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)