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- import torch
- from torch 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
- __all__ = ['Categorical']
- 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 `probs` is 1-dimensional with length-`K`, each element is the relative probability
- of sampling the class at that index.
- If `probs` is N-dimensional, the first N-1 dimensions are treated as a batch of
- relative probability vectors.
- .. 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.
- See also: :func:`torch.multinomial`
- Example::
- >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
- >>> 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 probabilities (unnormalized)
- """
- arg_constraints = {'probs': constraints.simplex,
- 'logits': constraints.real_vector}
- 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.")
- # Normalize
- 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().__init__(batch_shape, validate_args=validate_args)
- 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(is_discrete=True, event_dim=0)
- def support(self):
- return constraints.integer_interval(0, self._num_events - 1)
- @lazy_property
- def logits(self):
- return probs_to_logits(self.probs)
- @lazy_property
- def probs(self):
- return logits_to_probs(self.logits)
- @property
- def param_shape(self):
- return self._param.size()
- @property
- def mean(self):
- return torch.full(self._extended_shape(), nan, dtype=self.probs.dtype, device=self.probs.device)
- @property
- def mode(self):
- return self.probs.argmax(axis=-1)
- @property
- def variance(self):
- return torch.full(self._extended_shape(), nan, dtype=self.probs.dtype, device=self.probs.device)
- def sample(self, sample_shape=torch.Size()):
- if not isinstance(sample_shape, torch.Size):
- sample_shape = torch.Size(sample_shape)
- probs_2d = self.probs.reshape(-1, self._num_events)
- samples_2d = torch.multinomial(probs_2d, sample_shape.numel(), True).T
- return samples_2d.reshape(self._extended_shape(sample_shape))
- def log_prob(self, value):
- if self._validate_args:
- self._validate_sample(value)
- value = value.long().unsqueeze(-1)
- value, log_pmf = torch.broadcast_tensors(value, self.logits)
- value = value[..., :1]
- return log_pmf.gather(-1, value).squeeze(-1)
- def entropy(self):
- min_real = torch.finfo(self.logits.dtype).min
- logits = torch.clamp(self.logits, min=min_real)
- p_log_p = logits * self.probs
- return -p_log_p.sum(-1)
- 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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