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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 | |