Calculates (one point) Expected improvement.
Ei=EI(opt,x)
mlist of type EGO.
matrix (mx,nx) of points at which calculates (one point) Ei, mx rows for mx data points, nx columns for each dimension.
expected improvement at points x.
Calculates (one point) Expected improvement.
// 1D example function [y]=gpeak(x) y=sin(x*3)-exp(-(x+0.1)^2/0.01) endfunction; ftest=gpeak; x_bound=[-1; 1]; noise=0; X=(linspace(x_bound(1),x_bound(2),6))';// six initial points YExp=ftest(X); // response Xtest=(linspace(x_bound(1),x_bound(2),100))'; // test points // creaate initial kriging model [kmodel] =km(X,YExp,0,'gaussian',1,0.0000001); kmodel.estimateNoise=0; [kmodel p]=findTheta(kmodel,0,5,'MLL','RS',100); // ego boundaries lob=[-1]; upb=[1]; maxit=16; opt=EGO(kmodel,lob,upb,maxit,0.00000001,'RS'); x=RLHS(10,1,lob,upb); EI(opt,x) scf(); plot(Xtest,EI(opt,Xtest)); | ![]() | ![]() |
Jones D.R., Schonlau M. and Welch W.J. (1998), Efficient Global Optimization of Expensive Black-Box Functions, Journal of Global optimization, 13:, 455-492.
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