85 lines
2.4 KiB
Python
85 lines
2.4 KiB
Python
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"]
|
|
|
|
|
|
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)
|
|
|
|
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)
|
|
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):
|
|
return (-self.rate,)
|
|
|
|
def _log_normalizer(self, x):
|
|
return -torch.log(-x)
|