-06 10:39:34,949 DEBUG: Start: Getting Results
+2016-09-06 10:39:34,950 DEBUG: Done: Getting Results
+2016-09-06 10:39:34,950 INFO: Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 18)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 11
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:34,950 INFO: Done: Result Analysis
+2016-09-06 10:39:34,952 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:39:34,952 DEBUG: Start: Training
+2016-09-06 10:39:34,957 DEBUG: Info: Time for Training: 0.0618000030518[s]
+2016-09-06 10:39:34,957 DEBUG: Done: Training
+2016-09-06 10:39:34,957 DEBUG: Start: Predicting
+2016-09-06 10:39:34,960 DEBUG: Done: Predicting
+2016-09-06 10:39:34,961 DEBUG: Start: Getting Results
+2016-09-06 10:39:34,962 DEBUG: Done: Getting Results
+2016-09-06 10:39:34,963 INFO: Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 18)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:34,963 INFO: Done: Result Analysis
+2016-09-06 10:39:35,046 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:35,046 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:35,046 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:39:35,046 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:39:35,046 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:39:35,046 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:39:35,047 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 10:39:35,047 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 10:39:35,047 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 10:39:35,047 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 10:39:35,047 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:39:35,047 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:39:35,047 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:39:35,047 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:39:35,081 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:39:35,081 DEBUG: Start: Training
+2016-09-06 10:39:35,081 DEBUG: Info: Time for Training: 0.0358710289001[s]
+2016-09-06 10:39:35,081 DEBUG: Done: Training
+2016-09-06 10:39:35,081 DEBUG: Start: Predicting
+2016-09-06 10:39:35,089 DEBUG: Done: Predicting
+2016-09-06 10:39:35,089 DEBUG: Start: Getting Results
+2016-09-06 10:39:35,091 DEBUG: Done: Getting Results
+2016-09-06 10:39:35,091 INFO: Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.571428571429
+accuracy_score on test : 0.455555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 18)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 43
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.571428571429
+ - Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:35,091 INFO: Done: Result Analysis
+2016-09-06 10:39:35,328 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:39:35,328 DEBUG: Start: Training
+2016-09-06 10:39:35,368 DEBUG: Info: Time for Training: 0.322957992554[s]
+2016-09-06 10:39:35,369 DEBUG: Done: Training
+2016-09-06 10:39:35,369 DEBUG: Start: Predicting
+2016-09-06 10:39:35,374 DEBUG: Done: Predicting
+2016-09-06 10:39:35,374 DEBUG: Start: Getting Results
+2016-09-06 10:39:35,376 DEBUG: Done: Getting Results
+2016-09-06 10:39:35,376 INFO: Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.990476190476
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 18)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 15, max_depth : 11
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.990476190476
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:35,376 INFO: Done: Result Analysis
+2016-09-06 10:39:35,496 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:35,496 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:35,496 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:39:35,496 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:39:35,497 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:39:35,497 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:39:35,498 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 10:39:35,498 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 10:39:35,498 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 10:39:35,498 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 10:39:35,498 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:39:35,498 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:39:35,498 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:39:35,498 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:39:35,546 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:39:35,546 DEBUG: Start: Training
+2016-09-06 10:39:35,547 DEBUG: Info: Time for Training: 0.0519301891327[s]
+2016-09-06 10:39:35,547 DEBUG: Done: Training
+2016-09-06 10:39:35,547 DEBUG: Start: Predicting
+2016-09-06 10:39:35,553 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:39:35,554 DEBUG: Start: Training
+2016-09-06 10:39:35,565 DEBUG: Done: Predicting
+2016-09-06 10:39:35,565 DEBUG: Start: Getting Results
+2016-09-06 10:39:35,567 DEBUG: Done: Getting Results
+2016-09-06 10:39:35,567 INFO: Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.495238095238
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 18)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.495238095238
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:35,567 INFO: Done: Result Analysis
+2016-09-06 10:39:35,575 DEBUG: Info: Time for Training: 0.0798330307007[s]
+2016-09-06 10:39:35,575 DEBUG: Done: Training
+2016-09-06 10:39:35,575 DEBUG: Start: Predicting
+2016-09-06 10:39:35,579 DEBUG: Done: Predicting
+2016-09-06 10:39:35,579 DEBUG: Start: Getting Results
+2016-09-06 10:39:35,581 DEBUG: Done: Getting Results
+2016-09-06 10:39:35,581 INFO: Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.519047619048
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 18)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7957
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.519047619048
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:35,581 INFO: Done: Result Analysis
+2016-09-06 10:39:35,643 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:35,643 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:35,644 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:39:35,644 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:39:35,644 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:39:35,644 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:39:35,645 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 10:39:35,645 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 10:39:35,645 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 10:39:35,646 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:39:35,646 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 10:39:35,646 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:39:35,646 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:39:35,646 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:39:35,698 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:39:35,699 DEBUG: Start: Training
+2016-09-06 10:39:35,701 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:39:35,701 DEBUG: Start: Training
+2016-09-06 10:39:35,716 DEBUG: Info: Time for Training: 0.0735998153687[s]
+2016-09-06 10:39:35,717 DEBUG: Done: Training
+2016-09-06 10:39:35,717 DEBUG: Start: Predicting
+2016-09-06 10:39:35,720 DEBUG: Info: Time for Training: 0.0770709514618[s]
+2016-09-06 10:39:35,720 DEBUG: Done: Training
+2016-09-06 10:39:35,720 DEBUG: Start: Predicting
+2016-09-06 10:39:35,723 DEBUG: Done: Predicting
+2016-09-06 10:39:35,723 DEBUG: Start: Getting Results
+2016-09-06 10:39:35,724 DEBUG: Done: Predicting
+2016-09-06 10:39:35,724 DEBUG: Done: Getting Results
+2016-09-06 10:39:35,724 INFO: Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 18)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7957
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:35,724 DEBUG: Start: Getting Results
+2016-09-06 10:39:35,724 INFO: Done: Result Analysis
+2016-09-06 10:39:35,726 DEBUG: Done: Getting Results
+2016-09-06 10:39:35,726 INFO: Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 18)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7957
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:35,726 INFO: Done: Result Analysis
+2016-09-06 10:39:35,791 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:35,791 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:35,791 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:39:35,791 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:39:35,791 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:39:35,791 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:39:35,792 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:39:35,792 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:39:35,792 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:39:35,792 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:39:35,792 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:39:35,792 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:39:35,792 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:39:35,792 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:39:35,826 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:39:35,827 DEBUG: Start: Training
+2016-09-06 10:39:35,828 DEBUG: Info: Time for Training: 0.0375211238861[s]
+2016-09-06 10:39:35,828 DEBUG: Done: Training
+2016-09-06 10:39:35,828 DEBUG: Start: Predicting
+2016-09-06 10:39:35,831 DEBUG: Done: Predicting
+2016-09-06 10:39:35,831 DEBUG: Start: Getting Results
+2016-09-06 10:39:35,832 DEBUG: Done: Getting Results
+2016-09-06 10:39:35,832 INFO: Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.455555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 11
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:35,833 INFO: Done: Result Analysis
+2016-09-06 10:39:35,840 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:39:35,840 DEBUG: Start: Training
+2016-09-06 10:39:35,843 DEBUG: Info: Time for Training: 0.0527670383453[s]
+2016-09-06 10:39:35,843 DEBUG: Done: Training
+2016-09-06 10:39:35,843 DEBUG: Start: Predicting
+2016-09-06 10:39:35,846 DEBUG: Done: Predicting
+2016-09-06 10:39:35,846 DEBUG: Start: Getting Results
+2016-09-06 10:39:35,848 DEBUG: Done: Getting Results
+2016-09-06 10:39:35,848 INFO: Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:35,848 INFO: Done: Result Analysis
+2016-09-06 10:39:35,945 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:35,945 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:35,945 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:39:35,945 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:39:35,945 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:39:35,945 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:39:35,946 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:39:35,946 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:39:35,947 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:39:35,947 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:39:35,947 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:39:35,947 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:39:35,947 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:39:35,947 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:39:35,998 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:39:35,998 DEBUG: Start: Training
+2016-09-06 10:39:35,999 DEBUG: Info: Time for Training: 0.0549540519714[s]
+2016-09-06 10:39:35,999 DEBUG: Done: Training
+2016-09-06 10:39:35,999 DEBUG: Start: Predicting
+2016-09-06 10:39:36,008 DEBUG: Done: Predicting
+2016-09-06 10:39:36,008 DEBUG: Start: Getting Results
+2016-09-06 10:39:36,011 DEBUG: Done: Getting Results
+2016-09-06 10:39:36,011 INFO: Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.528571428571
+accuracy_score on test : 0.566666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 43
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.528571428571
+ - Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:36,011 INFO: Done: Result Analysis
+2016-09-06 10:39:36,240 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:39:36,240 DEBUG: Start: Training
+2016-09-06 10:39:36,279 DEBUG: Info: Time for Training: 0.334888935089[s]
+2016-09-06 10:39:36,279 DEBUG: Done: Training
+2016-09-06 10:39:36,279 DEBUG: Start: Predicting
+2016-09-06 10:39:36,284 DEBUG: Done: Predicting
+2016-09-06 10:39:36,285 DEBUG: Start: Getting Results
+2016-09-06 10:39:36,286 DEBUG: Done: Getting Results
+2016-09-06 10:39:36,286 INFO: Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.444444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 15, max_depth : 11
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:36,286 INFO: Done: Result Analysis
+2016-09-06 10:39:36,394 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:36,394 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:36,394 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:39:36,394 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:39:36,394 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:39:36,394 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:39:36,395 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:39:36,395 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:39:36,395 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:39:36,395 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:39:36,395 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:39:36,395 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:39:36,395 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:39:36,396 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:39:36,441 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:39:36,441 DEBUG: Start: Training
+2016-09-06 10:39:36,442 DEBUG: Info: Time for Training: 0.0489950180054[s]
+2016-09-06 10:39:36,442 DEBUG: Done: Training
+2016-09-06 10:39:36,442 DEBUG: Start: Predicting
+2016-09-06 10:39:36,445 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:39:36,445 DEBUG: Start: Training
+2016-09-06 10:39:36,463 DEBUG: Info: Time for Training: 0.0693230628967[s]
+2016-09-06 10:39:36,463 DEBUG: Done: Training
+2016-09-06 10:39:36,463 DEBUG: Start: Predicting
+2016-09-06 10:39:36,466 DEBUG: Done: Predicting
+2016-09-06 10:39:36,466 DEBUG: Start: Getting Results
+2016-09-06 10:39:36,467 DEBUG: Done: Predicting
+2016-09-06 10:39:36,467 DEBUG: Start: Getting Results
+2016-09-06 10:39:36,468 DEBUG: Done: Getting Results
