gpml.zip_gpml_多输入多输出_多高斯过程_高斯过程_高斯过程建模

% n=20; % rand('state',18); % randn('state',20); % covfunc={'covSum',{'covSEiso','covNoise'}}; % loghyper=[log(1.0);log(1.0);log(0.1)]; % x=15*(rand(n,1)-0.5); % y=chol(feval(covfunc{:},loghyper,x))'*randn(n,1); % plot(x,y,'k+','MarkerSize',17) % covfunc={'covSum',{'covSEiso','covNoise'}}; % feval(covfunc{:}) % loghyper=[log(1.0);log(sqrt(1.0));log(sqrt(0.1))]; % xstar=linspace(-7.5,7.5,201)'; % [mu S2]=gpr(loghyper,covfunc,x,y,xstar); clear; clc; load Xdata; load Ydata; mean_X=mean(X),std_X=std(X),mean_Y=mean(Y),std_Y=std(Y) offset=mean(Y); Y=Y-repmat(offset,106,1); X=X-repmat(mean(X),106,1); for i=1:size(X,2); X(:,i)=X(:,i)./std_X(i); end for i=1:size(Y,2); Y(:,i)=Y(:,i)./std_Y(i); end S1=lhsamp(30,3); for i=1:30; S(i,1)=3400+1100*S1(i,1); S(i,2)=550+100*S1(i,2); S(i,3)=220+50*S1(i,3); end xstar=S; std_xstar=std(xstar); xstar=xstar-repmat(mean(xstar),30,1); for i=1:size(xstar,2); xstar(:,i)=xstar(:,i)./std_xstar(i); end covfunc={'covSum',{'covSEard','covNoise'}}; for i=1:size(Y,2); % rand('state',0); logtheta0=[0;0;0;0;log(sqrt(0.1))]; [logtheta,fvals,iter]=minimize(logtheta0,'gpr',-100,covfunc,X,Y(:,i)); [mu S2]=gpr(logtheta,covfunc,X,Y(:,i),xstar); fstar(:,i)=mu; end for i=1:30 fstar(i,:)=fstar(i,:)+offset; end fstar % % [logtheta,fvals,iter]=minimize(logtheta0,'gpr',-100,covfunc,X,Y); %exp(logtheta)