d0273f567ef61481fb601398e98916dbc6852099,Tars/distributions/distributions.py,Distribution,__init__,#Distribution#Any#Any#Any#,14

Before Change

        // Set the distribution if all parameters are constant and
        // set at initialization.
        if len(self.constant_params) == len(self.params_keys):
            params = self.get_params()
            self.dist = self.DistributionTorch(**params)

    def _get_sample(self, reparam=True,
                    sample_shape=torch.Size()):

After Change

                ",".join(var)
            )
        else:
            self.prob_text = "p({}|{})".format(
                ",".join(var),
                ",".join(cond_var)
            )
        self.prob_factorized_text = self.prob_text

        // whether I"m a distribution with constant parameters
        self.dist = None

Instances