Revision b81e94e16826a1c8d996b0f590b8c3407a0e6a07 authored by Jesper Nielsen on 23 September 2022, 09:50:10 UTC, committed by Jesper Nielsen on 26 October 2022, 13:02:08 UTC
1 parent f66a708
bijectors.py
# Copyright 2019-2020 The GPflow Contributors. 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 typing import Optional
import tensorflow as tf
import tensorflow_probability as tfp
from check_shapes import check_shapes
from .. import config
from .misc import to_default_float
__all__ = ["positive", "triangular", "triangular_size"]
def positive(lower: Optional[float] = None, base: Optional[str] = None) -> tfp.bijectors.Bijector:
"""
Returns a positive bijector (a reversible transformation from real to positive numbers).
:param lower: overrides default lower bound
(if None, defaults to gpflow.config.default_positive_minimum())
:param base: overrides base positive bijector
(if None, defaults to gpflow.config.default_positive_bijector())
:returns: a bijector instance
"""
bijector = base if base is not None else config.default_positive_bijector()
bijector = config.positive_bijector_type_map()[bijector.lower()]()
lower_bound = lower if lower is not None else config.default_positive_minimum()
if lower_bound != 0.0:
shift = tfp.bijectors.Shift(to_default_float(lower_bound))
bijector = tfp.bijectors.Chain([shift, bijector]) # from unconstrained to constrained
return bijector
def triangular() -> tfp.bijectors.Bijector:
"""
Returns instance of a (lower) triangular bijector.
"""
return tfp.bijectors.FillTriangular()
@check_shapes(
"n: []",
"return: []",
)
def triangular_size(n: tf.Tensor) -> tf.Tensor:
"""
Returns the number of non-zero elements in an `n` by `n` triangular matrix.
"""
return n * (n + 1) // 2
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