# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""Invert Bijector"""
from mindspore._checkparam import Validator as validator
from .bijector import Bijector
[docs]class Invert(Bijector):
r"""
Invert Bijector. Compute the inverse function of the input bijector.
Args:
bijector (Bijector): Base Bijector.
name (str): The name of the Bijector. Default: 'Invert' + bijector.name.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> import mindspore
>>> import mindspore.nn as nn
>>> import mindspore.nn.probability.bijector as msb
>>> from mindspore import Tensor
>>> import mindspore.context as context
>>> context.set_context(mode=1)
>>>
>>> # To initialize an inverse Exp bijector.
>>> inv_exp = msb.Invert(msb.Exp())
>>> value = Tensor([1, 2, 3], dtype=mindspore.float32)
>>> ans1 = inv_exp.forward(value)
>>> print(ans1.shape)
(3,)
>>> ans2 = inv_exp.inverse(value)
>>> print(ans2.shape)
(3,)
>>> ans3 = inv_exp.forward_log_jacobian(value)
>>> print(ans3.shape)
(3,)
>>> ans4 = inv_exp.inverse_log_jacobian(value)
>>> print(ans4.shape)
(3,)
"""
def __init__(self,
bijector,
name=""):
param = dict(locals())
validator.check_value_type('bijector', bijector, [Bijector], "Invert")
name = name or ('Invert' + bijector.name)
param["name"] = name
super(Invert, self).__init__(is_constant_jacobian=bijector.is_constant_jacobian,
is_injective=bijector.is_injective,
name=name,
dtype=bijector.dtype,
param=param)
self._bijector = bijector
self._batch_shape = self.bijector.batch_shape
self._is_scalar_batch = self.bijector.is_scalar_batch
@property
def bijector(self):
return self._bijector
def inverse(self, y):
return self.bijector("forward", y)
def forward(self, x):
return self.bijector("inverse", x)
def inverse_log_jacobian(self, y):
return self.bijector("forward_log_jacobian", y)
def forward_log_jacobian(self, x):
return self.bijector("inverse_log_jacobian", x)