from typing import Dict import torch from torch.distributions import constraints from torch.distributions.distribution import Distribution from torch.distributions.utils import _sum_rightmost __all__ = ["Independent"] class Independent(Distribution): r""" Reinterprets some of the batch dims of a distribution as event dims. This is mainly useful for changing the shape of the result of :meth:`log_prob`. For example to create a diagonal Normal distribution with the same shape as a Multivariate Normal distribution (so they are interchangeable), you can:: >>> from torch.distributions.multivariate_normal import MultivariateNormal >>> from torch.distributions.normal import Normal >>> loc = torch.zeros(3) >>> scale = torch.ones(3) >>> mvn = MultivariateNormal(loc, scale_tril=torch.diag(scale)) >>> [mvn.batch_shape, mvn.event_shape] [torch.Size([]), torch.Size([3])] >>> normal = Normal(loc, scale) >>> [normal.batch_shape, normal.event_shape] [torch.Size([3]), torch.Size([])] >>> diagn = Independent(normal, 1) >>> [diagn.batch_shape, diagn.event_shape] [torch.Size([]), torch.Size([3])] Args: base_distribution (torch.distributions.distribution.Distribution): a base distribution reinterpreted_batch_ndims (int): the number of batch dims to reinterpret as event dims """ arg_constraints: Dict[str, constraints.Constraint] = {} def __init__( self, base_distribution, reinterpreted_batch_ndims, validate_args=None ): if reinterpreted_batch_ndims > len(base_distribution.batch_shape): raise ValueError( "Expected reinterpreted_batch_ndims <= len(base_distribution.batch_shape), " f"actual {reinterpreted_batch_ndims} vs {len(base_distribution.batch_shape)}" ) shape = base_distribution.batch_shape + base_distribution.event_shape event_dim = reinterpreted_batch_ndims + len(base_distribution.event_shape) batch_shape = shape[: len(shape) - event_dim] event_shape = shape[len(shape) - event_dim :] self.base_dist = base_distribution self.reinterpreted_batch_ndims = reinterpreted_batch_ndims super().__init__(batch_shape, event_shape, validate_args=validate_args) def expand(self, batch_shape, _instance=None): new = self._get_checked_instance(Independent, _instance) batch_shape = torch.Size(batch_shape) new.base_dist = self.base_dist.expand( batch_shape + self.event_shape[: self.reinterpreted_batch_ndims] ) new.reinterpreted_batch_ndims = self.reinterpreted_batch_ndims super(Independent, new).__init__( batch_shape, self.event_shape, validate_args=False ) new._validate_args = self._validate_args return new @property def has_rsample(self): return self.base_dist.has_rsample @property def has_enumerate_support(self): if self.reinterpreted_batch_ndims > 0: return False return self.base_dist.has_enumerate_support @constraints.dependent_property def support(self): result = self.base_dist.support if self.reinterpreted_batch_ndims: result = constraints.independent(result, self.reinterpreted_batch_ndims) return result @property def mean(self): return self.base_dist.mean @property def mode(self): return self.base_dist.mode @property def variance(self): return self.base_dist.variance def sample(self, sample_shape=torch.Size()): return self.base_dist.sample(sample_shape) def rsample(self, sample_shape=torch.Size()): return self.base_dist.rsample(sample_shape) def log_prob(self, value): log_prob = self.base_dist.log_prob(value) return _sum_rightmost(log_prob, self.reinterpreted_batch_ndims) def entropy(self): entropy = self.base_dist.entropy() return _sum_rightmost(entropy, self.reinterpreted_batch_ndims) def enumerate_support(self, expand=True): if self.reinterpreted_batch_ndims > 0: raise NotImplementedError( "Enumeration over cartesian product is not implemented" ) return self.base_dist.enumerate_support(expand=expand) def __repr__(self): return ( self.__class__.__name__ + f"({self.base_dist}, {self.reinterpreted_batch_ndims})" )