/****************************************************************************
 *   This file is part of the aGrUM/pyAgrum library.                        *
 *                                                                          *
 *   Copyright (c) 2005-2026 by                                             *
 *       - Pierre-Henri WUILLEMIN(_at_LIP6)                                 *
 *       - Christophe GONZALES(_at_AMU)                                     *
 *                                                                          *
 *   The aGrUM/pyAgrum library is free software; you can redistribute it    *
 *   and/or modify it under the terms of either :                           *
 *                                                                          *
 *    - the GNU Lesser General Public License as published by               *
 *      the Free Software Foundation, either version 3 of the License,      *
 *      or (at your option) any later version,                              *
 *    - the MIT license (MIT),                                              *
 *    - or both in dual license, as here.                                   *
 *                                                                          *
 *   (see https://agrum.gitlab.io/articles/dual-licenses-lgplv3mit.html)    *
 *                                                                          *
 *   This aGrUM/pyAgrum library is distributed in the hope that it will be  *
 *   useful, but WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,          *
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 *                                                                          *
 *   See LICENCES for more details.                                         *
 *                                                                          *
 *   SPDX-FileCopyrightText: Copyright 2005-2026                            *
 *       - Pierre-Henri WUILLEMIN(_at_LIP6)                                 *
 *       - Christophe GONZALES(_at_AMU)                                     *
 *   SPDX-License-Identifier: LGPL-3.0-or-later OR MIT                      *
 *                                                                          *
 *   Contact  : info_at_agrum_dot_org                                       *
 *   homepage : http://agrum.gitlab.io                                      *
 *   gitlab   : https://gitlab.com/agrumery/agrum                           *
 *                                                                          *
 ****************************************************************************/


/** @file
 * @brief A class that, given a structure and a parameter estimator returns a
 * full Bayes net
 *
 * @author Christophe GONZALES(_at_AMU) and Pierre-Henri WUILLEMIN(_at_LIP6)
 */

#include <agrum/BN/learning/paramUtils/DAG2BNLearner.h>

#ifndef DOXYGEN_SHOULD_SKIP_THIS

/// include the inlined functions if necessary
#  ifdef GUM_NO_INLINE
#    include <agrum/BN/learning/paramUtils/DAG2BNLearner_inl.h>
#  endif /* GUM_NO_INLINE */

namespace gum {

  namespace learning {

    /// default constructor
    DAG2BNLearner::DAG2BNLearner() : EMApproximationScheme() { GUM_CONSTRUCTOR(DAG2BNLearner); }

    /// copy constructor
    DAG2BNLearner::DAG2BNLearner(const DAG2BNLearner& from) :
        EMApproximationScheme(from), noiseEM_(from.noiseEM_),
        max_nb_dec_likelihood_iter_(from.max_nb_dec_likelihood_iter_) {
      GUM_CONS_CPY(DAG2BNLearner);
    }

    /// move constructor
    DAG2BNLearner::DAG2BNLearner(DAG2BNLearner&& from) noexcept :
        EMApproximationScheme(std::move(from)), noiseEM_(from.noiseEM_),
        max_nb_dec_likelihood_iter_(from.max_nb_dec_likelihood_iter_) {
      GUM_CONS_MOV(DAG2BNLearner);
    }

    /// virtual copy constructor
    DAG2BNLearner* DAG2BNLearner::clone() const { return new DAG2BNLearner(*this); }

    /// destructor
    DAG2BNLearner::~DAG2BNLearner() { GUM_DESTRUCTOR(DAG2BNLearner); }

    /// copy operator
    DAG2BNLearner& DAG2BNLearner::operator=(const DAG2BNLearner& from) {
      EMApproximationScheme::operator=(from);
      noiseEM_ = from.noiseEM_;
      return *this;
    }

    /// move operator
    DAG2BNLearner& DAG2BNLearner::operator=(DAG2BNLearner&& from) noexcept {
      EMApproximationScheme::operator=(std::move(from));
      noiseEM_ = from.noiseEM_;
      return *this;
    }


  } /* namespace learning */

} /* namespace gum */

#endif /* DOXYGEN_SHOULD_SKIP_THIS */
