score_inl.h

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 *   This file is part of the aGrUM/pyAgrum library.                        *
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 *   Copyright (c) 2005-2026 by                                             *
 *       - Pierre-Henri WUILLEMIN(_at_LIP6)                                 *
 *       - Christophe GONZALES(_at_AMU)                                     *
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 *       - Pierre-Henri WUILLEMIN(_at_LIP6)                                 *
 *       - Christophe GONZALES(_at_AMU)                                     *
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#pragma once
 

#include <agrum/BN/learning/scores/score.h>   // to ease IDE parser
#ifndef DOXYGEN_SHOULD_SKIP_THIS
 
namespace gum {
 
  namespace learning {

    INLINE Score::Score(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) :
        prior_(prior.clone()), counter_(parser, ranges, nodeId2columns) {
      GUM_CONSTRUCTOR(Score);
    }

    INLINE Score::Score(const DBRowGeneratorParser&             parser,
                        const Prior&                            prior,
                        const Bijection< NodeId, std::size_t >& nodeId2columns) :
        prior_(prior.clone()), counter_(parser, nodeId2columns) {
      GUM_CONSTRUCTOR(Score);
    }

    INLINE Score::Score(const Score& from) :
        prior_(from.prior_->clone()), counter_(from.counter_), cache_(from.cache_),
        use_cache_(from.use_cache_) {
      GUM_CONS_CPY(Score);
    }

    INLINE Score::Score(Score&& from) :
        prior_(from.prior_), counter_(std::move(from.counter_)), cache_(std::move(from.cache_)),
        use_cache_(from.use_cache_) {
      from.prior_ = nullptr;
      GUM_CONS_MOV(Score);
    }

    INLINE Score::~Score() {
      if (prior_ != nullptr) delete prior_;
      GUM_DESTRUCTOR(Score);
    }

    INLINE void Score::setNumberOfThreads(Size nb) { counter_.setNumberOfThreads(nb); }

    INLINE Size Score::getNumberOfThreads() const { return counter_.getNumberOfThreads(); }

    INLINE bool Score::isGumNumberOfThreadsOverriden() const {
      return counter_.isGumNumberOfThreadsOverriden();
    }

    INLINE void Score::setMinNbRowsPerThread(const std::size_t nb) const {
      counter_.setMinNbRowsPerThread(nb);
    }

    INLINE std::size_t Score::minNbRowsPerThread() const { return counter_.minNbRowsPerThread(); }

    INLINE const std::vector< std::pair< std::size_t, std::size_t > >& Score::ranges() const {
      return counter_.ranges();
    }

    INLINE double Score::score(const NodeId var) {
      IdCondSet idset(var, empty_ids_, true);
      if (use_cache_) {
        if (auto ptr_score = cache_.tryGet(idset)) { return *ptr_score; }
        double the_score = score_(idset);
        cache_.insert(std::move(idset), the_score);
        return the_score;
      } else {
        return score_(std::move(idset));
      }
    }


    INLINE double Score::score(const NodeId var, const std::vector< NodeId >& rhs_ids) {
      IdCondSet idset(var, rhs_ids, false);
      if (use_cache_) {
        if (auto ptr_score = cache_.tryGet(idset)) { return *ptr_score; }
        const double the_score = score_(idset);
        cache_.insert(std::move(idset), the_score);
        return the_score;
      } else {
        return score_(idset);
      }
    }

    INLINE void Score::clear() {
      counter_.clear();
      cache_.clear();
    }

    INLINE void Score::clearCache() { cache_.clear(); }

    INLINE void Score::useCache(const bool on_off) { use_cache_ = on_off; }

    INLINE bool Score::isUsingCache() const { return use_cache_; }

    INLINE const Bijection< NodeId, std::size_t >& Score::nodeId2Columns() const {
      return counter_.nodeId2Columns();
    }

    INLINE const DatabaseTable& Score::database() const { return counter_.database(); }
 
  } /* namespace learning */
 
} /* namespace gum */
 
#endif /* DOXYGEN_SHOULD_SKIP_THIS */