greedyHillClimbing.h

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#ifndef GUM_LEARNING_GREEDY_HILL_CLIMBING_H
#define GUM_LEARNING_GREEDY_HILL_CLIMBING_H
 
#include <string>
#include <vector>
 
#include <agrum/agrum.h>
 
#include <agrum/base/core/approximations/approximationScheme.h>
#include <agrum/BN/BayesNet.h>
 
namespace gum {
 
  namespace learning {

    class GUM_PUBLIC_BN GreedyHillClimbing: public ApproximationScheme {
      public:
      // ##########################################################################
      // ##########################################################################

      GreedyHillClimbing();

      GreedyHillClimbing(const GreedyHillClimbing& from);

      GreedyHillClimbing(GreedyHillClimbing&& from);

      ~GreedyHillClimbing() override;

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

      GreedyHillClimbing& operator=(const GreedyHillClimbing& from);

      GreedyHillClimbing& operator=(GreedyHillClimbing&& from);

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

      ApproximationScheme& approximationScheme();


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


      template < GUM_Numeric GUM_SCALAR = double,
                 typename GRAPH_CHANGES_SELECTOR,
                 typename PARAM_ESTIMATOR >
      BayesNet< GUM_SCALAR > learnBN(GRAPH_CHANGES_SELECTOR& selector,
                                     PARAM_ESTIMATOR&        estimator,
                                     DAG                     initial_dag = DAG());

    };
 
  } /* namespace learning */
 
} /* namespace gum */

#include <agrum/BN/learning/greedyHillClimbing_tpl.h>
 
#endif /* GUM_LEARNING_GREEDY_HILL_CLIMBING_H */