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sinh.py
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sinh.py
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# Copyright 2020 The TensorFlow Probability Authors.
#
# 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.
# ============================================================================
"""Sinh bijector."""
import tensorflow.compat.v2 as tf
from tensorflow_probability.python import math as tfp_math
from tensorflow_probability.python.bijectors import bijector
__all__ = [
'Sinh',
]
class Sinh(bijector.AutoCompositeTensorBijector):
"""Bijector that computes `Y = sinh(X)`.
#### Examples
```python
bijector.Sinh().forward(x=[[1., 0], [3, 2]])
# Result: [[1.1752012, 0.], [10.017875, 3.6268604]], i.e., sinh(x)
bijector.Sinh().inverse(y=[[1., 0], [3, 2]])
# Result: [[0.8813736, 0.], [1.8184465, 1.4436355]], i.e., asinh(y).
```
"""
def __init__(self, validate_args=False, name='sinh'):
parameters = dict(locals())
with tf.name_scope(name) as name:
super(Sinh, self).__init__(
forward_min_event_ndims=0,
validate_args=validate_args,
parameters=parameters,
name=name)
@classmethod
def _is_increasing(cls):
return True
@classmethod
def _parameter_properties(cls, dtype):
return dict()
def _forward(self, x):
return tf.sinh(x)
def _inverse(self, y):
return tf.asinh(y)
# We implicitly rely on _forward_log_det_jacobian rather than explicitly
# implement _inverse_log_det_jacobian because directly using
# `-0.5 * math.log1psquare(y)` has lower numerical precision.
def _forward_log_det_jacobian(self, x):
return tfp_math.log_cosh(x)