Table of Contents
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

    __all__ = ["Poisson"]

    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::

            >>> # xdoctest: +SKIP("poisson_cpu not implemented for 'Long'")
            >>> 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().__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)

## 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

To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. As the current maintainers of this site, Facebook’s Cookies Policy applies. Learn more, including about available controls: Cookies Policy.