+2016-09-06 10:39:36,469 INFO: Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.504761904762
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.504761904762
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:36,469 INFO: Done: Result Analysis
+2016-09-06 10:39:36,469 DEBUG: Done: Getting Results
+2016-09-06 10:39:36,469 INFO: Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.485714285714
+accuracy_score on test : 0.377777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7957
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.485714285714
+ - Score on test : 0.377777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:36,470 INFO: Done: Result Analysis
+2016-09-06 10:39:36,547 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:36,547 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:36,548 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:39:36,548 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:39:36,548 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:39:36,548 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:39:36,550 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:39:36,550 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:39:36,550 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:39:36,550 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:39:36,550 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:39:36,550 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:39:36,550 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:39:36,550 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:39:36,603 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:39:36,603 DEBUG: Start: Training
+2016-09-06 10:39:36,613 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:39:36,614 DEBUG: Start: Training
+2016-09-06 10:39:36,619 DEBUG: Info: Time for Training: 0.0724880695343[s]
+2016-09-06 10:39:36,619 DEBUG: Done: Training
+2016-09-06 10:39:36,619 DEBUG: Start: Predicting
+2016-09-06 10:39:36,624 DEBUG: Done: Predicting
+2016-09-06 10:39:36,624 DEBUG: Start: Getting Results
+2016-09-06 10:39:36,626 DEBUG: Done: Getting Results
+2016-09-06 10:39:36,626 INFO: Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7957
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:36,626 INFO: Done: Result Analysis
+2016-09-06 10:39:36,631 DEBUG: Info: Time for Training: 0.0851111412048[s]
+2016-09-06 10:39:36,631 DEBUG: Done: Training
+2016-09-06 10:39:36,632 DEBUG: Start: Predicting
+2016-09-06 10:39:36,635 DEBUG: Done: Predicting
+2016-09-06 10:39:36,635 DEBUG: Start: Getting Results
+2016-09-06 10:39:36,637 DEBUG: Done: Getting Results
+2016-09-06 10:39:36,637 INFO: Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 0.966666666667
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7957
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.966666666667
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:36,638 INFO: Done: Result Analysis
+2016-09-06 10:39:36,795 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:36,795 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:39:36,795 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:39:36,796 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:36,796 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:39:36,797 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:39:36,797 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:39:36,797 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:39:36,797 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:39:36,797 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:39:36,798 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:39:36,798 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:39:36,798 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:39:36,798 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:39:36,840 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:39:36,840 DEBUG: Start: Training
+2016-09-06 10:39:36,844 DEBUG: Info: Time for Training: 0.0481259822845[s]
+2016-09-06 10:39:36,844 DEBUG: Done: Training
+2016-09-06 10:39:36,844 DEBUG: Start: Predicting
+2016-09-06 10:39:36,846 DEBUG: Done: Predicting
+2016-09-06 10:39:36,847 DEBUG: Start: Getting Results
+2016-09-06 10:39:36,848 DEBUG: Done: Getting Results
+2016-09-06 10:39:36,848 INFO: Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 0.985714285714
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 11
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.985714285714
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:36,848 INFO: Done: Result Analysis
+2016-09-06 10:39:36,851 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:39:36,851 DEBUG: Start: Training
+2016-09-06 10:39:36,856 DEBUG: Info: Time for Training: 0.0621771812439[s]
+2016-09-06 10:39:36,856 DEBUG: Done: Training
+2016-09-06 10:39:36,856 DEBUG: Start: Predicting
+2016-09-06 10:39:36,859 DEBUG: Done: Predicting
+2016-09-06 10:39:36,860 DEBUG: Start: Getting Results
+2016-09-06 10:39:36,862 DEBUG: Done: Getting Results
+2016-09-06 10:39:36,862 INFO: Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:36,862 INFO: Done: Result Analysis
+2016-09-06 10:39:36,939 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:36,939 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:36,940 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:39:36,940 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:39:36,940 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:39:36,940 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:39:36,940 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:39:36,940 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:39:36,940 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:39:36,940 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:39:36,940 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:39:36,940 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:39:36,941 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:39:36,941 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:39:36,974 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:39:36,974 DEBUG: Start: Training
+2016-09-06 10:39:36,974 DEBUG: Info: Time for Training: 0.0355970859528[s]
+2016-09-06 10:39:36,975 DEBUG: Done: Training
+2016-09-06 10:39:36,975 DEBUG: Start: Predicting
+2016-09-06 10:39:36,983 DEBUG: Done: Predicting
+2016-09-06 10:39:36,983 DEBUG: Start: Getting Results
+2016-09-06 10:39:36,984 DEBUG: Done: Getting Results
+2016-09-06 10:39:36,984 INFO: Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.561904761905
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 43
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.561904761905
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:36,984 INFO: Done: Result Analysis
+2016-09-06 10:39:37,218 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:39:37,219 DEBUG: Start: Training
+2016-09-06 10:39:37,260 DEBUG: Info: Time for Training: 0.320749998093[s]
+2016-09-06 10:39:37,260 DEBUG: Done: Training
+2016-09-06 10:39:37,260 DEBUG: Start: Predicting
+2016-09-06 10:39:37,265 DEBUG: Done: Predicting
+2016-09-06 10:39:37,265 DEBUG: Start: Getting Results
+2016-09-06 10:39:37,267 DEBUG: Done: Getting Results
+2016-09-06 10:39:37,267 INFO: Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 15, max_depth : 11
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.995238095238
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:37,267 INFO: Done: Result Analysis
+2016-09-06 10:39:37,387 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:37,387 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:37,387 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:39:37,387 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:39:37,387 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:39:37,387 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:39:37,388 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:39:37,388 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:39:37,388 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:39:37,388 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:39:37,388 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:39:37,388 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:39:37,388 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:39:37,388 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:39:37,439 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:39:37,439 DEBUG: Start: Training
+2016-09-06 10:39:37,440 DEBUG: Info: Time for Training: 0.0537350177765[s]
+2016-09-06 10:39:37,440 DEBUG: Done: Training
+2016-09-06 10:39:37,440 DEBUG: Start: Predicting
+2016-09-06 10:39:37,440 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:39:37,440 DEBUG: Start: Training
+2016-09-06 10:39:37,460 DEBUG: Done: Predicting
+2016-09-06 10:39:37,461 DEBUG: Start: Getting Results
+2016-09-06 10:39:37,464 DEBUG: Done: Getting Results
+2016-09-06 10:39:37,464 INFO: Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.495238095238
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.495238095238
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:37,465 INFO: Done: Result Analysis
+2016-09-06 10:39:37,469 DEBUG: Info: Time for Training: 0.082640171051[s]
+2016-09-06 10:39:37,469 DEBUG: Done: Training
+2016-09-06 10:39:37,469 DEBUG: Start: Predicting
+2016-09-06 10:39:37,473 DEBUG: Done: Predicting
+2016-09-06 10:39:37,473 DEBUG: Start: Getting Results
+2016-09-06 10:39:37,474 DEBUG: Done: Getting Results
+2016-09-06 10:39:37,474 INFO: Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.519047619048
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7957
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.519047619048
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:37,475 INFO: Done: Result Analysis
+2016-09-06 10:39:37,532 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:37,532 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:37,532 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:39:37,532 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:39:37,532 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:39:37,532 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:39:37,533 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:39:37,533 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:39:37,533 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:39:37,533 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:39:37,533 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:39:37,533 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:39:37,533 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:39:37,533 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:39:37,582 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:39:37,582 DEBUG: Start: Training
+2016-09-06 10:39:37,587 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:39:37,588 DEBUG: Start: Training
+2016-09-06 10:39:37,601 DEBUG: Info: Time for Training: 0.0697820186615[s]
+2016-09-06 10:39:37,601 DEBUG: Done: Training
+2016-09-06 10:39:37,601 DEBUG: Start: Predicting
+2016-09-06 10:39:37,607 DEBUG: Info: Time for Training: 0.0757231712341[s]
+2016-09-06 10:39:37,607 DEBUG: Done: Training
+2016-09-06 10:39:37,607 DEBUG: Start: Predicting
+2016-09-06 10:39:37,607 DEBUG: Done: Predicting
+2016-09-06 10:39:37,607 DEBUG: Start: Getting Results
+2016-09-06 10:39:37,609 DEBUG: Done: Getting Results
+2016-09-06 10:39:37,609 INFO: Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7957
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:37,609 INFO: Done: Result Analysis
+2016-09-06 10:39:37,611 DEBUG: Done: Predicting
+2016-09-06 10:39:37,611 DEBUG: Start: Getting Results
+2016-09-06 10:39:37,613 DEBUG: Done: Getting Results
+2016-09-06 10:39:37,613 INFO: Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.411111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7957
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:37,613 INFO: Done: Result Analysis
+2016-09-06 10:39:37,681 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:37,681 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:37,681 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:39:37,681 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:39:37,682 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:39:37,682 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:39:37,682 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:39:37,682 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:39:37,683 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:39:37,683 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:39:37,683 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:39:37,683 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:39:37,683 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:39:37,683 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:39:37,723 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:39:37,723 DEBUG: Start: Training
+2016-09-06 10:39:37,725 DEBUG: Info: Time for Training: 0.0450918674469[s]
+2016-09-06 10:39:37,726 DEBUG: Done: Training
+2016-09-06 10:39:37,726 DEBUG: Start: Predicting
+2016-09-06 10:39:37,728 DEBUG: Done: Predicting
+2016-09-06 10:39:37,728 DEBUG: Start: Getting Results
+2016-09-06 10:39:37,730 DEBUG: Done: Getting Results
+2016-09-06 10:39:37,730 INFO: Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 11
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:37,730 INFO: Done: Result Analysis
+2016-09-06 10:39:37,739 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:39:37,739 DEBUG: Start: Training
+2016-09-06 10:39:37,743 DEBUG: Info: Time for Training: 0.0631110668182[s]
+2016-09-06 10:39:37,744 DEBUG: Done: Training
+2016-09-06 10:39:37,744 DEBUG: Start: Predicting
+2016-09-06 10:39:37,746 DEBUG: Done: Predicting
+2016-09-06 10:39:37,747 DEBUG: Start: Getting Results
+2016-09-06 10:39:37,748 DEBUG: Done: Getting Results
+2016-09-06 10:39:37,749 INFO: Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:37,749 INFO: Done: Result Analysis
