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Source code for torch.distributions.categorical

import torch
from torch._six import nan
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.utils import probs_to_logits, logits_to_probs, lazy_property

[docs]class Categorical(Distribution):
r"""
Creates a categorical distribution parameterized by either :attr:probs or
:attr:logits (but not both).

.. note::
It is equivalent to the distribution that :func:torch.multinomial
samples from.

Samples are integers from :math:\{0, \ldots, K-1\} where K is probs.size(-1).

If :attr:probs is 1D with length-K, each element is the relative
probability of sampling the class at that index.

If :attr:probs is 2D, it is treated as a batch of relative probability
vectors.

.. note:: :attr:probs must be non-negative, finite and have a non-zero sum,
and it will be normalized to sum to 1.

See also: :func:torch.multinomial

Example::

>>> m = Categorical(torch.tensor([ 0.25, 0.25, 0.25, 0.25 ]))
>>> m.sample()  # equal probability of 0, 1, 2, 3
tensor(3)

Args:
probs (Tensor): event probabilities
logits (Tensor): event log-odds
"""
arg_constraints = {'probs': constraints.simplex,
'logits': constraints.real}
has_enumerate_support = True

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:
if probs.dim() < 1:
raise ValueError("probs parameter must be at least one-dimensional.")
self.probs = probs / probs.sum(-1, keepdim=True)
else:
if logits.dim() < 1:
raise ValueError("logits parameter must be at least one-dimensional.")
self.logits = logits - logits.logsumexp(dim=-1, keepdim=True)
self._param = self.probs if probs is not None else self.logits
self._num_events = self._param.size()[-1]
batch_shape = self._param.size()[:-1] if self._param.ndimension() > 1 else torch.Size()
super(Categorical, self).__init__(batch_shape, validate_args=validate_args)

[docs]    def expand(self, batch_shape, _instance=None):
new = self._get_checked_instance(Categorical, _instance)
batch_shape = torch.Size(batch_shape)
param_shape = batch_shape + torch.Size((self._num_events,))
if 'probs' in self.__dict__:
new.probs = self.probs.expand(param_shape)
new._param = new.probs
if 'logits' in self.__dict__:
new.logits = self.logits.expand(param_shape)
new._param = new.logits
new._num_events = self._num_events
super(Categorical, new).__init__(batch_shape, validate_args=False)
new._validate_args = self._validate_args
return new

def _new(self, *args, **kwargs):
return self._param.new(*args, **kwargs)

@constraints.dependent_property
def support(self):
return constraints.integer_interval(0, self._num_events - 1)

[docs]    @lazy_property
def logits(self):
return probs_to_logits(self.probs)

[docs]    @lazy_property
def probs(self):
return logits_to_probs(self.logits)

@property
def param_shape(self):
return self._param.size()

@property
def mean(self):

@property
def variance(self):

[docs]    def sample(self, sample_shape=torch.Size()):
sample_shape = self._extended_shape(sample_shape)
param_shape = sample_shape + torch.Size((self._num_events,))
probs = self.probs.expand(param_shape)
probs_2d = probs.reshape(-1, self._num_events)
sample_2d = torch.multinomial(probs_2d, 1, True)
return sample_2d.reshape(sample_shape)

[docs]    def log_prob(self, value):
if self._validate_args:
self._validate_sample(value)
value = value.long().unsqueeze(-1)
value = value[..., :1]
return log_pmf.gather(-1, value).squeeze(-1)

[docs]    def entropy(self):
p_log_p = self.logits * self.probs
return -p_log_p.sum(-1)

[docs]    def enumerate_support(self, expand=True):
num_events = self._num_events
values = torch.arange(num_events, dtype=torch.long, device=self._param.device)
values = values.view((-1,) + (1,) * len(self._batch_shape))
if expand:
values = values.expand((-1,) + self._batch_shape)
return values


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