k-means基础实现
__author__ = 'Administrator' from numpy import * import time import matplotlib.pyplot as plt # 计算距离（欧式） def euclDistance(vector1, vector2): return sqrt(sum(power(vector2 - vector1, 2))) # 初始中心点(随机) def initCentroids(dataSet, k): numSamples,dim = dataSet.shape centroids = zeros((k, dim)) for i in range(k): index = int(random.uniform(0, numSamples)) centroids[i, :] = dataSet[index, :] return centroids def loaddata(name): dataMat=[] fe=open(name,'r') for line in fe: strs=line.restrip().split(',') flt=map(float,strs) dataMat.append(flt) return dataMat # k-means cluster def kmeans(dataSet, k): numSamples = dataSet.shape[0] # first column stores which cluster this sample belongs to, # second column stores the error between this sample and its centroid clusterAssment = mat(zeros((numSamples, 2))) clusterChanged = True ## step 1: init centroids centroids = initCentroids(dataSet, k) while clusterChanged: clusterChanged = False ## for each sample for i in xrange(numSamples): minDist = 100000.0 minIndex = 0 ## for each centroid ## step 2: find the centroid who is closest for j in range(k): distance = euclDistance(centroids[j, :], dataSet[i, :]) if distance < minDist: minDist = distance minIndex = j ## step 3: update its cluster if clusterAssment[i, 0] != minIndex: clusterChanged = True clusterAssment[i, :] = minIndex, minDist**2 ## step 4: update centroids for j in range(k): pointsInCluster = dataSet[nonzero(clusterAssment[:, 0].A == j)[0]] centroids[j, :] = mean(pointsInCluster, axis = 0) print 'Congratulations, cluster complete!' return centroids, clusterAssment # show your cluster only available with 2-D data def showCluster(dataSet, k, centroids, clusterAssment): numSamples, dim = dataSet.shape if dim != 2: print "Sorry! notice ,I can not draw because the dimension of your data is not 2!" return 1 mark = ['or', 'ob', 'og', 'ok', '^r', '+r', 'sr', 'dr', '<r', 'pr'] if k > len(mark): print "Sorry! Your k is too large! please contact Zouxy" return 1 # draw all samples for i in xrange(numSamples): markIndex = int(clusterAssment[i, 0]) plt.plot(dataSet[i, 0], dataSet[i, 1], mark[markIndex]) mark = ['Dr', 'Db', 'Dg', 'Dk', '^b', '+b', 'sb', 'db', '<b', 'pb'] # draw the centroids for i in range(k): plt.plot(centroids[i, 0], centroids[i, 1], mark[i], markersize = 12) plt.show() if __name__=='__main__': data=loaddata('data.txt') kmeans(data,5) 文章来源: https://blog.csdn.net/weixin_41442514/article/details/92141144