二分k均值聚类

from numpy import *import timeimport matplotlib.pyplot as plt# calculate Euclidean distancedef euclDistance(vector1, vector2):    return sqrt(sum(power(vector2 - vector1, 2)))# init centroids with random samplesdef initCentroids(dataSet, k):    numSamples, dim = dataSet.shape    centroids = zeros((k, dim))    for i in range(k):        index = int(random.uniform(0, numSamples))        centroids[i, :] = dataSet[index, :]    return centroids# k-means clusterdef kmeans(dataSet, k):    numSamples = dataSet.shape[0]    # first column stores which cluster this sample belongs to,    # second column stores the error between this sample and its centroid    clusterAssment = mat(zeros((numSamples, 2)))    clusterChanged = True    ## step 1: init centroids    centroids = initCentroids(dataSet, k)    while clusterChanged:        clusterChanged = False        ## for each sample        for i in range(numSamples):            minDist = 100000.0            minIndex = 0            ## for each centroid            ## step 2: find the centroid who is closest            for j in range(k):                distance = euclDistance(centroids[j, :], dataSet[i, :])                if distance < minDist:                    minDist = distance                    minIndex = j                    ## step 3: update its cluster            if clusterAssment[i, 0] != minIndex:                clusterChanged = True                clusterAssment[i, :] = minIndex, minDist ** 2                ## step 4: update centroids        for j in range(k):            pointsInCluster = dataSet[nonzero(clusterAssment[:, 0].A == j)[0]]            centroids[j, :] = mean(pointsInCluster, axis=0)    print('Congratulations, cluster using k-means complete!')    return centroids, clusterAssment# bisecting k-means clusterdef biKmeans(dataSet, k):    numSamples = dataSet.shape[0]    # first column stores which cluster this sample belongs to,    # second column stores the error between this sample and its centroid    clusterAssment = mat(zeros((numSamples, 2)))    # step 1: the init cluster is the whole data set    centroid = mean(dataSet, axis=0).tolist()[0]    centList = [centroid]    for i in range(numSamples):        clusterAssment[i, 1] = euclDistance(mat(centroid), dataSet[i, :]) ** 2    while len(centList) < k:        # min sum of square error        minSSE = 100000.0        numCurrCluster = len(centList)        # for each cluster        for i in range(numCurrCluster):            # step 2: get samples in cluster i            pointsInCurrCluster = dataSet[nonzero(clusterAssment[:, 0].A == i)[0], :]            # step 3: cluster it to 2 sub-clusters using k-means            centroids, splitClusterAssment = kmeans(pointsInCurrCluster, 2)            # step 4: calculate the sum of square error after split this cluster            splitSSE = sum(splitClusterAssment[:, 1])            notSplitSSE = sum(clusterAssment[nonzero(clusterAssment[:, 0].A != i)[0], 1])            currSplitSSE = splitSSE + notSplitSSE            # step 5: find the best split cluster which has the min sum of square error            if currSplitSSE < minSSE:                minSSE = currSplitSSE                bestCentroidToSplit = i                bestNewCentroids = centroids.copy()                bestClusterAssment = splitClusterAssment.copy()                # step 6: modify the cluster index for adding new cluster        bestClusterAssment[nonzero(bestClusterAssment[:, 0].A == 1)[0], 0] = numCurrCluster        bestClusterAssment[nonzero(bestClusterAssment[:, 0].A == 0)[0], 0] = bestCentroidToSplit        # step 7: update and append the centroids of the new 2 sub-cluster        centList[bestCentroidToSplit] = bestNewCentroids[0, :]        centList.append(bestNewCentroids[1, :])        # step 8: update the index and error of the samples whose cluster have been changed        clusterAssment[nonzero(clusterAssment[:, 0].A == bestCentroidToSplit), :] = bestClusterAssment    print('Congratulations, cluster using bi-kmeans complete!')    return mat(centList), clusterAssment# show your cluster only available with 2-D datadef showCluster(dataSet, k, centroids, clusterAssment):    numSamples, dim = dataSet.shape    if dim != 2:        print("Sorry! I can not draw because the dimension of your data is not 2!")        return 1    mark = ['or', 'ob', 'og', 'ok', '^r', '+r', 'sr', 'dr', '<r', 'pr']    if k > len(mark):        print("Sorry! Your k is too large! please contact Zouxy")        return 1        # draw all samples    for i in range(numSamples):        markIndex = int(clusterAssment[i, 0])        plt.plot(dataSet[i, 0], dataSet[i, 1], mark[markIndex])    mark = ['Dr', 'Db', 'Dg', 'Dk', '^b', '+b', 'sb', 'db', '<b', 'pb']    # draw the centroids    for i in range(k):        plt.plot(centroids[i, 0], centroids[i, 1], mark[i], markersize=12)    plt.show()from numpy import *import timeimport matplotlib.pyplot as plt## step 1: load dataprint("step 1: load data...")dataSet = []fileIn = open('/home/zengxl/PycharmProjects/test5.py/aaa/机器学习实战代码/Ch10/testSet.txt')for line in fileIn.readlines():    lineArr = line.strip().split('\t')    dataSet.append([float(lineArr[0]), float(lineArr[1])])## step 2: clustering...print"step 2: clustering..."dataSet = mat(dataSet)k = 4centroids, clusterAssment = biKmeans(dataSet, k)## step 3: show the resultprint("step 3: show the result...")showCluster(dataSet, k, centroids, clusterAssment)