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- from numbers import Real, Number
- 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
- class Beta(ExponentialFamily):
- r"""
- Beta distribution parameterized by :attr:`concentration1` and :attr:`concentration0`.
- Example::
- >>> 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(Beta, self).__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)
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