| | |
| | from typing import Optional, Union |
| |
|
| | import torch |
| | from torch import Tensor |
| | from torch.distributions import constraints |
| | from torch.distributions.exp_family import ExponentialFamily |
| | from torch.distributions.utils import broadcast_all |
| | from torch.types import _Number, _size |
| |
|
| |
|
| | __all__ = ["Exponential"] |
| |
|
| |
|
| | class Exponential(ExponentialFamily): |
| | r""" |
| | Creates a Exponential distribution parameterized by :attr:`rate`. |
| | |
| | Example:: |
| | |
| | >>> # xdoctest: +IGNORE_WANT("non-deterministic") |
| | >>> 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) -> Tensor: |
| | return self.rate.reciprocal() |
| |
|
| | @property |
| | def mode(self) -> Tensor: |
| | return torch.zeros_like(self.rate) |
| |
|
| | @property |
| | def stddev(self) -> Tensor: |
| | return self.rate.reciprocal() |
| |
|
| | @property |
| | def variance(self) -> Tensor: |
| | return self.rate.pow(-2) |
| |
|
| | def __init__( |
| | self, |
| | rate: Union[Tensor, float], |
| | validate_args: Optional[bool] = None, |
| | ) -> None: |
| | (self.rate,) = broadcast_all(rate) |
| | batch_shape = torch.Size() if isinstance(rate, _Number) else self.rate.size() |
| | super().__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: _size = torch.Size()) -> Tensor: |
| | shape = self._extended_shape(sample_shape) |
| | 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.log1p(-value) / self.rate |
| |
|
| | def entropy(self): |
| | return 1.0 - torch.log(self.rate) |
| |
|
| | @property |
| | def _natural_params(self) -> tuple[Tensor]: |
| | return (-self.rate,) |
| |
|
| | def _log_normalizer(self, x): |
| | return -torch.log(-x) |
| |
|
