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-8import pylab as plpoints = [[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 currentCenter<strong>本文来源gaodai#ma#com搞@@代~&码*网2</strong>1 = [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。
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转载请注明原文链接：Python聚类算法之基本K均值实例详解
转载请注明原文链接：Python聚类算法之基本K均值实例详解
