How to use the gaussian process regression function in matlab 2015b ?
Learn Gaussian Process Regression in MATLAB 2015b! This resource provides a step-by-step guide and examples to effectively use the gpr function. Start your proj
Learn Gaussian Process Regression in MATLAB 2015b! This resource provides a step-by-step guide and examples to effectively use the gpr function. Start your proj
% True curve. fun = @(x) x.*sin(x); xx = linspace(0,10,100)'; yy = fun(xx); % Example points (from doc). You can do xd = linspace(1,9,nd)' as well % but you will get a different fit. xd = [1,3,5,6,7,8]'; yd = fun(xd); % Fit a GP model. Initialize 'Sigma' to a small value. A GP estimates % its parameters by maximizing the marginal log likelihood. Depending % on the data, the marginal log likelihood can have multiple local % optima corresponding to different interpretations of the data. % Initializing 'Sigma' to a small value discourages the high noise % variance interpretation of the data. gp = fitrgp(xd,yd,'KernelFunction','squaredexponential','sigma',0.1,'verbose',1); % Plot. figure(1); clf plot(xx,yy, 'r-.') hold on; [ypred,~,yint] = predict(gp,xx); plot(xx,ypred, 'g-'); plot(xx,yint(:,1),'k-'); plot(xx,yint(:,2),'m-'); plot(xd,yd,'ro'); legend('f(x) = x.*sin(x)','GPR predictions','Lower 95% interval','Upper 95% interval','Observations','Location','Best');
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