diff --git a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/Adaboost.py b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/Adaboost.py
index 406861fe632b6e17b6617caa22c8fd4581c2b32e..fb5e6a741edb775c6864988566290bd028aca45d 100644
--- a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/Adaboost.py
+++ b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/Adaboost.py
@@ -32,7 +32,7 @@ def getKWARGS(kwargsList):
     return kwargsDict
 
 
-def gridSearch(X_train, y_train, nbFolds=4, metric=["accuracy_score", None], nIter=30, nbCores=1):
+def randomizedSearch(X_train, y_train, nbFolds=4, metric=["accuracy_score", None], nIter=30, nbCores=1):
     pipeline = Pipeline([('classifier', AdaBoostClassifier())])
 
     param= {"classifier__n_estimators": randint(1, 15),
diff --git a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/KNN.py b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/KNN.py
index 234ff43e82865962b6e52613a522495974490eb0..ee51ca59e77d8a916b4b3dd2142829a22fecdd23 100644
--- a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/KNN.py
+++ b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/KNN.py
@@ -28,7 +28,7 @@ def getKWARGS(kwargsList):
     return kwargsDict
 
 
-def gridSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], nIter=30 ):
+def randomizedSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], nIter=30 ):
     pipeline_KNN = Pipeline([('classifier', KNeighborsClassifier())])
     param_KNN = {"classifier__n_neighbors": randint(1, 50)}
     metricModule = getattr(Metrics, metric[0])
diff --git a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/RandomForest.py b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/RandomForest.py
index 370f679bb17f602424297e364d5970a6741f8160..3a3939062a5cefa0548c3a284002626eb80cfa0f 100644
--- a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/RandomForest.py
+++ b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/RandomForest.py
@@ -32,7 +32,7 @@ def getKWARGS(kwargsList):
     return kwargsDict
 
 
-def gridSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], nIter=30):
+def randomizedSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], nIter=30):
     pipeline_rf = Pipeline([('classifier', RandomForestClassifier())])
     param_rf = {"classifier__n_estimators": randint(1, 30),
                 "classifier__max_depth":randint(1, 30)}
diff --git a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SCM.py b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SCM.py
index 19a463555e343923717d6edfdb968372b5bff851..89e240fcb56343a893d6fc3fa0ca82ce4eafc36a 100644
--- a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SCM.py
+++ b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SCM.py
@@ -60,7 +60,7 @@ def getKWARGS(kwargsList):
 
 
 
-def gridSearch(X_train, y_train, nbFolds=4, metric=["accuracy_score", None], nIter=30, nbCores=1):
+def randomizedSearch(X_train, y_train, nbFolds=4, metric=["accuracy_score", None], nIter=30, nbCores=1):
 
     metricModule = getattr(Metrics, metric[0])
     if metric[1]!=None:
diff --git a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SGD.py b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SGD.py
index 93fb39107a52555f091c80d959c1761a6d769269..59a772b5606260ef622f9d05cf377a0a2efa4dcb 100644
--- a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SGD.py
+++ b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SGD.py
@@ -37,7 +37,7 @@ def getKWARGS(kwargsList):
     return kwargsDict
 
 
-def gridSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], nIter=30):
+def randomizedSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], nIter=30):
     pipeline_SGD = Pipeline([('classifier', SGDClassifier())])
     losses = ['log', 'modified_huber']
     penalties = ["l1", "l2", "elasticnet"]
diff --git a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMLinear.py b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMLinear.py
index 0229110d52ca1956245cbb6fc0e4c241c952ea92..3b98ef6697de2b03070aab088ea690638f918556 100644
--- a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMLinear.py
+++ b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMLinear.py
@@ -28,7 +28,7 @@ def getKWARGS(kwargsList):
     return kwargsDict
 
 
-def gridSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], nIter=30):
+def randomizedSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], nIter=30):
     pipeline_SVMLinear = Pipeline([('classifier', SVC(kernel="linear", max_iter=1000))])
     param_SVMLinear = {"classifier__C":randint(1, 10000)}
     metricModule = getattr(Metrics, metric[0])
diff --git a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMPoly.py b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMPoly.py
index d5506e6d58dce2292f2d91bd7ada81687b0a9176..2300e481653249d3275e6308c4f2bdc2c20ccacb 100644
--- a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMPoly.py
+++ b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMPoly.py
@@ -31,7 +31,7 @@ def getKWARGS(kwargsList):
     return kwargsDict
 
 
-def gridSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], nIter=30):
+def randomizedSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], nIter=30):
     pipeline_SVMPoly = Pipeline([('classifier', SVC(kernel="poly", max_iter=1000))])
     param_SVMPoly = {"classifier__C": randint(1, 10000), "classifier__degree":randint(1, 30)}
     metricModule = getattr(Metrics, metric[0])
diff --git a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMRBF.py b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMRBF.py
index e232c1ba30a9592c6e72bc28e36a51ee67a72c72..11415da2ab637fc5d558be51ab308cb7422d8230 100644
--- a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMRBF.py
+++ b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMRBF.py
@@ -29,7 +29,7 @@ def getKWARGS(kwargsList):
     return kwargsDict
 
 
-def gridSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], nIter=30):
+def randomizedSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], nIter=30):
     pipeline_SVMRBF = Pipeline([('classifier', SVC(kernel="rbf", max_iter=1000))])
     param_SVMRBF = {"classifier__C": randint(1, 10000)}
     metricModule = getattr(Metrics, metric[0])