kNN with kmeans++ code

kNN.py

@@ -6,14 +6,15 @@
 # Now that the initial centers have been chosen, proceed using standard k-means clustering.
 
 #kmeans++ initialization of labels. data being a dictionary
+
 def kMeansPPlusInitalization(data, distancematrix, numclusters):
     initialpoints = dict()
     initialpoints[numclusters] = [random.sample(data.keys(),1)]
     numclusters -=1
     while numclusters:
         initialpoints[numclusters] = getNextPoint(data.keys(), distancematrix, initialpoints)
         numclusters -=1
-    
+
     return initialpoints
 
 
@@ -29,6 +30,8 @@ def getNextPoint(keys, distancematrix, initialpoints):
                 minimum = distancematrix[i][j]
         genDistanceDict[i] = minimum
 
+
+
     #code for prob. pick to be written