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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
__all__ = ['Geometric']
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::
>>> # xdoctest: +IGNORE_WANT("non-deterinistic")
>>> 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:
self.probs, = broadcast_all(probs)
else:
self.logits, = broadcast_all(logits)
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)
if self._validate_args and probs is not None:
# Add an extra check beyond unit_interval
value = self.probs
valid = value > 0
if not valid.all():
invalid_value = value.data[~valid]
raise ValueError(
"Expected parameter probs "
f"({type(value).__name__} of shape {tuple(value.shape)}) "
f"of distribution {repr(self)} "
f"to be positive but found invalid values:\n{invalid_value}"
)
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 mode(self):
return torch.zeros_like(self.probs)
@property
def variance(self):
return (1. / self.probs - 1.) / self.probs
@lazy_property
def logits(self):
return probs_to_logits(self.probs, is_binary=True)
@lazy_property
def probs(self):
return logits_to_probs(self.logits, is_binary=True)
def sample(self, sample_shape=torch.Size()):
shape = self._extended_shape(sample_shape)
tiny = torch.finfo(self.probs.dtype).tiny
with torch.no_grad():
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()
def log_prob(self, value):
if self._validate_args:
self._validate_sample(value)
value, probs = broadcast_all(value, self.probs)
probs = probs.clone(memory_format=torch.contiguous_format)
probs[(probs == 1) & (value == 0)] = 0
return value * (-probs).log1p() + self.probs.log()
def entropy(self):
return binary_cross_entropy_with_logits(self.logits, self.probs, reduction='none') / self.probs
|
