dbb3361434fb06d61dcbbac36bd139bdedd076b8,test/examples/test_hadamard_multitask_gp_regression.py,HadamardMultitaskGPModel,__init__,#HadamardMultitaskGPModel#Any#Any#Any#,35

Before Change

class HadamardMultitaskGPModel(gpytorch.models.ExactGP):
    def __init__(self, train_x, train_y, likelihood):
        super(HadamardMultitaskGPModel, self).__init__(train_x, train_y, likelihood)
        self.mean_module = ConstantMean(prior=SmoothedBoxPrior(-1, 1))
        self.covar_module = RBFKernel(
            log_lengthscale_prior=SmoothedBoxPrior(exp(-6), exp(6), sigma=0.1, log_transform=True)
        )
        self.task_covar_module = IndexKernel(n_tasks=2, rank=1, prior=InverseWishartPrior(nu=2, K=torch.eye(2)))

After Change

        // Default bounds on mean are (-1e10, 1e10)
        self.mean_module = ConstantMean()
        // We use the very common RBF kernel
        self.covar_module = RBFKernel() + RBFKernel()
        // We learn an IndexKernel for 2 tasks
        // (so we"ll actually learn 2x2=4 tasks with correlations)
        self.task_covar_module = IndexKernel(n_tasks=2, rank=1, prior=InverseWishartPrior(nu=2, K=torch.eye(2)))

Instances