var_f2 = 3. # GP variance len_f2 = 1. # GP lengthscale prior1 = priors.Matern32(variance=var_f1, lengthscale=len_f1) prior2 = priors.Matern32(variance=var_f2, lengthscale=len_f2) prior = priors.Independent([prior1, prior2]) lik = likelihoods.HeteroscedasticNoise() step_size = 5e-2 if method == 0: inf_method = approx_inf.EEP(power=1, damping=0.5) elif method == 1: inf_method = approx_inf.EEP(power=0.5, damping=0.5) elif method == 2: inf_method = approx_inf.EKS(damping=0.5) elif method == 3: inf_method = approx_inf.UEP(power=1, damping=0.5) elif method == 4: inf_method = approx_inf.UEP(power=0.5, damping=0.5) elif method == 5: inf_method = approx_inf.UKS(damping=0.5) elif method == 6: inf_method = approx_inf.GHEP(power=1, damping=0.5) elif method == 7: inf_method = approx_inf.GHEP(power=0.5, damping=0.5) elif method == 8: inf_method = approx_inf.GHKS(damping=0.5)
train = np.setdiff1d(cvind, test) # Set training and test data X = inputs[train, :1] R = inputs[train, 1:] Y = Yall[train, :] XT = inputs[test, :1] RT = inputs[test, 1:] YT = Yall[test, :] if method == 0: inf_method = approx_inf.EEP(power=1) elif method == 1: inf_method = approx_inf.EEP(power=0.5) elif method == 2: inf_method = approx_inf.EKS() elif method == 3: inf_method = approx_inf.UEP(power=1) elif method == 4: inf_method = approx_inf.UEP(power=0.5) elif method == 5: inf_method = approx_inf.UKS() elif method == 6: inf_method = approx_inf.GHEP(power=1) elif method == 7: inf_method = approx_inf.GHEP(power=0.5) elif method == 8: inf_method = approx_inf.GHKS()