scoreLog2Likelihood_tpl.h

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/***************************************************************************
 *   Copyright (C) 2005 by Christophe GONZALES and Pierre-Henri WUILLEMIN  *
 *   {prenom.nom}_at_lip6.fr                                               *
 *                                                                         *
 *   This program is free software; you can redistribute it and/or modify  *
 *   it under the terms of the GNU General Public License as published by  *
 *   the Free Software Foundation; either version 2 of the License, or     *
 *   (at your option) any later version.                                   *
 *                                                                         *
 *   This program is distributed in the hope that it will be useful,       *
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of        *
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the         *
 *   GNU General Public License for more details.                          *
 *                                                                         *
 *   You should have received a copy of the GNU General Public License     *
 *   along with this program; if not, write to the                         *
 *   Free Software Foundation, Inc.,                                       *
 *   59 Temple Place - Suite 330, Boston, MA  02111-1307, USA.             *
 ***************************************************************************/
#ifndef DOXYGEN_SHOULD_SKIP_THIS

#  include <agrum/learning/scores_and_tests/scoreLog2Likelihood.h>
#  include <sstream>

namespace gum {

  namespace learning {

    template < template < typename > class ALLOC >
    INLINE ScoreLog2Likelihood< ALLOC >::ScoreLog2Likelihood(
       const DBRowGeneratorParser< ALLOC >& parser,
       const Apriori< ALLOC >&              apriori,
       const std::vector< std::pair< std::size_t, std::size_t >,
                          ALLOC< std::pair< std::size_t, std::size_t > > >& ranges,
       const Bijection< NodeId, std::size_t, ALLOC< std::size_t > >&
                                                                    nodeId2columns,
       const typename ScoreLog2Likelihood< ALLOC >::allocator_type& alloc) :
        Score< ALLOC >(parser, apriori, ranges, nodeId2columns, alloc),
        __internal_apriori(parser.database(), nodeId2columns) {
      GUM_CONSTRUCTOR(ScoreLog2Likelihood);
    }


    template < template < typename > class ALLOC >
    INLINE ScoreLog2Likelihood< ALLOC >::ScoreLog2Likelihood(
       const DBRowGeneratorParser< ALLOC >& parser,
       const Apriori< ALLOC >&              apriori,
       const Bijection< NodeId, std::size_t, ALLOC< std::size_t > >&
                                                                    nodeId2columns,
       const typename ScoreLog2Likelihood< ALLOC >::allocator_type& alloc) :
        Score< ALLOC >(parser, apriori, nodeId2columns, alloc),
        __internal_apriori(parser.database(), nodeId2columns) {
      GUM_CONSTRUCTOR(ScoreLog2Likelihood);
    }


    template < template < typename > class ALLOC >
    INLINE ScoreLog2Likelihood< ALLOC >::ScoreLog2Likelihood(
       const ScoreLog2Likelihood< ALLOC >&                          from,
       const typename ScoreLog2Likelihood< ALLOC >::allocator_type& alloc) :
        Score< ALLOC >(from, alloc),
        __internal_apriori(from.__internal_apriori, alloc) {
      GUM_CONS_CPY(ScoreLog2Likelihood);
    }


    template < template < typename > class ALLOC >
    INLINE ScoreLog2Likelihood< ALLOC >::ScoreLog2Likelihood(
       const ScoreLog2Likelihood< ALLOC >& from) :
        ScoreLog2Likelihood< ALLOC >(from, from.getAllocator()) {}


    template < template < typename > class ALLOC >
    INLINE ScoreLog2Likelihood< ALLOC >::ScoreLog2Likelihood(
       ScoreLog2Likelihood< ALLOC >&&                               from,
       const typename ScoreLog2Likelihood< ALLOC >::allocator_type& alloc) :
        Score< ALLOC >(std::move(from), alloc),
        __internal_apriori(std::move(from.__internal_apriori), alloc) {
      GUM_CONS_MOV(ScoreLog2Likelihood);
    }


    template < template < typename > class ALLOC >
    INLINE ScoreLog2Likelihood< ALLOC >::ScoreLog2Likelihood(
       ScoreLog2Likelihood< ALLOC >&& from) :
        ScoreLog2Likelihood< ALLOC >(std::move(from), from.getAllocator()) {}


