学步园

from numpy import *

def loadDataSet(fileName):
    dataMat = []
    fr = open(fileName)
    for line in fr.readlines():
        curLine = line.strip().split('\t')
        fltLine = map(float,curLine)
        dataMat.append(fltLine)
    return dataMat

def distEclude(vecA, vecB):
    return sqrt(sum(pow((vecA-vecB).A,2)))

def randCent(dataSet, k):
    n = shape(dataSet)[1]
    centroids = mat(zeros((k,n)))
    for j in range(n):
        print dataSet[:,j]
        minJ = min(dataSet[:,j])
        rangeJ = float(max(dataSet[:,j])-minJ)
        centroids[:,j] = minJ + rangeJ * random.rand(k,1)
    return centroids

def kMeans(dataSet, k, distMeas = distEclude, createCent=randCent):
    m = shape(dataSet)[0]
    clusterAssment = mat(zeros((m,2)))
    centroids = createCent(dataSet,k)
    clusterChanged = True
    while clusterChanged:
        for i in range(m):
            minDist = inf; minIndex = -1
            for j in range(k):
                distJI = distMeas(centroids[j,:],dataSet[i,:])
                if distJI < minDist:
                    minDist = distJI
                    minIndex = j
            clusterChanged = True if clusterAssment[i,0] != minIndex else False
            clusterAssment[i,:] = minIndex, minDist**2
        print centroids
        for cent in range(k):
            ptsInClust = dataSet[nonzero(clusterAssment[:,0].A == cent)[0]]  #get datas which belong to cent
            centroids[cent,:] = mean(ptsInClust, axis = 0)   #update the centroids
    return centroids, clusterAssment

# dataSet = loadDataSet('testSet.txt')
# kMeans(mat(dataSet), 3, distEclude, randCent)

def biKmeans(dataSet, k, distMeas = distEclude):
    m = shape(dataSet)[0]
    clusterAssment = mat(zeros((m,2)))
    centroid0 = mean(dataSet, axis=0).tolist()[0]
    centList = [centroid0]
    for j in range(m):
        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],:]
            centroidMat, splitClustAss = kMeans(ptsInCurrCluster, 2, distMeas)
            sseSplit = sum(splitClustAss[:,1])
            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
        '''after partition,the bestCentToSplit will replace the original cluster'''
        bestClustAss[nonzero(bestClustAss[:,0].A == 1)[0],0] = len(centList)
        bestClustAss[nonzero(bestClustAss[:,0].A == 0)[0],0] = bestCentToSplit
        print 'the bestCenttoSplit is:', bestCentToSplit
        print 'the len of bestCustAss is:', len(bestClustAss)
        centList[bestCentToSplit] = bestNewCents[0,:]
        centList.append(bestNewCents[1,:])
        '''update the clusterAssment'''
        clusterAssment[nonzero(clusterAssment[:,0].A == bestCentToSplit)[0],:] = bestClustAss
    return centList, clusterAssment
        
# datMat = mat(loadDataSet('testSet2.txt'))
# centList, myNewAssments = biKmeans(datMat, 3)
# print centList