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 (
from torch.nn.functional import binary_cross_entropy_with_logits

__all__ = ["Geometric"]

[docs]class Geometric(Distribution): r""" Creates a Geometric distribution parameterized by :attr:`probs`, where :attr:`probs` is the probability of success of Bernoulli trials. .. math:: P(X=k) = (1-p)^{k} p, k = 0, 1, ... .. note:: :func:`torch.distributions.geometric.Geometric` :math:`(k+1)`-th trial is the first success hence draws samples in :math:`\{0, 1, \ldots\}`, whereas :func:`torch.Tensor.geometric_` `k`-th trial is the first success hence draws samples in :math:`\{1, 2, \ldots\}`. Example:: >>> # xdoctest: +IGNORE_WANT("non-deterministic") >>> 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().__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 =[~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}" )
[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 1.0 / self.probs - 1.0 @property def mode(self): return torch.zeros_like(self.probs) @property def variance(self): return (1.0 / self.probs - 1.0) / 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)
[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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