a8cd2fb3b8b8aad1fb30dc7004f9fd3c44a80969,gpytorch/mlls/exact_marginal_log_likelihood.py,ExactMarginalLogLikelihood,forward,#ExactMarginalLogLikelihood#Any#Any#,25

Before Change

        // Add log probs of priors on the parameters
        for _, prior, params_and_tfs, prior_tf in self.named_priors():
            params = [p if ptf is None else ptf(p) for p, ptf in params_and_tfs]
            lp_arg = params[0] if prior_tf is None else prior_tf(*params)
            res.add_(prior.log_prob(lp_arg).sum())

        // Scale by the amount of data we have

After Change

        // Add log probs of priors on the (functions of) parameters
        for _, prior, closure in self.named_priors():
            res.add_(prior.log_prob(closure()).sum())

        // Scale by the amount of data we have
        num_data = target.size(-1)

Instances