GRLNeuro.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.                  *
 **************************************************************************/
 
#ifndef GRLNEURO_H
#define GRLNEURO_H
 
#include <trg/grl/dataobjects/GRLMLP.h>
#include <framework/datastore/StoreArray.h>
#include <framework/datastore/StoreObjPtr.h>
#include <framework/database/DBObjPtr.h>
 
 
namespace Belle2 {

  class GRLNeuro {
  public:
    struct Parameters {
      unsigned nMLP;
      std::vector<std::vector<float>> nHidden;
      int targetresult = 10;
      std::vector<unsigned> nOutput;
      std::vector<std::vector<float>> nn_thres;
      bool multiplyHidden = false;
      std::vector<std::vector<float>> outputScale = {{ -1., 1.}};
 

      unsigned n_cdc_sector;
      std::vector<float> i_cdc_sector;
      unsigned n_ecl_sector;
      std::vector<float> i_ecl_sector;

      std::vector<std::vector<int>> total_bit_bias;
      std::vector<std::vector<int>> int_bit_bias;
      std::vector<std::vector<bool>> is_signed_bias;
      std::vector<std::vector<int>> rounding_bias;
      std::vector<std::vector<int>> saturation_bias;
      std::vector<std::vector<int>> total_bit_accum;
      std::vector<std::vector<int>> int_bit_accum;
      std::vector<std::vector<bool>> is_signed_accum;
      std::vector<std::vector<int>> rounding_accum;
      std::vector<std::vector<int>> saturation_accum;
      std::vector<std::vector<int>> total_bit_weight;
      std::vector<std::vector<int>> int_bit_weight;
      std::vector<std::vector<bool>> is_signed_weight;
      std::vector<std::vector<int>> rounding_weight;
      std::vector<std::vector<int>> saturation_weight;
      std::vector<std::vector<int>> total_bit_relu;
      std::vector<std::vector<int>> int_bit_relu;
      std::vector<std::vector<bool>> is_signed_relu;
      std::vector<std::vector<int>> rounding_relu;
      std::vector<std::vector<int>> saturation_relu;
      std::vector<std::vector<int>> total_bit;
      std::vector<std::vector<int>> int_bit;
      std::vector<std::vector<bool>> is_signed;
      std::vector<std::vector<int>> rounding;
      std::vector<std::vector<int>> saturation;
      std::vector<std::vector<std::vector<int>>> W_input;
      std::vector<std::vector<std::vector<int>>> I_input;
      std::vector<std::vector<std::vector<int>>> W_input_layer;
      std::vector<std::vector<std::vector<int>>> I_input_layer;
    };
 
 

    GRLNeuro() {}

    virtual ~GRLNeuro() {}

    void initialize(const Parameters& p);

    GRLMLP& operator[](unsigned index) { return m_MLPs[index]; }
    const GRLMLP& operator[](unsigned index) const { return m_MLPs[index]; }
    unsigned nSectors() const { return m_MLPs.size(); }

    void save(const std::string& filename, const std::string& arrayname = "MLPs");
    bool load(unsigned isector, const std::string& wfilename, const std::string& bfilename);
 
    bool load(unsigned isector, std::vector<float> warray, std::vector<float> barray);//add
    std::vector<float> runMLP(unsigned isector,  const std::vector<float>& input);
 
  private:
    std::vector<GRLMLP> m_MLPs = {};
 
  };

}
#endif