python实现kMeans算法

import numpy as npimport matplotlib.pyplot as pltdef loadDataSet(fileName):  dataMat = []  with open(fileName) as f:  for line in f.readlines():   line = line.strip().split('/t')   dataMat.append(line) dataMat = np.array(dataMat).astype(np.float32) return dataMatdef distEclud(vecA,vecB): return np.sqrt(np.sum(np.power((vecA-vecB),2)))def randCent(dataSet,k): m = np.shape(dataSet)[1] center = np.mat(np.ones((k,m))) for i in range(m):  centmin = min(dataSet[:,i])  centmax = max(dataSet[:,i])  center[:,i] = centmin + (centmax - centmin) * np.random.rand(k,1) return centerdef kMeans(dataSet,k,distMeans = 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   for j in range(k):    distJI = distMeans(dataSet[i,:],centroids[j,:])    if distJI < minDist:     minDist = distJI     minIndex = j   if clusterAssment[i,0] != minIndex:    clusterChanged = True   clusterAssment[i,:] = minIndex,minDist**2  for cent in range(k):   ptsInClust = dataSet[np.nonzero(clusterAssment[:,0].A == cent)[0]]   centroids[cent,:] = np.mean(ptsInClust,axis = 0) return centroids,clusterAssmentdata = loadDataSet('testSet.txt')muCentroids, clusterAssing = kMeans(data,4)fig = plt.figure(0)ax = fig.add_subplot(111)ax.scatter(data[:,0],data[:,1],c = clusterAssing[:,0].A)plt.show()print(clusterAssing)

import numpy as npimport matplotlib.pyplot as pltdef loadDataSet(fileName):  dataMat = []  with open(fileName) as f:  for line in f.readlines():   line = line.strip().split('/t')   dataMat.append(line) dataMat = np.array(dataMat).astype(np.float32) return dataMatdef distEclud(vecA,vecB): return np.sqrt(np.sum(np.power((vecA-vecB),2)))def randCent(dataSet,k): m = np.shape(dataSet)[1] center = np.mat(np.ones((k,m))) for i in range(m):  centmin = min(dataSet[:,i])  centmax = max(dataSet[:,i])  center[:,i] = centmin + (centmax - centmin) * np.random.rand(k,1) return centerdef kMeans(dataSet,k,distMeans = 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   for j in range(k):    distJI = distMeans(dataSet[i,:],centroids[j,:])    if distJI < minDist:     minDist = distJI     minIndex = j   if clusterAssment[i,0] != minIndex:    clusterChanged = True   clusterAssment[i,:] = minIndex,minDist**2  for cent in range(k):   ptsInClust = dataSet[np.nonzero(clusterAssment[:,0].A == cent)[0]]   centroids[cent,:] = np.mean(ptsInClust,axis = 0) return centroids,clusterAssmentdef biKmeans(dataSet,k,distMeans = distEclud): m = np.shape(dataSet)[0] clusterAssment = np.mat(np.zeros((m,2))) centroid0 = np.mean(dataSet,axis=0).tolist() centList = [centroid0] for j in range(m):  clusterAssment[j,1] = distMeans(dataSet[j,:],np.mat(centroid0))**2 while (len(centList)<k):  lowestSSE = np.inf  for i in range(len(centList)):   ptsInCurrCluster = dataSet[np.nonzero(clusterAssment[:,0].A == i)[0],:]   centroidMat,splitClustAss = kMeans(ptsInCurrCluster,2,distMeans)   sseSplit = np.sum(splitClustAss[:,1])   sseNotSplit = np.sum(clusterAssment[np.nonzero(clusterAssment[:,0].A != i)[0],1])   if (sseSplit + sseNotSplit) < lowestSSE:    bestCentToSplit = i    bestNewCents = centroidMat.copy()    bestClustAss = splitClustAss.copy()    lowestSSE = sseSplit + sseNotSplit  print('the best cent to split is ',bestCentToSplit)#  print('the len of the bestClust')  bestClustAss[np.nonzero(bestClustAss[:,0].A == 1)[0],0] = len(centList)  bestClustAss[np.nonzero(bestClustAss[:,0].A == 0)[0],0] = bestCentToSplit  clusterAssment[np.nonzero(clusterAssment[:,0].A == bestCentToSplit)[0],:] = bestClustAss.copy()  centList[bestCentToSplit] = bestNewCents[0,:].tolist()[0]  centList.append(bestNewCents[1,:].tolist()[0]) return np.mat(centList),clusterAssmentdata = loadDataSet('testSet2.txt')muCentroids, clusterAssing = biKmeans(data,3)fig = plt.figure(0)ax = fig.add_subplot(111)ax.scatter(data[:,0],data[:,1],c = clusterAssing[:,0].A,cmap=plt.cm.Paired)ax.scatter(muCentroids[:,0],muCentroids[:,1])plt.show()print(clusterAssing)print(muCentroids)