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_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_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)
