pytorch

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# mypy: allow-untyped-defs
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from numbers import Number, Real
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import torch
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from torch.distributions import constraints
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from torch.distributions.dirichlet import Dirichlet
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from torch.distributions.exp_family import ExponentialFamily
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from torch.distributions.utils import broadcast_all
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from torch.types import _size
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__all__ = ["Beta"]
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class Beta(ExponentialFamily):
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    r"""
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    Beta distribution parameterized by :attr:`concentration1` and :attr:`concentration0`.
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    Example::
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        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
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        >>> m = Beta(torch.tensor([0.5]), torch.tensor([0.5]))
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        >>> m.sample()  # Beta distributed with concentration concentration1 and concentration0
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        tensor([ 0.1046])
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    Args:
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        concentration1 (float or Tensor): 1st concentration parameter of the distribution
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            (often referred to as alpha)
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        concentration0 (float or Tensor): 2nd concentration parameter of the distribution
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            (often referred to as beta)
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    """
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    arg_constraints = {
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        "concentration1": constraints.positive,
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        "concentration0": constraints.positive,
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    }
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    support = constraints.unit_interval
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    has_rsample = True
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    def __init__(self, concentration1, concentration0, validate_args=None):
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        if isinstance(concentration1, Real) and isinstance(concentration0, Real):
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            concentration1_concentration0 = torch.tensor(
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                [float(concentration1), float(concentration0)]
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            )
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        else:
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            concentration1, concentration0 = broadcast_all(
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                concentration1, concentration0
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            )
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            concentration1_concentration0 = torch.stack(
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                [concentration1, concentration0], -1
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            )
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        self._dirichlet = Dirichlet(
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            concentration1_concentration0, validate_args=validate_args
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        )
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        super().__init__(self._dirichlet._batch_shape, validate_args=validate_args)
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    def expand(self, batch_shape, _instance=None):
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        new = self._get_checked_instance(Beta, _instance)
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        batch_shape = torch.Size(batch_shape)
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        new._dirichlet = self._dirichlet.expand(batch_shape)
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        super(Beta, new).__init__(batch_shape, validate_args=False)
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        new._validate_args = self._validate_args
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        return new
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    @property
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    def mean(self):
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        return self.concentration1 / (self.concentration1 + self.concentration0)
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    @property
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    def mode(self):
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        return self._dirichlet.mode[..., 0]
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    @property
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    def variance(self):
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        total = self.concentration1 + self.concentration0
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        return self.concentration1 * self.concentration0 / (total.pow(2) * (total + 1))
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    def rsample(self, sample_shape: _size = ()) -> torch.Tensor:
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        return self._dirichlet.rsample(sample_shape).select(-1, 0)
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    def log_prob(self, value):
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        if self._validate_args:
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            self._validate_sample(value)
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        heads_tails = torch.stack([value, 1.0 - value], -1)
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        return self._dirichlet.log_prob(heads_tails)
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    def entropy(self):
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        return self._dirichlet.entropy()
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    @property
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    def concentration1(self):
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        result = self._dirichlet.concentration[..., 0]
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        if isinstance(result, Number):
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            return torch.tensor([result])
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        else:
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            return result
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    @property
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    def concentration0(self):
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        result = self._dirichlet.concentration[..., 1]
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        if isinstance(result, Number):
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            return torch.tensor([result])
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        else:
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            return result
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    @property
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    def _natural_params(self):
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        return (self.concentration1, self.concentration0)
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    def _log_normalizer(self, x, y):
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        return torch.lgamma(x) + torch.lgamma(y) - torch.lgamma(x + y)
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