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# Source code for torch.distributions.geometric

from numbers import Number

import torch
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.utils import broadcast_all, probs_to_logits, logits_to_probs, lazy_property
from torch.nn.functional import binary_cross_entropy_with_logits

[docs]class Geometric(Distribution):
r"""
Creates a Geometric distribution parameterized by :attr:probs,
where :attr:probs is the probability of success of Bernoulli trials.
It represents the probability that in :math:k + 1 Bernoulli trials, the
first :math:k trials failed, before seeing a success.

Samples are non-negative integers [0, :math:\inf).

Example::

>>> m = Geometric(torch.tensor([0.3]))
>>> m.sample()  # underlying Bernoulli has 30% chance 1; 70% chance 0
tensor([ 2.])

Args:
probs (Number, Tensor): the probability of sampling 1. Must be in range (0, 1]
logits (Number, Tensor): the log-odds of sampling 1.
"""
arg_constraints = {'probs': constraints.unit_interval,
'logits': constraints.real}
support = constraints.nonnegative_integer

def __init__(self, probs=None, logits=None, validate_args=None):
if (probs is None) == (logits is None):
raise ValueError("Either probs or logits must be specified, but not both.")
if probs is not None:
if not self.probs.gt(0).all():
raise ValueError('All elements of probs must be greater than 0')
else:
probs_or_logits = probs if probs is not None else logits
if isinstance(probs_or_logits, Number):
batch_shape = torch.Size()
else:
batch_shape = probs_or_logits.size()
super(Geometric, self).__init__(batch_shape, validate_args=validate_args)

[docs]    def expand(self, batch_shape, _instance=None):
new = self._get_checked_instance(Geometric, _instance)
batch_shape = torch.Size(batch_shape)
if 'probs' in self.__dict__:
new.probs = self.probs.expand(batch_shape)
if 'logits' in self.__dict__:
new.logits = self.logits.expand(batch_shape)
super(Geometric, new).__init__(batch_shape, validate_args=False)
new._validate_args = self._validate_args
return new

@property
def mean(self):
return 1. / self.probs - 1.

@property
def variance(self):
return (1. / self.probs - 1.) / self.probs

[docs]    @lazy_property
def logits(self):
return probs_to_logits(self.probs, is_binary=True)

[docs]    @lazy_property
def probs(self):
return logits_to_probs(self.logits, is_binary=True)

[docs]    def sample(self, sample_shape=torch.Size()):
shape = self._extended_shape(sample_shape)
tiny = torch.finfo(self.probs.dtype).tiny
if torch._C._get_tracing_state():
# [JIT WORKAROUND] lack of support for .uniform_()
u = torch.rand(shape, dtype=self.probs.dtype, device=self.probs.device)
u = u.clamp(min=tiny)
else:
u = self.probs.new(shape).uniform_(tiny, 1)
return (u.log() / (-self.probs).log1p()).floor()

[docs]    def log_prob(self, value):
if self._validate_args:
self._validate_sample(value)
probs[(probs == 1) & (value == 0)] = 0
return value * (-probs).log1p() + self.probs.log()

[docs]    def entropy(self):
return binary_cross_entropy_with_logits(self.logits, self.probs, reduction='none') / self.probs


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