+2016-09-06 10:39:37,835 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:37,835 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:37,836 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:39:37,836 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:39:37,836 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:39:37,836 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:39:37,836 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:39:37,837 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:39:37,837 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:39:37,837 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:39:37,837 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:39:37,837 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:39:37,837 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:39:37,837 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:39:37,875 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:39:37,875 DEBUG: Start: Training
+2016-09-06 10:39:37,876 DEBUG: Info: Time for Training: 0.0416429042816[s]
+2016-09-06 10:39:37,876 DEBUG: Done: Training
+2016-09-06 10:39:37,877 DEBUG: Start: Predicting
+2016-09-06 10:39:37,885 DEBUG: Done: Predicting
+2016-09-06 10:39:37,886 DEBUG: Start: Getting Results
+2016-09-06 10:39:37,887 DEBUG: Done: Getting Results
+2016-09-06 10:39:37,887 INFO: Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.52380952381
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 43
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.52380952381
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:37,887 INFO: Done: Result Analysis
+2016-09-06 10:39:38,178 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:39:38,178 DEBUG: Start: Training
+2016-09-06 10:39:38,218 DEBUG: Info: Time for Training: 0.383457183838[s]
+2016-09-06 10:39:38,218 DEBUG: Done: Training
+2016-09-06 10:39:38,218 DEBUG: Start: Predicting
+2016-09-06 10:39:38,224 DEBUG: Done: Predicting
+2016-09-06 10:39:38,224 DEBUG: Start: Getting Results
+2016-09-06 10:39:38,225 DEBUG: Done: Getting Results
+2016-09-06 10:39:38,226 INFO: Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.433333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 15, max_depth : 11
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:38,226 INFO: Done: Result Analysis
+2016-09-06 10:39:38,289 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:38,289 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:38,289 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:39:38,289 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:39:38,289 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:39:38,289 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:39:38,290 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:39:38,290 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:39:38,291 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:39:38,291 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:39:38,291 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:39:38,291 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:39:38,291 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:39:38,291 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:39:38,363 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:39:38,363 DEBUG: Start: Training
+2016-09-06 10:39:38,365 DEBUG: Info: Time for Training: 0.076936006546[s]
+2016-09-06 10:39:38,365 DEBUG: Done: Training
+2016-09-06 10:39:38,365 DEBUG: Start: Predicting
+2016-09-06 10:39:38,371 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:39:38,371 DEBUG: Start: Training
+2016-09-06 10:39:38,377 DEBUG: Done: Predicting
+2016-09-06 10:39:38,378 DEBUG: Start: Getting Results
+2016-09-06 10:39:38,380 DEBUG: Done: Getting Results
+2016-09-06 10:39:38,380 INFO: Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.495238095238
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.495238095238
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:38,380 INFO: Done: Result Analysis
+2016-09-06 10:39:38,396 DEBUG: Info: Time for Training: 0.108675003052[s]
+2016-09-06 10:39:38,397 DEBUG: Done: Training
+2016-09-06 10:39:38,397 DEBUG: Start: Predicting
+2016-09-06 10:39:38,400 DEBUG: Done: Predicting
+2016-09-06 10:39:38,401 DEBUG: Start: Getting Results
+2016-09-06 10:39:38,402 DEBUG: Done: Getting Results
+2016-09-06 10:39:38,402 INFO: Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.409523809524
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7957
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.409523809524
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:38,402 INFO: Done: Result Analysis
+2016-09-06 10:39:38,680 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:39:38,680 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-09-06 10:39:38,681 INFO: Info: Shape of View0 :(300, 18)
+2016-09-06 10:39:38,681 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:39:38,682 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-06 10:39:38,682 INFO: Info: Shape of View1 :(300, 8)
+2016-09-06 10:39:38,683 INFO: Info: Shape of View0 :(300, 18)
+2016-09-06 10:39:38,683 INFO: Info: Shape of View2 :(300, 19)
+2016-09-06 10:39:38,684 INFO: Info: Shape of View1 :(300, 8)
+2016-09-06 10:39:38,684 INFO: Info: Shape of View3 :(300, 20)
+2016-09-06 10:39:38,684 INFO: Done: Read Database Files
+2016-09-06 10:39:38,684 INFO: Start: Determine validation split for ratio 0.7
+2016-09-06 10:39:38,684 INFO: Info: Shape of View2 :(300, 19)
+2016-09-06 10:39:38,685 INFO: Info: Shape of View3 :(300, 20)
+2016-09-06 10:39:38,685 INFO: Done: Read Database Files
+2016-09-06 10:39:38,685 INFO: Start: Determine validation split for ratio 0.7
+2016-09-06 10:39:38,691 INFO: Done: Determine validation split
+2016-09-06 10:39:38,691 INFO: Start: Determine 5 folds
+2016-09-06 10:39:38,691 INFO: Done: Determine validation split
+2016-09-06 10:39:38,692 INFO: Start: Determine 5 folds
+2016-09-06 10:39:38,707 INFO: Info: Length of Learning Sets: 169
+2016-09-06 10:39:38,707 INFO: Info: Length of Testing Sets: 42
+2016-09-06 10:39:38,707 INFO: Info: Length of Validation Set: 89
+2016-09-06 10:39:38,707 INFO: Info: Length of Learning Sets: 169
+2016-09-06 10:39:38,707 INFO: Done: Determine folds
+2016-09-06 10:39:38,707 INFO: Info: Length of Testing Sets: 42
+2016-09-06 10:39:38,707 INFO: Start: Learning with Fusion and 5 folds
+2016-09-06 10:39:38,707 INFO: Info: Length of Validation Set: 89
+2016-09-06 10:39:38,708 INFO: Done: Determine folds
+2016-09-06 10:39:38,707 INFO: Start: Randomsearching best settings for monoview classifiers
+2016-09-06 10:39:38,708 INFO: Start: Learning with Mumbo and 5 folds
+2016-09-06 10:39:38,708 DEBUG: Start: Random search for Adaboost with 30 iterations
+2016-09-06 10:39:38,708 INFO: Start: Randomsearching best settings for monoview classifiers
+2016-09-06 10:39:38,708 DEBUG: Start: Gridsearch for DecisionTree on View0
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103934Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103934Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..7c2f9b7f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103934Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 18)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103934Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103934Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..225bfa22
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103934Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 18)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 11
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..ce956518
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..e4d36bfb
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.455555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 11
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..9d6ed1f4
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.571428571429
+accuracy_score on test : 0.455555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 18)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 43
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.571428571429
+ - Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..5ce22fa3
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.990476190476
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 18)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 15, max_depth : 11
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.990476190476
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..00f4dc53
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.495238095238
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 18)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.495238095238
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..cdf61b88
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.519047619048
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 18)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7957
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.519047619048
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..5a41a5a4
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 18)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7957
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..6ccf3592
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 18)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7957
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..13e69855
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..50dc139e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 0.985714285714
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 11
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.985714285714
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..3df2f276
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.561904761905
+accuracy_score on test : 0.555555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 43
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.561904761905
+ - Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..c50be0fa
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.444444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 15, max_depth : 11
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..9737bc60
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.504761904762
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.504761904762
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..37755036
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.485714285714
+accuracy_score on test : 0.377777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7957
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.485714285714
+ - Score on test : 0.377777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..c60418d9
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 0.966666666667
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7957
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.966666666667
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..61f4324f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 8)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7957
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..291654d0
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..d4f31147
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 11
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..2f3557c2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.52380952381
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 43
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.52380952381
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..38a3f4d6
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 15, max_depth : 11
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.995238095238
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..8c91b519
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.495238095238
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.495238095238
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..006bed3d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.519047619048
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7957
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.519047619048
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..9d41e795
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.411111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7957
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..482a6b43
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 19)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7957
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..9df5e00e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.433333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 15, max_depth : 11
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..69d5a4ea
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.495238095238
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.495238095238
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..241a8b39
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.409523809524
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 20)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7957
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.409523809524
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104015-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-104015-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 00000000..12848a0f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104015-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,1250 @@
+2016-09-06 10:40:15,413 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-06 10:40:15,414 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.000114625 Gbytes /!\
+2016-09-06 10:40:20,424 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-06 10:40:20,425 INFO: Start: Finding all available mono- & multiview algorithms
+2016-09-06 10:40:20,483 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:20,483 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:20,484 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:40:20,484 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:40:20,484 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:40:20,484 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:40:20,485 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:40:20,485 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:40:20,485 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:40:20,485 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:40:20,485 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:40:20,485 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:40:20,485 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:40:20,486 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:40:20,546 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:40:20,546 DEBUG: Start: Training
