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): 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)
[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) with torch.no_grad(): return torch.poisson(self.rate.expand(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): return torch.exp(x)


Access comprehensive developer documentation for PyTorch

View Docs


Get in-depth tutorials for beginners and advanced developers

View Tutorials


Find development resources and get your questions answered

View Resources