clipppy.utils.distributions.wrapper

Module Contents

clipppy.utils.distributions.wrapper._Distribution

clipppy.utils.distributions.wrapper._size

class clipppy.utils.distributions.wrapper.DistributionWrapper(base_dist: _Distribution, batch_shape: _size = None, event_shape: _size = None, validate_args=None)

Base class for a distribution that delegates to another one.

arg_constraints

property batch_dim(self)

cdf(self, value)

Returns the cumulative density/mass function evaluated at value.

Args:

value (Tensor):

entropy(self)

Returns entropy of distribution, batched over batch_shape.

Returns:

Tensor of shape batch_shape.

enumerate_support(self, expand=True)

Returns tensor containing all values supported by a discrete distribution. The result will enumerate over dimension 0, so the shape of the result will be (cardinality,) + batch_shape + event_shape (where event_shape = () for univariate distributions).

Note that this enumerates over all batched tensors in lock-step [[0, 0], [1, 1], ...]. With expand=False, enumeration happens along dim 0, but with the remaining batch dimensions being singleton dimensions, [[0], [1], ...

To iterate over the full Cartesian product use itertools.product(m.enumerate_support()).

Args:

expand (bool): whether to expand the support over the

batch dims to match the distribution’s batch_shape.

Returns:

Tensor iterating over dimension 0.

expand(self, batch_shape, _instance=None)

Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to batch_shape. This method calls expand on the distribution’s parameters. As such, this does not allocate new memory for the expanded distribution instance. Additionally, this does not repeat any args checking or parameter broadcasting in __init__.py, when an instance is first created.

Args:

batch_shape (torch.Size): the desired expanded size. _instance: new instance provided by subclasses that

need to override .expand.

Returns:

New distribution instance with batch dimensions expanded to batch_size.

property has_enumerate_support(self)

property has_rsample(self)

icdf(self, value)

Returns the inverse cumulative density/mass function evaluated at value.

Args:

value (Tensor):

log_prob(self, value)

Returns the log of the probability density/mass function evaluated at value.

Args:

value (Tensor):

property mean(self)

Returns the mean of the distribution.

rsample(self, sample_shape=Size())

Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples if the distribution parameters are batched.

sample(self, sample_shape=Size())

Generates a sample_shape shaped sample or sample_shape shaped batch of samples if the distribution parameters are batched.

support(self)

Returns a Constraint object representing this distribution’s support.

property variance(self)

Returns the variance of the distribution.