+2016-09-06 10:40:20,549 DEBUG: Info: Time for Training: 0.0666189193726[s]
+2016-09-06 10:40:20,549 DEBUG: Done: Training
+2016-09-06 10:40:20,549 DEBUG: Start: Predicting
+2016-09-06 10:40:20,553 DEBUG: Done: Predicting
+2016-09-06 10:40:20,553 DEBUG: Start: Getting Results
+2016-09-06 10:40:20,556 DEBUG: Done: Getting Results
+2016-09-06 10:40:20,556 INFO: Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 0.933333333333
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.933333333333
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:20,556 INFO: Done: Result Analysis
+2016-09-06 10:40:20,558 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:40:20,558 DEBUG: Start: Training
+2016-09-06 10:40:20,565 DEBUG: Info: Time for Training: 0.0827748775482[s]
+2016-09-06 10:40:20,565 DEBUG: Done: Training
+2016-09-06 10:40:20,566 DEBUG: Start: Predicting
+2016-09-06 10:40:20,570 DEBUG: Done: Predicting
+2016-09-06 10:40:20,570 DEBUG: Start: Getting Results
+2016-09-06 10:40:20,573 DEBUG: Done: Getting Results
+2016-09-06 10:40:20,573 INFO: Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:20,574 INFO: Done: Result Analysis
+2016-09-06 10:40:20,633 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:20,633 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:20,633 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:40:20,633 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:40:20,633 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:40:20,633 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:40:20,634 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:40:20,634 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:40:20,634 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:40:20,634 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:40:20,634 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:40:20,634 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:40:20,634 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:40:20,634 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:40:20,688 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:40:20,688 DEBUG: Start: Training
+2016-09-06 10:40:20,689 DEBUG: Info: Time for Training: 0.0570049285889[s]
+2016-09-06 10:40:20,689 DEBUG: Done: Training
+2016-09-06 10:40:20,689 DEBUG: Start: Predicting
+2016-09-06 10:40:20,698 DEBUG: Done: Predicting
+2016-09-06 10:40:20,698 DEBUG: Start: Getting Results
+2016-09-06 10:40:20,700 DEBUG: Done: Getting Results
+2016-09-06 10:40:20,701 INFO: Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.585714285714
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.585714285714
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:20,701 INFO: Done: Result Analysis
+2016-09-06 10:40:21,198 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:40:21,198 DEBUG: Start: Training
+2016-09-06 10:40:21,275 DEBUG: Info: Time for Training: 0.643486976624[s]
+2016-09-06 10:40:21,276 DEBUG: Done: Training
+2016-09-06 10:40:21,276 DEBUG: Start: Predicting
+2016-09-06 10:40:21,285 DEBUG: Done: Predicting
+2016-09-06 10:40:21,286 DEBUG: Start: Getting Results
+2016-09-06 10:40:21,287 DEBUG: Done: Getting Results
+2016-09-06 10:40:21,287 INFO: Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.990476190476
+accuracy_score on test : 0.455555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 29, max_depth : 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.990476190476
+ - Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:21,287 INFO: Done: Result Analysis
+2016-09-06 10:40:21,386 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:21,386 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:40:21,386 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:40:21,387 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:21,387 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:40:21,387 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:40:21,387 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:40:21,388 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:40:21,388 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:40:21,388 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:40:21,389 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:40:21,389 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:40:21,389 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:40:21,389 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:40:21,433 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:40:21,433 DEBUG: Start: Training
+2016-09-06 10:40:21,434 DEBUG: Info: Time for Training: 0.0487298965454[s]
+2016-09-06 10:40:21,434 DEBUG: Done: Training
+2016-09-06 10:40:21,434 DEBUG: Start: Predicting
+2016-09-06 10:40:21,446 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:40:21,446 DEBUG: Start: Training
+2016-09-06 10:40:21,453 DEBUG: Done: Predicting
+2016-09-06 10:40:21,453 DEBUG: Start: Getting Results
+2016-09-06 10:40:21,455 DEBUG: Done: Getting Results
+2016-09-06 10:40:21,455 INFO: Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.609523809524
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.609523809524
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:21,455 INFO: Done: Result Analysis
+2016-09-06 10:40:21,468 DEBUG: Info: Time for Training: 0.0820069313049[s]
+2016-09-06 10:40:21,468 DEBUG: Done: Training
+2016-09-06 10:40:21,468 DEBUG: Start: Predicting
+2016-09-06 10:40:21,472 DEBUG: Done: Predicting
+2016-09-06 10:40:21,472 DEBUG: Start: Getting Results
+2016-09-06 10:40:21,473 DEBUG: Done: Getting Results
+2016-09-06 10:40:21,473 INFO: Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.538095238095
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 1181
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.538095238095
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:21,473 INFO: Done: Result Analysis
+2016-09-06 10:40:21,527 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:21,527 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:21,527 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:40:21,527 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:40:21,527 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:40:21,527 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:40:21,528 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:40:21,528 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:40:21,528 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:40:21,528 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:40:21,528 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:40:21,528 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:40:21,529 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:40:21,529 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:40:21,580 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:40:21,580 DEBUG: Start: Training
+2016-09-06 10:40:21,590 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:40:21,590 DEBUG: Start: Training
+2016-09-06 10:40:21,597 DEBUG: Info: Time for Training: 0.0706040859222[s]
+2016-09-06 10:40:21,597 DEBUG: Done: Training
+2016-09-06 10:40:21,597 DEBUG: Start: Predicting
+2016-09-06 10:40:21,603 DEBUG: Done: Predicting
+2016-09-06 10:40:21,603 DEBUG: Start: Getting Results
+2016-09-06 10:40:21,605 DEBUG: Done: Getting Results
+2016-09-06 10:40:21,605 INFO: Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 1181
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:21,605 INFO: Done: Result Analysis
+2016-09-06 10:40:21,610 DEBUG: Info: Time for Training: 0.0829930305481[s]
+2016-09-06 10:40:21,610 DEBUG: Done: Training
+2016-09-06 10:40:21,610 DEBUG: Start: Predicting
+2016-09-06 10:40:21,614 DEBUG: Done: Predicting
+2016-09-06 10:40:21,614 DEBUG: Start: Getting Results
+2016-09-06 10:40:21,615 DEBUG: Done: Getting Results
+2016-09-06 10:40:21,615 INFO: Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 1181
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:21,615 INFO: Done: Result Analysis
+2016-09-06 10:40:21,678 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:21,678 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:40:21,678 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:40:21,678 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:21,679 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:40:21,679 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:40:21,679 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:40:21,679 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:40:21,679 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:40:21,679 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:40:21,679 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:40:21,679 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:40:21,679 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:40:21,679 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:40:21,715 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:40:21,715 DEBUG: Start: Training
+2016-09-06 10:40:21,717 DEBUG: Info: Time for Training: 0.0389919281006[s]
+2016-09-06 10:40:21,717 DEBUG: Done: Training
+2016-09-06 10:40:21,717 DEBUG: Start: Predicting
+2016-09-06 10:40:21,720 DEBUG: Done: Predicting
+2016-09-06 10:40:21,720 DEBUG: Start: Getting Results
+2016-09-06 10:40:21,721 DEBUG: Done: Getting Results
+2016-09-06 10:40:21,721 INFO: Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 0.814285714286
+accuracy_score on test : 0.544444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.814285714286
+ - Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:21,721 INFO: Done: Result Analysis
+2016-09-06 10:40:21,730 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:40:21,730 DEBUG: Start: Training
+2016-09-06 10:40:21,734 DEBUG: Info: Time for Training: 0.056380033493[s]
+2016-09-06 10:40:21,734 DEBUG: Done: Training
+2016-09-06 10:40:21,734 DEBUG: Start: Predicting
+2016-09-06 10:40:21,737 DEBUG: Done: Predicting
+2016-09-06 10:40:21,737 DEBUG: Start: Getting Results
+2016-09-06 10:40:21,740 DEBUG: Done: Getting Results
+2016-09-06 10:40:21,740 INFO: Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.433333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:21,741 INFO: Done: Result Analysis
+2016-09-06 10:40:21,832 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:21,832 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:21,833 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:40:21,833 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:40:21,833 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:40:21,833 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:40:21,834 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:40:21,834 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:40:21,834 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:40:21,834 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:40:21,835 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:40:21,835 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:40:21,835 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:40:21,835 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:40:21,881 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:40:21,882 DEBUG: Start: Training
+2016-09-06 10:40:21,883 DEBUG: Info: Time for Training: 0.0516860485077[s]
+2016-09-06 10:40:21,883 DEBUG: Done: Training
+2016-09-06 10:40:21,883 DEBUG: Start: Predicting
+2016-09-06 10:40:21,889 DEBUG: Done: Predicting
+2016-09-06 10:40:21,890 DEBUG: Start: Getting Results
+2016-09-06 10:40:21,892 DEBUG: Done: Getting Results
+2016-09-06 10:40:21,892 INFO: Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.652380952381
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.652380952381
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:21,892 INFO: Done: Result Analysis
+2016-09-06 10:40:22,343 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:40:22,343 DEBUG: Start: Training
+2016-09-06 10:40:22,417 DEBUG: Info: Time for Training: 0.585797071457[s]
+2016-09-06 10:40:22,417 DEBUG: Done: Training
+2016-09-06 10:40:22,417 DEBUG: Start: Predicting
+2016-09-06 10:40:22,425 DEBUG: Done: Predicting
+2016-09-06 10:40:22,425 DEBUG: Start: Getting Results
+2016-09-06 10:40:22,427 DEBUG: Done: Getting Results
+2016-09-06 10:40:22,427 INFO: Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 29, max_depth : 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.995238095238
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:22,427 INFO: Done: Result Analysis
+2016-09-06 10:40:22,574 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:22,574 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:22,574 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:40:22,574 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:40:22,574 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:40:22,574 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:40:22,575 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:40:22,575 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:40:22,575 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:40:22,575 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:40:22,575 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:40:22,575 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:40:22,575 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:40:22,575 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:40:22,619 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:40:22,619 DEBUG: Start: Training
+2016-09-06 10:40:22,620 DEBUG: Info: Time for Training: 0.0461812019348[s]
+2016-09-06 10:40:22,620 DEBUG: Done: Training
+2016-09-06 10:40:22,620 DEBUG: Start: Predicting
+2016-09-06 10:40:22,624 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:40:22,624 DEBUG: Start: Training
+2016-09-06 10:40:22,642 DEBUG: Done: Predicting
+2016-09-06 10:40:22,642 DEBUG: Start: Getting Results
+2016-09-06 10:40:22,643 DEBUG: Info: Time for Training: 0.0691771507263[s]
+2016-09-06 10:40:22,643 DEBUG: Done: Training
+2016-09-06 10:40:22,643 DEBUG: Start: Predicting
+2016-09-06 10:40:22,644 DEBUG: Done: Getting Results
+2016-09-06 10:40:22,644 INFO: Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.547619047619
+accuracy_score on test : 0.455555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.547619047619
+ - Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:22,644 INFO: Done: Result Analysis
