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- from numbers import Number
- import torch
- from torch.distributions import constraints
- from torch.distributions.distribution import Distribution
- from torch.distributions.utils import broadcast_all
- class Laplace(Distribution):
- r"""
- Creates a Laplace distribution parameterized by :attr:`loc` and :attr:`scale`.
- Example::
- >>> 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(Laplace, self).__init__(batch_shape, validate_args=validate_args)
- 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
- 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())
- 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
- 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)
- def icdf(self, value):
- term = value - 0.5
- return self.loc - self.scale * (term).sign() * torch.log1p(-2 * term.abs())
- def entropy(self):
- return 1 + torch.log(2 * self.scale)
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