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: self.probs, = broadcast_all(probs) if not raise ValueError('All elements of probs must be greater than 0') 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)
[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 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 =, 1) return (u.log() / (-self.probs).log1p()).floor()
[docs] 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()
[docs] def entropy(self): return binary_cross_entropy_with_logits(self.logits, self.probs, reduction='none') / self.probs


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