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- from torch.distributions import constraints
- from torch.distributions.transforms import ExpTransform
- from torch.distributions.normal import Normal
- from torch.distributions.transformed_distribution import TransformedDistribution
- __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-deterinistic")
- >>> 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)
- @property
- def loc(self):
- return self.base_dist.loc
- @property
- def scale(self):
- return self.base_dist.scale
- @property
- def mean(self):
- return (self.loc + self.scale.pow(2) / 2).exp()
- @property
- def mode(self):
- return (self.loc - self.scale.square()).exp()
- @property
- 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
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