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Source code for torch.distributions.independent

import torch
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.utils import _sum_rightmost


[docs]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:: >>> 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 = {} 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), " "actual {} vs {}".format(reinterpreted_batch_ndims, 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(Independent, self).__init__(batch_shape, event_shape, validate_args=validate_args)
[docs] 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): return self.base_dist.support @property def mean(self): return self.base_dist.mean @property def variance(self): return self.base_dist.variance
[docs] def sample(self, sample_shape=torch.Size()): return self.base_dist.sample(sample_shape)
[docs] def rsample(self, sample_shape=torch.Size()): return self.base_dist.rsample(sample_shape)
[docs] def log_prob(self, value): log_prob = self.base_dist.log_prob(value) return _sum_rightmost(log_prob, self.reinterpreted_batch_ndims)
[docs] def entropy(self): entropy = self.base_dist.entropy() return _sum_rightmost(entropy, self.reinterpreted_batch_ndims)
[docs] 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__ + '({}, {})'.format(self.base_dist, self.reinterpreted_batch_ndims)

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