matlab神经网络30个案例分析源代码demochapter

该案例作者申明 byliyang[faruto]@faruto'sStudio~ .cn/faruto 子函数functionfunctioncloseall;format载入测试数据wine,其中包含的数据为classnumber3,wine:178*13的矩阵,wine_labes:178*1的列向量loadchapter13_wine.mat;画出测试数据的可视化图holdforrun=1:178forrun=2:14holdon;str=['attrib',num2str(run-1)];fori=1:178%选定训练集和测试%将第一类的1-30,第二类的60-95,第三类的131-153做为训练train_wine=%相应的训练集 train_wine_labels= testwine=%相应的测试集 test_wine_labels=%数据预处理,将训练集和测试集归一化到[0,1]区%mapminmax [train_wine,pstrain]=将映射函数的范围参数分别置为0和1pstrain.ymin=0;pstrain.ymax=%对训练集进行[0,1]归一[train_wine,pstrain]=%mapminmax [test_wine,pstest]=将映射函数的范围参数分别置为0和1pstest.ymin=0;pstest.ymax=%对测试集进行[0,1]归一[test_wine,pstest]=%对训练集和测试集进行转置,以符合libsvm工具箱的数据train_wine=train_wine';test_wine=test_wine';选择最佳的SVM参数%%首先进行粗略选择:c&g的变化范围是2^(-10),2^(-[bestacc,bestc,bestg]=SVMcgForClass(train_wine_labels,train_wine,-10,10,-%打印粗略选择结str=sprintf('BestCrossValidationAccuracy=%g%%Bestc=%gBestg%%根据粗略选择的结果图再进行精细选择:c的变化范围是2^(-2),2^(-1.5),...,2^(4),g的变化范围是[bestacc,bestc,bestg]=SVMcgForClass(train_wine_labels,train_wine,-2,4,-%str=sprintf('BestCrossValidationAccuracy=%g%%Bestc=%gBestg打印粗略选择结BestCrossValidationAccuracy=98.8764%Bestc=0.5Bestg=打印精细选择结BestCrossValidationAccuracy=98.8764%Bestc=0.353553Bestg=利用最佳的参数进行SVMcmdcmd=['-c',num2str(bestc),'-g',num2str(bestg)];model=SVM[predict_label,accuracy][predict_label,accuracy]=%打印测试total=rightsum(predict_labeltest_wine_labels);str=sprintf('Accuracy=%g%%(%d/%d)',accuracy(1),right,total);Accuracy=96.6292%(86/89)打印测试集分类Accuracy=96.6292%%%测试集的实际分类和预测分%通过图可以看出只有三个测试样本是holdon;%web web /forum-31-1.html-子函数functionfunction[bestacc,bestc,bestg]=%%输入%%%%%%%%%cmin:惩罚参数c的变化范围的最小值(取以2为底的对数后),即c_min=2^(cmin).默认为cmax:惩罚参数c的变化范围的最大值(取以2为底的对数后),即c_max=2^(cmax).默认为gmin:参数g的变化范围的最小值(取以2为底的对数后),即g_min=2^(gmin).默认为--5gmax:参数g的变化范围的最小值(取以2为底的对数后),即g_minv:crossvalidation的参数,即给测试集分为几部分进行crosscstep:参数c步进的大小.默认为1gstep:参数g步进的大小.默认为=2^(gmax).默认为3%accstep:最后显示准确率图时的步进大小.默认为%输出%bestacc:CrossValidation过程中的最高分类准确%bestc:最佳的参数%bestg:最佳的参数%abouttheparametersofSVMcgForClassifnargin<10accstep=ifnargin<accstep=cstep=gstep=ifnargin<accstep=v=cstep=gstep=ifnargin<accstep=v=cstep=gstep=gmax=ifnargin<accstep=v=cstep=gstep=gmax=gmin=-ifnargin<accstep=v=cstep=gstep=gmax=gmin=-cmax=ifnargin<accstep=v=cstep=gstep=gmax=gmin=-cmax=cmin=-%X:cY:g[X,Y]=meshgrid(cmin:cstep:cmax,gmin:gstep:gmax);[m,n]=size(X);cg=%recordaccuracywithdifferentc&g,andfindthebestaccuracywiththesmallestcbestc=0;bestg=bestacc=basenum=2;fori=1:mforj=cmd=['-v',num2str(v),'-c',num2str(basenum^X(i,j)),'-g',num2str(basenum^Y(i,j))];cg(i,j)=svmtrain(train_label,train,ifcg(i,j)>bestaccbestacc=cg(i,j);bestc=basenum^X(i,j);bestg=if(cg(i,j)==bestacc&&bestc>basenum^X(i,j))bestacc=cg(i,j);bestc=base