Matlab学习笔记——（2）

load spectra_data.mat axis equal plot(NIR') %随机产生训练集和测试集 temp=randperm(size(NIR,1)); %训练集 P_train=NIR(temp(1:50),:)'; T_train=octane(temp(1:50),:)'; %测试集 P_test=NIR(temp(51:end),:)'; T_test=octane(temp(51:end),:)'; N=size(P_test,2); [p_train,ps_input]=mapminmax(P_train,0,1); p_test=mapminmax('apply',P_test,ps_input); [t_train,ps_output]=mapminmax(T_train,0,1); net=newff(p_train,t_train,9); net.trainParam.epochs=1000; net.trainParam.goal=1e-8; net.trainParam.lr=0.01; net=train(net,p_train,t_train); t_sim=sim(net,p_test); T_sim=mapminmax('reverse',t_sim,ps_output); error=abs(T_sim-T_test)./T_test; R2 = (N * sum(T_sim .* T_test) - sum(T_sim) * sum(T_test))^2 / ((N * sum((T_sim).^2) - (sum(T_sim))^2) * (N * sum((T_test).^2) - (sum(T_test))^2)); result=[T_test' T_sim' error']; figure plot(1:N,T_test,'b:x',1:N,T_sim,'r-o') legend('真实值','预测值'); xlabel('预测样本') ylabel('辛烷值') string={'测试集辛烷值含量预测结果对比';['R^2=' num2str(R2)]}; title(string);

mapminmax函数的官方解释

[Y,PS] = mapminmax(X,YMIN,YMAX) [Y,PS] = mapminmax(X,FP) Y = mapminmax('apply',X,PS) X = mapminmax('reverse',Y,PS) dx_dy = mapminmax('dx_dy',X,Y,PS)

可用于归一化及反归一化