def _get_fixed_prior_model(**tkwargs): train_X, train_Y = _get_random_mt_data(**tkwargs) sd_prior = GammaPrior(2.0, 0.15) sd_prior._event_shape = torch.Size([2]) model = MultiTaskGP(train_X, train_Y, task_feature=1, prior=LKJCovariancePrior(2, 0.6, sd_prior)) return model.to(**tkwargs)
def _get_fixed_noise_and_prior_model(**tkwargs): train_X, train_Y = _get_random_mt_data(**tkwargs) train_Yvar = torch.full_like(train_Y, 0.05) sd_prior = GammaPrior(2.0, 0.15) sd_prior._event_shape = torch.Size([2]) model = FixedNoiseMultiTaskGP( train_X, train_Y, train_Yvar, task_feature=1, task_covar_prior=LKJCovariancePrior(2, 0.6, sd_prior), ) return model.to(**tkwargs)