bdeuPrior.h

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#ifndef GUM_LEARNING_PRIOR_BDEU_H
#define GUM_LEARNING_PRIOR_BDEU_H
 
#include <vector>
 
#include <agrum/agrum.h>
 
#include <agrum/base/stattests/priors/prior.h>
 
namespace gum::learning {

  class GUM_SHARED_PUBLIC BDeuPrior: public Prior {
    public:
    // ##########################################################################
    // ##########################################################################


    explicit BDeuPrior(const DatabaseTable&                    database,
                       const Bijection< NodeId, std::size_t >& nodeId2columns
                       = Bijection< NodeId, std::size_t >());

    BDeuPrior(const BDeuPrior& from);

    BDeuPrior(BDeuPrior&& from) noexcept;

    [[nodiscard]] BDeuPrior* clone() const override;

    ~BDeuPrior() override;

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

    BDeuPrior& operator=(const BDeuPrior& from);

    BDeuPrior& operator=(BDeuPrior&& from) noexcept;

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

    void setWeight(double weight) final;

    void setEffectiveSampleSize(double weight);

    PriorType getType() const final;


    bool isInformative() const final;


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

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

  };
 
}   // namespace gum::learning
 
// include the inlined functions if necessary
#ifndef GUM_NO_INLINE
#  include <agrum/base/stattests/priors/bdeuPrior_inl.h>
#endif /* GUM_NO_INLINE */
 
#endif /* GUM_LEARNING_PRIOR_BDEU_H */