def _get_fixed_noise_model_single_output(**tkwargs): train_X, train_Y = _get_random_mt_data(**tkwargs) train_Yvar = torch.full_like(train_Y, 0.05) model = FixedNoiseMultiTaskGP( train_X, train_Y, train_Yvar, task_feature=1, output_tasks=[1] ) return model.to(**tkwargs)
def _get_fixed_noise_model_and_training_data(input_transform=None, **tkwargs): train_X, train_Y = _get_random_mt_data(**tkwargs) train_Yvar = torch.full_like(train_Y, 0.05) model = FixedNoiseMultiTaskGP( train_X, train_Y, train_Yvar, task_feature=1, input_transform=input_transform ) return model.to(**tkwargs), train_X, train_Y, train_Yvar
def _get_fixed_noise_and_given_covar_module_model(**tkwargs): train_X, train_Y = _get_random_mt_data(**tkwargs) train_Yvar = torch.full_like(train_Y, 0.05) model = FixedNoiseMultiTaskGP( train_X, train_Y, train_Yvar, task_feature=1, covar_module=MaternKernel(nu=1.5, lengthscale_prior=GammaPrior(1.0, 1.0)), ) 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)