+2016-09-06 10:40:22,646 DEBUG: Done: Predicting
+2016-09-06 10:40:22,647 DEBUG: Start: Getting Results
+2016-09-06 10:40:22,648 DEBUG: Done: Getting Results
+2016-09-06 10:40:22,648 INFO: Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.52380952381
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 1181
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.52380952381
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:22,648 INFO: Done: Result Analysis
+2016-09-06 10:40:22,725 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:22,725 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:22,726 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:40:22,726 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:40:22,726 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:40:22,726 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:40:22,727 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:40:22,727 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:40:22,727 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:40:22,727 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:40:22,727 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:40:22,727 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:40:22,727 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:40:22,727 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:40:22,798 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:40:22,798 DEBUG: Start: Training
+2016-09-06 10:40:22,807 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:40:22,807 DEBUG: Start: Training
+2016-09-06 10:40:22,822 DEBUG: Info: Time for Training: 0.0971689224243[s]
+2016-09-06 10:40:22,822 DEBUG: Done: Training
+2016-09-06 10:40:22,822 DEBUG: Start: Predicting
+2016-09-06 10:40:22,829 DEBUG: Done: Predicting
+2016-09-06 10:40:22,829 DEBUG: Start: Getting Results
+2016-09-06 10:40:22,831 DEBUG: Done: Getting Results
+2016-09-06 10:40:22,831 INFO: Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.622222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 1181
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.622222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:22,832 INFO: Done: Result Analysis
+2016-09-06 10:40:22,833 DEBUG: Info: Time for Training: 0.108348846436[s]
+2016-09-06 10:40:22,833 DEBUG: Done: Training
+2016-09-06 10:40:22,833 DEBUG: Start: Predicting
+2016-09-06 10:40:22,836 DEBUG: Done: Predicting
+2016-09-06 10:40:22,836 DEBUG: Start: Getting Results
+2016-09-06 10:40:22,837 DEBUG: Done: Getting Results
+2016-09-06 10:40:22,838 INFO: Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 0.6
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 1181
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.6
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:22,838 INFO: Done: Result Analysis
+2016-09-06 10:40:22,968 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:22,968 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:22,968 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:40:22,968 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:40:22,968 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:40:22,968 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:40:22,969 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:40:22,969 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:40:22,969 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:40:22,969 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:40:22,969 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:40:22,969 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:40:22,969 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:40:22,969 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:40:23,006 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:40:23,006 DEBUG: Start: Training
+2016-09-06 10:40:23,008 DEBUG: Info: Time for Training: 0.0404770374298[s]
+2016-09-06 10:40:23,008 DEBUG: Done: Training
+2016-09-06 10:40:23,008 DEBUG: Start: Predicting
+2016-09-06 10:40:23,010 DEBUG: Done: Predicting
+2016-09-06 10:40:23,011 DEBUG: Start: Getting Results
+2016-09-06 10:40:23,012 DEBUG: Done: Getting Results
+2016-09-06 10:40:23,012 INFO: Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 0.985714285714
+accuracy_score on test : 0.411111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.985714285714
+ - Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:23,012 INFO: Done: Result Analysis
+2016-09-06 10:40:23,021 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:40:23,021 DEBUG: Start: Training
+2016-09-06 10:40:23,025 DEBUG: Info: Time for Training: 0.0578100681305[s]
+2016-09-06 10:40:23,025 DEBUG: Done: Training
+2016-09-06 10:40:23,025 DEBUG: Start: Predicting
+2016-09-06 10:40:23,028 DEBUG: Done: Predicting
+2016-09-06 10:40:23,028 DEBUG: Start: Getting Results
+2016-09-06 10:40:23,030 DEBUG: Done: Getting Results
+2016-09-06 10:40:23,030 INFO: Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:23,031 INFO: Done: Result Analysis
+2016-09-06 10:40:23,122 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:23,122 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:23,122 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:40:23,122 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:40:23,123 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:40:23,123 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:40:23,124 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:40:23,124 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:40:23,124 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:40:23,124 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:40:23,124 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:40:23,124 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:40:23,124 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:40:23,124 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:40:23,174 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:40:23,175 DEBUG: Start: Training
+2016-09-06 10:40:23,175 DEBUG: Info: Time for Training: 0.0543978214264[s]
+2016-09-06 10:40:23,176 DEBUG: Done: Training
+2016-09-06 10:40:23,176 DEBUG: Start: Predicting
+2016-09-06 10:40:23,184 DEBUG: Done: Predicting
+2016-09-06 10:40:23,184 DEBUG: Start: Getting Results
+2016-09-06 10:40:23,186 DEBUG: Done: Getting Results
+2016-09-06 10:40:23,186 INFO: Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.619047619048
+accuracy_score on test : 0.433333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.619047619048
+ - Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:23,187 INFO: Done: Result Analysis
+2016-09-06 10:40:23,643 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:40:23,643 DEBUG: Start: Training
+2016-09-06 10:40:23,719 DEBUG: Info: Time for Training: 0.597998142242[s]
+2016-09-06 10:40:23,719 DEBUG: Done: Training
+2016-09-06 10:40:23,719 DEBUG: Start: Predicting
+2016-09-06 10:40:23,727 DEBUG: Done: Predicting
+2016-09-06 10:40:23,727 DEBUG: Start: Getting Results
+2016-09-06 10:40:23,729 DEBUG: Done: Getting Results
+2016-09-06 10:40:23,729 INFO: Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.411111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 29, max_depth : 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.995238095238
+ - Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:23,729 INFO: Done: Result Analysis
+2016-09-06 10:40:23,867 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:23,868 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:40:23,868 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:40:23,869 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:40:23,869 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:40:23,869 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:40:23,869 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:40:23,873 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:23,873 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:40:23,874 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:40:23,875 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:40:23,875 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:40:23,875 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:40:23,875 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:40:23,915 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:40:23,915 DEBUG: Start: Training
+2016-09-06 10:40:23,916 DEBUG: Info: Time for Training: 0.0492420196533[s]
+2016-09-06 10:40:23,916 DEBUG: Done: Training
+2016-09-06 10:40:23,916 DEBUG: Start: Predicting
+2016-09-06 10:40:23,927 DEBUG: Done: Predicting
+2016-09-06 10:40:23,928 DEBUG: Start: Getting Results
+2016-09-06 10:40:23,929 DEBUG: Done: Getting Results
+2016-09-06 10:40:23,929 INFO: Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.62380952381
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.62380952381
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:23,929 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:40:23,929 INFO: Done: Result Analysis
+2016-09-06 10:40:23,929 DEBUG: Start: Training
+2016-09-06 10:40:23,950 DEBUG: Info: Time for Training: 0.0777189731598[s]
+2016-09-06 10:40:23,950 DEBUG: Done: Training
+2016-09-06 10:40:23,950 DEBUG: Start: Predicting
+2016-09-06 10:40:23,953 DEBUG: Done: Predicting
+2016-09-06 10:40:23,953 DEBUG: Start: Getting Results
+2016-09-06 10:40:23,955 DEBUG: Done: Getting Results
+2016-09-06 10:40:23,955 INFO: Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.557142857143
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 1181
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.557142857143
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:23,955 INFO: Done: Result Analysis
+2016-09-06 10:40:24,016 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:24,016 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:24,016 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:40:24,016 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:40:24,016 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:40:24,016 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:40:24,017 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:40:24,017 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:40:24,017 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:40:24,017 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:40:24,017 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:40:24,017 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:40:24,017 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:40:24,017 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:40:24,065 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:40:24,065 DEBUG: Start: Training
+2016-09-06 10:40:24,072 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:40:24,072 DEBUG: Start: Training
+2016-09-06 10:40:24,082 DEBUG: Info: Time for Training: 0.066547870636[s]
+2016-09-06 10:40:24,082 DEBUG: Done: Training
+2016-09-06 10:40:24,082 DEBUG: Start: Predicting
+2016-09-06 10:40:24,088 DEBUG: Done: Predicting
+2016-09-06 10:40:24,088 DEBUG: Start: Getting Results
+2016-09-06 10:40:24,090 DEBUG: Done: Getting Results
+2016-09-06 10:40:24,090 INFO: Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 1181
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:24,090 INFO: Done: Result Analysis
+2016-09-06 10:40:24,093 DEBUG: Info: Time for Training: 0.0777978897095[s]
+2016-09-06 10:40:24,093 DEBUG: Done: Training
+2016-09-06 10:40:24,093 DEBUG: Start: Predicting
+2016-09-06 10:40:24,099 DEBUG: Done: Predicting
+2016-09-06 10:40:24,099 DEBUG: Start: Getting Results
+2016-09-06 10:40:24,100 DEBUG: Done: Getting Results
+2016-09-06 10:40:24,100 INFO: Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.433333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 1181
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:24,101 INFO: Done: Result Analysis
+2016-09-06 10:40:24,162 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:24,163 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:40:24,163 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:40:24,163 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:24,163 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:40:24,163 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:40:24,164 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:40:24,164 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:40:24,164 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:40:24,164 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:40:24,164 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:40:24,164 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:40:24,164 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:40:24,164 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:40:24,198 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:40:24,198 DEBUG: Start: Training
+2016-09-06 10:40:24,200 DEBUG: Info: Time for Training: 0.0375709533691[s]
+2016-09-06 10:40:24,200 DEBUG: Done: Training
+2016-09-06 10:40:24,200 DEBUG: Start: Predicting
+2016-09-06 10:40:24,202 DEBUG: Done: Predicting
+2016-09-06 10:40:24,202 DEBUG: Start: Getting Results
+2016-09-06 10:40:24,204 DEBUG: Done: Getting Results
+2016-09-06 10:40:24,204 INFO: Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.455555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.995238095238
+ - Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:24,204 INFO: Done: Result Analysis
+2016-09-06 10:40:24,213 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:40:24,213 DEBUG: Start: Training
+2016-09-06 10:40:24,216 DEBUG: Info: Time for Training: 0.0543420314789[s]
+2016-09-06 10:40:24,216 DEBUG: Done: Training
+2016-09-06 10:40:24,217 DEBUG: Start: Predicting
+2016-09-06 10:40:24,220 DEBUG: Done: Predicting
+2016-09-06 10:40:24,220 DEBUG: Start: Getting Results
+2016-09-06 10:40:24,222 DEBUG: Done: Getting Results
+2016-09-06 10:40:24,222 INFO: Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:24,222 INFO: Done: Result Analysis
