k-means.py

import random
import math

def euclideanDistance(x,y):
    return math.sqrt(sum([(a-b)**2 for (a,b) in zip(x,y)]))


def partition(points, k, means, d=euclideanDistance):
   thePartition = [[] for _ in means]  # list of k empty lists
   indices = range(k)

   for x in points:
      closestIndex = min(indices, key=lambda index: d(x, means[index]))
      thePartition[closestIndex].append(x)

   return thePartition


def mean(points):
   ''' assume the entries of the list of points are tuples;
       e.g. (3,4) or (6,3,1). '''
   n = len(points)
   return tuple(float(sum(x)) / n for x in zip(*points))


def kMeans(points, k, initialMeans, d=euclideanDistance):
   oldPartition = []
   newPartition = partition(points, k, initialMeans, d)

   while oldPartition != newPartition:
      oldPartition = newPartition
      newMeans = [mean(S) for S in oldPartition]
      newPartition = partition(points, k, newMeans, d)

   return newPartition


def importData():
   f = lambda name,b,d: [name, float(b), float(d)]

   with open('birth-death-rates.csv', 'r') as inputFile:
      return [f(*line.strip().split('\t')) for line in inputFile]



if __name__ == "__main__":
   L = [x[1:] for x in importData()] # remove names
   print str(L).replace('[','{').replace(']', '}')

   import matplotlib.pyplot as plt
   '''
   plt.scatter(*zip(*L))
   plt.show()
   '''

   import random
   k = 3
   partition = kMeans(L, k, random.sample(L, k))
   plt.scatter(*zip(*partition[0]), c='b')
   plt.scatter(*zip(*partition[1]), c='r')
   plt.scatter(*zip(*partition[2]), c='g')
   plt.show()