paramEstimator_tpl.h

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#ifndef DOXYGEN_SHOULD_SKIP_THIS

namespace gum {

  namespace learning {

    template < template < typename > class ALLOC >
    INLINE typename ParamEstimator< ALLOC >::allocator_type
       ParamEstimator< ALLOC >::getAllocator() const {
      return _counter.getAllocator();
    }


    template < template < typename > class ALLOC >
    ParamEstimator< ALLOC >::ParamEstimator(
       const DBRowGeneratorParser< ALLOC >& parser,
       const Apriori< ALLOC >&              external_apriori,
       const Apriori< ALLOC >&              score_internal_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 ParamEstimator< ALLOC >::allocator_type& alloc) :
        _counter(parser, ranges, nodeId2columns, alloc) {
      // copy the a prioris
      _external_apriori = external_apriori.clone(alloc);
      try {
        _score_internal_apriori = score_internal_apriori.clone();
      } catch (...) {
        ALLOC< Apriori< ALLOC > > allocator(alloc);
        allocator.destroy(_external_apriori);
        allocator.deallocate(_external_apriori, 1);
        throw;
      }

      GUM_CONSTRUCTOR(ParamEstimator);
    }


    template < template < typename > class ALLOC >
    ParamEstimator< ALLOC >::ParamEstimator(
       const DBRowGeneratorParser< ALLOC >& parser,
       const Apriori< ALLOC >&              external_apriori,
       const Apriori< ALLOC >&              score_internal_apriori,
       const Bijection< NodeId, std::size_t, ALLOC< std::size_t > >&
                                                               nodeId2columns,
       const typename ParamEstimator< ALLOC >::allocator_type& alloc) :
        _counter(parser, nodeId2columns, alloc) {
      // copy the a prioris
      _external_apriori = external_apriori.clone(alloc);
      try {
        _score_internal_apriori = score_internal_apriori.clone();
      } catch (...) {
        ALLOC< Apriori< ALLOC > > allocator(alloc);
        allocator.destroy(_external_apriori);
        allocator.deallocate(_external_apriori, 1);
        throw;
      }

      GUM_CONSTRUCTOR(ParamEstimator);
    }


    template < template < typename > class ALLOC >
    INLINE ParamEstimator< ALLOC >::ParamEstimator(
       const ParamEstimator< ALLOC >&                          from,
       const typename ParamEstimator< ALLOC >::allocator_type& alloc) :
        _external_apriori(from._external_apriori->clone(alloc)),
        _score_internal_apriori(from._score_internal_apriori->clone(alloc)),
        _counter(from._counter, alloc) {
      GUM_CONS_CPY(ParamEstimator);
    }


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


    template < template < typename > class ALLOC >
    INLINE ParamEstimator< ALLOC >::ParamEstimator(
       ParamEstimator< ALLOC >&&                               from,
       const typename ParamEstimator< ALLOC >::allocator_type& alloc) :
        _external_apriori(from._external_apriori),
        _score_internal_apriori(from._score_internal_apriori),
        _counter(std::move(from._counter), alloc) {
      from._external_apriori = nullptr;
      from._score_internal_apriori = nullptr;
      GUM_CONS_MOV(ParamEstimator);
    }


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


    template < template < typename > class ALLOC >
    ParamEstimator< ALLOC >::~ParamEstimator() {
      ALLOC< Apriori< ALLOC > > allocator(this->getAllocator());
      if (_external_apriori != nullptr) {
        allocator.destroy(_external_apriori);
        allocator.deallocate(_external_apriori, 1);
      }

      if (_score_internal_apriori != nullptr) {
        allocator.destroy(_score_internal_apriori);
        allocator.deallocate(_score_internal_apriori, 1);
      }

      GUM_DESTRUCTOR(ParamEstimator);
    }


    template < template < typename > class ALLOC >
    ParamEstimator< ALLOC >& ParamEstimator< ALLOC >::
                             operator=(const ParamEstimator< ALLOC >& from) {
      if (this != &from) {
        ALLOC< Apriori< ALLOC > > allocator(this->getAllocator());
        if (_external_apriori != nullptr) {
          allocator.destroy(_external_apriori);
          allocator.deallocate(_external_apriori, 1);
          _external_apriori = nullptr;
        }
        _external_apriori = from._external_apriori->clone(this->getAllocator());

        if (_score_internal_apriori != nullptr) {
          allocator.destroy(_score_internal_apriori);
          allocator.deallocate(_score_internal_apriori, 1);
          _external_apriori = nullptr;
        }
        _score_internal_apriori =
           from._score_internal_apriori->clone(this->getAllocator());