+2016-09-06 10:40:24,312 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:24,312 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:24,312 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:40:24,312 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:40:24,312 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:40:24,312 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:40:24,313 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:40:24,313 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:40:24,313 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:40:24,313 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:40:24,313 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:40:24,313 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:40:24,313 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:40:24,313 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:40:24,346 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:40:24,346 DEBUG: Start: Training
+2016-09-06 10:40:24,346 DEBUG: Info: Time for Training: 0.0354690551758[s]
+2016-09-06 10:40:24,347 DEBUG: Done: Training
+2016-09-06 10:40:24,347 DEBUG: Start: Predicting
+2016-09-06 10:40:24,352 DEBUG: Done: Predicting
+2016-09-06 10:40:24,352 DEBUG: Start: Getting Results
+2016-09-06 10:40:24,353 DEBUG: Done: Getting Results
+2016-09-06 10:40:24,353 INFO: Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.67619047619
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.67619047619
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:24,353 INFO: Done: Result Analysis
+2016-09-06 10:40:24,910 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:40:24,910 DEBUG: Start: Training
+2016-09-06 10:40:24,992 DEBUG: Info: Time for Training: 0.680758953094[s]
+2016-09-06 10:40:24,992 DEBUG: Done: Training
+2016-09-06 10:40:24,992 DEBUG: Start: Predicting
+2016-09-06 10:40:25,001 DEBUG: Done: Predicting
+2016-09-06 10:40:25,001 DEBUG: Start: Getting Results
+2016-09-06 10:40:25,003 DEBUG: Done: Getting Results
+2016-09-06 10:40:25,003 INFO: Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.455555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 29, max_depth : 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:25,003 INFO: Done: Result Analysis
+2016-09-06 10:40:25,067 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:25,067 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:40:25,067 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:40:25,068 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:40:25,068 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:40:25,068 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:25,068 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:40:25,068 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:40:25,068 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:40:25,068 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:40:25,069 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:40:25,069 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:40:25,069 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:40:25,069 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:40:25,122 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:40:25,122 DEBUG: Start: Training
+2016-09-06 10:40:25,122 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:40:25,123 DEBUG: Start: Training
+2016-09-06 10:40:25,123 DEBUG: Info: Time for Training: 0.056645154953[s]
+2016-09-06 10:40:25,123 DEBUG: Done: Training
+2016-09-06 10:40:25,123 DEBUG: Start: Predicting
+2016-09-06 10:40:25,137 DEBUG: Done: Predicting
+2016-09-06 10:40:25,137 DEBUG: Start: Getting Results
+2016-09-06 10:40:25,139 DEBUG: Done: Getting Results
+2016-09-06 10:40:25,139 INFO: Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.585714285714
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.585714285714
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:25,139 INFO: Done: Result Analysis
+2016-09-06 10:40:25,140 DEBUG: Info: Time for Training: 0.0731339454651[s]
+2016-09-06 10:40:25,140 DEBUG: Done: Training
+2016-09-06 10:40:25,141 DEBUG: Start: Predicting
+2016-09-06 10:40:25,143 DEBUG: Done: Predicting
+2016-09-06 10:40:25,144 DEBUG: Start: Getting Results
+2016-09-06 10:40:25,145 DEBUG: Done: Getting Results
+2016-09-06 10:40:25,145 INFO: Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.442857142857
+accuracy_score on test : 0.455555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 1181
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.442857142857
+ - Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:25,145 INFO: Done: Result Analysis
+2016-09-06 10:40:25,362 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:40:25,362 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-09-06 10:40:25,363 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:40:25,363 INFO: Info: Shape of View0 :(300, 13)
+2016-09-06 10:40:25,363 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-06 10:40:25,363 INFO: Info: Shape of View1 :(300, 7)
+2016-09-06 10:40:25,364 INFO: Info: Shape of View0 :(300, 13)
+2016-09-06 10:40:25,364 INFO: Info: Shape of View2 :(300, 14)
+2016-09-06 10:40:25,364 INFO: Info: Shape of View1 :(300, 7)
+2016-09-06 10:40:25,364 INFO: Info: Shape of View3 :(300, 7)
+2016-09-06 10:40:25,364 INFO: Done: Read Database Files
+2016-09-06 10:40:25,365 INFO: Start: Determine validation split for ratio 0.7
+2016-09-06 10:40:25,365 INFO: Info: Shape of View2 :(300, 14)
+2016-09-06 10:40:25,365 INFO: Info: Shape of View3 :(300, 7)
+2016-09-06 10:40:25,365 INFO: Done: Read Database Files
+2016-09-06 10:40:25,365 INFO: Start: Determine validation split for ratio 0.7
+2016-09-06 10:40:25,370 INFO: Done: Determine validation split
+2016-09-06 10:40:25,370 INFO: Start: Determine 5 folds
+2016-09-06 10:40:25,371 INFO: Done: Determine validation split
+2016-09-06 10:40:25,371 INFO: Start: Determine 5 folds
+2016-09-06 10:40:25,381 INFO: Info: Length of Learning Sets: 170
+2016-09-06 10:40:25,382 INFO: Info: Length of Testing Sets: 41
+2016-09-06 10:40:25,382 INFO: Info: Length of Validation Set: 89
+2016-09-06 10:40:25,382 INFO: Done: Determine folds
+2016-09-06 10:40:25,382 INFO: Start: Learning with Fusion and 5 folds
+2016-09-06 10:40:25,382 INFO: Start: Randomsearching best settings for monoview classifiers
+2016-09-06 10:40:25,382 DEBUG: Start: Random search for Adaboost with 30 iterations
+2016-09-06 10:40:25,383 INFO: Info: Length of Learning Sets: 170
+2016-09-06 10:40:25,383 INFO: Info: Length of Testing Sets: 41
+2016-09-06 10:40:25,383 INFO: Info: Length of Validation Set: 89
+2016-09-06 10:40:25,383 INFO: Done: Determine folds
+2016-09-06 10:40:25,383 INFO: Start: Learning with Mumbo and 5 folds
+2016-09-06 10:40:25,384 INFO: Start: Randomsearching best settings for monoview classifiers
+2016-09-06 10:40:25,384 DEBUG: Start: Gridsearch for DecisionTree on View0
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..3faa7965
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..c74ef47d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 0.933333333333
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.933333333333
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..5e8145f8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.585714285714
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.585714285714
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..c2e5210e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.433333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..0468fed6
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 0.814285714286
+accuracy_score on test : 0.544444444444
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.814285714286
+ - Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..879e5454
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.652380952381
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.652380952381
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..8c5ade65
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.990476190476
+accuracy_score on test : 0.455555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 29, max_depth : 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.990476190476
+ - Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..0a0d904e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.609523809524
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.609523809524
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..11b4dc01
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.538095238095
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 1181
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.538095238095
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..bc68c9fa
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 1181
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..bc4d679a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View0 View shape : (300, 13)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 1181
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..4fe0cec4
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.477777777778
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 29, max_depth : 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.995238095238
+ - Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..5b99d1af
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.547619047619
+accuracy_score on test : 0.455555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.547619047619
+ - Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..0fa62be2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.52380952381
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 1181
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.52380952381
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..9e75865c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 0.6
+accuracy_score on test : 0.511111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 1181
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.6
+ - Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..20513448
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.622222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View1 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 1181
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.622222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..c9dca693
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..26220077
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 0.985714285714
+accuracy_score on test : 0.411111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.985714285714
+ - Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..9a5e6d34
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.619047619048
+accuracy_score on test : 0.433333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.619047619048
+ - Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..bf83d84c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.411111111111
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 29, max_depth : 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.995238095238
+ - Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..05e8489b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.62380952381
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.62380952381
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..c1e2e693
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.557142857143
+accuracy_score on test : 0.5
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 1181
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.557142857143
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..8bffe493
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..f1942cf7
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.455555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.995238095238
+ - Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..07177040
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.67619047619
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.67619047619
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..cb100076
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.455555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 29, max_depth : 9
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..f433c885
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.433333333333
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 1181
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..d0ca9fbd
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.422222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View2 View shape : (300, 14)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 1181
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104025Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104025Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..3a184326
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104025Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.585714285714
+accuracy_score on test : 0.522222222222
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.585714285714
+ - Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104025Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104025Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 00000000..76add94d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104025Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.442857142857
+accuracy_score on test : 0.455555555556
+
+Database configuration :
+ - Database name : Fake
+ - View name : View3 View shape : (300, 7)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 1181
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.442857142857
+ - Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104051-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-104051-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 00000000..d46207b5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104051-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,664 @@
+2016-09-06 10:40:51,299 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-06 10:40:51,299 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.273145851562 Gbytes /!\
+2016-09-06 10:41:01,425 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-06 10:41:01,427 INFO: Start: Finding all available mono- & multiview algorithms
+2016-09-06 10:41:02,152 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:41:02,152 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:41:02,152 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:41:02,204 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-06 10:41:02,204 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-06 10:41:02,204 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:41:02,204 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:41:02,825 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:41:02,825 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:41:02,826 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:41:02,871 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-06 10:41:02,871 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-06 10:41:02,871 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:41:02,871 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:41:12,176 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:41:12,176 DEBUG: Start: Training
