Python实现k-means算法

yipeiwu_com5年前Python基础

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【听图阁-专注于Python设计】。

相关文章

python逆向入门教程

python逆向入门教程

1、开发环境 我们在Windows 10上开始python逆向之旅,首先开始搭建开发环境,python解释器使用最新的3.6.1,IDE使用PyCharm社区版2017.1.3,下载地址...

python3 中的字符串(单引号、双引号、三引号)以及字符串与数字的运算

python3中的字符串是一种常见的数据类型。 字符串有多种表现形式:单引号、双引号和三引号,且这些字符串的表现形式(单、双、三)都必须是成对出现的。 单、双引号是英文的:‘'和"",三...

Django ORM 查询管理器源码解析

ORM 查询管理器 对于 ORM 定义: 对象关系映射, Object Relational Mapping, ORM, 是一种程序设计技术,用于实现面向对象编程语言里不同类型系统的数...

Python 比较两个数组的元素的异同方法

通过set()获取两个数组的交/并/差集: print set(a).intersection(set(b)) # 交集 print set(a).union(set(b)) # 并...

Python EOL while scanning string literal问题解决方法

项目中有个定时任务,每天取到一些表数据传到一个外部接口,但是最近总是有异常,今天查了下原因。 首先本地和测试环境测试这个程序都没问题,只有线上环境会在日志中抛出异常,猜测异常主要产生的原...