        _counter = from._counter;
      }
      return *this;
    }


    template < template < typename > class ALLOC >
    ParamEstimator< ALLOC >& ParamEstimator< ALLOC >::
                             operator=(ParamEstimator< ALLOC >&& from) {
      if (this != &from) {
        _external_apriori = from._external_apriori;
        _score_internal_apriori = from._score_internal_apriori;
        _counter = std::move(from._counter);
        from._external_apriori = nullptr;
        from._score_internal_apriori = nullptr;
      }
      return *this;
    }


    template < template < typename > class ALLOC >
    INLINE void ParamEstimator< ALLOC >::clear() {
      _counter.clear();
    }


    template < template < typename > class ALLOC >
    void ParamEstimator< ALLOC >::setMaxNbThreads(std::size_t nb) const {
      _counter.setMaxNbThreads(nb);
    }


    template < template < typename > class ALLOC >
    std::size_t ParamEstimator< ALLOC >::nbThreads() const {
      return _counter.nbThreads();
    }


    template < template < typename > class ALLOC >
    INLINE void
       ParamEstimator< ALLOC >::setMinNbRowsPerThread(const std::size_t nb) const {
      _counter.setMinNbRowsPerThread(nb);
    }


    template < template < typename > class ALLOC >
    INLINE std::size_t ParamEstimator< ALLOC >::minNbRowsPerThread() const {
      return _counter.minNbRowsPerThread();
    }



    template < template < typename > class ALLOC >
    template < template < typename > class XALLOC >
    void ParamEstimator< ALLOC >::setRanges(
       const std::vector< std::pair< std::size_t, std::size_t >,
                          XALLOC< std::pair< std::size_t, std::size_t > > >&
          new_ranges) {
      std::vector< std::pair< std::size_t, std::size_t >,
                   ALLOC< std::pair< std::size_t, std::size_t > > >
         old_ranges = ranges();
      _counter.setRanges(new_ranges);
      if (old_ranges != ranges()) clear();
    }


    template < template < typename > class ALLOC >
    void ParamEstimator< ALLOC >::clearRanges() {
      std::vector< std::pair< std::size_t, std::size_t >,
                   ALLOC< std::pair< std::size_t, std::size_t > > >
         old_ranges = ranges();
      _counter.clearRanges();
      if (old_ranges != ranges()) clear();
    }


    template < template < typename > class ALLOC >
    INLINE const std::vector< std::pair< std::size_t, std::size_t >,
                              ALLOC< std::pair< std::size_t, std::size_t > > >&
                 ParamEstimator< ALLOC >::ranges() const {
      return _counter.ranges();
    }


    template < template < typename > class ALLOC >
    INLINE std::vector< double, ALLOC< double > >
           ParamEstimator< ALLOC >::parameters(const NodeId target_node) {
      return parameters(target_node, _empty_nodevect);
    }


    // check the coherency between the parameters passed to setParameters functions
    template < template < typename > class ALLOC >
    template < typename GUM_SCALAR >
    void ParamEstimator< ALLOC >::__checkParameters(
       const NodeId                                  target_node,
       const std::vector< NodeId, ALLOC< NodeId > >& conditioning_nodes,
       Potential< GUM_SCALAR >&                      pot) {
      // check that the nodes passed in arguments correspond to those of pot
      const Sequence< const DiscreteVariable* >& vars = pot.variablesSequence();
      if (vars.size() == 0) {
        GUM_ERROR(SizeError, "the potential contains no variable");
      }

