--- a/python/ml.py	Mon Jul 24 00:28:52 2017 -0400
+++ b/python/ml.py	Mon Jul 24 21:22:18 2017 -0400
@@ -167,9 +167,9 @@
             indices = [i for i in range(similarities.shape[0]) if labels[i] == smallestClusterIndex]
     return prototypeIndices, labels
 
-def prototypeCluster(instances, similarities, minSimilarity, similarityFunc = None, minClusterSize = 0, optimizeCentroid = True, randomInitialization = False, assign = True, initialPrototypeIndices = None):
+def prototypeCluster(instances, similarities, minSimilarity, similarityFunc = None, minClusterSize = 0, optimizeCentroid = True, randomInitialization = False, initialPrototypeIndices = None):
     '''Finds exemplar (prototype) instance that represent each cluster
-    Returns the prototype indices (in the instances list) and the cluster label of each instance
+    Returns the prototype indices (in the instances list)
 
     the elements in the instances list must have a length (method __len__), or one can use the random initialization
     the positions in the instances list corresponds to the similarities
@@ -236,14 +236,9 @@
                     newCentroidIdx = clusterIndices[clusterSimilarities.sum(0).argmax()]
                     if prototypeIndices[label] != newCentroidIdx:
                         prototypeIndices[label] = newCentroidIdx
-            elif randomInitialization: # replace prototype by current instance i if longer
-                if len(instances[prototypeIndices[label]]) < len(instances[i]):
-                    prototypeIndices[label] = i
-                    
-    if assign:
-        return assignToPrototypeClusters(instances, prototypeIndices, similarities, minSimilarity, similarityFunc, minClusterSize)
-    else:
-        return prototypeIndices, None
+            elif len(instances[prototypeIndices[label]]) < len(instances[i]): # replace prototype by current instance i if longer # otherwise, possible to test if randomInitialization or initialPrototypes is not None
+                prototypeIndices[label] = i
+    return prototypeIndices
 
 def computeClusterSizes(labels, prototypeIndices, outlierIndex = -1):
     clusterSizes = {i: sum(np.array(labels) == i) for i in prototypeIndices}