DBRowGeneratorSet.h

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#ifndef GUM_LEARNING_DBROW_GENERATOR_SET_H
#define GUM_LEARNING_DBROW_GENERATOR_SET_H
 
#include <vector>
 
#include <agrum/agrum.h>
 
#include <agrum/base/database/DBRowGeneratorWithBN.h>
 
namespace gum {
 
  namespace learning {
 

    class DBRowGeneratorSet final {
      public:
      // ##########################################################################
      // ##########################################################################


      DBRowGeneratorSet();

      DBRowGeneratorSet(const DBRowGeneratorSet& from);

      DBRowGeneratorSet(DBRowGeneratorSet&& from);

      virtual DBRowGeneratorSet* clone() const;

      virtual ~DBRowGeneratorSet();

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


      DBRowGeneratorSet& operator=(const DBRowGeneratorSet& from);

      DBRowGeneratorSet& operator=(DBRowGeneratorSet&& from);


      DBRowGenerator& operator[](const std::size_t i);


      const DBRowGenerator& operator[](const std::size_t i) const;

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



      template < class Generator >
      void insertGenerator(const Generator& generator);


      template < class Generator >
      void insertGenerator(const Generator& generator, const std::size_t i);

      std::size_t nbGenerators() const noexcept;

      std::size_t size() const noexcept;

      bool hasRows();


      bool setInputRow(const DBRow< DBTranslatedValue >& input_row);

      const DBRow< DBTranslatedValue >& generate();


      template < typename GUM_SCALAR >
      void setBayesNet(const BayesNet< GUM_SCALAR >& new_bn);

      void reset();

      void clear();

      void setColumnsOfInterest(const std::vector< std::size_t >& cols_of_interest);

      void setColumnsOfInterest(std::vector< std::size_t >&& cols_of_interest);

      const std::vector< std::size_t >& columnsOfInterest() const;

 
 
#ifndef DOXYGEN_SHOULD_SKIP_THIS
 
      private:
      // the vector of all the generators
      std::vector< DBRowGenerator* > _generators_;
 
      // the number of generators
      std::size_t _nb_generators_{std::size_t(0)};
 
      // the next output row to return when method generate is called
      const DBRow< DBTranslatedValue >* _output_row_{nullptr};
 
      // the generation of output rows can be viewed as the traversal of a
      // tree: each node of the tree correspond to the input row received by
      // a generator. So the root node is the row passed in argument to
      // the setInputDBrow() Method. From these input rows, generators produce
      // through their generate() method new output rows, which correspond to
      // the input rows of the next level of the tree. If we traverse this tree
      // in terms of generators rather than in terms of input rows, which makes
      // sense knowing our data structures, we need to know whether we should
      // call only Method generate() to move down the tree or whether we should
      // call first Method setInputDBrow() and then generate(). Actually, when
      // a generator receives a new input row, it should call Method
      // setInputDBrow(), which updates its data structure in order to be able
      // to subsequently produce new output rows using Method generate().
      // So, we need to discriminate between the situation in which Method
      // setInputDBrow() has already been called from the situation in which
      // we first need to call setInputDBrow(). The following vector allows this
      // discrimination: when its cells equal 0, we need to call setInputDBrow()
      // first, else when they equal 1, we just need to call the generate()
      // method.
      std::vector< int > _setInputRow_performed_;
 


      bool _produceNextRow_(const DBRow< DBTranslatedValue >* input_row, std::size_t i);
 
#endif /* DOXYGEN_SHOULD_SKIP_THIS */
    };
 
  } /* namespace learning */
 
} /* namespace gum */
 
// always include the template implementation
#include <agrum/base/database/DBRowGeneratorSet_tpl.h>

#ifndef GUM_NO_INLINE
#  include <agrum/base/database/DBRowGeneratorSet_inl.h>
#endif /* GUM_NO_INLINE */
 
 
#endif /* GUM_LEARNING_DBROW_GENERATOR_SET_H */