python中kmeans聚类实现

#encoding:utf-8
'''
Created on 2015年9月21日
@author: ZHOUMEIXU204
'''

path=u"D:\\Users\\zhoumeixu204\\Desktop\\python语言机器学习\\机器学习实战代码   python\\机器学习实战代码\\machinelearninginaction\\Ch10\\"
import  numpy  as np
def loadDataSet(fileName):  #读取数据
dataMat=[]
fr=open(fileName)
for line  in fr.readlines():
curLine=line.strip().split('\t')
fltLine=map(float,curLine)
dataMat.append(fltLine)
return  dataMat
def  distEclud(vecA,vecB):    #计算距离
return np.sqrt(np.sum(np.power(vecA-vecB,2)))
def randCent(dataSet,k):      #构建镞质心
n=np.shape(dataSet)[1]
centroids=np.mat(np.zeros((k,n)))
for j in  range(n):
minJ=np.min(dataSet[:,j])
rangeJ=float(np.max(dataSet[:,j])-minJ)
centroids[:,j]=minJ+rangeJ*np.random.rand(k,1)
return centroids
dataMat=np.mat(loadDataSet(path+'testSet.txt'))
print(dataMat[:,0])

# 所有数都比-inf大
# 所有数都比+inf小
def  kMeans(dataSet,k,distMeas=distEclud,createCent=randCent):
m=np.shape(dataSet)[0]
clusterAssment=np.mat(np.zeros((m,2)))
centroids=createCent(dataSet,k)
clusterChanged=True
while clusterChanged:
clusterChanged=False
for i in range(m):
minDist=np.inf;minIndex=-1  #np.inf表示无穷大
for j in range(k):
distJI=distMeas(centroids[j,:],dataSet[i,:])
if distJI
minDist=distJI;minIndex=j
if clusterAssment[i,0]!=minIndex:clusterChanged=True
clusterAssment[i,:]=minIndex,minDist**2
print centroids
for cent in range(k):
ptsInClust=dataSet[np.nonzero(clusterAssment[:,0].A==cent)[0]]  #[0]这里取0是指去除坐标索引值，结果会有两个
#np.nonzero函数，寻找非0元素的下标  nz=np.nonzero([1,2,3,0,0,4,0])结果为0,1,2
centroids[cent,:]=np.mean(ptsInClust,axis=0)

return  centroids,clusterAssment
myCentroids,clustAssing=kMeans(dataMat,4)
print(myCentroids,clustAssing)

#二分均值聚类(bisecting  k-means)
def   biKmeans(dataSet,k,distMeas=distEclud):
m=np.shape(dataSet)[0]
clusterAssment=np.mat(np.zeros((m,2)))
centroid0=np.mean(dataSet,axis=0).tolist()[0]
centList=[centroid0]
for j in range(m):
clusterAssment[j,1]=distMeas(np.mat(centroid0),dataSet[j,:])**2
while (len(centList)
lowestSSE=np.Inf
for  i  in range(len(centList)):
ptsInCurrCluster=dataSet[np.nonzero(clusterAssment[:,0].A==i)[0],:]
centroidMat,splitClusAss=kMeans(ptsInCurrCluster,2,distMeas)
sseSplit=np.sum(splitClusAss[:,1])
sseNotSplit=np.sum(clusterAssment[np.nonzero(clusterAssment[:,0].A!=i)[0],1])
print "sseSplit, and notSplit:",sseSplit,sseNotSplit
if (sseSplit+sseNotSplit)
bestCenToSplit=i
bestNewCents=centroidMat
bestClustAss=splitClusAss.copy()
lowestSSE=sseSplit+sseNotSplit
bestClustAss[np.nonzero(bestClustAss[:,0].A==1)[0],0]=len(centList)
bestClustAss[np.nonzero(bestClustAss[:,0].A==0)[0],0]=bestCenToSplit
print "the bestCentToSplit is:",bestCenToSplit
print 'the len of  bestClustAss is:',len(bestClustAss)
centList[bestCenToSplit]=bestNewCents[0,:]
centList.append(bestNewCents[1,:])
clusterAssment[np.nonzero(clusterAssment[:,0].A==bestCenToSplit)[0],:]=bestClustAss
return centList,clusterAssment
print(u"二分聚类分析结果开始")
dataMat3=np.mat(loadDataSet(path+'testSet2.txt'))
centList,myNewAssments=biKmeans(dataMat3, 3)
print(centList)