机器学习-Logistic回归之梯度上升法

来源：互联网发布：c语言计算闰年循环编辑：程序博客网时间：2024/06/05 01:18

运行环境：ubuntu 16.10+MATLAB2016a

数据集：

　　　　特征1　　　　特征2　　　标签

-0.017612   14.053064   0-1.395634   4.662541    1-0.752157   6.538620    0-1.322371   7.152853    00.423363    11.054677   00.406704    7.067335    10.667394    12.741452   0-2.460150   6.866805    10.569411    9.548755    0-0.026632   10.427743   00.850433    6.920334    11.347183    13.175500   01.176813    3.167020    1-1.781871   9.097953    0-0.566606   5.749003    10.931635    1.589505    1-0.024205   6.151823    1-0.036453   2.690988    1-0.196949   0.444165    11.014459    5.754399    11.985298    3.230619    1-1.693453   -0.557540   1-0.576525   11.778922   0-0.346811   -1.678730   1-2.124484   2.672471    11.217916    9.597015    0-0.733928   9.098687    0-3.642001   -1.618087   10.315985    3.523953    11.416614    9.619232    0-0.386323   3.989286    10.556921    8.294984    11.224863    11.587360   0-1.347803   -2.406051   11.196604    4.951851    10.275221    9.543647    00.470575    9.332488    0-1.889567   9.542662    0-1.527893   12.150579   0-1.185247   11.309318   0-0.445678   3.297303    11.042222    6.105155    1-0.618787   10.320986   01.152083    0.548467    10.828534    2.676045    1-1.237728   10.549033   0-0.683565   -2.166125   10.229456    5.921938    1-0.959885   11.555336   00.492911    10.993324   00.184992    8.721488    0-0.355715   10.325976   0-0.397822   8.058397    00.824839    13.730343   01.507278    5.027866    10.099671    6.835839    1-0.344008   10.717485   01.785928    7.718645    1-0.918801   11.560217   0-0.364009   4.747300    1-0.841722   4.119083    10.490426    1.960539    1-0.007194   9.075792    00.356107    12.447863   00.342578    12.281162   0-0.810823   -1.466018   12.530777    6.476801    11.296683    11.607559   00.475487    12.040035   0-0.783277   11.009725   00.074798    11.023650   0-1.337472   0.468339    1-0.102781   13.763651   0-0.147324   2.874846    10.518389    9.887035    01.015399    7.571882    0-1.658086   -0.027255   11.319944    2.171228    12.056216    5.019981    1-0.851633   4.375691    1-1.510047   6.061992    0-1.076637   -3.181888   11.821096    10.283990   03.010150    8.401766    1-1.099458   1.688274    1-0.834872   -1.733869   1-0.846637   3.849075    11.400102    12.628781   01.752842    5.468166    10.078557    0.059736    10.089392    -0.715300   11.825662    12.693808   00.197445    9.744638    00.126117    0.922311    1-0.679797   1.220530    10.677983    2.556666    10.761349    10.693862   0-2.168791   0.143632    11.388610    9.341997    00.317029    14.739025   0

基于MATLAB的代码:

%%机器学习-logistic回归-梯度上升优化算法%%machine learning-logistic regression-gradient ascent optimizationclear;testSet = importdata('testSet.txt');[m,n] = size(testSet);data = zeros(m,n);data(:,1) = ones(m,1);data(:,2:end) = testSet(:,1:end-1);labels = testSet(:,end);alpha = 0.001;   %步长maxCycles = 500;   %最大迭代次数weights = ones(n,1);   %回归系数for i = 1:maxCycles    h = 1.0./(1 + exp(-data * weights));    error = labels - h;    weights = weights + alpha * data' * error;endclass1_vectors = testSet(testSet(:,3)==1,[1,2]);class0_vectors = testSet(testSet(:,3)==0,[1,2]);plot(class0_vectors(:,1),class0_vectors(:,2),'*',class1_vectors(:,1),class1_vectors(:,2),'o');ylim([-10,20]);x = -3.0:0.01:3.0;y = (-weights(1) - weights(2) * x) / weights(3);hold on;plot(x,y,'r','linewidth',2);

运行结果:

123
　　深蓝色雪花代表标签为“０”的一类，空心圆圈代表标签为“１”的一类，红色直线为拟合直线。

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