f01591a685be78db2c440e8024895601017a5b72,gpytorch/models/variational_gp.py,VariationalGP,__call__,#VariationalGP#Any#,26

Before Change

                q_mat, t_mat = lq_object.lanczos_batch(induc_induc_matmul, init_vector)
                self.prior_chol = Variable(q_mat[0].matmul(t_mat[0].potrf().inverse()))

                self.variational_chol = gpytorch.inv_matmul(induc_induc_covar, variational_output.covar().lhs)
                self.has_computed_chol = True

            // Test mean
            predictive_mean = torch.add(test_mean, test_induc_covar.matmul(self.alpha))

After Change

                self.prior_chol = Variable(q_mat[0].matmul(t_mat[0].potrf().inverse()))

                chol_variational_output = variational_output.covar().root.evaluate()
                self.variational_chol = gpytorch.inv_matmul(induc_induc_covar, chol_variational_output)
                self.has_computed_chol = True

            // Test mean
            predictive_mean = torch.add(test_mean, test_induc_covar.matmul(self.alpha))

Instances