Spaces:
Sleeping
Sleeping
File size: 2,294 Bytes
c61ccee |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 |
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)
|