scorefNML.cpp

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

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

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

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

    std::string ScorefNML::isPriorCompatible(PriorType prior_type, double weight) {
      // check that the prior is compatible with the score
      if ((prior_type == PriorType::DirichletPriorType)
          || (prior_type == PriorType::SmoothingPriorType)
          || (prior_type == PriorType::NoPriorType)) {
        return "";
      }
 
      // prior types unsupported by the type checker
      std::stringstream msg;
      msg << "The prior '" << priorTypeToString(prior_type)
          << "' is not yet compatible with the score 'fNML'.";
      return msg.str();
    }

    double ScorefNML::score_(const IdCondSet& idset) {
      // 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 bool            informative_external_prior = this->prior_->isInformative();
      if (informative_external_prior) this->prior_->addJointPseudoCount(idset, N_ijk);
      const std::size_t all_size = N_ijk.size();
 
      // 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     target_domsize = all_size / N_ij.size();
 
        // compute the score: it remains to compute the log likelihood, i.e.,
        // sum_k=1^r_i sum_j=1^q_i N_ijk log (N_ijk / N_ij), which is also
        // equivalent to:
        // sum_j=1^q_i sum_k=1^r_i N_ijk log N_ijk - sum_j=1^q_i N_ij log N_ij
        double score = 0.0;
        for (const auto n_ijk: N_ijk) {
          if (n_ijk) { score += n_ijk * std::log(n_ijk); }
        }
        for (const auto n_ij: N_ij) {
          if (n_ij) { score -= n_ij * std::log(n_ij); }
        }
 
        // divide by log(2), since the log likelihood uses log_2
        score *= this->one_log2_;
 
        // finally, remove the penalty
        double penalty = 0.0;
        for (const auto n_ij: N_ij) {
          penalty += _ctable_.log2Cnr(target_domsize, n_ij);
        }
 
        score -= penalty;
 
        return score;
      } else {
        // here, there are no conditioning nodes
 
        // compute the score: it remains to compute the log likelihood, i.e.,
        // sum_k=1^r_i N_ijk log (N_ijk / N), which is also
        // equivalent to:
        // sum_j=1^q_i sum_k=1^r_i N_ijk log N_ijk - N log N
        double N     = 0.0;
        double score = 0.0;
        for (const auto n_ijk: N_ijk) {
          if (n_ijk) {
            score += n_ijk * std::log(n_ijk);
            N += n_ijk;
          }
        }
        score -= N * std::log(N);
 
        // divide by log(2), since the log likelihood uses log_2
        score *= this->one_log2_;
 
        // finally, remove the penalty
        score -= _ctable_.log2Cnr(all_size, N);
 
        return score;
      }
    }
 
  } /* namespace learning */
 
} /* namespace gum */
 
#endif /* DOXYGEN_SHOULD_SKIP_THIS */