def copy(self, **override_parameters_kwargs):
"""Creates a deep copy of the distribution.
Note: the copy distribution may continue to depend on the original
initialization arguments.
Args:
**override_parameters_kwargs: String/value dictionary of initialization
arguments to override with new values.
Returns:
distribution: A new instance of `type(self)` initialized from the union
of self.parameters and override_parameters_kwargs, i.e.,
`dict(self.parameters, **override_parameters_kwargs)`.
"""
try:
# We want track provenance from origin variables, so we use batch_slice
# if this distribution supports slicing. See the comment on
# PROVENANCE_ATTR in slicing.py
return slicing.batch_slice(self, self._params_event_ndims(),
override_parameters_kwargs, Ellipsis)
except NotImplementedError:
parameters = dict(self.parameters, **override_parameters_kwargs)
d = type(self)(**parameters)
# pylint: disable=protected-access
d._parameters = parameters
d._parameters_sanitized = True
# pylint: enable=protected-access
return d
def __getitem__(self, slices):
"""Slices the batch axes of this distribution, returning a new instance.
```python
b = tfd.Bernoulli(logits=tf.zeros([3, 5, 7, 9]))
b.batch_shape # => [3, 5, 7, 9]
b2 = b[:, tf.newaxis, ..., -2:, 1::2]
b2.batch_shape # => [3, 1, 5, 2, 4]
x = tf.random.normal([5, 3, 2, 2])
cov = tf.matmul(x, x, transpose_b=True)
chol = tf.cholesky(cov)
loc = tf.random.normal([4, 1, 3, 1])
mvn = tfd.MultivariateNormalTriL(loc, chol)
mvn.batch_shape # => [4, 5, 3]
mvn.event_shape # => [2]
mvn2 = mvn[:, 3:, ..., ::-1, tf.newaxis]
mvn2.batch_shape # => [4, 2, 3, 1]
mvn2.event_shape # => [2]
```
Args:
slices: slices from the [] operator
Returns:
dist: A new `tfd.Distribution` instance with sliced parameters.
"""
return slicing.batch_slice(self, self._params_event_ndims(), {},
slices)