# HG changeset patch
# User Nicolas Saunier <nicolas.saunier@polymtl.ca>
# Date 1439243539 14400
# Node ID dad99b86a1046421c350904d7c6dd8b894b51afb
# Parent  4e89341edd29f6fe41b9a0fc668e27da030446bb# Parent  c6d4ea05a2d0493f33d270b78465ef5891954254
merge with default

diff -r c6d4ea05a2d0 -r dad99b86a104 python/events.py
--- a/python/events.py	Mon Aug 10 01:06:59 2015 -0400
+++ b/python/events.py	Mon Aug 10 17:52:19 2015 -0400
@@ -295,13 +295,15 @@
         print('unknown type of point: '+pointType)
     return allPoints
 
-def prototypeCluster(interactions, similarityMatrix, alignmentMatrix, indicatorName, minSimilarity):
+def prototypeCluster(interactions, similarityMatrix, alignmentMatrix, indicatorName, minSimilarity, minClusterSize = None):
     '''Finds exemplar indicator time series for all interactions
     Returns the prototype indices (in the interaction list) and the label of each indicator (interaction)
 
     if an indicator profile (time series) is different enough (<minSimilarity), 
     it will become a new prototype. 
-    Non-prototype interactions will be assigned to an existing prototype'''
+    Non-prototype interactions will be assigned to an existing prototype
+    if minClusterSize is not None, the clusters will be refined by removing iteratively the smallest clusters
+    and reassigning all elements in the cluster until no cluster is smaller than minClusterSize'''
 
     # sort indicators based on length
     indices = range(similarityMatrix.shape[0])
@@ -320,13 +322,22 @@
              prototypeIndices.append(i)
 
     # assignment
-    labels = [-1]*similarityMatrix.shape[0]
     indices = [i for i in range(similarityMatrix.shape[0]) if i not in prototypeIndices]
-    for i in prototypeIndices:
-        labels[i] = i
-    for i in indices:
-        prototypeIndex = similarityMatrix[i][prototypeIndices].argmax()
-        labels[i] = prototypeIndices[prototypeIndex]
+    assign = True
+    while assign:
+        labels = [-1]*similarityMatrix.shape[0]
+        for i in prototypeIndices:
+            labels[i] = i
+        for i in indices:
+            prototypeIndex = similarityMatrix[i][prototypeIndices].argmax()
+            labels[i] = prototypeIndices[prototypeIndex]
+        clusterSizes = {i: sum(np.array(labels) == i) for i in prototypeIndices}
+        smallestClusterIndex = min(clusterSizes, key = clusterSizes.get) 
+        assign = (clusterSizes[smallestClusterIndex] < minClusterSize)
+        print prototypeIndices, smallestClusterIndex, clusterSizes[smallestClusterIndex]
+        if assign:
+            prototypeIndices.remove(smallestClusterIndex)
+            indices.append(smallestClusterIndex)
 
     return prototypeIndices, labels