I’m having a weird issue when I use two GPRegression models in the same program.
say I have two GPRegression models -
gp1 = gp.models.GPRegression(X1, y1, kernel, noise=torch.tensor(0.1), jitter=1.0e-4)
gp2 = gp.models.GPRegression(X2, y2, kernel, noise=torch.tensor(0.1), jitter=1.0e-4)
after training, I can use them for prediction -
mu1, variance1 = gp1(x_new, full_cov=False, noiseless=False)
mu2, variance2 = gp2(x_new, full_cov=False, noiseless=False)
If now I train gp1 with more new data, as expected the gp1 now predicting mu1, variance1 differently because the gp1 is changed by the new data.
however, for some unknown/mysterious reason that the prediction from gp2 is also changed… I didn’t train the gp2 with new data therefore there should be no change for gp2 at all… I notice that the gp2 is predicting smaller and smaller variance which seems to be following the variance from gp1…
not sure where I did wrong…