www.gusucode.com > stats 源码程序 matlab案例代码 > stats/ComputeRegressionLossforTestDataExample.m
%% Compute Regression Loss for Test Data %% % Load the sample data. load(fullfile(matlabroot,'examples','stats','gprdata.mat')) %% % The data has 8 predictor variables and contains 500 observations in training % data and 100 observations in test data. This is simulated data. %% % Fit a GPR model using the squared exponential kernel function with separate % length scales for each predictor. Standardize the predictor values in % the training data. Use the exact method for fitting and prediction. gprMdl = fitrgp(Xtrain,ytrain,'FitMethod','exact',... 'PredictMethod','exact','KernelFunction','ardsquaredexponential',... 'Standardize',1); %% % Compute the regression error for the test data. L = loss(gprMdl,Xtest,ytest) %% % Predict the responses for test data. ypredtest = predict(gprMdl,Xtest); %% % Plot the test response along with the predictions. figure; plot(ytest,'r'); hold on; plot(ypredtest,'b'); legend('Data','Predictions','Location','Best'); %% % Manually compute the regression loss. L = (ytest - ypredtest)'*(ytest - ypredtest)/length(ytest)