K-Means算法

from numpy import *

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

def distEclud(vecA, vecB):
return sqrt(sum(power(vecA-vecB, 2)))

def randCent(dataSet, k):
n = shape(dataSet)[1]
centroids = mat(zeros((k, n)))
for j in range(n):
minJ = min(dataSet[:, 0])
rangeJ = float(max(dataSet[:, j]) - minJ)
centroids[:, j] = minJ + rangeJ * random.rand(k, 1)
return centroids

def kMeans(dataSet, k, distMeas=distEclud, createCent=randCent):
m = shape(dataSet)[0]
clusterAssment = mat(zeros((m, 2)))
centroids = createCent(dataSet, k)
clusterChanged = True
while clusterChanged:
clusterChanged = False
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
if clusterAssment[i, 0] != minIndex:
clusterChanged = True
clusterAssment[i, :] = minIndex, minDist ** 2
# print centroids
for cent in  range(k):
ptsInClust = dataSet[nonzero(clusterAssment[:,0].A==cent)[0]]
# print nonzero(clusterAssment[:, 0].A == cent)[0]
centroids[cent,:] = mean(ptsInClust)
return centroids, clusterAssment

def biKmeans(dataSet, k, distMeans=distEclud):
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] = distMeans(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, distMeans)
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
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 bestClustAss is: ', len(bestClustAss)
centList[bestCentToSplit] = bestNewCents[0,:].tolist()[0]
centList.append(bestNewCents[1,:].tolist()[0])
clusterAssment[nonzero(clusterAssment[:,0].A == bestCentToSplit)[0],:]= bestClustAss
return mat(centList), clusterAssment