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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) | |
def loc(self): | |
return self.base_dist.base_dist.loc | |
def scale(self): | |
return self.base_dist.base_dist.scale | |