SA模拟退火.zip_matlab_支持向量机_模拟退火 分类_退火_退火 分类

% 使用模拟退火法寻优SVM中的参数c和g % 使用METROPOLIS接受准则进行模拟 %% 清空环境 tic % 计时 clear clc close all 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 % 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),: ); %% SA算法主程序 lb=[0.01,0.01]; % 参数取值下界 ub=[100,100]; % 参数取值上界 % 冷却表参数 MarkovLength=100; % 马可夫链长度 DecayScale=0.85; % 衰减参数 StepFactor=0.2; % Metropolis步长因子 Temperature0=8; % 初始温度 Temperatureend=3; % 最终温度 Boltzmann_con=1; % Boltzmann常数 AcceptPoints=0.0; % Metropolis过程中总接受点 % 随机初始化参数 range=ub-lb; Par_cur=rand(size(lb)).*range+lb; % 用Par_cur表示当前解 Par_best_cur=Par_cur; % 用Par_best_cur表示当前最优解 Par_best=rand(size(lb)).*range+lb; % 用Par_best表示冷却中的最好解 % 每迭代一次退火(降温)一次，直到满足迭代条件为止 t=Temperature0; itr_num=0; % 记录迭代次数 while t>Temperatureend itr_num=itr_num+1; t=DecayScale*t; % 温度更新（降温) for i=1:MarkovLength % 在此当前参数点附近随机选下一点 p=0; while p==0 Par_new=Par_cur+StepFactor.*range.*(rand(size(lb))-0.5); % 防止越界 if sum(Par_new>ub)+sum(Par_new<lb)==0 p=1; end end % 检验当前解是否为全局最优解 if (objfun_svm(Par_best,train_wine_labels,train_wine,test_wine_labels,test_wine)>... objfun_svm(Par_new,train_wine_labels,train_wine,test_wine_labels,test_wine)) % 保留上一个最优解 Par_best_cur=Par_best; % 此为新的最优解 Par_best=Par_new; end % Metropolis过程 if (objfun_svm(Par_cur,train_wine_labels,train_wine,test_wine_labels,test_wine)-... objfun_svm(Par_new,train_wine_labels,train_wine,test_wine_labels,test_wine)>0) % 接受新解 Par_cur=Par_new; AcceptPoints=AcceptPoints+1; else changer=-1*(objfun_svm(Par_new,train_wine_labels,train_wine,test_wine_labels,test_wine)... -objfun_svm(Par_cur,train_wine_labels,train_wine,test_wine_labels,test_wine))/Boltzmann_con*Temperature0; p1=exp(changer); if p1>rand Par_cur=Par_new; AcceptPoints=AcceptPoints+1; end end end end %% 结果显示 disp(['最小值在点:',num2str(Par_best)]); Objval_best= objfun_svm(Par_best,train_wine_labels,train_wine,test_wine_labels,test_wine); disp(['目标最小值为:',num2str(Objval_best)]); bestc=Par_best(1);bestg=Par_best(2); %% cmd_gwosvm = ['-c ',num2str(bestc),' -g ',num2str(bestg)]; model_gwosvm = svmtrain(train_wine_labels,train_wine,cmd_gwosvm); %% SVM网络预测 [predict_label,accuracy] = svmpredict(test_wine_labels,test_wine,model_gwosvm); % 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 %% 显示程序运行时间 toc %% 显示运行时间 toc