Shortcuts

# Source code for torch.distributions.poisson

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

[docs]class Poisson(ExponentialFamily):
r"""
Creates a Poisson distribution parameterized by :attr:rate, the rate parameter.

Samples are nonnegative integers, with a pmf given by

.. math::
\mathrm{rate}^k \frac{e^{-\mathrm{rate}}}{k!}

Example::

>>> m = Poisson(torch.tensor([4]))
>>> m.sample()
tensor([ 3.])

Args:
rate (Number, Tensor): the rate parameter
"""
arg_constraints = {'rate': constraints.nonnegative}
support = constraints.nonnegative_integer

@property
def mean(self):
return self.rate

@property
def variance(self):
return self.rate

def __init__(self, rate, validate_args=None):
if isinstance(rate, Number):
batch_shape = torch.Size()
else:
batch_shape = self.rate.size()
super(Poisson, self).__init__(batch_shape, validate_args=validate_args)

[docs]    def expand(self, batch_shape, _instance=None):
new = self._get_checked_instance(Poisson, _instance)
batch_shape = torch.Size(batch_shape)
new.rate = self.rate.expand(batch_shape)
super(Poisson, new).__init__(batch_shape, validate_args=False)
new._validate_args = self._validate_args
return new

[docs]    def sample(self, sample_shape=torch.Size()):
shape = self._extended_shape(sample_shape)

[docs]    def log_prob(self, value):
if self._validate_args:
self._validate_sample(value)
rate, value = broadcast_all(self.rate, value)
return value.xlogy(rate) - rate - (value + 1).lgamma()

@property
def _natural_params(self):
return (torch.log(self.rate), )

def _log_normalizer(self, x):


## Docs

Access comprehensive developer documentation for PyTorch

View Docs

## Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

## Resources

Find development resources and get your questions answered

View Resources