回归预测 - MATLAB实现SVR(支持向量机回归)多输入单输出（完整源码和数据）

%% %% 附本程序为SVR支持向量机回归模型 clc;clear; warning off; %% 导入数据 %% %% load data data=[attributes' strength']; %计算数据长度 input =data(:,1:7); output=data(:,8); nwhole=length(data); train_ratio=0.90; ntrain=round(nwhole*train_ratio); ntest =nwhole-ntrain; % 准备输入和输出训练数据 input_train =input(1:ntrain,:); output_train=output(1:ntrain,:); % 准备测试数据 input_test =input(ntrain+1:end,:); output_test=output(ntrain+1:end,:); %% 预测 %% 预测 SVMRP= fitrsvm(input_train,output_train); SVMoutput_train= predict(SVMRP,input_train); SVMoutput_test= predict(SVMRP,input_test); %------------------------------------------------------------------------------------- %% SVM数据输出 %% 训练误差集评价 SVMerror_train=SVMoutput_train-output_train; SVMpererror_train=SVMerror_train./output_train; % RMSE RMSEtrain = sqrt(sumsqr(SVMerror_train)/length(output_train)); % MAPE MAPEtrain = mean(abs(SVMpererror_train)); %------------------------------------------------------------------------------------- %% 测试集误差评价 SVMerror_test=SVMoutput_test-output_test; SVMpererror_test=SVMerror_test./output_test; % RMSE RMSEtest = sqrt(sumsqr(output_test - SVMoutput_test)/length(output_test)); % MAPE MAPEtest = mean(abs(SVMpererror_test)); SVMerror=[SVMerror_train' SVMerror_test']; SVMpererror=[SVMpererror_train' SVMpererror_test']; %-------------------------------------------------------------------------- disp('SVM模型训练平均绝对误差百分比MAPE'); disp(MAPEtrain) disp('SVM模型训练均方根误差RMSE') disp(RMSEtrain) disp('SVM模型测试平均绝对误差百分比MAPE'); disp(MAPEtest) disp('SVM模型测试均方根误差RMSE') disp(RMSEtest) %-------------------------------------------------------------------------- %% %% 数据可视化分析 %% %------------------------------------------------------------------------------------- figure(1) plot(SVMoutput_test,':.','Color',[0 0 255]./255,'linewidth',2,'Markersize',8,'MarkerFaceColor',[0 0 255]./255) hold on plot(output_test,'-','Color',[0 0 0]./255,'linewidth',2,'Markersize',3,'MarkerFaceColor',[0 0 0]./255) legend('SVM预测测试数据','实际分析数据','Location','NorthWest','FontName','华文宋体'); title('SVM模型预测结果及真实值','fontsize',12,'FontName','华文宋体') xlabel('时间','fontsize',12,'FontName','华文宋体'); ylabel('数值','fontsize',12,'FontName','华文宋体'); axis tight %------------------------------------------------------------------------------------- figure(2) bar(SVMpererror_test) legend('SVM模型相对误差','Location','NorthEast','FontName','华文宋体') title('SVM模型相对误差','fontsize',12,'FontName','华文宋体') ylabel('误差','fontsize',12,'FontName','华文宋体') xlabel('样本','fontsize',12,'FontName','华文宋体') axis tight