DBRowGeneratorEM.h

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#ifndef GUM_LEARNING_DBROW_GENERATOR_EM_H
#define GUM_LEARNING_DBROW_GENERATOR_EM_H

#include <vector>

#include <agrum/agrum.h>
#include <agrum/core/bijection.h>
#include <agrum/BN/BayesNet.h>
#include <agrum/BN/inference/variableElimination.h>
#include <agrum/learning/database/DBRowGeneratorWithBN.h>

namespace gum {

  namespace learning {

    template < typename GUM_SCALAR = double,
               template < typename > class ALLOC = std::allocator >
    class DBRowGeneratorEM : public DBRowGeneratorWithBN< GUM_SCALAR, ALLOC > {
      public:
      using allocator_type = ALLOC< DBTranslatedValue >;

      // ##########################################################################
      // ##########################################################################


      DBRowGeneratorEM(
         const std::vector< DBTranslatedValueType, ALLOC< DBTranslatedValueType > >
                                       column_types,
         const BayesNet< GUM_SCALAR >& bn,
         const Bijection< NodeId, std::size_t, ALLOC< std::size_t > >&
            nodeId2columns =
               Bijection< NodeId, std::size_t, ALLOC< std::size_t > >(),
         const allocator_type& alloc = allocator_type());

      DBRowGeneratorEM(const DBRowGeneratorEM< GUM_SCALAR, ALLOC >& from);

      DBRowGeneratorEM(const DBRowGeneratorEM< GUM_SCALAR, ALLOC >& from,
                       const allocator_type&                        alloc);

      DBRowGeneratorEM(DBRowGeneratorEM< GUM_SCALAR, ALLOC >&& from);

      DBRowGeneratorEM(DBRowGeneratorEM< GUM_SCALAR, ALLOC >&& from,
                       const allocator_type&                   alloc);

      virtual DBRowGeneratorEM< GUM_SCALAR, ALLOC >* clone() const override final;

      virtual DBRowGeneratorEM< GUM_SCALAR, ALLOC >*
         clone(const allocator_type& alloc) const override final;

      ~DBRowGeneratorEM();



      // ##########################################################################
      // ##########################################################################


      DBRowGeneratorEM< GUM_SCALAR, ALLOC >&
         operator=(const DBRowGeneratorEM< GUM_SCALAR, ALLOC >& from);

      DBRowGeneratorEM< GUM_SCALAR, ALLOC >&
         operator=(DBRowGeneratorEM< GUM_SCALAR, ALLOC >&& from);



      // ##########################################################################
      // ##########################################################################


      virtual const DBRow< DBTranslatedValue, ALLOC >& generate() override final;

      virtual void
         setBayesNet(const BayesNet< GUM_SCALAR >& new_bn) override final;

      allocator_type getAllocator() const;



      protected:
      virtual std::size_t
         _computeRows(const DBRow< DBTranslatedValue, ALLOC >& row) override final;


#ifndef DOXYGEN_SHOULD_SKIP_THIS

      private:
      const DBRow< DBTranslatedValue, ALLOC >* __input_row{nullptr};

      std::vector< std::size_t, ALLOC< std::size_t > > __missing_cols;

      std::size_t __nb_miss;

      Potential< GUM_SCALAR > __joint_proba;

      Instantiation* __joint_inst{nullptr};


      DBRow< DBTranslatedValue, ALLOC > __filled_row1;

      DBRow< DBTranslatedValue, ALLOC > __filled_row2;

      bool __use_filled_row1{true};

      double __original_weight;


#endif /* DOXYGEN_SHOULD_SKIP_THIS */
    };

  } /* namespace learning */

} /* namespace gum */


// always include the template implementation
#include <agrum/learning/database/DBRowGeneratorEM_tpl.h>

#endif /* GUM_LEARNING_DBROW_GENERATOR_EM_H */