b61a525baee25d2ea31ab088540dd4d2ffed2251,test/examples/multitask_gp_regression_test.py,MultitaskGPModel,__init__,#MultitaskGPModel#,27

Before Change

        self.mean_module = ConstantMean()
        self.covar_module = RBFKernel()
        self.task_covar_module = IndexKernel()
        self.register_parameter("constant_mean", nn.Parameter(torch.randn(1)), bounds=(-1, 1))
        self.register_parameter("log_noise", nn.Parameter(torch.randn(1)), bounds=(-6, 6))
        self.register_parameter("log_lengthscale", nn.Parameter(torch.randn(1)), bounds=(-6, 6))
        self.register_parameter("task_matrix", nn.Parameter(torch.randn(2, 1)), bounds=(-6, 6))
        self.register_parameter("task_log_vars", nn.Parameter(torch.randn(2)), bounds=(-6, 6))

    def forward(self, x, i):
        mean_x = self.mean_module(x, constant=self.constant_mean)

After Change

class MultitaskGPModel(gpytorch.GPModel):
    def __init__(self):
        likelihood = GaussianLikelihood(log_noise_bounds=(-6, 6))
        super(MultitaskGPModel, self).__init__(likelihood)
        self.mean_module = ConstantMean(constant_bounds=(-1, 1))
        self.covar_module = RBFKernel(log_lengthscale_bounds=(-6, 6))
        self.task_covar_module = IndexKernel(n_tasks=2, rank=1, covar_factor_bounds=(-6, 6), log_var_bounds=(-6, 6))

Instances