BNLearner.h

Go to the documentation of this file.

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

#include <agrum/BN/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/BN/learning/BNLearner_tpl.h>

#endif /* GUM_LEARNING_BN_LEARNER_H */