I have an Nx2 input matrix called X. I also have the output values Y which is a vector Nx1. I create some data to test as follows:
Xtest=linspace(x_min,x_max,n);
Ytest=linspace(y_min,y_max,n);
So, matrix Z is of nx2 dimensions and is going to be used as my test points. I use the default tuning of the parameters found in the demo provided with the GPML lib which is as follows:
covfunc = {@covMaterniso, 3};
ell = 1/4; sf = 1;
hyp.cov = log([ell; sf]);
likfunc = @likGauss;
sn = 0.1;
hyp.lik = log(sn);
and then use the gp function:
[ymu ys2 fmu fs2] = gp(hyp, @infExact, [], covfunc, likfunc, x, y, z);
I expected ymu to be the predicted value for each testing value in z. When I plot this like this:
[L1,L2]=meshgrid(Xtest',Ytest');
[mu,~]=meshgrid(ymu,ymu);
surf(L1,L2,ymu);
I get a strange surface. i.e i get stripes of coloured area rather some Gaussian like structure which is expected. The data in X and Y are real life data.
What I would expect: