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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 StickBreakingTransform
- __all__ = ['LogisticNormal']
- class LogisticNormal(TransformedDistribution):
- r"""
- Creates a logistic-normal distribution parameterized by :attr:`loc` and :attr:`scale`
- that define the base `Normal` distribution transformed with the
- `StickBreakingTransform` such that::
- X ~ LogisticNormal(loc, scale)
- Y = log(X / (1 - X.cumsum(-1)))[..., :-1] ~ Normal(loc, scale)
- Args:
- loc (float or Tensor): mean of the base distribution
- scale (float or Tensor): standard deviation of the base distribution
- Example::
- >>> # logistic-normal distributed with mean=(0, 0, 0) and stddev=(1, 1, 1)
- >>> # of the base Normal distribution
- >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
- >>> m = LogisticNormal(torch.tensor([0.0] * 3), torch.tensor([1.0] * 3))
- >>> m.sample()
- tensor([ 0.7653, 0.0341, 0.0579, 0.1427])
- """
- arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}
- support = constraints.simplex
- has_rsample = True
- def __init__(self, loc, scale, validate_args=None):
- base_dist = Normal(loc, scale, validate_args=validate_args)
- if not base_dist.batch_shape:
- base_dist = base_dist.expand([1])
- super().__init__(base_dist, StickBreakingTransform(), validate_args=validate_args)
- def expand(self, batch_shape, _instance=None):
- new = self._get_checked_instance(LogisticNormal, _instance)
- return super().expand(batch_shape, _instance=new)
- @property
- def loc(self):
- return self.base_dist.base_dist.loc
- @property
- def scale(self):
- return self.base_dist.base_dist.scale
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