# 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
[文档]class Invert(Bijector):
r"""
Invert Bijector. Compute the inverse function of the input bijector. If the function of the forward mapping,
namely the input of `bijector` below, is :math:`Y = g(X)`,
then the function of corresponding inverse mapping Bijector is :math:`Y = h(X) = g^{-1}(X)`.
Args:
bijector (Bijector): Base Bijector.
name (str): The name of the Bijector. Default: "". When name is set to "", it is actually
'Invert' + bijector.name.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> import mindspore
>>> import mindspore.nn as nn
>>> import mindspore.nn.probability.bijector as msb
>>> from mindspore import Tensor
>>> class Net(nn.Cell):
... def __init__(self):
... super(Net, self).__init__()
... self.origin = msb.ScalarAffine(scale=2.0, shift=1.0)
... self.invert = msb.Invert(self.origin)
...
... def construct(self, x_):
... return self.invert.forward(x_)
>>> forward = Net()
>>> x = np.array([2.0, 3.0, 4.0, 5.0]).astype(np.float32)
>>> ans = forward(Tensor(x, dtype=mindspore.float32))
>>> print(ans.shape)
(4,)
"""
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 base bijector."""
return self._bijector
[文档] def inverse(self, y):
"""
Perform the inverse transformation of the inverse bijector,
namely the forward transformation of the underlying bijector.
Args:
y (Tensor): the value of the transformed random variable.
Output:
Tensor, the value of the input random variable.
"""
return self.bijector("forward", y)
[文档] def forward(self, x):
"""
Perform the forward transformation of the inverse bijector,
namely the inverse transformation of the underlying bijector.
Args:
x (Tensor): the value of the input random variable.
Output:
Tensor, the value of the transformed random variable.
"""
return self.bijector("inverse", x)
[文档] def inverse_log_jacobian(self, y):
"""
Logarithm of the derivative of the inverse transformation of the inverse bijector,
namely logarithm of the derivative of the forward transformation of the underlying bijector.
Args:
y (Tensor): the value of the transformed random variable.
Output:
Tensor, logarithm of the derivative of the inverse transformation of the inverse bijector.
"""
return self.bijector("forward_log_jacobian", y)
[文档] def forward_log_jacobian(self, x):
"""
Logarithm of the derivative of the forward transformation of the inverse bijector,
namely logarithm of the derivative of the inverse transformation of the underlying bijector.
Args:
x (Tensor): the value of the input random variable.
Output:
Tensor, logarithm of the derivative of the forward transformation of the inverse bijector.
"""
return self.bijector("inverse_log_jacobian", x)