Source code for torch.distributions.exponential

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

__all__ = ["Exponential"]

[docs]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): 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().__init__(batch_shape, validate_args=validate_args)
[docs] 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
[docs] def rsample(self, sample_shape=torch.Size()): shape = self._extended_shape(sample_shape) return / self.rate
[docs] def log_prob(self, value): if self._validate_args: self._validate_sample(value) return self.rate.log() - self.rate * value
[docs] def cdf(self, value): if self._validate_args: self._validate_sample(value) return 1 - torch.exp(-self.rate * value)
[docs] def icdf(self, value): return -torch.log1p(-value) / self.rate
[docs] 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)


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