pytorch

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# mypy: allow-untyped-defs
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from numbers import Number
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import torch
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from torch.distributions import constraints
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from torch.distributions.distribution import Distribution
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from torch.distributions.utils import (
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    broadcast_all,
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    lazy_property,
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    logits_to_probs,
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    probs_to_logits,
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)
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from torch.nn.functional import binary_cross_entropy_with_logits
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__all__ = ["Geometric"]
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class Geometric(Distribution):
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    r"""
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    Creates a Geometric distribution parameterized by :attr:`probs`,
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    where :attr:`probs` is the probability of success of Bernoulli trials.
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    .. math::
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        P(X=k) = (1-p)^{k} p, k = 0, 1, ...
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    .. note::
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        :func:`torch.distributions.geometric.Geometric` :math:`(k+1)`-th trial is the first success
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        hence draws samples in :math:`\{0, 1, \ldots\}`, whereas
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        :func:`torch.Tensor.geometric_` `k`-th trial is the first success hence draws samples in :math:`\{1, 2, \ldots\}`.
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    Example::
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        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
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        >>> m = Geometric(torch.tensor([0.3]))
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        >>> m.sample()  # underlying Bernoulli has 30% chance 1; 70% chance 0
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        tensor([ 2.])
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    Args:
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        probs (Number, Tensor): the probability of sampling `1`. Must be in range (0, 1]
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        logits (Number, Tensor): the log-odds of sampling `1`.
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    """
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    arg_constraints = {"probs": constraints.unit_interval, "logits": constraints.real}
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    support = constraints.nonnegative_integer
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    def __init__(self, probs=None, logits=None, validate_args=None):
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        if (probs is None) == (logits is None):
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            raise ValueError(
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                "Either `probs` or `logits` must be specified, but not both."
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            )
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        if probs is not None:
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            (self.probs,) = broadcast_all(probs)
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        else:
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            (self.logits,) = broadcast_all(logits)
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        probs_or_logits = probs if probs is not None else logits
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        if isinstance(probs_or_logits, Number):
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            batch_shape = torch.Size()
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        else:
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            batch_shape = probs_or_logits.size()
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        super().__init__(batch_shape, validate_args=validate_args)
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        if self._validate_args and probs is not None:
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            # Add an extra check beyond unit_interval
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            value = self.probs
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            valid = value > 0
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            if not valid.all():
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                invalid_value = value.data[~valid]
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                raise ValueError(
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                    "Expected parameter probs "
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                    f"({type(value).__name__} of shape {tuple(value.shape)}) "
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                    f"of distribution {repr(self)} "
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                    f"to be positive but found invalid values:\n{invalid_value}"
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                )
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    def expand(self, batch_shape, _instance=None):
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        new = self._get_checked_instance(Geometric, _instance)
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        batch_shape = torch.Size(batch_shape)
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        if "probs" in self.__dict__:
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            new.probs = self.probs.expand(batch_shape)
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        if "logits" in self.__dict__:
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            new.logits = self.logits.expand(batch_shape)
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        super(Geometric, new).__init__(batch_shape, validate_args=False)
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        new._validate_args = self._validate_args
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        return new
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    @property
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    def mean(self):
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        return 1.0 / self.probs - 1.0
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    @property
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    def mode(self):
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        return torch.zeros_like(self.probs)
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    @property
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    def variance(self):
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        return (1.0 / self.probs - 1.0) / self.probs
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    @lazy_property
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    def logits(self):
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        return probs_to_logits(self.probs, is_binary=True)
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    @lazy_property
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    def probs(self):
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        return logits_to_probs(self.logits, is_binary=True)
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    def sample(self, sample_shape=torch.Size()):
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        shape = self._extended_shape(sample_shape)
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        tiny = torch.finfo(self.probs.dtype).tiny
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        with torch.no_grad():
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            if torch._C._get_tracing_state():
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                # [JIT WORKAROUND] lack of support for .uniform_()
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                u = torch.rand(shape, dtype=self.probs.dtype, device=self.probs.device)
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                u = u.clamp(min=tiny)
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            else:
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                u = self.probs.new(shape).uniform_(tiny, 1)
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            return (u.log() / (-self.probs).log1p()).floor()
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    def log_prob(self, value):
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        if self._validate_args:
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            self._validate_sample(value)
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        value, probs = broadcast_all(value, self.probs)
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        probs = probs.clone(memory_format=torch.contiguous_format)
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        probs[(probs == 1) & (value == 0)] = 0
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        return value * (-probs).log1p() + self.probs.log()
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    def entropy(self):
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        return (
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            binary_cross_entropy_with_logits(self.logits, self.probs, reduction="none")
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            / self.probs
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        )
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