+2016-09-06 10:41:13,078 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:41:13,078 DEBUG: Start: Training
+2016-09-06 10:41:14,378 DEBUG: Info: Time for Training: 12.8995170593[s]
+2016-09-06 10:41:14,378 DEBUG: Done: Training
+2016-09-06 10:41:14,378 DEBUG: Start: Predicting
+2016-09-06 10:41:14,451 DEBUG: Done: Predicting
+2016-09-06 10:41:14,452 DEBUG: Start: Getting Results
+2016-09-06 10:41:14,454 DEBUG: Done: Getting Results
+2016-09-06 10:41:14,454 INFO: Classification on MultiOmic database for Methyl with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.895238095238
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 15
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.895238095238
+
+
+ Classification took 0:00:12
+2016-09-06 10:41:14,466 INFO: Done: Result Analysis
+2016-09-06 10:41:15,257 DEBUG: Info: Time for Training: 13.7781460285[s]
+2016-09-06 10:41:15,257 DEBUG: Done: Training
+2016-09-06 10:41:15,257 DEBUG: Start: Predicting
+2016-09-06 10:41:15,271 DEBUG: Done: Predicting
+2016-09-06 10:41:15,271 DEBUG: Start: Getting Results
+2016-09-06 10:41:15,273 DEBUG: Done: Getting Results
+2016-09-06 10:41:15,273 INFO: Classification on MultiOmic database for Methyl with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.885714285714
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.885714285714
+
+
+ Classification took 0:00:13
+2016-09-06 10:41:15,273 INFO: Done: Result Analysis
+2016-09-06 10:41:15,379 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:41:15,379 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:41:15,379 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:41:15,380 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:41:15,381 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:41:15,381 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:41:15,405 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-06 10:41:15,406 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-06 10:41:15,406 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:41:15,406 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:41:15,407 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-06 10:41:15,407 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-06 10:41:15,407 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:41:15,408 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:41:16,396 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:41:16,396 DEBUG: Start: Training
+2016-09-06 10:41:16,569 DEBUG: Info: Time for Training: 1.2121629715[s]
+2016-09-06 10:41:16,569 DEBUG: Done: Training
+2016-09-06 10:41:16,569 DEBUG: Start: Predicting
+2016-09-06 10:41:16,587 DEBUG: Done: Predicting
+2016-09-06 10:41:16,587 DEBUG: Start: Getting Results
+2016-09-06 10:41:16,589 DEBUG: Done: Getting Results
+2016-09-06 10:41:16,589 INFO: Classification on MultiOmic database for Methyl with RandomForest
+
+accuracy_score on train : 0.99173553719
+accuracy_score on test : 0.885714285714
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 9, max_depth : 15
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.99173553719
+ - Score on test : 0.885714285714
+
+
+ Classification took 0:00:01
+2016-09-06 10:41:16,599 INFO: Done: Result Analysis
+2016-09-06 10:41:18,706 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:41:18,706 DEBUG: Start: Training
+2016-09-06 10:41:18,881 DEBUG: Info: Time for Training: 3.52481889725[s]
+2016-09-06 10:41:18,882 DEBUG: Done: Training
+2016-09-06 10:41:18,882 DEBUG: Start: Predicting
+2016-09-06 10:41:23,403 DEBUG: Done: Predicting
+2016-09-06 10:41:23,403 DEBUG: Start: Getting Results
+2016-09-06 10:41:23,405 DEBUG: Done: Getting Results
+2016-09-06 10:41:23,405 INFO: Classification on MultiOmic database for Methyl with KNN
+
+accuracy_score on train : 0.834710743802
+accuracy_score on test : 0.933333333333
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 15
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.834710743802
+ - Score on test : 0.933333333333
+
+
+ Classification took 0:00:03
+2016-09-06 10:41:23,405 INFO: Done: Result Analysis
+2016-09-06 10:41:23,544 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:41:23,545 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:41:23,545 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:41:23,546 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:41:23,546 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:41:23,546 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:41:23,571 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-06 10:41:23,571 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-06 10:41:23,572 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:41:23,572 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:41:23,573 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-06 10:41:23,573 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-06 10:41:23,573 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:41:23,573 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:41:26,298 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:41:26,298 DEBUG: Start: Training
+2016-09-06 10:41:26,749 DEBUG: Info: Time for Training: 3.22519683838[s]
+2016-09-06 10:41:26,749 DEBUG: Done: Training
+2016-09-06 10:41:26,749 DEBUG: Start: Predicting
+2016-09-06 10:41:26,811 DEBUG: Done: Predicting
+2016-09-06 10:41:26,812 DEBUG: Start: Getting Results
+2016-09-06 10:41:26,814 DEBUG: Done: Getting Results
+2016-09-06 10:41:26,814 INFO: Classification on MultiOmic database for Methyl with SGD
+
+accuracy_score on train : 0.710743801653
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.710743801653
+ - Score on test : 0.780952380952
+
+
+ Classification took 0:00:03
+2016-09-06 10:41:26,814 INFO: Done: Result Analysis
+2016-09-06 10:41:30,732 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:41:30,732 DEBUG: Start: Training
+2016-09-06 10:41:37,215 DEBUG: Info: Time for Training: 13.6910610199[s]
+2016-09-06 10:41:37,215 DEBUG: Done: Training
+2016-09-06 10:41:37,215 DEBUG: Start: Predicting
+2016-09-06 10:41:38,558 DEBUG: Done: Predicting
+2016-09-06 10:41:38,558 DEBUG: Start: Getting Results
+2016-09-06 10:41:38,560 DEBUG: Done: Getting Results
+2016-09-06 10:41:38,560 INFO: Classification on MultiOmic database for Methyl with SVMLinear
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.961904761905
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 6019
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.961904761905
+
+
+ Classification took 0:00:13
+2016-09-06 10:41:38,583 INFO: Done: Result Analysis
+2016-09-06 10:41:38,729 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:41:38,730 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:41:38,730 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:41:38,730 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:41:38,731 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:41:38,731 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:41:38,766 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-06 10:41:38,766 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-06 10:41:38,766 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-06 10:41:38,766 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-06 10:41:38,766 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:41:38,766 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:41:38,767 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:41:38,767 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:41:45,735 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:41:45,735 DEBUG: Start: Training
+2016-09-06 10:41:47,508 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:41:47,508 DEBUG: Start: Training
+2016-09-06 10:41:52,200 DEBUG: Info: Time for Training: 13.4952919483[s]
+2016-09-06 10:41:52,200 DEBUG: Done: Training
+2016-09-06 10:41:52,201 DEBUG: Start: Predicting
+2016-09-06 10:41:54,059 DEBUG: Done: Predicting
+2016-09-06 10:41:54,059 DEBUG: Start: Getting Results
+2016-09-06 10:41:54,061 DEBUG: Done: Getting Results
+2016-09-06 10:41:54,061 INFO: Classification on MultiOmic database for Methyl with SVMPoly
+
+accuracy_score on train : 0.710743801653
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 6019
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.710743801653
+ - Score on test : 0.780952380952
+
+
+ Classification took 0:00:13
+2016-09-06 10:41:54,061 INFO: Done: Result Analysis
+2016-09-06 10:41:55,637 DEBUG: Info: Time for Training: 16.932240963[s]
+2016-09-06 10:41:55,637 DEBUG: Done: Training
+2016-09-06 10:41:55,637 DEBUG: Start: Predicting
+2016-09-06 10:41:57,044 DEBUG: Done: Predicting
+2016-09-06 10:41:57,044 DEBUG: Start: Getting Results
+2016-09-06 10:41:57,045 DEBUG: Done: Getting Results
+2016-09-06 10:41:57,045 INFO: Classification on MultiOmic database for Methyl with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.961904761905
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 6019
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.961904761905
+
+
+ Classification took 0:00:16
+2016-09-06 10:41:57,058 INFO: Done: Result Analysis
+2016-09-06 10:41:57,256 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:41:57,256 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:41:57,256 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:41:57,258 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-06 10:41:57,258 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-06 10:41:57,258 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:41:57,259 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:41:57,267 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:41:57,267 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:41:57,267 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:41:57,269 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-06 10:41:57,269 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-06 10:41:57,269 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:41:57,269 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:41:57,528 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:41:57,528 DEBUG: Start: Training
+2016-09-06 10:41:57,534 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:41:57,534 DEBUG: Start: Training
+2016-09-06 10:41:57,581 DEBUG: Info: Time for Training: 0.376816987991[s]
+2016-09-06 10:41:57,582 DEBUG: Done: Training
+2016-09-06 10:41:57,582 DEBUG: Start: Predicting
+2016-09-06 10:41:57,585 DEBUG: Done: Predicting
+2016-09-06 10:41:57,585 DEBUG: Start: Getting Results
+2016-09-06 10:41:57,586 DEBUG: Done: Getting Results
+2016-09-06 10:41:57,586 INFO: Classification on MultiOmic database for MiRNA_ with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.819047619048
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 15
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.819047619048
+
+
+ Classification took 0:00:00
+2016-09-06 10:41:57,587 INFO: Done: Result Analysis
+2016-09-06 10:41:57,591 DEBUG: Info: Time for Training: 0.386079072952[s]
+2016-09-06 10:41:57,591 DEBUG: Done: Training
+2016-09-06 10:41:57,591 DEBUG: Start: Predicting
+2016-09-06 10:41:57,594 DEBUG: Done: Predicting
+2016-09-06 10:41:57,594 DEBUG: Start: Getting Results
+2016-09-06 10:41:57,596 DEBUG: Done: Getting Results
+2016-09-06 10:41:57,596 INFO: Classification on MultiOmic database for MiRNA_ with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.838095238095
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.838095238095
+
+
+ Classification took 0:00:00
+2016-09-06 10:41:57,607 INFO: Done: Result Analysis
+2016-09-06 10:41:57,755 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:41:57,755 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:41:57,755 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:41:57,755 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:41:57,756 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:41:57,756 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:41:57,758 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-06 10:41:57,758 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-06 10:41:57,759 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-06 10:41:57,759 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-06 10:41:57,759 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:41:57,759 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:41:57,759 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:41:57,759 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:41:57,949 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:41:57,949 DEBUG: Start: Training
+2016-09-06 10:41:57,957 DEBUG: Info: Time for Training: 0.203402996063[s]
+2016-09-06 10:41:57,957 DEBUG: Done: Training
+2016-09-06 10:41:57,957 DEBUG: Start: Predicting
+2016-09-06 10:41:58,094 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:41:58,094 DEBUG: Start: Training
+2016-09-06 10:41:58,145 DEBUG: Info: Time for Training: 0.391664028168[s]
+2016-09-06 10:41:58,145 DEBUG: Done: Training
+2016-09-06 10:41:58,145 DEBUG: Start: Predicting
+2016-09-06 10:41:58,151 DEBUG: Done: Predicting
+2016-09-06 10:41:58,152 DEBUG: Start: Getting Results
+2016-09-06 10:41:58,154 DEBUG: Done: Getting Results
+2016-09-06 10:41:58,154 INFO: Classification on MultiOmic database for MiRNA_ with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.866666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 9, max_depth : 15
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.866666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:41:58,154 INFO: Done: Result Analysis
+2016-09-06 10:41:58,180 DEBUG: Done: Predicting
+2016-09-06 10:41:58,180 DEBUG: Start: Getting Results
+2016-09-06 10:41:58,182 DEBUG: Done: Getting Results
+2016-09-06 10:41:58,182 INFO: Classification on MultiOmic database for MiRNA_ with KNN
+
+accuracy_score on train : 0.789256198347
+accuracy_score on test : 0.866666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 15
