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# mypy: allow-untyped-defs
from typing import Optional, Union
from torch import Tensor
from torch.distributions import constraints
from torch.distributions.normal import Normal
from torch.distributions.transformed_distribution import TransformedDistribution
from torch.distributions.transforms import ExpTransform
__all__ = ["LogNormal"]
class LogNormal(TransformedDistribution):
r"""
Creates a log-normal distribution parameterized by
:attr:`loc` and :attr:`scale` where::
X ~ Normal(loc, scale)
Y = exp(X) ~ LogNormal(loc, scale)
Example::
>>> # xdoctest: +IGNORE_WANT("non-deterministic")
>>> m = LogNormal(torch.tensor([0.0]), torch.tensor([1.0]))
>>> m.sample() # log-normal distributed with mean=0 and stddev=1
tensor([ 0.1046])
Args:
loc (float or Tensor): mean of log of distribution
scale (float or Tensor): standard deviation of log of the distribution
"""
arg_constraints = {"loc": constraints.real, "scale": constraints.positive}
support = constraints.positive
has_rsample = True
base_dist: Normal
def __init__(
self,
loc: Union[Tensor, float],
scale: Union[Tensor, float],
validate_args: Optional[bool] = None,
) -> None:
base_dist = Normal(loc, scale, validate_args=validate_args)
super().__init__(base_dist, ExpTransform(), validate_args=validate_args)
def expand(self, batch_shape, _instance=None):
new = self._get_checked_instance(LogNormal, _instance)
return super().expand(batch_shape, _instance=new)
@property
def loc(self) -> Tensor:
return self.base_dist.loc
@property
def scale(self) -> Tensor:
return self.base_dist.scale
@property
def mean(self) -> Tensor:
return (self.loc + self.scale.pow(2) / 2).exp()
@property
def mode(self) -> Tensor:
return (self.loc - self.scale.square()).exp()
@property
def variance(self) -> Tensor:
scale_sq = self.scale.pow(2)
return scale_sq.expm1() * (2 * self.loc + scale_sq).exp()
def entropy(self):
return self.base_dist.entropy() + self.loc
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