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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18598 ghimport-source-id: c74597e5e7437e94a43c163cee0639b20d0d0c6a Stack from [ghstack](https://github.com/ezyang/ghstack): * **#18598 Turn on F401: Unused import warning.** This was requested by someone at Facebook; this lint is turned on for Facebook by default. "Sure, why not." I had to noqa a number of imports in __init__. Hypothetically we're supposed to use __all__ in this case, but I was too lazy to fix it. Left for future work. Be careful! flake8-2 and flake8-3 behave differently with respect to import resolution for # type: comments. flake8-3 will report an import unused; flake8-2 will not. For now, I just noqa'd all these sites. All the changes were done by hand. Signed-off-by: Edward Z. Yang <ezyang@fb.com> Differential Revision: D14687478 fbshipit-source-id: 30d532381e914091aadfa0d2a5a89404819663e3
53 lines
2.0 KiB
Python
53 lines
2.0 KiB
Python
import torch
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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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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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>>> m = distributions.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)
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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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# Adjust event shape since StickBreakingTransform adds 1 dimension
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self._event_shape = torch.Size([s + 1 for s in self._event_shape])
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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.loc
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@property
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def scale(self):
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return self.base_dist.scale
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