kNN initialization added

kNN.py

@@ -0,0 +1,41 @@
+import random 
+# Choose one center uniformly at random from among the data points.
+# For each data point x, compute D(x), the distance between x and the nearest center that has already been chosen.
+# Choose one new data point at random as a new center, using a weighted probability distribution where a point x is chosen with probability proportional to D(x)2.
+# Repeat Steps 2 and 3 until k centers have been chosen.
+# 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
+
+
+
+def getNextPoint(keys, distancematrix, initialpoints):
+    genDistanceDict = dict()
+    for i in keys:
+        minimum = 999999999
+        if i in initialpoints.values():
+            continue
+        for j in initialpoints.values():
+            if(distancematrix[i][j] < minimum):
+                minimum = distancematrix[i][j]
+        genDistanceDict[i] = minimum
+
+    #code for prob. pick to be written
+
+
+
+
+#nearestPoints be the list of list of two elements with point and label
+def classifyPoints(nearestPoints, givenPoint):
+
+
+

testmaps.py

@@ -1,6 +1,6 @@
 import googlemaps 
 
-def getnodedata(file_location):
+def getNodeData(file_location):
     f = open(file_location)
     lines = f.readlines()
     placesToVisit = []
@@ -22,7 +22,7 @@ def getnodedata(file_location):
 def genDistanceMatrix(file_location = '/home/metalcyanide/cp/sample.txt', api_key):
     gmaps = googlemaps.Client(key=api_key) 
     placeDict = dict()
-    placeDict = getnodedata(file_location)
+    placeDict = getNodeData(file_location)
     print(placeDict.keys())
     distancedict = dict()
     # return 0
@@ -41,7 +41,9 @@ def genDistanceMatrix(file_location = '/home/metalcyanide/cp/sample.txt', api_ke
 
     return distancedict
 
-apikey = "YOUR_API_KEY"
-mydict = dict()
-mydict = genDistanceMatrix(apikey)
-print(mydict)
+
+def __init__ == main :
+    apikey = "YOUR_API_KEY"
+    mydict = dict()
+    mydict = genDistanceMatrix(apikey)
+    print(mydict)