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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-deterinistic") |
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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(LogisticNormal, self).__init__(base_dist, |
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StickBreakingTransform(), |
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validate_args=validate_args) |
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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(LogisticNormal, self).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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