MAT之PLS：利用PLS(两个主成分的贡献率就可达100%)提高测试集辛烷值含量预测准确度并《测试集辛烷值含量预测结果对比》

输出结果

实现代码

load spectra;

temp = randperm(size(NIR, 1));

P_train = NIR(temp(1:50),:);

T_train = octane(temp(1:50),:);

P_test = NIR(temp(51:end),:);

T_test = octane(temp(51:end),:);

k = 2;

[Xloadings,Yloadings,Xscores,Yscores,betaPLS,PLSPctVar,MSE,stats] = plsregress(P_train,T_train,k);

figure

percent_explained = 100 * PLSPctVar(2,:) / sum(PLSPctVar(2,:));

pareto(percent_explained)

xlabel('主成分')

ylabel('贡献率(%)')

title('PLS：各个主成分的贡献率—Jason niu')

N = size(P_test,1);

T_sim = [ones(N,1) P_test] * betaPLS;

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:*',1:N,T_sim,'r-o')

legend('真实值','预测值','location','best')

xlabel('预测样本')

ylabel('辛烷值')

string = {'PLS：利用PLS(两个主成分的贡献率就可达100%)提高《测试集辛烷值含量预测结果对比》的准确度—Jason niu';['R^2=' num2str(R2)]};

title(string)