Source code for torch.distributions.log_normal

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']

[docs]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(LogNormal, self).__init__(base_dist, ExpTransform(), validate_args=validate_args)
[docs] def expand(self, batch_shape, _instance=None): new = self._get_checked_instance(LogNormal, _instance) return super(LogNormal, self).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): return (self.scale.pow(2).exp() - 1) * (2 * self.loc + self.scale.pow(2)).exp()
[docs] def entropy(self): return self.base_dist.entropy() + self.loc


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