    template < template < typename > class ALLOC >
    ScoreLog2Likelihood< ALLOC >* ScoreLog2Likelihood< ALLOC >::clone(
       const typename ScoreLog2Likelihood< ALLOC >::allocator_type& alloc) const {
      ALLOC< ScoreLog2Likelihood< ALLOC > > allocator(alloc);
      ScoreLog2Likelihood< ALLOC >*         new_score = allocator.allocate(1);
      try {
        allocator.construct(new_score, *this, alloc);
      } catch (...) {
        allocator.deallocate(new_score, 1);
        throw;
      }

      return new_score;
    }


    template < template < typename > class ALLOC >
    ScoreLog2Likelihood< ALLOC >* ScoreLog2Likelihood< ALLOC >::clone() const {
      return clone(this->getAllocator());
    }


    template < template < typename > class ALLOC >
    ScoreLog2Likelihood< ALLOC >::~ScoreLog2Likelihood() {
      GUM_DESTRUCTOR(ScoreLog2Likelihood);
    }


    template < template < typename > class ALLOC >
    ScoreLog2Likelihood< ALLOC >& ScoreLog2Likelihood< ALLOC >::
                                  operator=(const ScoreLog2Likelihood< ALLOC >& from) {
      if (this != &from) {
        Score< ALLOC >::operator=(from);
        __internal_apriori = from.__internal_apriori;
      }
      return *this;
    }


    template < template < typename > class ALLOC >
    ScoreLog2Likelihood< ALLOC >& ScoreLog2Likelihood< ALLOC >::
                                  operator=(ScoreLog2Likelihood< ALLOC >&& from) {
      if (this != &from) {
        Score< ALLOC >::operator=(std::move(from));
        __internal_apriori = std::move(from.__internal_apriori);
      }
      return *this;
    }


    template < template < typename > class ALLOC >
    std::string ScoreLog2Likelihood< ALLOC >::isAprioriCompatible(
       const std::string& apriori_type, double weight) {
      // check that the apriori is compatible with the score
      if ((apriori_type == AprioriDirichletType::type)
          || (apriori_type == AprioriSmoothingType::type)
          || (apriori_type == AprioriNoAprioriType::type)) {
        return "";
      }

      // apriori types unsupported by the type checker
      std::stringstream msg;
      msg << "The apriori '" << apriori_type
          << "' is not yet supported by method isAprioriCompatible of "
          << "Score Log2Likelihood";
      return msg.str();
    }


    template < template < typename > class ALLOC >
    INLINE std::string ScoreLog2Likelihood< ALLOC >::isAprioriCompatible(
       const Apriori< ALLOC >& apriori) {
      return isAprioriCompatible(apriori.getType(), apriori.weight());
    }


    template < template < typename > class ALLOC >
    INLINE std::string ScoreLog2Likelihood< ALLOC >::isAprioriCompatible() const {
      return isAprioriCompatible(*(this->_apriori));
    }


    template < template < typename > class ALLOC >
    INLINE const Apriori< ALLOC >&
                 ScoreLog2Likelihood< ALLOC >::internalApriori() const {
      return __internal_apriori;
    }


    template < template < typename > class ALLOC >
    double ScoreLog2Likelihood< ALLOC >::_score(const IdSet< ALLOC >& idset) {
      // get the counts for all the nodes in the idset and add the apriori
      std::vector< double, ALLOC< double > > N_ijk(
         this->_counter.counts(idset, true));
      const bool informative_external_apriori = this->_apriori->isInformative();
      if (informative_external_apriori)
        this->_apriori->addAllApriori(idset, N_ijk);

      // 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, ALLOC< double > > N_ij(
           this->_marginalize(idset[0], N_ijk));

        // 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->_1log2;

        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->_1log2;

        return score;
      }
    }


    template < template < typename > class ALLOC >
    INLINE double
       ScoreLog2Likelihood< ALLOC >::score(const IdSet< ALLOC >& idset) {
      return _score(idset);
    }


  } /* namespace learning */

} /* namespace gum */

#endif /* DOXYGEN_SHOULD_SKIP_THIS */