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):
# 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.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。