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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-deterministic")
>>> 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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