Source code for torch.distributions.multinomial

import torch
from torch import inf
from torch.distributions import Categorical, constraints
from torch.distributions.binomial import Binomial
from torch.distributions.distribution import Distribution
from torch.distributions.utils import broadcast_all

__all__ = ["Multinomial"]

[docs]class Multinomial(Distribution): r""" Creates a Multinomial distribution parameterized by :attr:`total_count` and either :attr:`probs` or :attr:`logits` (but not both). The innermost dimension of :attr:`probs` indexes over categories. All other dimensions index over batches. Note that :attr:`total_count` need not be specified if only :meth:`log_prob` is called (see example below) .. note:: The `probs` argument must be non-negative, finite and have a non-zero sum, and it will be normalized to sum to 1 along the last dimension. :attr:`probs` will return this normalized value. The `logits` argument will be interpreted as unnormalized log probabilities and can therefore be any real number. It will likewise be normalized so that the resulting probabilities sum to 1 along the last dimension. :attr:`logits` will return this normalized value. - :meth:`sample` requires a single shared `total_count` for all parameters and samples. - :meth:`log_prob` allows different `total_count` for each parameter and sample. Example:: >>> # xdoctest: +SKIP("FIXME: found invalid values") >>> m = Multinomial(100, torch.tensor([ 1., 1., 1., 1.])) >>> x = m.sample() # equal probability of 0, 1, 2, 3 tensor([ 21., 24., 30., 25.]) >>> Multinomial(probs=torch.tensor([1., 1., 1., 1.])).log_prob(x) tensor([-4.1338]) Args: total_count (int): number of trials probs (Tensor): event probabilities logits (Tensor): event log probabilities (unnormalized) """ arg_constraints = {"probs": constraints.simplex, "logits": constraints.real_vector} total_count: int @property def mean(self): return self.probs * self.total_count @property def variance(self): return self.total_count * self.probs * (1 - self.probs) def __init__(self, total_count=1, probs=None, logits=None, validate_args=None): if not isinstance(total_count, int): raise NotImplementedError("inhomogeneous total_count is not supported") self.total_count = total_count self._categorical = Categorical(probs=probs, logits=logits) self._binomial = Binomial(total_count=total_count, probs=self.probs) batch_shape = self._categorical.batch_shape event_shape = self._categorical.param_shape[-1:] super().__init__(batch_shape, event_shape, validate_args=validate_args)
[docs] def expand(self, batch_shape, _instance=None): new = self._get_checked_instance(Multinomial, _instance) batch_shape = torch.Size(batch_shape) new.total_count = self.total_count new._categorical = self._categorical.expand(batch_shape) super(Multinomial, new).__init__( batch_shape, self.event_shape, validate_args=False ) new._validate_args = self._validate_args return new
def _new(self, *args, **kwargs): return self._categorical._new(*args, **kwargs) @constraints.dependent_property(is_discrete=True, event_dim=1) def support(self): return constraints.multinomial(self.total_count) @property def logits(self): return self._categorical.logits @property def probs(self): return self._categorical.probs @property def param_shape(self): return self._categorical.param_shape
[docs] def sample(self, sample_shape=torch.Size()): sample_shape = torch.Size(sample_shape) samples = self._categorical.sample( torch.Size((self.total_count,)) + sample_shape ) # samples.shape is (total_count, sample_shape, batch_shape), need to change it to # (sample_shape, batch_shape, total_count) shifted_idx = list(range(samples.dim())) shifted_idx.append(shifted_idx.pop(0)) samples = samples.permute(*shifted_idx) counts = counts.scatter_add_(-1, samples, torch.ones_like(samples)) return counts.type_as(self.probs)
[docs] def entropy(self): n = torch.tensor(self.total_count) cat_entropy = self._categorical.entropy() term1 = n * cat_entropy - torch.lgamma(n + 1) support = self._binomial.enumerate_support(expand=False)[1:] binomial_probs = torch.exp(self._binomial.log_prob(support)) weights = torch.lgamma(support + 1) term2 = (binomial_probs * weights).sum([0, -1]) return term1 + term2
[docs] def log_prob(self, value): if self._validate_args: self._validate_sample(value) logits, value = broadcast_all(self.logits, value) logits = logits.clone(memory_format=torch.contiguous_format) log_factorial_n = torch.lgamma(value.sum(-1) + 1) log_factorial_xs = torch.lgamma(value + 1).sum(-1) logits[(value == 0) & (logits == -inf)] = 0 log_powers = (logits * value).sum(-1) return log_factorial_n - log_factorial_xs + log_powers


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