scoreBD.cpp

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 *       - Pierre-Henri WUILLEMIN(_at_LIP6)                                 *
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#include <agrum/BN/learning/scores/scoreBD.h>
 
#ifndef DOXYGEN_SHOULD_SKIP_THIS

#  ifdef GUM_NO_INLINE
#    include <agrum/BN/learning/scores/scoreBD_inl.h>
#  endif /* GUM_NO_INLINE */
 
namespace gum {
 
  namespace learning {

    ScoreBD& ScoreBD::operator=(const ScoreBD& from) {
      if (this != &from) {
        Score::operator=(from);
        _internal_prior_ = from._internal_prior_;
      }
      return *this;
    }

    ScoreBD& ScoreBD::operator=(ScoreBD&& from) {
      if (this != &from) {
        Score::operator=(std::move(from));
        _internal_prior_ = std::move(from._internal_prior_);
      }
      return *this;
    }

    std::string ScoreBD::isPriorCompatible(PriorType prior_type, double weight) {
      if (prior_type == PriorType::NoPriorType) { return "The BD score requires an prior"; }
 
      if (weight != 0.0) {
        return "The prior is currently compatible with the BD score but if "
               "you change the weight, it may become biased";
      }
 
      // prior types unsupported by the type checker
      return std::format("The prior '{}' is not yet compatible with the score 'BD'.",
                         priorTypeToString(prior_type));
    }

    double ScoreBD::score_(const IdCondSet& idset) {
      // if the weight of the prior is 0, then gammaLog2 will fail
      if (!this->prior_->isInformative()) {
        GUM_ERROR(OutOfBounds,
                  "The BD score requires its external prior to " << "be strictly positive");
      }
 
      // get the counts for all the nodes in the idset and add the prior
      std::vector< double > N_ijk(this->counter_.counts(idset, true));
      const std::size_t     all_size = N_ijk.size();
      std::vector< double > N_prime_ijk(all_size, 0.0);
      this->prior_->addJointPseudoCount(idset, N_prime_ijk);
 
      double score = 0.0;
 
      // here, we distinguish idsets with conditioning nodes from those
      // without conditioning nodes
      if (idset.hasConditioningSet()) {
        // get the counts for the conditioning nodes
        std::vector< double > N_ij(this->marginalize_(idset[0], N_ijk));
        const std::size_t     conditioning_size = N_ij.size();
 
        std::vector< double > N_prime_ij(N_ij.size(), 0.0);
        this->prior_->addConditioningPseudoCount(idset, N_prime_ij);
 
        // the BD score can be computed as follows:
        // sum_j=1^qi [ gammalog2 ( N'_ij ) - gammalog2 ( N_ij + N'_ij )
        //              + sum_k=1^ri { gammlog2 ( N_ijk + N'_ijk ) -
        //                             gammalog2 ( N'_ijk ) } ]
        for (std::size_t j = std::size_t(0); j < conditioning_size; ++j) {
          score += _gammalog2_(N_prime_ij[j]) - _gammalog2_(N_ij[j] + N_prime_ij[j]);
        }
        for (std::size_t k = std::size_t(0); k < all_size; ++k) {
          score += _gammalog2_(N_ijk[k] + N_prime_ijk[k]) - _gammalog2_(N_prime_ijk[k]);
        }
      } else {
        // the BD score can be computed as follows:
        // gammalog2 ( N' ) - gammalog2 ( N + N' )
        // + sum_k=1^ri { gammlog2 ( N_i + N'_i ) - gammalog2 ( N'_i ) }
        double N       = 0.0;
        double N_prime = 0.0;
        for (std::size_t k = std::size_t(0); k < all_size; ++k) {
          score += _gammalog2_(N_ijk[k] + N_prime_ijk[k]) - _gammalog2_(N_prime_ijk[k]);
          N += N_ijk[k];
          N_prime += N_prime_ijk[k];
        }
        score += _gammalog2_(N_prime) - _gammalog2_(N + N_prime);
      }
 
      return score;
    }
 
  } /* namespace learning */
 
} /* namespace gum */
 
#endif /* DOXYGEN_SHOULD_SKIP_THIS */