| |
| from typing import Optional, Union |
|
|
| from torch import Tensor |
| from torch.distributions import constraints, Independent |
| 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 |
| base_dist: Independent[Normal] |
|
|
| def __init__( |
| self, |
| loc: Union[Tensor, float], |
| scale: Union[Tensor, float], |
| validate_args: Optional[bool] = None, |
| ) -> 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) -> Tensor: |
| return self.base_dist.base_dist.loc |
|
|
| @property |
| def scale(self) -> Tensor: |
| return self.base_dist.base_dist.scale |
|
|
