K2.h

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/***************************************************************************
 *   Copyright (C) 2005 by Christophe GONZALES and Pierre-Henri WUILLEMIN  *
 *   {prenom.nom}@lip6.fr                                                  *
 *                                                                         *
 *   This program is free software; you can redistribute it and/or modify  *
 *   it under the terms of the GNU General Public License as published by  *
 *   the Free Software Foundation; either version 2 of the License, or     *
 *   (at your option) any later version.                                   *
 *                                                                         *
 *   This program is distributed in the hope that it wil be useful,        *
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of        *
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the         *
 *   GNU General Public License for more details.                          *
 *                                                                         *
 *   You should have received a copy of the GNU General Public License     *
 *   along with this program; if not, write to the                         *
 *   Free Software Foundation, Inc.,                                       *
 *   59 Temple Place - Suite 330, Boston, MA  02111-1307, USA.             *
 ***************************************************************************/
#ifndef GUM_LEARNING_K2_H
#define GUM_LEARNING_K2_H

#include <string>
#include <vector>

#include <agrum/BN/BayesNet.h>
#include <agrum/core/sequence.h>
#include <agrum/graphs/DAG.h>
#include <agrum/learning/greedyHillClimbing.h>

namespace gum {

  namespace learning {

    class K2 : private GreedyHillClimbing {
      public:
      // ##########################################################################
      // ##########################################################################

      K2();

      K2(const K2& from);

      K2(K2&& from);

      ~K2();


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

      K2& operator=(const K2& from);

      K2& operator=(K2&& from);


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

      ApproximationScheme& approximationScheme();

      void setOrder(const Sequence< NodeId >& order);

      void setOrder(const std::vector< NodeId >& order);

      const Sequence< NodeId >& order() const noexcept;


      template < typename GRAPH_CHANGES_SELECTOR >
      DAG learnStructure(GRAPH_CHANGES_SELECTOR& selector,
                         DAG                     initial_dag = DAG());

      template < typename GUM_SCALAR,
                 typename GRAPH_CHANGES_SELECTOR,
                 typename PARAM_ESTIMATOR >
      BayesNet< GUM_SCALAR > learnBN(GRAPH_CHANGES_SELECTOR& selector,
                                     PARAM_ESTIMATOR&        estimator,
                                     DAG                     initial_dag = DAG());

      private:
      Sequence< NodeId > __order;

      void __checkOrder(const std::vector< Size >& modal);
    };

  } /* namespace learning */

} /* namespace gum */

#ifndef GUM_NO_INLINE
#  include <agrum/learning/K2_inl.h>
#endif /* GUM_NO_INLINE */

#include <agrum/learning/K2_tpl.h>

#endif /* GUM_LEARNING_K2_H */