FANN.h

/**************************************************************************
 * basf2 (Belle II Analysis Software Framework)                           *
 * Author: The Belle II Collaboration                                     *
 *                                                                        *
 * See git log for contributors and copyright holders.                    *
 * This file is licensed under LGPL-3.0, see LICENSE.md.                  *
 **************************************************************************/
 
#pragma once
#ifndef INCLUDE_GUARD_BELLE2_MVA_FANN_HEADER
#define INCLUDE_GUARD_BELLE2_MVA_FANN_HEADER
 
#include <mva/interface/Options.h>
#include <mva/interface/Teacher.h>
#include <mva/interface/Expert.h>
 
#include <fann.h>
 
namespace Belle2 {
  namespace MVA {
 
    class FANNOptions : public SpecificOptions {
 
    public:
      virtual void load(const boost::property_tree::ptree& pt) override;
 
      virtual void save(boost::property_tree::ptree& pt) const override;
 
      virtual po::options_description getDescription() override;
 
      virtual std::string getMethod() const override { return "FANN"; }
 
      std::vector<unsigned int> getHiddenLayerNeurons(unsigned int nf) const;
 
      unsigned int m_max_epochs = 10000; 
      bool m_verbose_mode = true; 
      std::string m_hidden_layers_architecture = "3*N"; 
      std::string m_hidden_activiation_function = "FANN_SIGMOID_SYMMETRIC"; 
      std::string m_output_activiation_function = "FANN_SIGMOID_SYMMETRIC"; 
      std::string m_error_function = "FANN_ERRORFUNC_LINEAR"; 
      std::string m_training_method = "FANN_TRAIN_RPROP"; 
      double m_validation_fraction = 0.5; 
      unsigned int m_random_seeds =
        3; 
      unsigned int m_test_rate =
        500; 
      unsigned int m_number_of_threads = 8; 
      bool m_scale_features = true; 
      bool m_scale_target = true; 
    };
 
    class FANNTeacher : public Teacher {
 
    public:
      FANNTeacher(const GeneralOptions& general_options, const FANNOptions& specific_options);
 
      virtual Weightfile train(Dataset& training_data) const override;
 
    private:
      FANNOptions m_specific_options; 
    };
 
 
    class FANNExpert : public MVA::Expert {
 
    public:
 
      virtual ~FANNExpert();
 
      virtual void load(Weightfile& weightfile) override;
 
      virtual std::vector<float> apply(Dataset& test_data) const override;
 
    private:
      FANNOptions m_specific_options; 
      struct fann* m_ann = nullptr; 
    };
 
  }
}
#endif