机器学习实战 logistic回归 python代码

# -*- 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

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