k-means.py

import numpy as np
from scipy import cluster
from matplotlib import pyplot

#fake some data
tests = np.reshape( np.random.uniform(0,100,60), (30,2) )

#plot remaining variance for each value for 'k' between 1,10
initial = [cluster.vq.kmeans(tests,i) for i in range(1,10)]
pyplot.plot([var for (cent,var) in initial])
pyplot.show()

#pick a winner
cent, var = initial[3]
#use vq() to get as assignment for each obs.
assignment,cdist = cluster.vq.vq(tests,cent)
pyplot.scatter(tests[:,0], tests[:,1], c=assignment)
pyplot.show()