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