%matplotlib inline
# -*- coding: utf-8 -*-
'''K均值聚类'''
from numpy import *
import numpy as np
def loadDataSet(fileName):
'''导入数据'''
dataMat = []
fr = open(fileName)
for line in fr.readlines():
curLine = line.strip().split('\t')
fltLine = list(curLine)
dataMat.append(fltLine)
dataMat = np.array(dataMat,dtype=np.float64)
return dataMat
def distEclud(vecA, vecB):
'''距离计算公式，本处计算欧式距离'''
return sqrt(sum(power(vecA - vecB, 2)))
def randCent(dataSet, k):
'''构建一个包含k个随机质心的集合'''
n = np.shape(dataSet)[1]
centroids = mat(zeros((k,n)))
for j in range(n):
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):
'''
K-均值聚类算法主函数
本算法会创建k个质心，然后将每个点分配到最近的质心，再重新计算质心
重复上述过程，知道数据点的簇分配结果不再改变为止
'''
m = shape(dataSet)[0]
clusterAssment = mat(zeros((m,2)))#存储每个点的簇分配结果
centroids = createCent(dataSet, k)#初始质心
clusterChanged = True
while clusterChanged:
clusterChanged = False
for i in range(m):#循环所有数据点
minDist = inf; minIndex = -1
for j in range(k):#计算数据点到每个质心的距离
distJI = distMeas(centroids[j,:],dataSet[i,:])
if distJI < minDist:#寻找最近的质心
minDist = distJI; minIndex = j
if clusterAssment[i,0] != minIndex: clusterChanged = True
clusterAssment[i,:] = minIndex,minDist**2
#第一列记录索引值，第二列存储误差
print(centroids)
for cent in range(k):#更新质心的位置
ptsInClust = dataSet[nonzero(clusterAssment[:,0].A==cent)[0]]
centroids[cent,:] = mean(ptsInClust, axis=0)
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):
'''从Yahoo返回一个字典'''
apiStem = 'https://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):
'''将数据封装并且保存到文件中'''
fw = open('places2.txt', 'w')
for line in open(fileName).readlines():
line = line.strip()
lineArr = line.split('\t')
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\t%f\t%f" % (lineArr[0], lat, lng))
fw.write('%s\t%f\t%f\n' % (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('F:\places.txt').readlines():
lineArr = line.split('\t')
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('F:\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()
#def ""
#datMat = mat(loadDataSet('testSet.txt'))
#center = randCent(datMat,2)
#myCentroids,clustAssing=kMeans(datMat,4)
#datMat2=mat(loadDataSet('testSet2.txt'))
#centList,myNewAssments = biKmeans(datMat2,3)
#geoResults=geoGrab('a VA Center','Augusta,ME')
clusterClubs(5)
#%matplotlib inline
# -*- coding: utf-8 -*-
''
阅读背景：
机器学习----K-means（二）
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