score.h

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#ifndef GUM_LEARNING_SCORE_H
#define GUM_LEARNING_SCORE_H
 
#include <utility>
 
#include <agrum/agrum.h>
 
#include <agrum/base/stattests/recordCounter.h>
#include <agrum/base/stattests/scoringCache.h>
#include <agrum/BN/learning/priors/prior.h>
#include <agrum/BN/learning/structureUtils/graphChange.h>
 
namespace gum {
 
  namespace learning {

    class Score: public IThreadNumberManager {
      public:
      // ##########################################################################
      // ##########################################################################


      Score(const DBRowGeneratorParser&                                 parser,
            const Prior&                                                external_prior,
            const std::vector< std::pair< std::size_t, std::size_t > >& ranges,
            const Bijection< NodeId, std::size_t >&                     nodeId2columns
            = Bijection< NodeId, std::size_t >());
 


      Score(const DBRowGeneratorParser&             parser,
            const Prior&                            external_prior,
            const Bijection< NodeId, std::size_t >& nodeId2columns
            = Bijection< NodeId, std::size_t >());

      [[nodiscard]] virtual Score* clone() const = 0;

      virtual ~Score();

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


      void setNumberOfThreads(Size nb) override;

      Size getNumberOfThreads() const override;

      bool isGumNumberOfThreadsOverriden() const override;

      virtual void setMinNbRowsPerThread(const std::size_t nb) const;

      virtual std::size_t minNbRowsPerThread() const;


      void setRanges(const std::vector< std::pair< std::size_t, std::size_t > >& new_ranges);

      void clearRanges();

      const std::vector< std::pair< std::size_t, std::size_t > >& ranges() const;

      double score(const NodeId var);


      double score(const NodeId var, const std::vector< NodeId >& rhs_ids);

      void clear();

      void clearCache();

      void useCache(const bool on_off);

      bool isUsingCache() const;


      const Bijection< NodeId, std::size_t >& nodeId2Columns() const;

      const DatabaseTable& database() const;


      virtual std::string isPriorCompatible() const = 0;


      virtual const Prior& internalPrior() const = 0;

 
 
      protected:
      const double one_log2_{M_LOG2E};

      Prior* prior_{nullptr};

      RecordCounter counter_;

      ScoringCache cache_;

      bool use_cache_{true};

      const std::vector< NodeId > empty_ids_;
 

      Score(const Score& from);

      Score(Score&& from);

      Score& operator=(const Score& from);

      Score& operator=(Score&& from);


      virtual double score_(const IdCondSet& idset) = 0;


      std::vector< double > marginalize_(const NodeId                 X_id,
                                         const std::vector< double >& N_xyz) const;
    };
 
  } /* namespace learning */
 
} /* namespace gum */
 
// include the inlined functions if necessary
#ifndef GUM_NO_INLINE
#  include <agrum/BN/learning/scores/score_inl.h>
#endif /* GUM_NO_INLINE */
 
#endif /* GUM_LEARNING_SCORE_H */