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机器学习实战(10) ——K均值聚类算法(python实现)

2018-04-02 23:06 274 查看
这是学习机器学习算法实战这本书时,写的代码实战。让自己对各个算法有更直观的了解,不能一直不写啊。不管简单还是不简单都亲自一行一行的敲一遍啊。
具体的源码和和数据链接:https://pan.baidu.com/s/1G2S2pb5gfBnxGNNTFgTkEA 密码:fov0
这个第十章的代码和自己做的测试kMeans.py .这章代码较少,但后面的调用雅虎接口一直调不通。# -*- coding: utf-8 -*-
# author: Yufeng Song
from numpy import*
import matplotlib.pyplot as plt
def loadDataSet(fileName): #general function to parse tab -delimited floats
dataMat = [] #assume last column is target value
dataMat = genfromtxt(fileName,delimiter="\t",dtype=float)#可以用这一行代替
# fr = open(fileName)
# for line in fr.readlines():
# curLine = line.strip().split('\t')
# # fltLine = map(float,curLine) #map all elements to float()
# # dataMat.append(fltLine)
# temp = [] #这一行为我自己加的
# curLine = line.strip().split('\t')
# #fltLine = map(float, curLine) #书上代码
# temp.append(float(curLine[0]))
# temp.append(float(curLine[1]))
# dataMat.append(temp)
# genfromtxt("bikeSpeedVsIq_test.txt",delimiter="\t",dtype=float)
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 showCluster(dataSet, k, centroids, clusterAssment):
m, dim = shape(dataSet)
if dim != 2:
print ("Sorry! i
4000
can not draw because the dimension of 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!")
return 1
#draw all samples
for i in range(m):
markIndex = int(clusterAssment[i,0]) #为样本指定颜色
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],marker= '+',color = 'red',markersize=18)
#用marker来指定质心样式,用color和markersize来指定颜色和大小

plt.show()

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.urlencode(params)
url_params = urllib.parse.urlencode(params)
yahooApi = apiStem + url_params #print url_params
print(yahooApi)
# c=urllib.urlopen(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('\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('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('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()

if __name__ == '__main__':
#dataMat = mat(loadDataSet('testSet.txt'))
# print(min(dataMat[:,0]))
# print(randCent(dataMat,2))
# print(random.rand(2,1))
# print(distEclud(dataMat[0],dataMat[1]))
# print(kMeans(dataMat,4))
# myCentroids,clustAssing = kMeans(dataMat,4)
# showCluster(dataMat,4,myCentroids,clustAssing)
# datMat3 = mat(loadDataSet('testSet2.txt'))
# # centList,myNewAssments = biKmeans(datMat3,3)
# centList,myNewAssments = kMeans(datMat3,3, distMeas=distEclud, createCent=randCent)
#
# print(centList)
# showCluster(datMat3,3,centList,myNewAssments)

geoResult = geoGrab('1 VA Center','Augusta, ME')
print(geoResult)

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