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