Source code for torch.distributions.half_normal

import math

from torch._six import inf
from torch.distributions import constraints
from torch.distributions.transforms import AbsTransform
from torch.distributions.normal import Normal
from torch.distributions.transformed_distribution import TransformedDistribution

[docs]class HalfNormal(TransformedDistribution): r""" Creates a half-normal distribution parameterized by `scale` where:: X ~ Normal(0, scale) Y = |X| ~ HalfNormal(scale) Example:: >>> m = HalfNormal(torch.tensor([1.0])) >>> m.sample() # half-normal distributed with scale=1 tensor([ 0.1046]) Args: scale (float or Tensor): scale of the full Normal distribution """ arg_constraints = {'scale': constraints.positive} support = constraints.positive has_rsample = True def __init__(self, scale, validate_args=None): base_dist = Normal(0, scale) super(HalfNormal, self).__init__(base_dist, AbsTransform(), validate_args=validate_args)
[docs] def expand(self, batch_shape, _instance=None): new = self._get_checked_instance(HalfNormal, _instance) return super(HalfNormal, self).expand(batch_shape, _instance=new)
@property def scale(self): return self.base_dist.scale @property def mean(self): return self.scale * math.sqrt(2 / math.pi) @property def variance(self): return self.scale.pow(2) * (1 - 2 / math.pi)
[docs] def log_prob(self, value): log_prob = self.base_dist.log_prob(value) + math.log(2) log_prob[value.expand(log_prob.shape) < 0] = -inf return log_prob
[docs] def cdf(self, 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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