diff --git a/Code/MonoMutliViewClassifiers/ExecClassif.py b/Code/MonoMutliViewClassifiers/ExecClassif.py
index f8fda518905289312b29dd0ccc55b7dd63034ce7..6f4ce211b298be608eb7a337239d39c192fef097 100644
--- a/Code/MonoMutliViewClassifiers/ExecClassif.py
+++ b/Code/MonoMutliViewClassifiers/ExecClassif.py
@@ -266,22 +266,22 @@ for viewIndex, viewArguments in enumerate(argumentDictionaries["Monoview"].value
 # bestClassifiersConfigs = [["1"],["1"],["1"],["1"]]
 try:
     if benchmark["Multiview"]:
-        # try:
-        #     if benchmark["Multiview"]["Mumbo"]:
-        #         for combination in itertools.combinations_with_replacement(range(len(benchmark["Multiview"]["Mumbo"])), NB_VIEW):
-        #             classifiersNames = [benchmark["Multiview"]["Mumbo"][index] for index in combination]
-        #             arguments = {"CL_type": "Mumbo",
-        #                          "views": args.views.split(":"),
-        #                          "NB_VIEW": len(args.views.split(":")),
-        #                          "NB_CLASS": len(args.CL_classes.split(":")),
-        #                          "LABELS_NAMES": args.CL_classes.split(":"),
-        #                          "MumboKWARGS": {"classifiersNames": classifiersNames,
-        #                                          "maxIter":int(args.MU_iter[0]), "minIter":int(args.MU_iter[1]),
-        #                                          "threshold":args.MU_iter[2],
-        #                                          "classifiersConfigs": [argument.split(":") for argument in args.MU_config]}}
-        #             argumentDictionaries["Multiview"].append(arguments)
-        # except:
-        #     pass
+        try:
+            if benchmark["Multiview"]["Mumbo"]:
+                for combination in itertools.combinations_with_replacement(range(len(benchmark["Multiview"]["Mumbo"])), NB_VIEW):
+                    classifiersNames = [benchmark["Multiview"]["Mumbo"][index] for index in combination]
+                    arguments = {"CL_type": "Mumbo",
+                                 "views": args.views.split(":"),
+                                 "NB_VIEW": len(args.views.split(":")),
+                                 "NB_CLASS": len(args.CL_classes.split(":")),
+                                 "LABELS_NAMES": args.CL_classes.split(":"),
+                                 "MumboKWARGS": {"classifiersNames": classifiersNames,
+                                                 "maxIter":int(args.MU_iter[0]), "minIter":int(args.MU_iter[1]),
+                                                 "threshold":args.MU_iter[2],
+                                                 "classifiersConfigs": [argument.split(":") for argument in args.MU_config]}}
+                    argumentDictionaries["Multiview"].append(arguments)
+        except:
+            pass
         try:
             if benchmark["Multiview"]["Fusion"]:
                 try:
diff --git a/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py b/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
index 8b154b6c558ca8e19e7b559c5b1e5011edaa74e9..c12e3cc6ae3e1b7f55c2edbeb6bd5e2bc337fd27 100644
--- a/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
+++ b/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
@@ -60,46 +60,39 @@ def ExecMonoview(X, Y, name, learningRate, nbFolds, nbCores, databaseType, path,
     logging.debug("Done:\t Determine Train/Test split")
 
     # Begin Classification RandomForest
-    logging.debug("Start:\t Classification")
 
     classifierModule = getattr(MonoviewClassifiers, CL_type)
     classifierGridSearch = getattr(classifierModule, "gridSearch")
 
     if gridSearch:
+        logging.debug("Start:\t RandomSearch best settings")
         cl_desc = classifierGridSearch(X_train, y_train, nbFolds=nbFolds, nbCores=nbCores, metric=metric, nIter=nIter)
         clKWARGS = dict((str(index), desc) for index, desc in enumerate(cl_desc))
+        logging.debug("Done:\t RandomSearch best settings")
+    logging.debug("Start:\t Training")
     cl_res = classifierModule.fit(X_train, y_train, NB_CORES=nbCores, **clKWARGS)
     t_end  = time.time() - t_start
 
-    # Add result to Results DF
-    df_class_res = pd.DataFrame()
-    # df_class_res = df_class_res.append({'a_class_time':t_end, 'b_cl_desc': cl_desc, 'c_cl_res': cl_res,
-    #                                                 'd_cl_score': cl_res.best_score_}, ignore_index=True)
-
-    logging.debug("Info:\t Time for Classification: " + str(t_end) + "[s]")
-    logging.debug("Done:\t Classification")
-
-    # CSV Export
-    # logging.debug("Start:\t Exporting to CSV")
-    # directory = os.path.dirname(os.path.abspath(__file__)) + "/Results-ClassMonoView/"
-    # filename = datetime.datetime.now().strftime("%Y_%m_%d") + "-CMV-" + name + "-" + feat
-    # ExportResults.exportPandasToCSV(df_class_res, directory, filename)
-    # logging.debug("Done:\t Exporting to CSV")
+    logging.debug("Info:\t Time for Training: " + str(t_end) + "[s]")
+    logging.debug("Done:\t Training")
 
