DAG2BNLearner.h

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#ifndef GUM_LEARNING_DAG_2_BN_LEARNER_H
#define GUM_LEARNING_DAG_2_BN_LEARNER_H

#include <vector>

#include <agrum/agrum.h>
#include <agrum/tools/core/approximations/approximationScheme.h>
#include <agrum/tools/core/approximations/approximationSchemeListener.h>
#include <agrum/BN/BayesNet.h>
#include <agrum/tools/graphs/DAG.h>
#include <agrum/BN/learning/paramUtils/paramEstimator.h>

namespace gum {

  namespace learning {

    template < template < typename > class ALLOC = std::allocator >
    class DAG2BNLearner: public ApproximationScheme, private ALLOC< NodeId > {
      public:
      using allocator_type = ALLOC< NodeId >;

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

      DAG2BNLearner(const allocator_type& alloc = allocator_type());

      DAG2BNLearner(const DAG2BNLearner< ALLOC >& from);

      DAG2BNLearner(const DAG2BNLearner< ALLOC >& from,
                    const allocator_type&         alloc);

      DAG2BNLearner(DAG2BNLearner< ALLOC >&& from);

      DAG2BNLearner(DAG2BNLearner< ALLOC >&& from, const allocator_type& alloc);

      virtual DAG2BNLearner< ALLOC >* clone() const;

      virtual DAG2BNLearner< ALLOC >* clone(const allocator_type& alloc) const;

      virtual ~DAG2BNLearner();



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


      DAG2BNLearner< ALLOC >& operator=(const DAG2BNLearner< ALLOC >& from);

      DAG2BNLearner< ALLOC >& operator=(DAG2BNLearner< ALLOC >&& from);



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

      template < typename GUM_SCALAR = double >
      static BayesNet< GUM_SCALAR > createBN(ParamEstimator< ALLOC >& estimator,
                                             const DAG&               dag);


      template < typename GUM_SCALAR = double >
      BayesNet< GUM_SCALAR > createBN(ParamEstimator< ALLOC >& bootstrap_estimator,
                                      ParamEstimator< ALLOC >& general_estimator,
                                      const DAG&               dag);

      ApproximationScheme& approximationScheme();

      allocator_type getAllocator() const;


#ifndef DOXYGEN_SHOULD_SKIP_THIS

      private:

      template < typename GUM_SCALAR = double >
      static void
         __probaVarReordering(gum::Potential< GUM_SCALAR >&       pot,
                              const gum::Potential< GUM_SCALAR >& other_pot);

#endif /* DOXYGEN_SHOULD_SKIP_THIS */
    };

  } /* namespace learning */

} /* namespace gum */

#include <agrum/BN/learning/paramUtils/DAG2BNLearner_tpl.h>

#endif /* GUM_LEARNING_DAG_2_BN_LEARNER_H */