Python K-mean

# from pylab import plot,show
import pylab as py
from numpy import vstack,array
from numpy.random import rand
from scipy.cluster.vq import kmeans,vq

# data generation
data = vstack((rand(150,2) + array([.5,.5]),rand(150,2)))

# computing K-Means with K = 2 (2 clusters)
centroids,_ = kmeans(data,2)
# assign each sample to a cluster
idx,_ = vq(data,centroids)

# some plotting using numpy's logical indexing
py.plot(data[idx==0,0],data[idx==0,1],'ob',
        data[idx==1,0],data[idx==1,1],'or')
py.plot(centroids[:,0],centroids[:,1],'sg',markersize=8)

py.show()
py.savefig('01_kk.png', dpi=300 )


# now with K = 3 (3 clusters)
centroids,_ = kmeans(data,3)
idx,_ = vq(data,centroids)

py.plot(data[idx==0,0],data[idx==0,1],'ob',
        data[idx==1,0],data[idx==1,1],'or',
        data[idx==2,0],data[idx==2,1],'og') # third cluster points
py.plot(centroids[:,0],centroids[:,1],'sm',markersize=8)

py.show()
py.savefig('02_kk.png', dpi=300 )