| 1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071727374 |
- 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 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 mode(self):
- return self.rate.floor()
- @property
- def variance(self):
- return self.rate
- def __init__(self, rate, validate_args=None):
- self.rate, = broadcast_all(rate)
- if isinstance(rate, Number):
- batch_shape = torch.Size()
- else:
- batch_shape = self.rate.size()
- super(Poisson, self).__init__(batch_shape, validate_args=validate_args)
- 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
- def sample(self, sample_shape=torch.Size()):
- shape = self._extended_shape(sample_shape)
- with torch.no_grad():
- return torch.poisson(self.rate.expand(shape))
- 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):
- return torch.exp(x)
|
