#!/usr/bin/python

#coding=utf-8

import time

import numpy as np

import pylab as pl

from sklearn.cluster import KMeans

from sklearn.metrics.pairwise import euclidean_distances

from sklearn.datasets.samples_generator import make_blobs

np.random.seed(0)

centers = [[1,1], [-1,-1], [1, -1]]

k = len(centers)

x , labels = make_blobs(n_samples=3000, centers=centers, cluster_std=.7)

kmeans = KMeans(init='k-means++', n_clusters=3, n_init = 10)

t0 = time.time()

kmeans.fit(x)

t_end = time.time() - t0

colors = ['r', 'b', 'g']

for k , col in zip( range(k) , colors):

members = (kmeans.labels_ == k )

pl.plot( x[members, 0] , x[members,1] , 'w', markerfacecolor=col, marker='.')

pl.plot(kmeans.cluster_centers_[k,0], kmeans.cluster_centers_[k,1], 'o', markerfacecolor=col,\

markeredgecolor='k', markersize=10)

pl.show()