Python实现k-means算法

# -*- coding: utf-8 -*- """Excercise 9.4"""import numpy as npimport pandas as pdimport matplotlib.pyplot as pltimport sysimport randomdata = pd.read_csv(filepath_or_buffer = '../dataset/watermelon4.0.csv', sep = ',')[["密度","含糖率"]].values########################################## K-means ####################################### k = int(sys.argv[1])#Randomly choose k samples from data as mean vectorsmean_vectors = random.sample(data,k)def dist(p1,p2):  return np.sqrt(sum((p1-p2)*(p1-p2)))while True:  print mean_vectors  clusters = map ((lambda x:[x]), mean_vectors)   for sample in data:    distances = map((lambda m: dist(sample,m)), mean_vectors)     min_index = distances.index(min(distances))    clusters[min_index].append(sample)  new_mean_vectors = []  for c,v in zip(clusters,mean_vectors):    new_mean_vector = sum(c)/len(c)    #If the difference betweenthe new mean vector and the old mean vector is less than 0.0001    #then do not updata the mean vector    if all(np.divide((new_mean_vector-v),v) < np.array([0.0001,0.0001]) ):      new_mean_vectors.append(v)      else:      new_mean_vectors.append(new_mean_vector)    if np.array_equal(mean_vectors,new_mean_vectors):    break  else:    mean_vectors = new_mean_vectors #Show the clustering resulttotal_colors = ['r','y','g','b','c','m','k']colors = random.sample(total_colors,k)for cluster,color in zip(clusters,colors):  density = map(lambda arr:arr[0],cluster)  sugar_content = map(lambda arr:arr[1],cluster)  plt.scatter(density,sugar_content,c = color)plt.show()