EastmindYe-SVM-SMO之Python程序svmMLiA上手练习

学习使人进步SVM-SMO之Python程序svmMLiA上手练习ByEastmindYe天涯笔名：毛片物语，新浪笔名：叶片物语20160713>>>importos>>>os.chdir("D:\eclipse\workspace\Python2\svmMLiA")>>>fromsvmMLiAimport*#先对线性可分数据进行分类，>>>plotdatafile('testSet.txt') #显示原始数据分布图像原始数据文件结构x1 x2 y 3.542485 1.977398 -13.018896 2.556416 -17.551510 -1.580030 12.114999 -0.004466 -1……#先用最初的简单SMO方法求解，需要十几秒时间>>>dataArr,labelArr=loadDataSet('testSet.txt')>>>dataArr>>>labelArr>>>b,alphas=smoSimple(dataArr,labelArr,0.6,0.001,40)>>>bmatrix([[-3.85136778]])>>>alphas[alphas>0]matrix([[0.08383344,0.27559541,0.04824315,0.31118571]])>>>shape(alphas[alphas>0])(1,4)>>>foriinrange(100):ifalphas[i]>0.0:printdataArr[i],labelArr[i]#求得原样本中有4个是支持向量[4.658191,3.507396]-1.0[3.457096,-0.082216]-1.0[5.286862,-2.358286]1.0[6.080573,0.418886]1.0>>>#接下来用改进型方法加速搜索，1秒完成>>>#dataArr,labelArr=loadDataSet('testSet.txt')>>>b,alphas=smoP(dataArr,labelArr,0.6,0.001,40)#求得W向量，以及对应的判别函数W*X+b>>>ws=calcWs(alphas,dataArr,labelArr)>>>wsarray([[0.65307162],[-0.17196128]])>>>datMat=mat(dataArr)>>>datMat[0]*mat(ws)+bmatrix([[-0.92555695]])>>>labelArr[0]-1.0>>>>>>datMat[1]*mat(ws)+bmatrix([[-1.36706674]])>>>labelArr[1]-1.0>>>datMat[2]*mat(ws)+bmatrix([[2.30436336]])>>>labelArr[2]1.0#再对线性不可分数据采用RBF核函数分类>>>plotdatafile('testSetRBF.txt') #训练集数据>>>plotdatafile('testSetRBF2.txt') #测试集数据0000000111111111111111110000000000000000111111111111111000000000000000001111111111111110000000000000000001111111111111100000000000000000000000001111110000000000000000000000000011111100000000000000000000000001111110000000000000000000000000011111100000000000000000000000000111111000000000000000000000000011111100000000000000000000000001111111000000000000000000000000011111100000000000000000000000000111111000000000000000000000000011111110000000000000000000000000111111100000000000000000000000000111111000000000000000000000000000111100000000000000>>>testDigits(('rbf',20))做好人力资源，企业无忧iterationnumber:7thereare50