TBNN.h

/****************************************************************************
* Copyright (c) 2023, CEA
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
* 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
* 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
* IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
*****************************************************************************/
#ifndef TBNN_included
#define TBNN_included
#include <keras_model.h>
#include <PrePostNN.h>
#include <string>
#include <vector>
 
class TBNN
{
public:
  TBNN(string keras_model_file,string preproc_file);
  ~TBNN();
 
  std::vector<double> predict(std::vector<double> lambda, std::vector<std::vector<double>> T);
 
private:
 
// Neural network
  KerasModel _model;              // Neural network
  std::vector<double> _g;              // output of the Neural network
  PrePostNN *_ppNN;               // objet pre et post processing
 
// Lambda pre-processing
  std::vector<double> _plambda;        // pre-processed lambda
 
// T pre-processing
  std::vector<std::vector<double>> _pT;     // pre-processed T
 
// b post-processing
  std::vector<double> _pb;             // result of the prediction not post-processed
  std::vector<double> _b;              // result of the prediction post-processed
 
// process methods
  void process_lambda(std::vector<double> lambda); // calculate the pre-processed lambda
  void process_T(std::vector<std::vector<double>> T);   // calculate the pre-processed T
  void applyNN();                             // prédiction du réseau de neurones
  void process_b();                           // calculate the post-processed b
};
 
#endif