BNLearner.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_BN_LEARNER_H
#define GUM_LEARNING_BN_LEARNER_H

#include <sstream>
#include <algorithm>
#include <vector>

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

#include <agrum/learning/BNLearnUtils/genericBNLearner.h>

namespace gum {

  namespace learning {

    class BNLearnerListener;

    template < typename GUM_SCALAR >
    class BNLearner : public genericBNLearner {
      public:
      // ##########################################################################
      // ##########################################################################


      BNLearner(const std::string&                filename,
                const std::vector< std::string >& missing_symbols = {"?"});
      BNLearner(const DatabaseTable<>& db);

      /*
      BNLearner(const std::string&                             filename,
                const NodeProperty< Sequence< std::string > >& modalities );
      */


      BNLearner(const std::string&                 filename,
                const gum::BayesNet< GUM_SCALAR >& src,
                const std::vector< std::string >&  missing_symbols = {"?"});

      BNLearner(const BNLearner&);

      BNLearner(BNLearner&&);

      virtual ~BNLearner();


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

      BNLearner& operator=(const BNLearner&);

      BNLearner& operator=(BNLearner&&);


      BayesNet< GUM_SCALAR > learnBN();


      BayesNet< GUM_SCALAR > learnParameters(const DAG& dag,
                                             bool take_into_account_score = true);

      // learns a BN (its parameters) when its structure is known
      BayesNet< GUM_SCALAR > learnParameters(bool take_into_account_score = true);

      private:
      NodeProperty< Sequence< std::string > >
         __labelsFromBN(const std::string&            filename,
                        const BayesNet< GUM_SCALAR >& src);
    };

  } /* namespace learning */

} /* namespace gum */

#include <agrum/learning/BNLearner_tpl.h>

#endif /* GUM_LEARNING_BN_LEARNER_H */