Python实现k-means算法

# -*- coding: utf-8 -*- 
"""Excercise 9.4"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sys
import random

data = 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 vectors
mean_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 result
total_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()