paramEstimatorML.h

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#ifndef GUM_LEARNING_PARAM_ESTIMATOR_ML_H
#define GUM_LEARNING_PARAM_ESTIMATOR_ML_H
 
#include <sstream>
 
#include <agrum/agrum.h>
 
#include <agrum/BN/learning/paramUtils/paramEstimator.h>
 
namespace gum {
 
  namespace learning {

    class ParamEstimatorML: public ParamEstimator {
      public:
      // ##########################################################################
      // ##########################################################################


      ParamEstimatorML(const DBRowGeneratorParser& parser,
                       const Prior&                external_prior,
                       const Prior&                _score_internal_prior,
                       const std::vector< std::pair< std::size_t, std::size_t > >& ranges,
                       const Bijection< NodeId, std::size_t >&                     nodeId2columns
                       = Bijection< NodeId, std::size_t >());


      ParamEstimatorML(const DBRowGeneratorParser&             parser,
                       const Prior&                            external_prior,
                       const Prior&                            _score_internal_prior,
                       const Bijection< NodeId, std::size_t >& nodeId2columns
                       = Bijection< NodeId, std::size_t >());

      ParamEstimatorML(const ParamEstimatorML& from);

      ParamEstimatorML(ParamEstimatorML&& from);

      virtual ParamEstimatorML* clone() const;

      virtual ~ParamEstimatorML();

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


      ParamEstimatorML& operator=(const ParamEstimatorML& from);

      ParamEstimatorML& operator=(ParamEstimatorML&& from);

 
 
      // ##########################################################################
      // ##########################################################################
 
      using ParamEstimator::parameters;


      virtual std::vector< double > parameters(const NodeId                 target_node,
                                               const std::vector< NodeId >& conditioning_nodes);

      virtual std::pair< std::vector< double >, double >
          parametersAndLogLikelihood(const NodeId                 target_node,
                                     const std::vector< NodeId >& conditioning_nodes);

 
      private:
      std::pair< std::vector< double >, double >
          _parametersAndLogLikelihood_(const NodeId                 target_node,
                                       const std::vector< NodeId >& conditioning_nodes,
                                       const bool                   compute_log_likelihood);
    };
 
  } /* namespace learning */
 
} /* namespace gum */
 
// include the inlined functions if necessary
#ifndef GUM_NO_INLINE
#  include <agrum/BN/learning/paramUtils/paramEstimatorML_inl.h>
#endif /* GUM_NO_INLINE */
 
#endif /* GUM_LEARNING_PARAM_ESTIMATOR_ML_H */