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% SVM神经网络中的参数优化-如何更好的提升分类器的性能 % 清空环境变量function chapter13_GAclose all;clear;clc;format compact;% 数据提取% 载入测试数据wine,其中包含的数据为classnumber = 3,wine:178*13的矩阵,wine_labes:178*1的列向量load chapter13_wine.mat;% 画出测试数据的box可视化图figure;boxplot(wine,orientation,horizontal,labels,categories);title(wine数据的box可视化图,FontSize,12);xlabel(属性值,FontSize,12);grid on;% 画出测试数据的分维可视化图figuresubplot(3,5,1);hold onfor run = 1:178 plot(run,wine_labels(run),*);endxlabel(样本,FontSize,10);ylabel(类别标签,FontSize,10);title(class,FontSize,10);for run = 2:14 subplot(3,5,run); hold on; str = attrib ,num2str(run-1); for i = 1:178 plot(i,wine(i,run-1),*); end xlabel(样本,FontSize,10); ylabel(属性值,FontSize,10); title(str,FontSize,10);end% 选定训练集和测试集% 将第一类的1-30,第二类的60-95,第三类的131-153做为训练集train_wine = wine(1:30,:);wine(60:95,:);wine(131:153,:);% 相应的训练集的标签也要分离出来train_wine_labels = wine_labels(1:30);wine_labels(60:95);wine_labels(131:153);% 将第一类的31-59,第二类的96-130,第三类的154-178做为测试集test_wine = wine(31:59,:);wine(96:130,:);wine(154:178,:);% 相应的测试集的标签也要分离出来test_wine_labels = wine_labels(31:59);wine_labels(96:130);wine_labels(154:178);% 数据预处理% 数据预处理,将训练集和测试集归一化到0,1区间mtrain,ntrain = size(train_wine);mtest,ntest = size(test_wine);dataset = train_wine;test_wine;% mapminmax为MATLAB自带的归一化函数dataset_scale,ps = mapminmax(dataset,0,1);dataset_scale = dataset_scale;train_wine = dataset_scale(1:mtrain,:);test_wine = dataset_scale( (mtrain+1):(mtrain+mtest),: );% 选择GA最佳的SVM参数c&g% GA的参数选项初始化ga_option.maxgen = 200;ga_option.sizepop = 20; ga_option.cbound = 0,100;ga_option.gbound = 0,100;ga_option.v = 5;ga_option.ggap = 0.9;bestacc,bestc,bestg = gaSVMcgForClass(train_wine_labels,train_wine,ga_option);% 打印选择结果disp(打印选择结果);str = sprintf( Best Cross Validation Accuracy = %g% Best c = %g Best g = %g,bestacc,bestc,bestg);disp(str);% 利用最佳的参数进行SVM网络训练cmd = -c ,num2str(bestc), -g ,num2str(bestg);model = svmtrain(train_wine_labels,train_wine,cmd);% SVM网络预测predict_label,accuracy = svmpredict(test_wine_labels,test_wine,model);% 打印测试集分类准确率total = length(test_wine_labels);right = sum(predict_label = test_wine_labels);disp(打印测试集分类准确率);str = sprintf( Accuracy = %g% (%d/%d),accuracy(1),right,total);disp(str);% 结果分析% 测试集的实际分类和预测分类图figure;hold on;plot(test_wine_labels,o);plot(predict_label,r*);xlabel(测试集样本,FontSize,12);ylabel(类别标签,FontSize,12);legend(实际测试集分类,预测测试集分类);title(测试集的实际分类和预测分类图,FontSize,12);grid on;snapnow;% 子函数 gaSVMcgForClass.mfunction BestCVaccuracy,Bestc,Bestg,ga_option = gaSVMcgForClass(train_label,train_data,ga_option)% gaSVMcgForClass% by faruto%Email:patrick.lee QQ:516667408 BNU%last modified 2010.01.17%Super Moderator % 若转载请注明：% faruto and liyang , LIBSVM-farutoUltimateVersion % a toolbox with implements for support vector machines based on libsvm, 2009. % Software available at % % Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for% support vector machines, 2001. Software available at% http:/www.csie.ntu.edu.tw/cjlin/libsvm% 参数初始化if nargin = 2 ga_option = struct(maxgen,200,sizepop,20,ggap,0.9,. cbound,0,100,gbound,0,1000,v,5);end% maxgen:最大的进化代数,默认为200,一般取值范围为100,500% sizepop:种群最大数量,默认为20,一般取值范围为20,100% cbound = cmin,cmax,参数c的变化范围,默认为(0,100% gbound = gmin,gmax,参数g的变化范围,默认为0,1000% v:SVM Cross Validation参数,默认为5%MAXGEN = ga_option.maxgen;NIND = ga_option.sizepop;NVAR = 2;PRECI = 20;GGAP = ga_option.ggap;trace = zeros(MAXGEN,2);FieldID = .rep(PRECI,1,NVAR);ga_option.cbound(1),ga_option.gbound(1);ga_option.cbound(2),ga_option.gbound(2); . 1,1;0,0;0,1;1,1;Chrom = crtbp(NIND,NVAR*PRECI);gen = 1;v = ga_option.v;BestCVaccuracy = 0;Bestc = 0;Bestg = 0;%cg = bs2rv(Chrom,FieldID);for nind = 1:NIND cmd = -v ,num2str(v), -c ,num2str(cg(nind,1), -g ,num2str(cg(nind,2); ObjV(nind,1) = svmtrain(train_label,train_data,cmd);endBestCVaccuracy,I = max(ObjV);Bestc = cg(I,1);Bestg = cg(I,2);for gen = 1:MAXGEN FitnV = ranking(-ObjV); SelCh = select(sus,Chrom,FitnV,GGAP); SelCh = recombin(xovsp,SelCh,0.7); SelCh = mut(SelCh); cg = bs2rv(SelCh,FieldID); for nind = 1:size(SelCh,1) cmd = -v ,num2str(v), -c ,num2str(cg(nind,1), -g ,num2str(cg(nind,2); ObjVSel(nind,1) = svmtrain(train_label,train_data,cmd); end Chrom,ObjV = reins(Chrom,SelCh,1,1,ObjV,ObjVSel); if max(ObjV) BestCVaccuracy BestCVaccuracy = NewBestCVaccuracy; Bestc = cg_temp(I,1); Bestg = cg_temp(I,2); end if abs( NewBestCVaccuracy-BestCVaccuracy ) = 10(-2) & . cg_temp(I,1) Bestc BestCVaccuracy = NewBestCVaccuracy; Bestc = cg_temp(I,1); Bestg = cg_temp(I,2); end trace(gen,1) = max(ObjV); trace(gen,2) = sum(ObjV)/length(ObjV); end%figure;hold on;trace = round(trace*10000)/10000;plot(trace(1:gen,1),r*-,LineWidth,1.5);plot(trace(1:gen,2),o-,LineWidth,1.5);legend(最佳适应度,平均适应度,3);xlabel(进化代数,FontSize,12);ylabel(适应度,FontSize,12);axis(0 gen 0 100);grid on;axis auto;line1 = 适应度曲线AccuracyGAmethod;line2 = (终止代数=, . num2str(gen),种群数量pop=, . num2str(NIND),);line3 = Best c=,num2str(Bestc), g=,num2str(Bestg), . CVAccuracy=,num2str(BestCVaccuracy),%;title(line1;line2;line3,FontSize,12);
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