Table of Contents
Shortcuts

# Source code for torch.distributions.beta

    from numbers import Number, Real

    import torch
    from torch.distributions import constraints
    from torch.distributions.dirichlet import Dirichlet
    from torch.distributions.exp_family import ExponentialFamily
    from torch.distributions.utils import broadcast_all

    __all__ = ["Beta"]

    class Beta(ExponentialFamily):
        r"""
        Beta distribution parameterized by :attr:`concentration1` and :attr:`concentration0`.

        Example::

            >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
            >>> m = Beta(torch.tensor([0.5]), torch.tensor([0.5]))
            >>> m.sample()  # Beta distributed with concentration concentration1 and concentration0
            tensor([ 0.1046])

        Args:
            concentration1 (float or Tensor): 1st concentration parameter of the distribution
                (often referred to as alpha)
            concentration0 (float or Tensor): 2nd concentration parameter of the distribution
                (often referred to as beta)
        """
        arg_constraints = {
            "concentration1": constraints.positive,
            "concentration0": constraints.positive,
        }
        support = constraints.unit_interval
        has_rsample = True

        def __init__(self, concentration1, concentration0, validate_args=None):
            if isinstance(concentration1, Real) and isinstance(concentration0, Real):
                concentration1_concentration0 = torch.tensor(
                    [float(concentration1), float(concentration0)]
                )
            else:
                concentration1, concentration0 = broadcast_all(
                    concentration1, concentration0
                )
                concentration1_concentration0 = torch.stack(
                    [concentration1, concentration0], -1
                )
            self._dirichlet = Dirichlet(
                concentration1_concentration0, validate_args=validate_args
            )
            super().__init__(self._dirichlet._batch_shape, validate_args=validate_args)

        def expand(self, batch_shape, _instance=None):
            new = self._get_checked_instance(Beta, _instance)
            batch_shape = torch.Size(batch_shape)
            new._dirichlet = self._dirichlet.expand(batch_shape)
            super(Beta, new).__init__(batch_shape, validate_args=False)
            new._validate_args = self._validate_args
            return new

        @property
        def mean(self):
            return self.concentration1 / (self.concentration1 + self.concentration0)

        @property
        def mode(self):
            return self._dirichlet.mode[..., 0]

        @property
        def variance(self):
            total = self.concentration1 + self.concentration0
            return self.concentration1 * self.concentration0 / (total.pow(2) * (total + 1))

        def rsample(self, sample_shape=()):
            return self._dirichlet.rsample(sample_shape).select(-1, 0)

        def log_prob(self, value):
            if self._validate_args:
                self._validate_sample(value)
            heads_tails = torch.stack([value, 1.0 - value], -1)
            return self._dirichlet.log_prob(heads_tails)

        def entropy(self):
            return self._dirichlet.entropy()

        @property
        def concentration1(self):
            result = self._dirichlet.concentration[..., 0]
            if isinstance(result, Number):
                return torch.tensor([result])
            else:
                return result

        @property
        def concentration0(self):
            result = self._dirichlet.concentration[..., 1]
            if isinstance(result, Number):
                return torch.tensor([result])
            else:
                return result

        @property
        def _natural_params(self):
            return (self.concentration1, self.concentration0)

        def _log_normalizer(self, x, y):
            return torch.lgamma(x) + torch.lgamma(y) - torch.lgamma(x + y)

## Docs

Access comprehensive developer documentation for PyTorch

View Docs

## Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

## Resources

Find development resources and get your questions answered

View Resources

To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. As the current maintainers of this site, Facebook’s Cookies Policy applies. Learn more, including about available controls: Cookies Policy.