DAG2BNLearner.h

Go to the documentation of this file.

/****************************************************************************
 *   This file is part of the aGrUM/pyAgrum library.                        *
 *                                                                          *
 *   Copyright (c) 2005-2025 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,          *
 *   INCLUDING BUT NOT LIMITED TO THE WARRANTIES MERCHANTABILITY or FITNESS *
 *   FOR A PARTICULAR PURPOSE  AND NONINFRINGEMENT. IN NO EVENT SHALL THE   *
 *   AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER *
 *   LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,        *
 *   ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR  *
 *   OTHER DEALINGS IN THE SOFTWARE.                                        *
 *                                                                          *
 *   See LICENCES for more details.                                         *
 *                                                                          *
 *   SPDX-FileCopyrightText: Copyright 2005-2025                            *
 *       - 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                           *
 *                                                                          *
 ****************************************************************************/
 

#ifndef GUM_LEARNING_DAG_2_BN_LEARNER_H
#define GUM_LEARNING_DAG_2_BN_LEARNER_H
 
#include <algorithm>
#include <string>
#include <vector>
 
#include <agrum/agrum.h>
 
#include <agrum/BN/learning/paramUtils/EMApproximationScheme.h>
#include <agrum/BN/learning/paramUtils/paramEstimator.h>
 
namespace gum {
 
  namespace learning {

    class DAG2BNLearner: public EMApproximationScheme {
      public:
      // ##########################################################################
      // ##########################################################################

      DAG2BNLearner();

      DAG2BNLearner(const DAG2BNLearner& from);

      DAG2BNLearner(DAG2BNLearner&& from);

      virtual DAG2BNLearner* clone() const;

      virtual ~DAG2BNLearner();

 
 
      // ##########################################################################
      // ##########################################################################


      DAG2BNLearner& operator=(const DAG2BNLearner& from);

      DAG2BNLearner& operator=(DAG2BNLearner&& from);

 
 
      // ##########################################################################
      // ##########################################################################

      DAG2BNLearner& setNoise(const double noise);

      template < typename GUM_SCALAR = double >
      static BayesNet< GUM_SCALAR > createBN(ParamEstimator& estimator, const DAG& dag);

      template < typename GUM_SCALAR = double >
      BayesNet< GUM_SCALAR > createBNwithEM(ParamEstimator& bootstrap_estimator,
                                            ParamEstimator& EM_estimator,
                                            const DAG&      dag);

      template < typename GUM_SCALAR >
      BayesNet< GUM_SCALAR > createBNwithEM(ParamEstimator&               bootstrap_estimator,
                                            ParamEstimator&               EM_estimator,
                                            const BayesNet< GUM_SCALAR >& bn);

      template < typename GUM_SCALAR >
      BayesNet< GUM_SCALAR > createBNwithEM(ParamEstimator&          bootstrap_estimator,
                                            ParamEstimator&          EM_estimator,
                                            BayesNet< GUM_SCALAR >&& bn);

      EMApproximationScheme& approximationScheme();

 
#ifndef DOXYGEN_SHOULD_SKIP_THIS
 
      private:
      double noiseEM_{0.1};


      double log_likelihood_EM_{0.0};

      unsigned int max_nb_dec_likelihood_iter_{3};


      template < typename GUM_SCALAR = double >
      static void _probaVarReordering_(gum::Tensor< GUM_SCALAR >&       pot,
                                       const gum::Tensor< GUM_SCALAR >& other_pot);


      template < typename GUM_SCALAR = double >
      BayesNet< GUM_SCALAR >
          _createBN_(ParamEstimator& estimator, const DAG& dag, const bool compute_log_likelihood);
      template < typename GUM_SCALAR >
      BayesNet< GUM_SCALAR > _performEM_(ParamEstimator&          bootstrap_estimator,
                                         ParamEstimator&          EM_estimator,
                                         BayesNet< GUM_SCALAR >&& bn);
 
 
#endif /* DOXYGEN_SHOULD_SKIP_THIS */
    };
 
  } /* namespace learning */
 
} /* namespace gum */

#include <agrum/BN/learning/paramUtils/DAG2BNLearner_tpl.h>
 
// include the inlined functions if necessary
#ifndef GUM_NO_INLINE
#  include <agrum/BN/learning/paramUtils/DAG2BNLearner_inl.h>
#endif /* GUM_NO_INLINE */
 
#endif /* GUM_LEARNING_DAG_2_BN_LEARNER_H */