[Question] Train a multi-input multi-output model with corregionalization. #63
Description
I managed to train a multi-input single-output with no issues. Consider the fake data:
x1 = -5:0.8:5
x2 = -5:0.8:5
xx1 = vec([i for i in x1, j in x2])
xx2 = vec([j for i in x1, j in x2])
X = [x1 x2]
h(x, y) = 10 * (sin(x^2 + y^2) / (x^2 + y^2)) + 10
Y = [h(i,j) for (i,j) in zip(xx1, xx2)];i simply used this model
kernel = KernelFunctions.SqExponentialKernel()
m = GP(X, Y, kernel,opt_noise=false)Now imagine that I want to a multi-output setting, such that my data is now
x1 = -5:0.8:5
x2 = -5:0.8:5
xx1 = vec([i for i in x1, j in x2])
xx2 = vec([j for i in x1, j in x2])
X = [x1 x2]
h(x, y) = 10 * (sin(x^2 + y^2) / (x^2 + y^2)) + 10
g(x, y) = -1 / (x^2 + y^2 + 1) + 10
y1 = [h(i,j) for (i,j) in zip(xx1, xx2)];
y2 = [g(i,j) for (i,j) in zip(xx1, xx2)];
Y = [y1 y2]How should I proceed? There aren't any examples for this case in the docs.