independent.py

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


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)

    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

    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)