correctedMutualInformation.h

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#ifndef GUM_LEARNING_CORRECTED_MUTUAL_INFORMATION_H
#define GUM_LEARNING_CORRECTED_MUTUAL_INFORMATION_H
 
#include <vector>
 
#include <agrum/config.h>
 
#include <agrum/base/stattests/kNML.h>
#include <agrum/BN/learning/scores/scoreLog2Likelihood.h>
#include <agrum/BN/learning/scores/scoreMDL.h>
 
namespace gum {
  namespace learning {
    class CorrectedMutualInformation: public IThreadNumberManager {
      public:
      // ##########################################################################
      // ##########################################################################


      CorrectedMutualInformation(const DBRowGeneratorParser&                                 parser,
                                 const Prior&                                                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 >());


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

      CorrectedMutualInformation(const CorrectedMutualInformation& from);

      CorrectedMutualInformation(CorrectedMutualInformation&& from);

      [[nodiscard]] virtual CorrectedMutualInformation* clone() const;

      virtual ~CorrectedMutualInformation();

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


      CorrectedMutualInformation& operator=(const CorrectedMutualInformation& from);

      CorrectedMutualInformation& operator=(CorrectedMutualInformation&& from);

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

      virtual void clear();


      virtual void clearCache();


      virtual void useCache(bool on_off);

      void useICache(bool on_off);

      void clearICache();

      void useHCache(bool on_off);

      void clearHCache();

      void useKCache(bool on_off);

      void clearKCache();

      void useCnrCache(bool on_off);

      void clearCnrCache();

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

      double score(NodeId var1, NodeId var2);

      double score(NodeId var1, NodeId var2, const std::vector< NodeId >& conditioning_ids);

      double score(NodeId var1, NodeId var2, NodeId var3);

      double score(NodeId                       var1,
                   NodeId                       var2,
                   NodeId                       var3,
                   const std::vector< NodeId >& conditioning_ids);

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

      void useMDL();

      void useNML();

      void useNoCorr();

      void setNumberOfThreads(Size nb) override;

      std::size_t 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;

 

      enum class KModeTypes { MDL, NML, NoCorr };
 
 
#ifndef DOXYGEN_SHOULD_SKIP_THIS
 
      private:
      /* Note that the log2-likelihood is equal to N times the entropy H */
      ScoreLog2Likelihood _NH_;

      KNML _k_NML_;

      ScoreMDL _score_MDL_;

      KModeTypes _kmode_{KModeTypes::MDL};
 


      bool _use_ICache_{true};


      bool _use_HCache_{true};


      bool _use_KCache_{true};


      bool _use_CnrCache_{true};
 

      ScoringCache _ICache_;

      ScoringCache _KCache_;
 

      const std::vector< NodeId > _empty_conditioning_set_;

      const double _threshold_{1e-10};
 

      double _NI_score_(NodeId var_x, NodeId var_y, const std::vector< NodeId >& vars_z);

      double _NI_score_(NodeId                       var_x,
                        NodeId                       var_y,
                        NodeId                       var_z,
                        const std::vector< NodeId >& vars_ui);

      double _K_score_(NodeId var_x, NodeId var_y, const std::vector< NodeId >& vars_z);

      double
          _K_score_(NodeId var_x, NodeId var_y, NodeId var_z, const std::vector< NodeId >& vars_ui);
 
#endif /* DOXYGEN_SHOULD_SKIP_THIS */
    };
 
  } /* namespace learning */
 
} /* namespace gum */
 
// include the inlined functions if necessary
#ifndef GUM_NO_INLINE
#  include <agrum/BN/learning/correctedMutualInformation_inl.h>
#endif /* GUM_NO_INLINE */
 
#endif /* GUM_LEARNING_CORRECTED_MUTUAL_INFORMATION_H */