佛山做app网站,惠州seo按天计费,iis7.5发布网站教程,建筑八大员培训机构下面这个是测试的M文件%% Matlab神经网络43个案例分析%% 清空环境变量function chapter_GridSearchclose 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 wine.mat;% 画出测试数…下面这个是测试的M文件%% Matlab神经网络43个案例分析%% 清空环境变量function chapter_GridSearchclose 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 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 1:178plot(run,wine_labels(run),*);endxlabel(样本,FontSize,10);ylabel(类别标签,FontSize,10);title(class,FontSize,10);for run 2:14subplot(3,5,run);hold on;str [attrib ,num2str(run-1)];for i 1:178plot(i,wine(i,run-1),*);endxlabel(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( (mtrain1):(mtrainmtest),: );%% 选择最佳的SVM参数cg% 首先进行粗略选择: cg 的变化范围是 2^(-10),2^(-9),...,2^(10)[bestacc,bestc,bestg] SVMcgForClass(train_wine_labels,train_wine,-10,10,-10,10);% 打印粗略选择结果disp(打印粗略选择结果);str sprintf( Best Cross Validation Accuracy %g%% Best c %g Best g %g,bestacc,bestc,bestg);disp(str);% 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 2^(-4),2^(-3.5),...,2^(4),[bestacc,bestc,bestg] SVMcgForClass(train_wine_labels,train_wine,-2,4,-4,4,3,0.5,0.5,0.9);% 打印精细选择结果disp(打印精细选择结果);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% 通过图可以看出只有三个测试样本是被错分的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 SVMcgForClass.mfunction [bestacc,bestc,bestg] SVMcgForClass(train_label,train,cmin,cmax,gmin,gmax,v,cstep,gstep,accstep)%SVMcg cross validation by faruto% about the parameters of SVMcgif nargin 10accstep 4.5;endif nargin 8cstep 0.8;gstep 0.8;endif nargin 7v 5;endif nargin 5gmax 8;gmin -8;endif nargin 3cmax 8;cmin -8;end% X:c Y:g cg:CVaccuracy[X,Y] meshgrid(cmin:cstep:cmax,gmin:gstep:gmax);[m,n] size(X);cg zeros(m,n);eps 10^(-4);% record acc with different c g,and find the bestacc with the smallest cbestc 1;bestg 0.1;bestacc 0;basenum 2;for i 1:mfor j 1:ncmd [-v ,num2str(v), -c ,num2str( basenum^X(i,j) ), -g ,num2str( basenum^Y(i,j) )];cg(i,j) svmtrain(train_label, train, cmd);if cg(i,j) 55continue;endif cg(i,j) bestaccbestacc cg(i,j);bestc basenum^X(i,j);bestg basenum^Y(i,j);endif abs( cg(i,j)-bestacc )eps bestc basenum^X(i,j)bestacc cg(i,j);bestc basenum^X(i,j);bestg basenum^Y(i,j);endendend% to draw the acc with different c gfigure;[C,h] contour(X,Y,cg,70:accstep:100);clabel(C,h,Color,r);xlabel(log2c,FontSize,12);ylabel(log2g,FontSize,12);firstline SVC参数选择结果图(等高线图)[GridSearchMethod];secondline [Best c,num2str(bestc), g,num2str(bestg), ... CVAccuracy,num2str(bestacc),%];title({firstline;secondline},Fontsize,12);grid on;figure;meshc(X,Y,cg);% mesh(X,Y,cg);% surf(X,Y,cg);axis([cmin,cmax,gmin,gmax,30,100]);xlabel(log2c,FontSize,12);ylabel(log2g,FontSize,12);zlabel(Accuracy(%),FontSize,12);firstline SVC参数选择结果图(3D视图)[GridSearchMethod];secondline [Best c,num2str(bestc), g,num2str(bestg), ... CVAccuracy,num2str(bestacc),%];title({firstline;secondline},Fontsize,12);
