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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 | |
def __init__(self, loc, scale, validate_args=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) | |
def loc(self): | |
return self.base_dist.loc | |
def scale(self): | |
return self.base_dist.scale | |
def mean(self): | |
return (self.loc + self.scale.pow(2) / 2).exp() | |
def mode(self): | |
return (self.loc - self.scale.square()).exp() | |
def variance(self): | |
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 | |