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independent.py
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independent.py
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.distribution import distribution
class Independent(distribution.Distribution):
r"""
Reinterprets some of the batch dimensions of a distribution as event dimensions.
This is mainly useful for changing the shape of the result of
:meth:`log_prob`.
Args:
base (Distribution): The base distribution.
reinterpreted_batch_rank (int): The number of batch dimensions to
reinterpret as event dimensions.
Examples:
.. code-block:: python
import paddle
from paddle.distribution import independent
beta = paddle.distribution.Beta(paddle.to_tensor([0.5, 0.5]), paddle.to_tensor([0.5, 0.5]))
print(beta.batch_shape, beta.event_shape)
# (2,) ()
print(beta.log_prob(paddle.to_tensor(0.2)))
# Tensor(shape=[2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# [-0.22843921, -0.22843921])
reinterpreted_beta = independent.Independent(beta, 1)
print(reinterpreted_beta.batch_shape, reinterpreted_beta.event_shape)
# () (2,)
print(reinterpreted_beta.log_prob(paddle.to_tensor([0.2, 0.2])))
# Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# -0.45687842)
"""
def __init__(self, base, reinterpreted_batch_rank):
if not isinstance(base, distribution.Distribution):
raise TypeError(
f"Expected type of 'base' is Distribution, but got {type(base)}"
)
if not (0 < reinterpreted_batch_rank <= len(base.batch_shape)):
raise ValueError(
f"Expected 0 < reinterpreted_batch_rank <= {len(base.batch_shape)}, but got {reinterpreted_batch_rank}"
)
self._base = base
self._reinterpreted_batch_rank = reinterpreted_batch_rank
shape = base.batch_shape + base.event_shape
super().__init__(
batch_shape=shape[
: len(base.batch_shape) - reinterpreted_batch_rank
],
event_shape=shape[
len(base.batch_shape) - reinterpreted_batch_rank :
],
)
@property
def mean(self):
return self._base.mean
@property
def variance(self):
return self._base.variance
def sample(self, shape=()):
return self._base.sample(shape)
def log_prob(self, value):
return self._sum_rightmost(
self._base.log_prob(value), self._reinterpreted_batch_rank
)
def prob(self, value):
return self.log_prob(value).exp()
def entropy(self):
return self._sum_rightmost(
self._base.entropy(), self._reinterpreted_batch_rank
)
def _sum_rightmost(self, value, n):
return value.sum(list(range(-n, 0))) if n > 0 else value