机器学习实战之K-Means算法
test10.py
#-*- coding：utf-8 import sys sys.path.append("kMeans.py") import kMeans from numpy import * # datMat = mat(kMeans.loadDataSet('testSet.txt')) # mindata = min(datMat[:, 0]) # print(mindata) # # # ranCentK = kMeans.randCent(datMat, 2) # print(ranCentK) # # dis = kMeans.distEclud(datMat[0], datMat[1]) # print(dis) # datMat3 = mat(kMeans.loadDataSet('testSet2.txt')) # centList, myNewAssments = kMeans.biKmeans(datMat3, 3) # print(centList) geoResults = kMeans.geoGrab('1 VA Center', 'Augusta, ME') print(geoResults) res = geoResults['ResultSet']['Error'] print(res) print('over!!!')
kMeans.py
''' Created on Feb 16, 2011 k Means Clustering for Ch10 of Machine Learning in Action @author: Peter Harrington ''' from numpy import * def loadDataSet(fileName): #general function to parse tab -delimited floats dataMat = [] #assume last column is target value fr = open(fileName) for line in fr.readlines(): curLine = line.strip().split(' ') fltLine = list(map(float,curLine)) #map all elements to float() dataMat.append(fltLine) return dataMat def distEclud(vecA, vecB): return sqrt(sum(power(vecA - vecB, 2))) #la.norm(vecA-vecB) def randCent(dataSet, k): n = shape(dataSet)[1] centroids = mat(zeros((k,n)))#create centroid mat for j in range(n):#create random cluster centers, within bounds of each dimension minJ = min(dataSet[:,j]) rangeJ = float(max(dataSet[:,j]) - minJ) centroids[:,j] = mat(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)))#create mat to assign data points #to a centroid, also holds SE of each point centroids = createCent(dataSet, k) clusterChanged = True while clusterChanged: clusterChanged = False for i in range(m):#for each data point assign it to the closest centroid 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):#recalculate centroids ptsInClust = dataSet[nonzero(clusterAssment[:,0].A==cent)[0]]#get all the point in this cluster centroids[cent,:] = mean(ptsInClust, axis=0) #assign centroid to mean return centroids, clusterAssment def biKmeans(dataSet, k, distMeas=distEclud): m = shape(dataSet)[0] clusterAssment = mat(zeros((m,2))) centroid0 = mean(dataSet, axis=0).tolist()[0] centList =[centroid0] #create a list with one centroid for j in range(m):#calc initial Error 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],:]#get the data points currently in cluster i centroidMat, splitClustAss = kMeans(ptsInCurrCluster, 2, distMeas) sseSplit = sum(splitClustAss[:,1])#compare the SSE to the currrent minimum 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) #change 1 to 3,4, or whatever 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]#replace a centroid with two best centroids centList.append(bestNewCents[1,:].tolist()[0]) clusterAssment[nonzero(clusterAssment[:,0].A == bestCentToSplit)[0],:]= bestClustAss#reassign new clusters, and SSE return mat(centList), clusterAssment import urllib import json def geoGrab(stAddress, city): apiStem = 'http://where.yahooapis.com/geocode?' #create a dict and constants for the goecoder params = {} params['flags'] = 'J'#JSON return type params['appid'] = 'aaa0VN6k' params['location'] = '%s %s' % (stAddress, city) url_params = urllib.parse.urlencode(params) yahooApi = apiStem + url_params #print url_params print(yahooApi) c = urllib.request.urlopen(yahooApi) return json.loads(c.read()) from time import sleep def massPlaceFind(fileName): fw = open('places.txt', 'w') for line in open(fileName).readlines(): line = line.strip() lineArr = line.split(' ') retDict = geoGrab(lineArr[1], lineArr[2]) if retDict['ResultSet']['Error'] == 0: lat = float(retDict['ResultSet']['Results'][0]['latitude']) lng = float(retDict['ResultSet']['Results'][0]['longitude']) print("%s %f %f" % (lineArr[0], lat, lng)) fw.write('%s %f %f ' % (line, lat, lng)) else: print("error fetching") sleep(1) fw.close() def distSLC(vecA, vecB):#Spherical Law of Cosines a = sin(vecA[0,1]*pi/180) * sin(vecB[0,1]*pi/180) b = cos(vecA[0,1]*pi/180) * cos(vecB[0,1]*pi/180) * cos(pi * (vecB[0,0]-vecA[0,0]) /180) return arccos(a + b)*6371.0 #pi is imported with numpy import matplotlib import matplotlib.pyplot as plt def clusterClubs(numClust=5): datList = [] for line in open('places.txt').readlines(): lineArr = line.split(' ') datList.append([float(lineArr[4]), float(lineArr[3])]) datMat = mat(datList) myCentroids, clustAssing = biKmeans(datMat, numClust, distMeas=distSLC) fig = plt.figure() rect=[0.1,0.1,0.8,0.8] scatterMarkers=['s', 'o', '^', '8', 'p', 'd', 'v', 'h', '>', '<'] axprops = dict(xticks=[], yticks=[]) ax0=fig.add_axes(rect, label='ax0', **axprops) imgP = plt.imread('Portland.png') ax0.imshow(imgP) ax1=fig.add_axes(rect, label='ax1', frameon=False) for i in range(numClust): ptsInCurrCluster = datMat[nonzero(clustAssing[:,0].A==i)[0],:] markerStyle = scatterMarkers[i % len(scatterMarkers)] ax1.scatter(ptsInCurrCluster[:,0].flatten().A[0], ptsInCurrCluster[:,1].flatten().A[0], marker=markerStyle, s=90) ax1.scatter(myCentroids[:,0].flatten().A[0], myCentroids[:,1].flatten().A[0], marker='+', s=300) plt.show()