K2.h

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#ifndef GUM_LEARNING_K2_H
#define GUM_LEARNING_K2_H
 
#include <string>
#include <vector>
 
#include <agrum/BN/learning/greedyHillClimbing.h>
 
namespace gum {
 
  namespace learning {

    class K2: private GreedyHillClimbing {
      public:
      // ##########################################################################
      // ##########################################################################

      K2();

      K2(const K2& from);

      K2(K2&& from);

      ~K2();

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

      K2& operator=(const K2& from);

      K2& operator=(K2&& from);

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

      ApproximationScheme& approximationScheme();

      void setOrder(const Sequence< NodeId >& order);

      void setOrder(const std::vector< NodeId >& order);

      const Sequence< NodeId >& order() const noexcept;


      template < typename GRAPH_CHANGES_SELECTOR >
      DAG learnStructure(GRAPH_CHANGES_SELECTOR& selector, DAG initial_dag = DAG());

      template < typename GUM_SCALAR, typename GRAPH_CHANGES_SELECTOR, typename PARAM_ESTIMATOR >
      BayesNet< GUM_SCALAR > learnBN(GRAPH_CHANGES_SELECTOR& selector,
                                     PARAM_ESTIMATOR&        estimator,
                                     DAG                     initial_dag = DAG());
 
      private:
      Sequence< NodeId > _order_;

      void _checkOrder_(const std::vector< Size >& modal);
    };
 
  } /* namespace learning */
 
} /* namespace gum */

#ifndef GUM_NO_INLINE
#  include <agrum/BN/learning/K2_inl.h>
#endif /* GUM_NO_INLINE */

#include <agrum/BN/learning/K2_tpl.h>
 
#endif /* GUM_LEARNING_K2_H */