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106 lines
3.8 KiB
106 lines
3.8 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""Transformed Distribution"""
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from mindspore.ops import operations as P
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from mindspore._checkparam import Validator as validator
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from mindspore.common import dtype as mstype
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import mindspore.nn as nn
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from .distribution import Distribution
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from ._utils.utils import check_type
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class TransformedDistribution(Distribution):
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"""
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Transformed Distribution.
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This class contains a bijector and a distribution and transforms the original distribution
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to a new distribution through the operation defined by the bijector.
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Args:
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bijector (Bijector): transformation to perform.
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distribution (Distribution): The original distribution.
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name (str): name of the transformed distribution. Default: transformed_distribution.
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Note:
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The arguments used to initialize the original distribution cannot be None.
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For example, mynormal = nn.Normal(dtype=dtyple.float32) cannot be used to initialized a
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TransformedDistribution since mean and sd are not specified.
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"""
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def __init__(self,
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bijector,
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distribution,
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dtype,
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seed=0,
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name="transformed_distribution"):
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"""
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Constructor of transformed_distribution class.
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"""
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param = dict(locals())
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validator.check_value_type('bijector', bijector, [nn.probability.bijector.Bijector], name)
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validator.check_value_type('distribution', distribution, [Distribution], name)
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valid_dtype = mstype.number_type
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check_type(dtype, valid_dtype, "transformed_distribution")
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super(TransformedDistribution, self).__init__(seed, dtype, name, param)
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self._bijector = bijector
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self._distribution = distribution
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self._is_linear_transformation = bijector.is_constant_jacobian
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self.exp = P.Exp()
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@property
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def bijector(self):
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return self._bijector
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@property
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def distribution(self):
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return self._distribution
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@property
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def is_linear_transformation(self):
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return self._is_linear_transformation
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def _cdf(self, value):
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r"""
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.. math::
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Y = g(X)
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P(Y <= a) = P(X <= g^{-1}(a))
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"""
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inverse_value = self.bijector.inverse(value)
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return self.distribution.cdf(inverse_value)
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def _log_prob(self, value):
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r"""
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.. math::
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Y = g(X)
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Py(a) = Px(g^{-1}(a)) * (g^{-1})'(a)
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\log(Py(a)) = \log(Px(g^{-1}(a))) + \log((g^{-1})'(a))
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"""
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inverse_value = self.bijector.inverse(value)
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unadjust_prob = self.distribution.log_prob(inverse_value)
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log_jacobian = self.bijector.inverse_log_jacobian(value)
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return unadjust_prob + log_jacobian
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def _prob(self, value):
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return self.exp(self._log_prob(value))
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def _sample(self, shape):
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org_sample = self.distribution.sample(shape)
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return self.bijector.forward(org_sample)
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def _mean(self):
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"""
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Note:
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This function maybe overridden by derived class.
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"""
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return self.bijector.forward(self.distribution.mean())
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