108 lines
3.6 KiB
Python
108 lines
3.6 KiB
Python
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)
|