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