new_contents: #! /usr/bin/env python
  # -*- coding:utf-8
  # Introduction to Kmeans clustering 
  # Special thanks: Harisson@pythonprogramming.net
  
  '''
      File name: kmeans_test.py
      Author: chimney37
      Date created: 09/05/2017
      Python Version: 3.62
  '''
  import matplotlib.pyplot as plt
  from matplotlib import style
  import numpy as np
  from sklearn.cluster import KMeans
  style.use('ggplot')
  
  X = np.array([[1,2],
                [1.5,1.8],
                [5,8],
                [8,8],
                [1,0.6],
                [9,11]])
  
  #plt.scatter(X[:,0],X[:,1], s=150, linewidths=5, zorder=10)
  #plt.show()
  
  clf = KMeans(n_clusters=2)
  clf.fit(X)
  
  centroids = clf.cluster_centers_
  labels = clf.labels_
  
  colors = ["g.","r.","c.","y."]
  for i in range(len(X)):
      plt.plot(X[i][0], X[i][1], colors[labels[i]], markersize=10)
  plt.scatter(centroids[:,0],centroids[:,1],marker="x",s=150,linewidths=5,zorder=10)
  plt.show()
  
subject: test kmeans clustering with centroid plotting