Python实现k-means算法_Python

Python实现k-means算法

2021-01-17 00:01the_Chain_Warden Python

这篇文章主要为大家详细介绍了Python实现k-means算法，具有一定的参考价值，感兴趣的小伙伴们可以参考一下

本文实例为大家分享了Python实现k-means算法的具体代码，供大家参考，具体内容如下

这也是周志华《机器学习》的习题9.4。

数据集是西瓜数据集4.0，如下

编号,密度,含糖率
1,0.697,0.46
2,0.774,0.376
3,0.634,0.264
4,0.608,0.318
5,0.556,0.215
6,0.403,0.237
7,0.481,0.149
8,0.437,0.211
9,0.666,0.091
10,0.243,0.267
11,0.245,0.057
12,0.343,0.099
13,0.639,0.161
14,0.657,0.198
15,0.36,0.37
16,0.593,0.042
17,0.719,0.103
18,0.359,0.188
19,0.339,0.241
20,0.282,0.257
21,0.784,0.232
22,0.714,0.346
23,0.483,0.312
24,0.478,0.437
25,0.525,0.369
26,0.751,0.489
27,0.532,0.472
28,0.473,0.376
29,0.725,0.445
30,0.446,0.459

算法很简单，就不解释了，代码也不复杂，直接放上来：

									# -*- 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()