| | |
| | from typing import Optional, Union |
| |
|
| | import torch |
| | from torch import Tensor |
| | 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 |
| | from torch.types import _Number, _size |
| |
|
| |
|
| | __all__ = ["Beta"] |
| |
|
| |
|
| | class Beta(ExponentialFamily): |
| | r""" |
| | Beta distribution parameterized by :attr:`concentration1` and :attr:`concentration0`. |
| | |
| | Example:: |
| | |
| | >>> # xdoctest: +IGNORE_WANT("non-deterministic") |
| | >>> 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: Union[Tensor, float], |
| | concentration0: Union[Tensor, float], |
| | validate_args: Optional[bool] = None, |
| | ) -> None: |
| | if isinstance(concentration1, _Number) and isinstance(concentration0, _Number): |
| | 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) -> Tensor: |
| | return self.concentration1 / (self.concentration1 + self.concentration0) |
| |
|
| | @property |
| | def mode(self) -> Tensor: |
| | return self._dirichlet.mode[..., 0] |
| |
|
| | @property |
| | def variance(self) -> Tensor: |
| | total = self.concentration1 + self.concentration0 |
| | return self.concentration1 * self.concentration0 / (total.pow(2) * (total + 1)) |
| |
|
| | def rsample(self, sample_shape: _size = ()) -> Tensor: |
| | 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) -> Tensor: |
| | result = self._dirichlet.concentration[..., 0] |
| | if isinstance(result, _Number): |
| | return torch.tensor([result]) |
| | else: |
| | return result |
| |
|
| | @property |
| | def concentration0(self) -> Tensor: |
| | result = self._dirichlet.concentration[..., 1] |
| | if isinstance(result, _Number): |
| | return torch.tensor([result]) |
| | else: |
| | return result |
| |
|
| | @property |
| | def _natural_params(self) -> tuple[Tensor, Tensor]: |
| | return (self.concentration1, self.concentration0) |
| |
|
| | def _log_normalizer(self, x, y): |
| | return torch.lgamma(x) + torch.lgamma(y) - torch.lgamma(x + y) |
| |
|