      const auto& database = _counter.database();
      const auto& node2cols = _counter.nodeId2Columns();
      if (node2cols.empty()) {
        if (database.domainSize(target_node) != vars[0]->domainSize()) {
          GUM_ERROR(SizeError,
                    "Variable "
                       << vars[0]->name() << "of the potential to be filled "
                       << "has a domain size of " << vars[0]->domainSize()
                       << ", which is different from that of node " << target_node
                       << " which is equal to "
                       << database.domainSize(target_node));
        }
        for (std::size_t i = 1; i < vars.size(); ++i) {
          if (database.domainSize(conditioning_nodes[i - 1])
              != vars[i]->domainSize()) {
            GUM_ERROR(SizeError,
                      "Variable "
                         << vars[i]->name() << "of the potential to be filled "
                         << "has a domain size of " << vars[i]->domainSize()
                         << ", which is different from that of node "
                         << conditioning_nodes[i - 1] << " which is equal to "
                         << database.domainSize(conditioning_nodes[i - 1]));
          }
        }
      } else {
        std::size_t col = node2cols.second(target_node);
        if (database.domainSize(col) != vars[0]->domainSize()) {
          GUM_ERROR(SizeError,
                    "Variable "
                       << vars[0]->name() << "of the potential to be filled "
                       << "has a domain size of " << vars[0]->domainSize()
                       << ", which is different from that of node " << target_node
                       << " which is equal to " << database.domainSize(col));
        }
        for (std::size_t i = 1; i < vars.size(); ++i) {
          col = node2cols.second(conditioning_nodes[i - 1]);
          if (database.domainSize(col) != vars[i]->domainSize()) {
            GUM_ERROR(SizeError,
                      "Variable "
                         << vars[i]->name() << "of the potential to be filled "
                         << "has a domain size of " << vars[i]->domainSize()
                         << ", which is different from that of node "
                         << conditioning_nodes[i - 1] << " which is equal to "
                         << database.domainSize(col));
          }
        }
      }
    }


    template < template < typename > class ALLOC >
    template < typename GUM_SCALAR >
    INLINE typename std::enable_if< !std::is_same< GUM_SCALAR, double >::value,
                                    void >::type
       ParamEstimator< ALLOC >::__setParameters(
          const NodeId                                  target_node,
          const std::vector< NodeId, ALLOC< NodeId > >& conditioning_nodes,
          Potential< GUM_SCALAR >&                      pot) {
      __checkParameters(target_node, conditioning_nodes, pot);

      const std::vector< double, ALLOC< double > > params(
         parameters(target_node, conditioning_nodes));

      // transform the vector of double into a vector of GUM_SCALAR
      const std::size_t                              size = params.size();
      std::vector< GUM_SCALAR, ALLOC< GUM_SCALAR > > xparams(size);
      for (std::size_t i = std::size_t(0); i < size; ++i)
        xparams[i] = GUM_SCALAR(params[i]);

      pot.fillWith(xparams);
    }


    template < template < typename > class ALLOC >
    template < typename GUM_SCALAR >
    INLINE typename std::enable_if< std::is_same< GUM_SCALAR, double >::value,
                                    void >::type
       ParamEstimator< ALLOC >::__setParameters(
          const NodeId                                  target_node,
          const std::vector< NodeId, ALLOC< NodeId > >& conditioning_nodes,
          Potential< GUM_SCALAR >&                      pot) {
      __checkParameters(target_node, conditioning_nodes, pot);

      const std::vector< double, ALLOC< double > > params(
         parameters(target_node, conditioning_nodes));
      pot.fillWith(params);
    }


    template < template < typename > class ALLOC >
    template < typename GUM_SCALAR >
    INLINE void ParamEstimator< ALLOC >::setParameters(
       const NodeId                                  target_node,
       const std::vector< NodeId, ALLOC< NodeId > >& conditioning_nodes,
       Potential< GUM_SCALAR >&                      pot) {
      __setParameters(target_node, conditioning_nodes, pot);
    }


    template < template < typename > class ALLOC >
    INLINE const Bijection< NodeId, std::size_t, ALLOC< std::size_t > >&
                 ParamEstimator< ALLOC >::nodeId2Columns() const {
      return _counter.nodeId2Columns();
    }


    template < template < typename > class ALLOC >
    INLINE const DatabaseTable< ALLOC >&
                 ParamEstimator< ALLOC >::database() const {
      return _counter.database();
    }


    template < template < typename > class ALLOC >
    template < typename GUM_SCALAR >
    INLINE void
       ParamEstimator< ALLOC >::setBayesNet(const BayesNet< GUM_SCALAR >& new_bn) {
      _counter.setBayesNet(new_bn);
    }


  } /* namespace learning */

} /* namespace gum */

#endif /* DOXYGEN_SHOULD_SKIP_THIS */