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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-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, 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)
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