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# Copyright 2016 The TensorFlow Authors. 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.
# ==============================================================================
"""Classes representing statistical distributions and ops for working with them.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.util import deprecation
# pylint: disable=unused-import,wildcard-import,line-too-long,g-importing-member,g-import-not-at-top
with deprecation.silence():
from tensorflow.contrib.distributions.python.ops import bijectors
from tensorflow.contrib.distributions.python.ops.autoregressive import *
from tensorflow.contrib.distributions.python.ops.batch_reshape import *
from tensorflow.contrib.distributions.python.ops.binomial import *
from tensorflow.contrib.distributions.python.ops.cauchy import *
from tensorflow.contrib.distributions.python.ops.chi2 import *
from tensorflow.contrib.distributions.python.ops.conditional_distribution import *
from tensorflow.contrib.distributions.python.ops.conditional_transformed_distribution import *
from tensorflow.contrib.distributions.python.ops.deterministic import *
from tensorflow.contrib.distributions.python.ops.distribution_util import fill_triangular
from tensorflow.contrib.distributions.python.ops.distribution_util import fill_triangular_inverse
from tensorflow.contrib.distributions.python.ops.distribution_util import matrix_diag_transform
from tensorflow.contrib.distributions.python.ops.distribution_util import reduce_weighted_logsumexp
from tensorflow.contrib.distributions.python.ops.distribution_util import softplus_inverse
from tensorflow.contrib.distributions.python.ops.distribution_util import tridiag
from tensorflow.contrib.distributions.python.ops.estimator import *
from tensorflow.contrib.distributions.python.ops.geometric import *
from tensorflow.contrib.distributions.python.ops.half_normal import *
from tensorflow.contrib.distributions.python.ops.independent import *
from tensorflow.contrib.distributions.python.ops.inverse_gamma import *
from tensorflow.contrib.distributions.python.ops.kumaraswamy import *
from tensorflow.contrib.distributions.python.ops.logistic import *
from tensorflow.contrib.distributions.python.ops.mixture import *
from tensorflow.contrib.distributions.python.ops.mixture_same_family import *
from tensorflow.contrib.distributions.python.ops.moving_stats import *
from tensorflow.contrib.distributions.python.ops.mvn_diag import *
from tensorflow.contrib.distributions.python.ops.mvn_diag_plus_low_rank import *
from tensorflow.contrib.distributions.python.ops.mvn_full_covariance import *
from tensorflow.contrib.distributions.python.ops.mvn_tril import *
from tensorflow.contrib.distributions.python.ops.negative_binomial import *
from tensorflow.contrib.distributions.python.ops.normal_conjugate_posteriors import *
from tensorflow.contrib.distributions.python.ops.onehot_categorical import *
from tensorflow.contrib.distributions.python.ops.poisson import *
from tensorflow.contrib.distributions.python.ops.poisson_lognormal import *
from tensorflow.contrib.distributions.python.ops.quantized_distribution import *
from tensorflow.contrib.distributions.python.ops.relaxed_bernoulli import *
from tensorflow.contrib.distributions.python.ops.relaxed_onehot_categorical import *
from tensorflow.contrib.distributions.python.ops.sample_stats import *
from tensorflow.contrib.distributions.python.ops.seed_stream import *
from tensorflow.contrib.distributions.python.ops.sinh_arcsinh import *
from tensorflow.contrib.distributions.python.ops.test_util import *
from tensorflow.contrib.distributions.python.ops.vector_diffeomixture import *
from tensorflow.contrib.distributions.python.ops.vector_exponential_diag import *
from tensorflow.contrib.distributions.python.ops.vector_laplace_diag import *
from tensorflow.contrib.distributions.python.ops.vector_sinh_arcsinh_diag import *
from tensorflow.contrib.distributions.python.ops.wishart import *
from tensorflow.python.ops.distributions.bernoulli import *
from tensorflow.python.ops.distributions.beta import *
from tensorflow.python.ops.distributions.categorical import *
from tensorflow.python.ops.distributions.dirichlet import *
from tensorflow.python.ops.distributions.dirichlet_multinomial import *
from tensorflow.python.ops.distributions.distribution import *
from tensorflow.python.ops.distributions.exponential import *
from tensorflow.python.ops.distributions.gamma import *
from tensorflow.python.ops.distributions.kullback_leibler import *
from tensorflow.python.ops.distributions.laplace import *
from tensorflow.python.ops.distributions.multinomial import *
from tensorflow.python.ops.distributions.normal import *
from tensorflow.python.ops.distributions.student_t import *
from tensorflow.python.ops.distributions.transformed_distribution import *
from tensorflow.python.ops.distributions.uniform import *
# pylint: enable=unused-import,wildcard-import,line-too-long,g-importing-member
from tensorflow.python.util.all_util import remove_undocumented
_allowed_symbols = [
'auto_correlation',
'bijectors',
'Cauchy',
'ConditionalDistribution',
'ConditionalTransformedDistribution',
'FULLY_REPARAMETERIZED',
'NOT_REPARAMETERIZED',
'ReparameterizationType',
'Distribution',
'Autoregressive',
'BatchReshape',
'Bernoulli',
'Beta',
'Binomial',
'BetaWithSoftplusConcentration',
'Categorical',
'Chi2',
'Chi2WithAbsDf',
'Deterministic',
'VectorDeterministic',
'Exponential',
'ExponentialWithSoftplusRate',
'VectorExponentialDiag',
'Gamma',
'GammaWithSoftplusConcentrationRate',
'Geometric',
'HalfNormal',
'Independent',
'InverseGamma',
'InverseGammaWithSoftplusConcentrationRate',
'Kumaraswamy',
'Laplace',
'LaplaceWithSoftplusScale',
'Logistic',
'NegativeBinomial',
'Normal',
'NormalWithSoftplusScale',
'Poisson',
'PoissonLogNormalQuadratureCompound',
'SeedStream',
'SinhArcsinh',
'StudentT',
'StudentTWithAbsDfSoftplusScale',
'Uniform',
'MultivariateNormalDiag',
'MultivariateNormalFullCovariance',
'MultivariateNormalTriL',
'MultivariateNormalDiagPlusLowRank',
'MultivariateNormalDiagWithSoftplusScale',
'Dirichlet',
'DirichletMultinomial',
'Multinomial',
'VectorDiffeomixture',
'VectorLaplaceDiag',
'VectorSinhArcsinhDiag',
'WishartCholesky',
'WishartFull',
'TransformedDistribution',
'QuantizedDistribution',
'Mixture',
'MixtureSameFamily',
'ExpRelaxedOneHotCategorical',
'OneHotCategorical',
'RelaxedBernoulli',
'RelaxedOneHotCategorical',
'kl_divergence',
'RegisterKL',
'fill_triangular',
'fill_triangular_inverse',
'matrix_diag_transform',
'reduce_weighted_logsumexp',
'softplus_inverse',
'tridiag',
'normal_conjugates_known_scale_posterior',
'normal_conjugates_known_scale_predictive',
'percentile',
'assign_moving_mean_variance',
'assign_log_moving_mean_exp',
'moving_mean_variance',
'estimator_head_distribution_regression',
'quadrature_scheme_softmaxnormal_gauss_hermite',
'quadrature_scheme_softmaxnormal_quantiles',
'quadrature_scheme_lognormal_gauss_hermite',
'quadrature_scheme_lognormal_quantiles',
]
remove_undocumented(__name__, _allowed_symbols)
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