# -*- coding: utf-8 -*-

"""

Created on Sun Aug 06 15:57:18 2017

@author: mdz

"""

‘‘‘http://blog.chinaunix.net/xmlrpc.php?r=blog/article&uid=9162199&id=4223505‘‘‘

import numpy as np

#读取数据

def loadDataSet():

dataList=[];labelList=[]

fr=open(‘testSet.txt‘)

for line in fr.readlines():

lineArr=line.strip().split()

dataList.append([1.0,float(lineArr[0]),float(lineArr[1])])

labelList.append(int(lineArr[2]))

return dataList,labelList

#引入Logistic函数

def sigmoid(inx):

return 1.0/(1+np.exp(-inx))

#梯度下降法拟合回归系数

def gradAscent(dataList,labelList):

dataMat=np.mat(dataList)

labelMat=np.mat(labelList).transpose()

m,n=np.shape(dataMat)

alpha=0.001

maxCycles=500

weights=np.ones((n,1))

for k in range (maxCycles):

h=sigmoid(dataMat*weights)

error=(labelMat-h)

weights=weights+alpha*dataMat.transpose()*error

return weights

#画图呈现分类效果

def plotBestFit(weights,dataList,labelList):

import matplotlib.pyplot as plt

weights=weights.getA()#返回narray

dataArr=np.array(dataList)

n=np.shape(dataArr)[0]

xcord1=[];ycord1=[]

xcord2=[];ycord2=[]

for i in range(n):

if int (labelList[i])==1:

xcord1.append(dataArr[i][1]);ycord1.append(dataArr[i][2])

else:

xcord2.append(dataArr[i][1]);ycord2.append(dataArr[i][2])

fig=plt.figure()

ax=fig.add_subplot(111)

ax.scatter(xcord1,ycord1,s=100,c=‘red‘,marker=‘s‘)

ax.scatter(xcord2,ycord2,s=100,c=‘green‘,marker=‘o‘)

x=np.arange(-3.0,3.0,0.1)

y=(-weights[0]-weights[1]*x)/weights[2]

ax.plot(x,y)

plt.xlabel(‘X1‘)

plt.ylabel(‘X2‘)

plt.show()

#脚本

‘‘‘import temp

dataList,labelList=temp.loadDataSet()

weights=temp.gradAscent(dataList,labelList)

temp.plotBestFit(weights,dataList,labelList)‘‘‘

testSet.txt

‘‘‘

-0.017612 14.053064 0

-1.395634 4.662541 1

-0.752157 6.538620 0

-1.322371 7.152853 0

0.423363 11.054677 0

0.406704 7.067335 1

0.667394 12.741452 0

-2.460150 6.866805 1

0.569411 9.548755 0

-0.026632 10.427743 0

0.850433 6.920334 1

1.347183 13.175500 0

1.176813 3.167020 1

-1.781871 9.097953 0

-0.566606 5.749003 1

0.931635 1.589505 1

-0.024205 6.151823 1

-0.036453 2.690988 1

-0.196949 0.444165 1

1.014459 5.754399 1

1.985298 3.230619 1

-1.693453 -0.557540 1

-0.576525 11.778922 0

-0.346811 -1.678730 1

-2.124484 2.672471 1

1.217916 9.597015 0

-0.733928 9.098687 0

-3.642001 -1.618087 1

0.315985 3.523953 1

1.416614 9.619232 0

-0.386323 3.989286 1

0.556921 8.294984 1

1.224863 11.587360 0

-1.347803 -2.406051 1

1.196604 4.951851 1

0.275221 9.543647 0

0.470575 9.332488 0

-1.889567 9.542662 0

-1.527893 12.150579 0

-1.185247 11.309318 0

-0.445678 3.297303 1

1.042222 6.105155 1

-0.618787 10.320986 0

1.152083 0.548467 1

0.828534 2.676045 1

-1.237728 10.549033 0

-0.683565 -2.166125 1

0.229456 5.921938 1

-0.959885 11.555336 0

0.492911 10.993324 0

0.184992 8.721488 0

-0.355715 10.325976 0

-0.397822 8.058397 0

0.824839 13.730343 0

1.507278 5.027866 1

0.099671 6.835839 1

-0.344008 10.717485 0

1.785928 7.718645 1

-0.918801 11.560217 0

-0.364009 4.747300 1

-0.841722 4.119083 1

0.490426 1.960539 1

-0.007194 9.075792 0

0.356107 12.447863 0

0.342578 12.281162 0

-0.810823 -1.466018 1

2.530777 6.476801 1

1.296683 11.607559 0

0.475487 12.040035 0

-0.783277 11.009725 0

0.074798 11.023650 0

-1.337472 0.468339 1

-0.102781 13.763651 0

-0.147324 2.874846 1

0.518389 9.887035 0

1.015399 7.571882 0

-1.658086 -0.027255 1

1.319944 2.171228 1

2.056216 5.019981 1

-0.851633 4.375691 1

-1.510047 6.061992 0

-1.076637 -3.181888 1

1.821096 10.283990 0

3.010150 8.401766 1

-1.099458 1.688274 1

-0.834872 -1.733869 1

-0.846637 3.849075 1

1.400102 12.628781 0

1.752842 5.468166 1

0.078557 0.059736 1

0.089392 -0.715300 1

1.825662 12.693808 0

0.197445 9.744638 0

0.126117 0.922311 1

-0.679797 1.220530 1

0.677983 2.556666 1

0.761349 10.693862 0

-2.168791 0.143632 1

1.388610 9.341997 0

0.317029 14.739025 0