aGrUM
3.2.0
a C++ library for (probabilistic) graphical models
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DAG2BNLearner.cpp
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* This file is part of the aGrUM/pyAgrum library. *
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* Copyright (c) 2005-2026 by *
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* - Pierre-Henri WUILLEMIN(_at_LIP6) *
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* - Christophe GONZALES(_at_AMU) *
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* The aGrUM/pyAgrum library is free software; you can redistribute it *
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* SPDX-FileCopyrightText: Copyright 2005-2026 *
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* - Pierre-Henri WUILLEMIN(_at_LIP6) *
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* - Christophe GONZALES(_at_AMU) *
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#include <
agrum/BN/learning/paramUtils/DAG2BNLearner.h
>
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#ifndef DOXYGEN_SHOULD_SKIP_THIS
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# ifdef GUM_NO_INLINE
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# include <
agrum/BN/learning/paramUtils/DAG2BNLearner_inl.h
>
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# endif
/* GUM_NO_INLINE */
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namespace
gum
{
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namespace
learning
{
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DAG2BNLearner::DAG2BNLearner
() :
EMApproximationScheme
() { GUM_CONSTRUCTOR(DAG2BNLearner); }
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DAG2BNLearner::DAG2BNLearner(
const
DAG2BNLearner& from) :
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EMApproximationScheme(from), noiseEM_(from.noiseEM_),
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max_nb_dec_likelihood_iter_(from.max_nb_dec_likelihood_iter_) {
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GUM_CONS_CPY(DAG2BNLearner);
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}
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DAG2BNLearner::DAG2BNLearner(DAG2BNLearner&& from) noexcept :
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EMApproximationScheme(std::move(from)), noiseEM_(from.noiseEM_),
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max_nb_dec_likelihood_iter_(from.max_nb_dec_likelihood_iter_) {
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GUM_CONS_MOV(DAG2BNLearner);
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}
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DAG2BNLearner* DAG2BNLearner::clone()
const
{
return
new
DAG2BNLearner(*
this
); }
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DAG2BNLearner::~DAG2BNLearner() { GUM_DESTRUCTOR(DAG2BNLearner); }
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DAG2BNLearner& DAG2BNLearner::operator=(
const
DAG2BNLearner& from) {
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EMApproximationScheme::operator=(from);
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noiseEM_ = from.noiseEM_;
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return
*
this
;
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}
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DAG2BNLearner& DAG2BNLearner::operator=(DAG2BNLearner&& from)
noexcept
{
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EMApproximationScheme::operator=(std::move(from));
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noiseEM_ = from.noiseEM_;
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return
*
this
;
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}
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}
/* namespace learning */
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}
/* namespace gum */
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#endif
/* DOXYGEN_SHOULD_SKIP_THIS */
DAG2BNLearner.h
A class that, given a structure and a parameter estimator returns a full Bayes net.
DAG2BNLearner_inl.h
A class that, given a structure and a parameter estimator returns a full Bayes net.
gum::learning::DAG2BNLearner::DAG2BNLearner
DAG2BNLearner()
default constructor
gum::learning::EMApproximationScheme
A class for parameterizing EM's parameter learning approximations.
Definition
EMApproximationScheme.h:68
gum::learning
include the inlined functions if necessary
Definition
CSVParser.h:55
gum
gum is the global namespace for all aGrUM entities
Definition
agrum.h:46
aGrUM
3.2.0
© PHW&CG&others - 2022
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