| 12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667686970717273747576777879808182838485 |
- from numbers import Number
- import torch
- from torch.distributions import constraints
- from torch.distributions.exp_family import ExponentialFamily
- from torch.distributions.utils import broadcast_all
- class Exponential(ExponentialFamily):
- r"""
- Creates a Exponential distribution parameterized by :attr:`rate`.
- Example::
- >>> m = Exponential(torch.tensor([1.0]))
- >>> m.sample() # Exponential distributed with rate=1
- tensor([ 0.1046])
- Args:
- rate (float or Tensor): rate = 1 / scale of the distribution
- """
- arg_constraints = {'rate': constraints.positive}
- support = constraints.nonnegative
- has_rsample = True
- _mean_carrier_measure = 0
- @property
- def mean(self):
- return self.rate.reciprocal()
- @property
- def mode(self):
- return torch.zeros_like(self.rate)
- @property
- def stddev(self):
- return self.rate.reciprocal()
- @property
- def variance(self):
- return self.rate.pow(-2)
- def __init__(self, rate, validate_args=None):
- self.rate, = broadcast_all(rate)
- batch_shape = torch.Size() if isinstance(rate, Number) else self.rate.size()
- super(Exponential, self).__init__(batch_shape, validate_args=validate_args)
- def expand(self, batch_shape, _instance=None):
- new = self._get_checked_instance(Exponential, _instance)
- batch_shape = torch.Size(batch_shape)
- new.rate = self.rate.expand(batch_shape)
- super(Exponential, 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)
- if torch._C._get_tracing_state():
- # [JIT WORKAROUND] lack of support for ._exponential()
- u = torch.rand(shape, dtype=self.rate.dtype, device=self.rate.device)
- return -(-u).log1p() / self.rate
- return self.rate.new(shape).exponential_() / self.rate
- def log_prob(self, value):
- if self._validate_args:
- self._validate_sample(value)
- return self.rate.log() - self.rate * value
- def cdf(self, value):
- if self._validate_args:
- self._validate_sample(value)
- return 1 - torch.exp(-self.rate * value)
- def icdf(self, value):
- return -torch.log(1 - value) / self.rate
- def entropy(self):
- return 1.0 - torch.log(self.rate)
- @property
- def _natural_params(self):
- return (-self.rate, )
- def _log_normalizer(self, x):
- return -torch.log(-x)
|
