819eae17016f0b262534d0fe50288b8892e8bc08,gpytorch/models/exact_prediction_strategies.py,DefaultPredictionStrategy,get_fantasy_strategy,#DefaultPredictionStrategy#Any#Any#Any#Any#Any#,92

Before Change

        // Evaluate fant x train and fant x fant covariance matrices, leave train x train unevaluated.
        fant_fant_covar = full_covar[..., num_train:, num_train:]
        fant_mean = full_mean[..., num_train:]
        mvn = self.likelihood(MultivariateNormal(fant_mean, fant_fant_covar), inputs)
        fant_fant_covar = mvn.covariance_matrix

        fant_train_covar = delazify(full_covar[..., num_train:, :num_train])

After Change

        // Evaluate fant x train and fant x fant covariance matrices, leave train x train unevaluated.
        fant_fant_covar = full_covar[..., num_train:, num_train:]
        fant_mean = full_mean[..., num_train:]
        mvn = MultivariateNormal(fant_mean, fant_fant_covar)
        self.likelihood.fantasize(**kwargs)
        if "noise" in kwargs:
            mvn_obs = self.likelihood(mvn, inputs, observation_noise=kwargs.get("noise"))
        else:
            mvn_obs = self.likelihood(mvn, inputs)

        fant_fant_covar = mvn_obs.covariance_matrix
        fant_train_covar = delazify(full_covar[..., num_train:, :num_train])

Instances