GRLMLP.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 GRLMLP_H
#define GRLMLP_H
 
#include <TObject.h>
#include <framework/logging/Logger.h>
 
namespace Belle2 {
  class GRLMLP : public TObject {
 
    // weights etc. are set only by the trainer
    friend class GRLNeuroTrainerModule;
 
  public:
    GRLMLP();
 
    GRLMLP(std::vector<unsigned short>& nodes, unsigned short targets, const std::vector<float>& outputscale);
 
    ~GRLMLP() { }
 
    bool isTrained() const { return m_trained; }
    unsigned getNumberOfLayers() const { return m_nNodes.size(); }
    unsigned getNumberOfNodesLayer(unsigned iLayer) const { return m_nNodes[iLayer]; }
    unsigned getNumberOfWeights() const { return m_weights.size(); }
    unsigned nWeightsCal() const;
    unsigned nBiasCal() const;
    std::vector<float> getWeights() const { return m_weights; }
    std::vector<float> getBias() const { return m_bias; }
    void setWeights(std::vector<float>& weights) { m_weights = weights; }
    void setBias(std::vector<float>& bias) { m_bias = bias; }
 
    void Trained(bool trained) { m_trained = trained; }
 
  private:
    std::vector<unsigned short> m_nNodes;
    std::vector<float> m_weights;
    std::vector<float> m_bias;
    bool m_trained;
 
    unsigned short m_targetVars;
    std::vector<float> m_outputScale;
 
    ClassDef(GRLMLP, 2);
  };
}
#endif