机器学习实战：K-均值及二分K-均值聚类算法

# coding=utf-8'''Created on Feb 16, 2011k Means Clustering for Ch10 of Machine Learning in Action#@author: Peter Harrington'''from numpy import *def loadDataSet(fileName):      #general function to parse tab -delimited floats    dataMat = []                #assume last column is target value    fr = open(fileName)    for line in fr.readlines():        curLine = line.strip().split('\t')        fltLine = list(map(float,curLine)) #map all elements to float()        dataMat.append(fltLine)    return mat(dataMat)  #mat(list[])先变成矩阵类型后面才能进行[:,j] min ,max 等运算def distEclud(vecA, vecB):    return sqrt(sum(power(vecA - vecB, 2))) #la.norm(vecA-vecB)def randCent(dataSet, k):    n = shape(dataSet)[1]    centroids = mat(zeros((k,n)))#create centroid mat    for j in range(n):#create random cluster centers, within bounds of each dimension        minJ = min(dataSet[:,j])        rangeJ = float(max(dataSet[:,j]) - minJ)        centroids[:,j] = mat(minJ + rangeJ * random.rand(k,1))    return centroids    def kMeans(dataSet, k, distMeas=distEclud, createCent=randCent):    m = shape(dataSet)[0]    clusterAssment = mat(zeros((m,2)))#create mat to assign data points                                       #to a centroid, also holds SE of each point    centroids = createCent(dataSet, k)    clusterChanged = True    while clusterChanged:        clusterChanged = False        for i in range(m):#for each data point assign it to the closest centroid            minDist = inf; minIndex = -1            for j in range(k):                distJI = distMeas(centroids[j,:],dataSet[i,:])                if distJI < minDist:                    minDist = distJI; minIndex = j            if clusterAssment[i,0] != minIndex: clusterChanged = True            clusterAssment[i,:] = minIndex,minDist**2        print (centroids)        for cent in range(k):#recalculate centroids            ptsInClust = dataSet[nonzero(clusterAssment[:,0].A==cent)[0]]#get all the point in this cluster            centroids[cent,:] = mean(ptsInClust, axis=0) #assign centroid to mean     return centroids, clusterAssmentdef biKmeans(dataSet, k, distMeas=distEclud):    m = shape(dataSet)[0]    clusterAssment = mat(zeros((m,2)))    centroid0 = mean(dataSet, axis=0).tolist()[0]    centList =[centroid0] #create a list with one centroid    for j in range(m):#calc initial Error        clusterAssment[j,1] = distMeas(mat(centroid0), dataSet[j,:])**2    while (len(centList) < k):        lowestSSE = inf        for i in range(len(centList)):            ptsInCurrCluster = dataSet[nonzero(clusterAssment[:,0].A==i)[0],:]#get the data points currently in cluster i            centroidMat, splitClustAss = kMeans(ptsInCurrCluster, 2, distMeas)            sseSplit = sum(splitClustAss[:,1])#compare the SSE to the currrent minimum            sseNotSplit = sum(clusterAssment[nonzero(clusterAssment[:,0].A!=i)[0],1])            print ("sseSplit, and notSplit: ",sseSplit,sseNotSplit)            if (sseSplit + sseNotSplit) < lowestSSE:                bestCentToSplit = i                bestNewCents = centroidMat                bestClustAss = splitClustAss.copy()                lowestSSE = sseSplit + sseNotSplit        bestClustAss[nonzero(bestClustAss[:,0].A == 1)[0],0] = len(centList) #change 1 to 3,4, or whatever        bestClustAss[nonzero(bestClustAss[:,0].A == 0)[0],0] = bestCentToSplit        print ('the bestCentToSplit is: ',bestCentToSplit)        print ('the len of bestClustAss is: ', len(bestClustAss))        centList[bestCentToSplit] = bestNewCents[0,:].tolist()[0]#replace a centroid with two best centroids         centList.append(bestNewCents[1,:].tolist()[0])        clusterAssment[nonzero(clusterAssment[:,0].A == bestCentToSplit)[0],:]= bestClustAss#reassign new clusters, and SSE    return mat(centList), clusterAssmentdatMat=loadDataSet(r'C:\Users\li\Downloads\machinelearninginaction\Ch10\testSet2.txt')#myCentroids,clustAssing=kMeans(datMat, 4)myCentroids,clustAssing=biKmeans(datMat, 3)print(myCentroids)