cbfd111192d2b1ed7a43ebfac3178d31b844b4cd,gpytorch/likelihoods/gaussian_likelihood.py,_GaussianLikelihoodBase,forward,#_GaussianLikelihoodBase#Any#,20

Before Change

        else:
            // here shape[:-1] is the batch shape requested, and shape[-1] is `n`, the number of points
            shape = mean.shape
        noise_covar = self.noise_covar(*params, shape=shape)
        full_covar = covar + noise_covar
        return input.__class__(mean, full_covar)

    def variational_log_probability(self, input, target):
        raise NotImplementedError

After Change

        return self.noise_covar(*params, shape=shape)

    def forward(self, function_samples, *params, **kwargs):
        return base_distributions.Normal(
            function_samples, self._shaped_noise_covar(function_samples.shape, *params).diag()
        )

    def marginal(self, function_dist, *params, **kwargs):
        mean, covar = function_dist.mean, function_dist.lazy_covariance_matrix
        full_covar = covar + self._shaped_noise_covar(mean.shape, *params)

Instances