83 lines
2.3 KiB
Python
83 lines
2.3 KiB
Python
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import math
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import torch
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from torch import inf
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from torch.distributions import constraints
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from torch.distributions.cauchy import Cauchy
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from torch.distributions.transformed_distribution import TransformedDistribution
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from torch.distributions.transforms import AbsTransform
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__all__ = ["HalfCauchy"]
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class HalfCauchy(TransformedDistribution):
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r"""
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Creates a half-Cauchy distribution parameterized by `scale` where::
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X ~ Cauchy(0, scale)
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Y = |X| ~ HalfCauchy(scale)
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Example::
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>>> # xdoctest: +IGNORE_WANT("non-deterministic")
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>>> m = HalfCauchy(torch.tensor([1.0]))
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>>> m.sample() # half-cauchy distributed with scale=1
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tensor([ 2.3214])
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Args:
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scale (float or Tensor): scale of the full Cauchy distribution
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"""
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arg_constraints = {"scale": constraints.positive}
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support = constraints.nonnegative
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has_rsample = True
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def __init__(self, scale, validate_args=None):
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base_dist = Cauchy(0, scale, validate_args=False)
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super().__init__(base_dist, AbsTransform(), validate_args=validate_args)
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def expand(self, batch_shape, _instance=None):
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new = self._get_checked_instance(HalfCauchy, _instance)
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return super().expand(batch_shape, _instance=new)
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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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@property
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def mean(self):
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return torch.full(
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self._extended_shape(),
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math.inf,
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dtype=self.scale.dtype,
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device=self.scale.device,
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)
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@property
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def mode(self):
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return torch.zeros_like(self.scale)
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@property
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def variance(self):
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return self.base_dist.variance
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def log_prob(self, value):
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if self._validate_args:
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self._validate_sample(value)
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value = torch.as_tensor(
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value, dtype=self.base_dist.scale.dtype, device=self.base_dist.scale.device
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)
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log_prob = self.base_dist.log_prob(value) + math.log(2)
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log_prob = torch.where(value >= 0, log_prob, -inf)
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return log_prob
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def cdf(self, value):
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if self._validate_args:
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self._validate_sample(value)
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return 2 * self.base_dist.cdf(value) - 1
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def icdf(self, prob):
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return self.base_dist.icdf((prob + 1) / 2)
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def entropy(self):
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return self.base_dist.entropy() - math.log(2)
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