案例13SVM神经网络中的参数优化---提升分类器性能.zip

共34个文件

png：26个

html：4个

m：3个

需积分: 5 74 浏览量 2023-05-26 10:56:56 上传评论 1 收藏 280KB ZIP 举报

资源推荐

资源详情

资源评论

收起资源包目录

案例13 SVM神经网络中的参数优化---提升分类器性能.zip （34个子文件）

chapter13_GridSearch.m 8KB

html

chapter13.html 28KB

chapter13_PSO.png 3KB

chapter13_04.png 14KB

chapter13_GridSearch_04.png 899B

chapter13_05.png 18KB

chapter13_GridSearch.html 26KB

chapter13_01.png 7KB

chapter13_GA_02.png 11KB

chapter13.png 3KB

chapter13_GA.png 3KB

chapter13_PSO_01.png 7KB

chapter13_PSO_04.png 9KB

chapter13_GridSearch.png 3KB

chapter13_GridSearch_06.png 15KB

chapter13_GA.html 27KB

chapter13_07.png 9KB

chapter13_GA_04.png 9KB

chapter13_GridSearch_02.png 11KB

chapter13_06.png 15KB

chapter13_GridSearch_07.png 9KB

chapter13_02.png 11KB

chapter13_GA_01.png 7KB

chapter13_PSO_03.png 14KB

chapter13_PSO.html 33KB

chapter13_GridSearch_01.png 7KB

chapter13_GridSearch_05.png 22KB

chapter13_GridSearch_03.png 12KB

chapter13_03.png 11KB

chapter13_PSO_02.png 11KB

chapter13_GA_03.png 13KB

chapter13_GA.m 8KB

chapter13_PSO.m 10KB

chapter13_wine.mat 20KB

%% SVM神经网络中的参数优化---如何更好的提升分类器的性能 % % <html> % <table border="0" width="600px" id="table1"> <tr> <td>该案例作者申明：</td> </tr> <tr> <td>1：本人长期驻扎在此<a target="_blank" href="http://www.ilovematlab.cn/forum-158-1.html">板块</a>里，对<a target="_blank" href="http://www.ilovematlab.cn/thread-48362-1-1.html">该案例</a>提问，做到有问必答。</td></tr><tr> <td>2：此案例有配套的教学视频，配套的完整可运行Matlab程序。</td> </tr> <tr> <td> 3：以下内容为该案例的部分内容（约占该案例完整内容的1/10）。</td> </tr> <tr> <td> 4：此案例为原创案例，转载请注明出处（<a target="_blank" href="http://www.ilovematlab.cn/">Matlab中文论坛</a>，<a target="_blank" href="http://www.ilovematlab.cn/forum-158-1.html">《Matlab神经网络30个案例分析》</a>）。</td> </tr> <tr> <td> 5：若此案例碰巧与您的研究有关联，我们欢迎您提意见，要求等，我们考虑后可以加在案例里。</td> </tr> <tr> <td> 6：您看到的以下内容为初稿，书籍的实际内容可能有少许出入，以书籍实际发行内容为准。</td> </tr><tr> <td> 7：此书其他常见问题、预定方式等，<a target="_blank" href="http://www.ilovematlab.cn/thread-47939-1-1.html">请点击这里</a>。</td> </tr></table> % </html> % %% 清空环境变量 function chapter13_PSO close 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 figure subplot(3,5,1); hold on for run = 1:178 plot(run,wine_labels(run),'*'); end xlabel('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),: ); %% 选择最佳的SVM参数c&g [bestacc,bestc,bestg] = psoSVMcgForClass(train_wine_labels,train_wine); % 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 % 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 psoSVMcgForClass.m function [bestCVaccuarcy,bestc,bestg,pso_option] = psoSVMcgForClass(train_label,train,pso_option) % psoSVMcgForClass % % by faruto %Email:[email protected] QQ:516667408 http://blog.sina.com.cn/faruto BNU %last modified 2010.01.17 %Super Moderator @ www.ilovematlab.cn % 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 http://www.ilovematlab.cn % % 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 pso_option = struct('c1',1.5,'c2',1.7,'maxgen',200,'sizepop',20, ... 'k',0.6,'wV',1,'wP',1,'v',5, ... 'popcmax',10^2,'popcmin',10^(-1),'popgmax',10^3,'popgmin',10^(-2)); end % c1:初始为1.5,pso参数局部搜索能力 % c2:初始为1.7,pso参数全局搜索能力 % maxgen:初始为200,最大进化数量 % sizepop:初始为20,种群最大数量 % k:初始为0.6(k belongs to [0.1,1.0]),速率和x的关系(V = kX) % wV:初始为1(wV best belongs to [0.8,1.2]),速率更新公式中速度前面的弹性系数 % wP:初始为1,种群更新公式中速度前面的弹性系数 % v:初始为3,SVM Cross Validation参数 % popcmax:初始为100,SVM 参数c的变化的最大值. % popcmin:初始为0.1,SVM 参数c的变化的最小值. % popgmax:初始为1000,SVM 参数g的变化的最大值. % popgmin:初始为0.01,SVM 参数c的变化的最小值. Vcmax = pso_option.k*pso_option.popcmax; Vcmin = -Vcmax ; Vgmax = pso_option.k*pso_option.popgmax; Vgmin = -Vgmax ; eps = 10^(-3); % 产生初始粒子和速度 for i=1:pso_option.sizepop % 随机产生种群和速度 pop(i,1) = (pso_option.popcmax-pso_option.popcmin)*rand+pso_option.popcmin; pop(i,2) = (pso_option.popgmax-pso_option.popgmin)*rand+pso_option.popgmin; V(i,1)=Vcmax*rands(1,1); V(i,2)=Vgmax*rands(1,1); % 计算初始适应度 cmd = ['-v ',num2str(pso_option.v),' -c ',num2str( pop(i,1) ),' -g ',num2str( pop(i,2) )]; fitness(i) = svmtrain(train_label, train, cmd); fitness(i) = -fitness(i); end % 找极值和极值点 [global_fitness bestindex]=min(fitness); % 全局极值 local_fitness=fitness; % 个体极值初始化 global_x=pop(bestindex,:); % 全局极值点 local_x=pop; % 个体极值点初始化 % 每一代种群的平均适应度 avgfitness_gen = zeros(1,pso_option.maxgen); % 迭代寻优 for i=1:pso_option.maxgen for j=1:pso_option.sizepop %速度更新 V(j,:) = pso_option.wV*V(j,:) + pso_option.c1*rand*(local_x(j,:) - pop(j,:)) + pso_option.c2*rand*(global_x - pop(j,:)); if V(j,1) > Vcmax V(j,1) = Vcmax; end if V(j,1) < Vcmin V(j,1) = Vcmin; end if V(j,2) > Vgmax V(j,2) = Vgmax; end if V(j,2) < Vgmin V(j,2) = Vgmin; end %种群更新 pop(j,:)=pop(j,:) + pso_option.wP*V(j,:); if pop(j,1) > pso_option.popcmax pop(j,1) = pso_option.popcmax; end if pop(j,1) < pso_option.popcmin pop(j,1) = pso_option.popcmin; end if pop(j,2) > pso_option.popgmax pop(j,2) = pso_option.popgmax; end if pop(j,2) < pso_option.popgmin pop(j,2) = pso_option.popgmin; end % 自适应粒子变异 if rand>0.5 k=ceil(2*rand); if k == 1 pop(j,k) = (20-1)*rand+1; end if k == 2 pop(j,k) = (pso_option.popgmax-pso_option.popgmin)*rand + pso_option.popgmin; end end %适应度值 cmd = ['-v ',num2st

评论收藏

内容反馈