本文實例講述了Python聚類算法之基本K均值運算技巧。分享給大家供大家參考，具體如下：
基本K均值 ：選擇 K 個初始質心，其中 K 是用戶指定的參數，即所期望的簇的個數。每次循環中，每個點被指派到最近的質心，指派到同一個質心的點集構成一個。然后，根據指派到簇的點，更新每個簇的質心。重復指派和更新操作，直到質心不發生明顯的變化。
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# scoding=utf-8import pylab as plpoints = [[int(eachpoint.split("#")[0]), int(eachpoint.split("#")[1])] for eachpoint in open("points","r")]# 指定三個初始質心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')# 記錄每次迭代后每個簇的質心的更新軌跡center1 = [currentCenter1]; center2 = [currentCenter2]; center3 = [currentCenter3]# 三個簇group1 = []; group2 = []; group3 = []for runtime in range(50): group1 = []; group2 = []; group3 = [] for eachpoint in points: # 計算每個點到三個質心的距離 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) # 將該點指派到離它最近的質心所在的簇 mindis = min(distance1,distance2,distance3) if(mindis == distance1): group1.append(eachpoint) elif(mindis == distance2): group2.append(eachpoint) else: group3.append(eachpoint) # 指派完所有的點后，更新每個簇的質心 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)] # 記錄該次對質心的更新 center1.append(currentCenter1) center2.append(currentCenter2) center3.append(currentCenter3)# 打印所有的點，用顏色標識該點所屬的簇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')# 打印每個簇的質心的更新軌跡for center in [center1,center2,center3]: pl.plot([eachcenter[0] for eachcenter in center], [eachcenter[1] for eachcenter in center],'k')pl.show() |
運行效果截圖如下：
希望本文所述對大家Python程序設計有所幫助。
