/usr/local/lib64/python3.6/site-packages/torch/distributions
Edit: /usr/local/lib64/python3.6/site-packages/torch/distributions/independent.py (4361B)
import torch
from torch.distributions import constraints
from torch.distributions.distribution import Distribution
from torch.distributions.utils import _sum_rightmost
from typing import Dict
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: 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), "
"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):
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 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__ + '({}, {})'.format(self.base_dist, self.reinterpreted_batch_ndims)