c63cc933782e2de32c9fe74c18b337b2bbe0f242,gpytorch/kernels/index_kernel.py,IndexKernel,__init__,#IndexKernel#Any#Any#Any#Any#Any#Any#,18

Before Change

        covar_factor_bounds=(-100, 100),
        log_var_bounds=(-100, 100),
    ):
        if active_dims is not None and len(active_dims) > 1:
            raise ValueError(
                "Index must be with respect to a single column. Received {}".format(
                    active_dims
                )
            )
        super(IndexKernel, self).__init__(active_dims=active_dims)
        self.register_parameter(
            name="covar_factor",
            parameter=torch.nn.Parameter(torch.randn(n_tasks, rank)),

After Change

            )

    def forward(self, i1, i2):
        covar_matrix = _eval_covar_matrix(self.covar_factor, self.log_var).unsqueeze(0)
        return covar_matrix.index_select(-2, i1.view(-1)).index_select(-1, i2.view(-1))

Instances