I have a case, where I’m trying to train a GP process regression model based continuous and categorical inputs. I read that traditional covariance functions that are used with Gaussian Process regression may not work well with categorical inputs.
I tried to understand the the covariance functions that we have with PyMC3, but cannot find whether we have any covariance function that works well with categorical inputs. I appreciate any help to identify a suitable covariance function to use with categorical inputs.
Also is there a way to combine two kernels, yet use different features with each kernel.
As an example :
X1, X2 = X[features1], X[features2] cov = nu ** 2 * pm.gp.cov.Matern32(X1.shape, l1) * pm.gp.cov.ExpQuad(X2.shape, l2) gp = pm.gp.Marginal(cov_func=cov)
Notice that each kernel has different set of features. Can we do this? Then how do we pass data to each kernel?
Thanks a lot.