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Source code for torch.distributions.laplace

from numbers import Number

import torch
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.utils import broadcast_all

__all__ = ["Laplace"]


[docs]class Laplace(Distribution): r""" Creates a Laplace distribution parameterized by :attr:`loc` and :attr:`scale`. Example:: >>> # xdoctest: +IGNORE_WANT("non-deterinistic") >>> m = Laplace(torch.tensor([0.0]), torch.tensor([1.0])) >>> m.sample() # Laplace distributed with loc=0, scale=1 tensor([ 0.1046]) Args: loc (float or Tensor): mean of the distribution scale (float or Tensor): scale of the distribution """ arg_constraints = {"loc": constraints.real, "scale": constraints.positive} support = constraints.real has_rsample = True @property def mean(self): return self.loc @property def mode(self): return self.loc @property def variance(self): return 2 * self.scale.pow(2) @property def stddev(self): return (2**0.5) * self.scale def __init__(self, loc, scale, validate_args=None): self.loc, self.scale = broadcast_all(loc, scale) if isinstance(loc, Number) and isinstance(scale, Number): batch_shape = torch.Size() else: batch_shape = self.loc.size() super().__init__(batch_shape, validate_args=validate_args)
[docs] def expand(self, batch_shape, _instance=None): new = self._get_checked_instance(Laplace, _instance) batch_shape = torch.Size(batch_shape) new.loc = self.loc.expand(batch_shape) new.scale = self.scale.expand(batch_shape) super(Laplace, new).__init__(batch_shape, validate_args=False) new._validate_args = self._validate_args return new
[docs] def rsample(self, sample_shape=torch.Size()): shape = self._extended_shape(sample_shape) finfo = torch.finfo(self.loc.dtype) if torch._C._get_tracing_state(): # [JIT WORKAROUND] lack of support for .uniform_() u = torch.rand(shape, dtype=self.loc.dtype, device=self.loc.device) * 2 - 1 return self.loc - self.scale * u.sign() * torch.log1p( -u.abs().clamp(min=finfo.tiny) ) u = self.loc.new(shape).uniform_(finfo.eps - 1, 1) # TODO: If we ever implement tensor.nextafter, below is what we want ideally. # u = self.loc.new(shape).uniform_(self.loc.nextafter(-.5, 0), .5) return self.loc - self.scale * u.sign() * torch.log1p(-u.abs())
[docs] def log_prob(self, value): if self._validate_args: self._validate_sample(value) return -torch.log(2 * self.scale) - torch.abs(value - self.loc) / self.scale
[docs] def cdf(self, value): if self._validate_args: self._validate_sample(value) return 0.5 - 0.5 * (value - self.loc).sign() * torch.expm1( -(value - self.loc).abs() / self.scale )
[docs] def icdf(self, value): term = value - 0.5 return self.loc - self.scale * (term).sign() * torch.log1p(-2 * term.abs())
[docs] def entropy(self): return 1 + torch.log(2 * self.scale)

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