Python聚类算法之基本K均值实例详解

Python聚类算法之基本K均值实例详解

本文实例讲述了Python聚类算法之基本K均值运算技巧。分享给大家供大家参考，具体如下：

基本K均值 ：选择 K 个初始质心，其中 K
是用户指定的参数，即所期望的簇的个数。每次循环中，每个点被指派到最近的质心，指派到同一个质心的点集构成一个。然后，根据指派到簇的点，更新每个簇的质心。重复指派和更新操作，直到质心不发生明显的变化。  
 

# scoding=utf-8

import pylab as pl

points = [[int(eachpoint.split("#")[0]),
int(eachpoint.split("#")[1])] for eachpoint in
open("points","r")]

# 指定三个初始质心

currentCenter1 = [20,190]; currentCenter2 = [120,90];
currentCenter3 = [170,140]

pl.plot([currentCenter1[0]], [currentCenter1[1]],'ok')

pl.plot([currentCenter2[0]], [currentCenter2[1]],'ok')

pl.plot([currentCenter3[0]], [currentCenter3[1]],'ok')

# 记录每次迭代后每个簇的质心的更新轨迹

center1 = [currentCenter1]; center2 = [currentCenter2]; center3 =
[currentCenter3]

# 三个簇

group1 = []; group2 = []; group3 = []

for runtime in range(50):

  group1 = []; group2 = []; group3 = []

  for eachpoint in points:

    #
计算每个点到三个质心的距离

    distance1 =
pow(abs(eachpoint[0]-currentCenter1[0]),2)
pow(abs(eachpoint[1]-currentCenter1[1]),2)

    distance2 =
pow(abs(eachpoint[0]-currentCenter2[0]),2)
pow(abs(eachpoint[1]-currentCenter2[1]),2)

    distance3 =
pow(abs(eachpoint[0]-currentCenter3[0]),2)
pow(abs(eachpoint[1]-currentCenter3[1]),2)

    #
将该点指派到离它最近的质心所在的簇

    mindis =
min(distance1,distance2,distance3)

    if(mindis ==
distance1):

     
group1.append(eachpoint)

    elif(mindis
== distance2):

     
group2.append(eachpoint)

    else:

     
group3.append(eachpoint)

  # 指派完所有的点后，更新每个簇的质心

  currentCenter1 = [sum([eachpoint[0] for
eachpoint in group1])/len(group1),sum([eachpoint[1] for eachpoint
in group1])/len(group1)]

  currentCenter2 = [sum([eachpoint[0] for
eachpoint in group2])/len(group2),sum([eachpoint[1] for eachpoint
in group2])/len(group2)]

  currentCenter3 = [sum([eachpoint[0] for
eachpoint in group3])/len(group3),sum([eachpoint[1] for eachpoint
in group3])/len(group3)]

  # 记录该次对质心的更新

  center1.append(currentCenter1)

  center2.append(currentCenter2)

  center3.append(currentCenter3)

# 打印所有的点，用颜色标识该点所属的簇

pl.plot([eachpoint[0] for eachpoint in group1], [eachpoint[1] for
eachpoint in group1], 'or')

pl.plot([eachpoint[0] for eachpoint in group2], [eachpoint[1] for
eachpoint in group2], 'oy')

pl.plot([eachpoint[0] for eachpoint in group3], [eachpoint[1] for
eachpoint in group3], 'og')

# 打印每个簇的质心的更新轨迹

for center in [center1,center2,center3]:

  pl.plot([eachcenter[0] for eachcenter in
center], [eachcenter[1] for eachcenter in center],'k')

pl.show()

运行效果截图如下：

希望本文所述对大家Python程序设计有所帮助。