76c081b840cd71b20d8ac8692b312ebef95eae75,gpytorch/models/exact_prediction_strategies.py,DefaultPredictionStrategy,exact_predictive_mean,#DefaultPredictionStrategy#Any#Any#,272

Before Change

        
        precomputed_cache = self.mean_cache

        if self.train_train_covar.dim() == 3:
            res = test_train_covar.matmul(
                precomputed_cache.expand(*test_train_covar.shape[:-2], test_train_covar.shape[-1]).unsqueeze(-1)
            ).squeeze(-1)
        else:
            if self.non_batch_train and precomputed_cache.dim() == 2:
                precomputed_cache = precomputed_cache[0]
            res = test_train_covar.matmul(precomputed_cache)

        res = res + test_mean

        return res

After Change

            :obj:`torch.tensor`: The predictive posterior mean of the test points
        
        // For efficiency - we can use addmv in the 2d case
        if test_train_covar.dim() == 2:
            res = torch.addmv(test_mean, delazify(test_train_covar), self.mean_cache)
        // In other cases - we"ll use the standard infrastructure
        else:
            res = (test_train_covar @ self.mean_cache.unsqueeze(-1)).squeeze(-1)
            res = res + test_mean
        return res

    def exact_predictive_covar(self, test_test_covar, test_train_covar):
        

Instances