def test_ModelListGP_single(self): tkwargs = {"device": self.device, "dtype": torch.float} train_x1, train_x2, train_y1, train_y2 = _get_random_data(n=10, **tkwargs) model1 = SingleTaskGP(train_X=train_x1, train_Y=train_y1) model = ModelListGP(model1) model.to(**tkwargs) test_x = torch.tensor([[0.25], [0.75]], **tkwargs) posterior = model.posterior(test_x) self.assertIsInstance(posterior, GPyTorchPosterior) self.assertIsInstance(posterior.mvn, MultivariateNormal)
def test_ModelListGPSingle(self, cuda=False): tkwargs = { "device": torch.device("cuda") if cuda else torch.device("cpu"), "dtype": torch.float, } train_x1, train_x2, train_y1, train_y2 = _get_random_data(n=10, **tkwargs) model1 = SingleTaskGP(train_X=train_x1, train_Y=train_y1) model = ModelListGP(gp_models=[model1]) model.to(**tkwargs) test_x = (torch.tensor([0.25, 0.75]).type_as(model.train_targets[0]),) posterior = model.posterior(test_x) self.assertIsInstance(posterior, GPyTorchPosterior) self.assertIsInstance(posterior.mvn, MultivariateNormal)
def test_ModelListGPSingle(self, cuda=False): tkwargs = { "device": torch.device("cuda") if cuda else torch.device("cpu"), "dtype": torch.float, } train_x1, train_x2, train_y1, train_y2 = _get_random_data(n=10, **tkwargs) model1 = SingleTaskGP(train_X=train_x1, train_Y=train_y1) model = ModelListGP(gp_models=[model1]) model.to(**tkwargs) test_x = (torch.tensor([0.25, 0.75]).type_as(model.train_targets[0]),) posterior = model.posterior(test_x) self.assertIsInstance(posterior, GPyTorchPosterior) self.assertIsInstance(posterior.mvn, MultivariateNormal)