+    logging.debug("Start:\t Predicting")
     # Stats Result
     y_test_pred = cl_res.predict(X_test)
     classLabelsDesc = pd.read_csv(path + fileCLD, sep=";", names=['label', 'name'])
     classLabelsNames = classLabelsDesc.name
+    logging.debug("Done:\t Predicting")
     #logging.debug("" + str(classLabelsNames))
     classLabelsNamesList = classLabelsNames.values.tolist()
     #logging.debug(""+ str(classLabelsNamesList))
 
-    logging.debug("Start:\t Statistic Results")
+    logging.debug("Start:\t Getting Results")
 
     #Accuracy classification score
     accuracy_score = ExportResults.accuracy_score(y_test, y_test_pred)
     logging.info("Accuracy :" +str(accuracy_score))
     cl_desc = [value for key, value in sorted(clKWARGS.iteritems())]
+    logging.debug("Done:\t Getting Results")
     return [CL_type, accuracy_score, cl_desc, feat]
     # # Classification Report with Precision, Recall, F1 , Support
     # logging.debug("Info:\t Classification report:")
diff --git a/Code/MonoMutliViewClassifiers/Monoview/ExecPlot.py b/Code/MonoMutliViewClassifiers/Monoview/ExecPlot.py
index 5edc1d9e1d38f33564a43d0aeaf13b186cb81679..979636c29b84d4561934377f971bae7ce017c800 100644
--- a/Code/MonoMutliViewClassifiers/Monoview/ExecPlot.py
+++ b/Code/MonoMutliViewClassifiers/Monoview/ExecPlot.py
@@ -6,7 +6,8 @@
 import argparse                         # for acommand line arguments
 import datetime                         # for TimeStamp in CSVFile
 import os                               # to geth path of the running script
-
+import matplotlib
+matplotlib.use('Agg')
 # Import 3rd party modules
 import pandas as pd                     # for Series
 import numpy as np                      # for DataFrames
diff --git a/Code/MonoMutliViewClassifiers/Monoview/ExportResults.py b/Code/MonoMutliViewClassifiers/Monoview/ExportResults.py
index 85841f4f258de885d4af3161c2eb27d3a6f078e3..68368d389708b768cac2ac8d649ffbf5c87d65ef 100644
--- a/Code/MonoMutliViewClassifiers/Monoview/ExportResults.py
+++ b/Code/MonoMutliViewClassifiers/Monoview/ExportResults.py
@@ -11,6 +11,8 @@ import pandas as pd  # for Series and DataFrames
 import numpy as np  # for Numpy Arrays
 import matplotlib.pyplot as plt  # for Plots
 from scipy.interpolate import interp1d  # to Interpolate Data
+import matplotlib
+matplotlib.use('Agg')
 from matplotlib.offsetbox import AnchoredOffsetbox, TextArea, HPacker  # to generate the Annotations in plot
 from pylab import rcParams  # to change size of plot
 from sklearn import metrics  # For stastics on classification
diff --git a/Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py b/Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py
index ad434d7f7180cf529db28205f7dc11db45b1178c..01557c51d67e90677c137488c1a5656d35d24de8 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py
@@ -79,7 +79,7 @@ def ExecMultiview(DATASET, name, learningRate, nbFolds, nbCores, databaseType, p
 
 
     if gridSearch:
-        logging.info("Start:\t Gridsearching best settings for monoview classifiers")
+        logging.info("Start:\t Randomsearching best settings for monoview classifiers")
         bestSettings, fusionConfig = classifierGridSearch(DATASET, classificationKWARGS, learningIndices
                                                           , metric=metric, nIter=nIter)
         classificationKWARGS["classifiersConfigs"] = bestSettings
@@ -87,8 +87,9 @@ def ExecMultiview(DATASET, name, learningRate, nbFolds, nbCores, databaseType, p
             classificationKWARGS["fusionMethodConfig"] = fusionConfig
         except:
             pass
-        logging.info("Done:\t Gridsearching best settings for monoview classifiers")
+        logging.info("Done:\t Randomsearching best settings for monoview classifiers")
 
