DirichletPriorFromDatabase.h

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#ifndef GUM_LEARNING_PRIOR_DIRICHLET_FROM_DATABASE_H
#define GUM_LEARNING_PRIOR_DIRICHLET_FROM_DATABASE_H
 
#include <vector>
 
#include <agrum/agrum.h>
 
#include <agrum/base/stattests/recordCounter.h>
#include <agrum/BN/learning/priors/prior.h>
 
namespace gum {
 
  namespace learning {

    class DirichletPriorFromDatabase: public Prior {
      public:
      // ##########################################################################
      // ##########################################################################


      DirichletPriorFromDatabase(const DatabaseTable&                    learning_db,
                                 const DBRowGeneratorParser&             prior_parser,
                                 const Bijection< NodeId, std::size_t >& nodeId2columns
                                 = Bijection< NodeId, std::size_t >());

      DirichletPriorFromDatabase(const DirichletPriorFromDatabase& from);

      DirichletPriorFromDatabase(DirichletPriorFromDatabase&& from) noexcept;

      virtual DirichletPriorFromDatabase* clone() const;

      virtual ~DirichletPriorFromDatabase();

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

      DirichletPriorFromDatabase& operator=(const DirichletPriorFromDatabase& from);

      DirichletPriorFromDatabase& operator=(DirichletPriorFromDatabase&& from);

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

      PriorType getType() const final;


      bool isInformative() const final;

      void setWeight(double weight) final;


      virtual void addJointPseudoCount(const IdCondSet& idset, std::vector< double >& counts) final;

      void addConditioningPseudoCount(const IdCondSet& idset, std::vector< double >& counts) final;

 
 
#ifndef DOXYGEN_SHOULD_SKIP_THIS
 
      private:
      // the record counter used to parse the prior database
      RecordCounter _counter_;
 
      // the internal weight is equal to weight_ / nb rows of prior database
      // this internal weight is used to ensure that assigning a weight of 1
      // to the prior is equivalent to adding just one row to the learning
      // database
      double _internal_weight_;
 
#endif /* DOXYGEN_SHOULD_SKIP_THIS */
    };
 
  } /* namespace learning */
 
} /* namespace gum */
 
// include the inlined functions if necessary
#ifndef GUM_NO_INLINE
#  include <agrum/BN/learning/priors/DirichletPriorFromDatabase_inl.h>
#endif /* GUM_NO_INLINE */
 
#endif /* GUM_LEARNING_PRIOR_DIRICHLET_FROM_DATABASE_H */