f81c70c85ca6a2cbf9adb31d26ccc49007d5a335,pyro/distributions/torch.py,Binomial,sample,#Binomial#Any#,58

Before Change

                    shift = (mean - variance).round()
                    result = torch.poisson(variance.expand(shape))
                    result = torch.min(result + shift, self.total_count)
                    return torch.where(p < q, result, self.total_count - result)
        return super().sample(sample_shape)


// This overloads .log_prob() and .enumerate_support() to speed up evaluating

After Change

                    shift = (mean - variance).round()
                    result = torch.poisson(variance.expand(shape))
                    result = torch.min(result + shift, self.total_count)
                    sample = torch.where(p < q, result, self.total_count - result)
                // Draw exact samples for remaining items.
                if exact.any():
                    total_count = torch.where(exact, self.total_count,
                                              torch.zeros_like(self.total_count))
                    exact_sample = torch.distributions.Binomial(
                        total_count, self.probs, validate_args=False).sample(sample_shape)
                    sample = torch.where(exact, exact_sample, sample)
                return sample
        return super().sample(sample_shape)


// This overloads .log_prob() and .enumerate_support() to speed up evaluating

Instances