Machine learning Combat Logistic regression Python code

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#-*-Coding:utf-8-*-"" "Created on Sun, 15:57:18 2017@author:mdz" "" "Http://blog.chinaunix.net/xmlrpc.php?r=blo g/article&uid=9162199&id=4223505 ' Import numpy as np# read Data def loaddataset (): datalist=[];labellist=[] Fr=ope N (' testSet.txt ') for line in Fr.readlines (): Linearr=line.strip (). Split () Datalist.append ([1.0,float] (line Arr[0]), float (linearr[1]) labellist.append (int (linearr[2))) return datalist,labellist# introduce the logistic function 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) Labelma T=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.t Ranspose () *error return weights #画图呈现分类效果def plotbestfit (weights,datalist,labellist): import matplotlib.pyplot as pl T 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.ara Nge ( -3.0,3.0,0.1) y= (-weights[0]-weights[1]*x)/weights[2] Ax.plot (x, y) plt.xlabel (' X1 ') plt.ylabel (' X2 ') pl T.show () #脚本 "Import Tempdatalist,labellist=temp.loaddataset () weights=temp.gradascent (datalist,labellist) Temp.plotbestfit (weights,datalist,labellist) "
TestSet.txt
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-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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Machine learning Combat Logistic regression Python code