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.789256198347
+ - Score on test : 0.866666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:41:58,199 INFO: Done: Result Analysis
+2016-09-06 10:41:58,303 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:41:58,303 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:41:58,303 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:41:58,303 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:41:58,303 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:41:58,303 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:41:58,305 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-06 10:41:58,305 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-06 10:41:58,306 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-06 10:41:58,306 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-06 10:41:58,306 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:41:58,306 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:41:58,306 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:41:58,306 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:41:58,464 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:41:58,464 DEBUG: Start: Training
+2016-09-06 10:41:58,477 DEBUG: Info: Time for Training: 0.175203084946[s]
+2016-09-06 10:41:58,477 DEBUG: Done: Training
+2016-09-06 10:41:58,477 DEBUG: Start: Predicting
+2016-09-06 10:41:58,481 DEBUG: Done: Predicting
+2016-09-06 10:41:58,481 DEBUG: Start: Getting Results
+2016-09-06 10:41:58,483 DEBUG: Done: Getting Results
+2016-09-06 10:41:58,483 INFO: Classification on MultiOmic database for MiRNA_ with SGD
+
+accuracy_score on train : 0.760330578512
+accuracy_score on test : 0.752380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.760330578512
+ - Score on test : 0.752380952381
+
+
+ Classification took 0:00:00
+2016-09-06 10:41:58,483 INFO: Done: Result Analysis
+2016-09-06 10:41:58,563 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:41:58,563 DEBUG: Start: Training
+2016-09-06 10:41:58,747 DEBUG: Info: Time for Training: 0.445248126984[s]
+2016-09-06 10:41:58,747 DEBUG: Done: Training
+2016-09-06 10:41:58,747 DEBUG: Start: Predicting
+2016-09-06 10:41:58,788 DEBUG: Done: Predicting
+2016-09-06 10:41:58,789 DEBUG: Start: Getting Results
+2016-09-06 10:41:58,790 DEBUG: Done: Getting Results
+2016-09-06 10:41:58,790 INFO: Classification on MultiOmic database for MiRNA_ with SVMLinear
+
+accuracy_score on train : 0.776859504132
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 6019
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.776859504132
+ - Score on test : 0.8
+
+
+ Classification took 0:00:00
+2016-09-06 10:41:58,790 INFO: Done: Result Analysis
+2016-09-06 10:41:58,853 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:41:58,853 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:41:58,853 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:41:58,853 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:41:58,854 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:41:58,854 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:41:58,856 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-06 10:41:58,856 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-06 10:41:58,856 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-06 10:41:58,856 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-06 10:41:58,856 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:41:58,856 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:41:58,857 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:41:58,857 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:41:58,930 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:41:58,930 DEBUG: Start: Training
+2016-09-06 10:41:58,941 DEBUG: Info: Time for Training: 0.0891480445862[s]
+2016-09-06 10:41:58,941 DEBUG: Done: Training
+2016-09-06 10:41:58,941 DEBUG: Start: Predicting
+2016-09-06 10:41:58,947 DEBUG: Done: Predicting
+2016-09-06 10:41:58,947 DEBUG: Start: Getting Results
+2016-09-06 10:41:58,949 DEBUG: Done: Getting Results
+2016-09-06 10:41:58,949 INFO: Classification on MultiOmic database for MiRNA_ with SVMPoly
+
+accuracy_score on train : 0.289256198347
+accuracy_score on test : 0.219047619048
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 6019
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.289256198347
+ - Score on test : 0.219047619048
+
+
+ Classification took 0:00:00
+2016-09-06 10:41:58,949 INFO: Done: Result Analysis
+2016-09-06 10:41:59,282 DEBUG: Done: RandomSearch best settings
+2016-09-06 10:41:59,282 DEBUG: Start: Training
+2016-09-06 10:41:59,624 DEBUG: Info: Time for Training: 0.773069143295[s]
+2016-09-06 10:41:59,625 DEBUG: Done: Training
+2016-09-06 10:41:59,625 DEBUG: Start: Predicting
+2016-09-06 10:41:59,728 DEBUG: Done: Predicting
+2016-09-06 10:41:59,728 DEBUG: Start: Getting Results
+2016-09-06 10:41:59,729 DEBUG: Done: Getting Results
+2016-09-06 10:41:59,729 INFO: Classification on MultiOmic database for MiRNA_ with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 6019
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.780952380952
+
+
+ Classification took 0:00:00
+2016-09-06 10:41:59,729 INFO: Done: Result Analysis
+2016-09-06 10:42:02,068 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:42:02,068 DEBUG: ### Classification - Database:MultiOmic Feature:RANSeq train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:42:02,068 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:42:02,117 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:42:02,117 DEBUG: ### Classification - Database:MultiOmic Feature:RANSeq train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:42:02,117 DEBUG: Start: Determine Train/Test split
+2016-09-06 10:42:02,161 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-09-06 10:42:02,161 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-09-06 10:42:02,161 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:42:02,161 DEBUG: Start: RandomSearch best settings with 1 iterations
+2016-09-06 10:42:02,210 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-09-06 10:42:02,210 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-09-06 10:42:02,210 DEBUG: Done: Determine Train/Test split
+2016-09-06 10:42:02,211 DEBUG: Start: RandomSearch best settings with 1 iterations
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104114Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104114Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 00000000..4d429abf
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104114Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.895238095238
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 15
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.895238095238
+
+
+ Classification took 0:00:12
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104115Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104115Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 00000000..70c256f2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104115Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for Methyl with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.885714285714
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.885714285714
+
+
+ Classification took 0:00:13
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104116Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104116Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 00000000..49c842a8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104116Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with RandomForest
+
+accuracy_score on train : 0.99173553719
+accuracy_score on test : 0.885714285714
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 9, max_depth : 15
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.99173553719
+ - Score on test : 0.885714285714
+
+
+ Classification took 0:00:01
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104123Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104123Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 00000000..f6012164
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104123Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with KNN
+
+accuracy_score on train : 0.834710743802
+accuracy_score on test : 0.933333333333
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 15
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.834710743802
+ - Score on test : 0.933333333333
+
+
+ Classification took 0:00:03
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104126Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104126Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 00000000..b94f7218
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104126Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with SGD
+
+accuracy_score on train : 0.710743801653
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.710743801653
+ - Score on test : 0.780952380952
+
+
+ Classification took 0:00:03
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104138Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104138Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 00000000..ff7fb5c5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104138Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with SVMLinear
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.961904761905
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 6019
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.961904761905
+
+
+ Classification took 0:00:13
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104154Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104154Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 00000000..8c4d7034
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104154Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with SVMPoly
+
+accuracy_score on train : 0.710743801653
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 6019
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.710743801653
+ - Score on test : 0.780952380952
+
+
+ Classification took 0:00:13
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104157Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104157Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 00000000..6044fc66
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104157Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for MiRNA_ with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.838095238095
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.838095238095
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104157Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104157Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 00000000..68547d67
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104157Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.819047619048
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 15
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.819047619048
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104157Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104157Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 00000000..c553126f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104157Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.961904761905
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 6019
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.961904761905
+
+
+ Classification took 0:00:16
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104158Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104158Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 00000000..7d687935
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104158Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with KNN
+
+accuracy_score on train : 0.789256198347
+accuracy_score on test : 0.866666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 15
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.789256198347
+ - Score on test : 0.866666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104158Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104158Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 00000000..57c8d5f7
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104158Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.866666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 9, max_depth : 15
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.866666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104158Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104158Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 00000000..c1d0d088
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104158Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SGD
+
+accuracy_score on train : 0.760330578512
+accuracy_score on test : 0.752380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.760330578512
+ - Score on test : 0.752380952381
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104158Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104158Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 00000000..4a2f9c3f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104158Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SVMLinear
+
+accuracy_score on train : 0.776859504132
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 6019
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.776859504132
+ - Score on test : 0.8
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104158Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104158Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 00000000..1e70623c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104158Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SVMPoly
+
+accuracy_score on train : 0.289256198347
+accuracy_score on test : 0.219047619048
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 6019
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.289256198347
+ - Score on test : 0.219047619048
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104159Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104159Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 00000000..a092ae6a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104159Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 6019
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 1 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.780952380952
+
+
+ Classification took 0:00:00
\ No newline at end of file
--
GitLab