python中kmeans聚类实现代码
k-means算法思想较简单，说的通俗易懂点就是物以类聚，花了一点时间在python中实现k-means算法，k-means算法有本身的缺点，比如说k初始位置的选择，针对这个有不少人提出k-means++算法进行改进；另外一种是要对k大小的选择也没有很完善的理论，针对这个比较经典的理论是轮廓系数，二分聚类的算法确定k的大小，在最后还写了二分聚类算法的实现，代码主要参考机器学习实战那本书：
#encoding:utf-8
'''''
Createdon2015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\\"
importnumpyasnp
defloadDataSet(fileName):#读取数据
dataMat=[]
fr=open(fileName)
forlineinfr.readlines():
curLine=line.strip().split('\t')
fltLine=map(float,curLine)
dataMat.append(fltLine)
returndataMat
defdistEclud(vecA,vecB):#计算距离
returnnp.sqrt(np.sum(np.power(vecA-vecB,2)))
defrandCent(dataSet,k):#构建镞质心
n=np.shape(dataSet)[1]
centroids=np.mat(np.zeros((k,n)))
forjinrange(n):
minJ=np.min(dataSet[:,j])
rangeJ=float(np.max(dataSet[:,j])-minJ)
centroids[:,j]=minJ+rangeJ*np.random.rand(k,1)
returncentroids
dataMat=np.mat(loadDataSet(path+'testSet.txt'))
print(dataMat[:,0])
#所有数都比-inf大
#所有数都比+inf小
defkMeans(dataSet,k,distMeas=distEclud,createCent=randCent):
m=np.shape(dataSet)[0]
clusterAssment=np.mat(np.zeros((m,2)))
centroids=createCent(dataSet,k)
clusterChanged=True
whileclusterChanged:
clusterChanged=False
foriinrange(m):
minDist=np.inf;minIndex=-1#np.inf表示无穷大
forjinrange(k):
distJI=distMeas(centroids[j,:],dataSet[i,:])
ifdistJI
minDist=distJI;minIndex=j
ifclusterAssment[i,0]!=minIndex:clusterChanged=True
clusterAssment[i,:]=minIndex,minDist**2
printcentroids
forcentinrange(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)
returncentroids,clusterAssment
myCentroids,clustAssing=kMeans(dataMat,4)
print(myCentroids,clustAssing)
#二分均值聚类(bisectingk-means)
defbiKmeans(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]
forjinrange(m):
clusterAssment[j,1]=distMeas(np.mat(centroid0),dataSet[j,:])**2
while(len(centList)
lowestSSE=np.Inf
foriinrange(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,andnotSplit:",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"thebestCentToSplitis:",bestCenToSplit
print'thelenofbestClustAssis:',len(bestClustAss)
centList[bestCenToSplit]=bestNewCents[0,:]
centList.append(bestNewCents[1,:])
clusterAssment[np.nonzero(clusterAssment[:,0].A==bestCenToSplit)[0],:]=bestClustAss
returncentList,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)
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