Source code for torch.distributions.half_cauchy

import math

import torch
from torch import inf
from torch.distributions import constraints
from torch.distributions.cauchy import Cauchy
from torch.distributions.transformed_distribution import TransformedDistribution
from torch.distributions.transforms import AbsTransform

__all__ = ["HalfCauchy"]

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


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