DBRowGeneratorWithBN.h

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#ifndef GUM_LEARNING_DBROW_GENERATOR_WITH_BN_H
#define GUM_LEARNING_DBROW_GENERATOR_WITH_BN_H
 
#include <vector>
 
#include <agrum/agrum.h>
 
#include <agrum/base/database/DBRowGenerator.h>
#include <agrum/BN/inference/variableElimination.h>
 
namespace gum {
 
  namespace learning {

    template < typename GUM_SCALAR = double >
    class DBRowGeneratorWithBN: public DBRowGenerator {
      public:
      // ##########################################################################
      // ##########################################################################


      DBRowGeneratorWithBN(const std::vector< DBTranslatedValueType >& column_types,
                           const BayesNet< GUM_SCALAR >&               bn,
                           const DBRowGeneratorGoal                    goal,
                           const Bijection< NodeId, std::size_t >&     nodeId2columns
                           = Bijection< NodeId, std::size_t >());

      DBRowGeneratorWithBN(const DBRowGeneratorWithBN< GUM_SCALAR >& from);

      DBRowGeneratorWithBN(DBRowGeneratorWithBN< GUM_SCALAR >&& from);

      ~DBRowGeneratorWithBN();

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


      virtual void setBayesNet(const BayesNet< GUM_SCALAR >& new_bn);

      const BayesNet< GUM_SCALAR >& getBayesNet() const;

 
 
      protected:
      const BayesNet< GUM_SCALAR >* bn_;

      Bijection< NodeId, std::size_t > nodeId2columns_;
 

      DBRowGeneratorWithBN< GUM_SCALAR >& operator=(const DBRowGeneratorWithBN< GUM_SCALAR >& from);

      DBRowGeneratorWithBN< GUM_SCALAR >& operator=(DBRowGeneratorWithBN< GUM_SCALAR >&& from);
    };
 
  } /* namespace learning */
 
} /* namespace gum */
 
// always include the template implementation
#include <agrum/base/database/DBRowGeneratorWithBN_tpl.h>
 
#endif /* GUM_LEARNING_DBROW_GENERATOR_WITH_BN_H */