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
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()
运行方式：在命令行输入 python k_means.py 4。其中4就是k。 下面是k分别等于3，4，5的运行结果，因为一开始的均值向量是随机的，所以每次运行结果会有不同。
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持脚本之家。
Python k means