/usr/local/lib64/python3.6/site-packages/torch/distributions/__pycache__
Edit: /usr/local/lib64/python3.6/site-packages/torch/distributions/__pycache__/independent.cpython-36.pyc (4506B)
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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
Nc s |t |jkr$tdj|t |j|j|j }|t |j }|d t || }|t || d }|| _|| _tt| j |||d d S )NzYExpected reinterpreted_batch_ndims <= len(base_distribution.batch_shape), actual {} vs {})
validate_args)
len batch_shape
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mean)r r r r r L s zIndependent.meanc C s | j jS )N)r
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