+    logging.info("Start:\t Classification")
     # Begin Classification
     for foldIdx, fold in enumerate(kFolds):
         if fold != range(datasetLength):
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Fusion/analyzeResults.py b/Code/MonoMutliViewClassifiers/Multiview/Fusion/analyzeResults.py
index 6c30126c80ec228f09abe7730dd402284c68ca59..2ecd6283b687a413fdc3e51c5cd48a8821e37f6f 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/Fusion/analyzeResults.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Fusion/analyzeResults.py
@@ -1,11 +1,14 @@
 from sklearn.metrics import precision_recall_fscore_support, accuracy_score, classification_report
 import numpy as np
+import matplotlib
+matplotlib.use('Agg')
 import matplotlib.pyplot as plt
 import operator
 from datetime import timedelta as hms
 from Methods import *
 import Methods.LateFusion
 
+
 def error(testLabels, computedLabels):
     error = sum(map(operator.ne, computedLabels, testLabels))
     return float(error) * 100 / len(computedLabels)
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Mumbo/analyzeResults.py b/Code/MonoMutliViewClassifiers/Multiview/Mumbo/analyzeResults.py
index 057b64744eecd7d6c56689cd6f68036ebdfd9b2c..7a71219a0f8b03e5ee32d5183466288a8df632db 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/Mumbo/analyzeResults.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Mumbo/analyzeResults.py
@@ -1,5 +1,7 @@
 from sklearn.metrics import precision_recall_fscore_support, accuracy_score, classification_report
 import numpy as np
+import matplotlib
+matplotlib.use('Agg')
 import matplotlib.pyplot as plt
 import operator
 from datetime import timedelta as hms
@@ -8,6 +10,7 @@ from Classifiers import *
 import logging
 
 
+
 def findMainView(bestViews):
     views = list(set(bestViews))
     viewCount = np.array([list(bestViews).count(view) for view in views])
diff --git a/Code/MonoMutliViewClassifiers/ResultAnalysis.py b/Code/MonoMutliViewClassifiers/ResultAnalysis.py
index c175a59a0240deda78cdac3eb5c8ce8f96e93d4b..eee8f24cb77316958843b1b8d178d6446d1a7a0a 100644
--- a/Code/MonoMutliViewClassifiers/ResultAnalysis.py
+++ b/Code/MonoMutliViewClassifiers/ResultAnalysis.py
@@ -1,3 +1,5 @@
+import matplotlib
+matplotlib.use('Agg')
 import matplotlib.pyplot as plt
 import time
 import pylab
diff --git a/Code/MonoMutliViewClassifiers/Versions.py b/Code/MonoMutliViewClassifiers/Versions.py
index 8c6fbbe08debe650a2b92729b25d7f5493130ea5..b2409d64a6c098c17c9b2b959e2e408653b07d5d 100644
--- a/Code/MonoMutliViewClassifiers/Versions.py
+++ b/Code/MonoMutliViewClassifiers/Versions.py
@@ -14,32 +14,58 @@ __author__ 	= "Nikolas Huelsmann"
 __status__ 	= "Prototype"           # Production, Development, Prototype
 __date__	= 2016-03-25
 
-import sys
-print("Python-V.: " + sys.version)
-
-import cv2
-print("OpenCV2-V.: " + cv2.__version__)
-
-import pandas as pd
-print("Pandas-V.: " + pd.__version__)
-
-import numpy
-print("Numpy-V.: " + numpy.version.version)
-
-import scipy
-print("Scipy-V.: " + scipy.__version__)
-
-import matplotlib
-print("Matplotlib-V.: " + matplotlib.__version__)
-
-import sklearn
-print("Sklearn-V.: " + sklearn.__version__)
-
-
-import logging                          # To create Log-Files  
-print("Logging: " + logging.__version__)
-
-import joblib
-print("joblib: " + joblib.__version__)
+try:
+    import sys
+    print("Python-V.: " + sys.version)
+except:
+    print "Please install Python 2.7"
+
+try:
+    import cv2
+    print("OpenCV2-V.: " + cv2.__version__)
+except:
+    print "Please install cv2 module"
+
+try:
+    import pandas as pd
+    print("Pandas-V.: " + pd.__version__)
+except:
+    print "Please install pandas module"
+
+try:
+    import numpy
+    print("Numpy-V.: " + numpy.version.version)
+except:
+    print "Please install numpy module"
+
+try:
+    import scipy
+    print("Scipy-V.: " + scipy.__version__)
+except:
+    print "Please install scipy module"
+
+try:
+    import matplotlib
+    print("Matplotlib-V.: " + matplotlib.__version__)
+except:
+    print "Please install matplotlib module"
+
+try:
+    import sklearn
+    print("Sklearn-V.: " + sklearn.__version__)
+except:
+    print "Please install sklearn module"
+
+try:
+    import logging                          # To create Log-Files
+    print("Logging: " + logging.__version__)
+except:
+    print "Please install logging module"
+
+try:
+    import joblib
+    print("joblib: " + joblib.__version__)
+except:
+    print "Pease install joblib module"