EmgGradientDescent.h

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// Copyright (c) 2002-present, The OpenMS Team -- EKU Tuebingen, ETH Zurich, and FU Berlin
// SPDX-License-Identifier: BSD-3-Clause
//
// --------------------------------------------------------------------------
// $Maintainer: Douglas McCloskey, Pasquale Domenico Colaianni $
// $Authors: Douglas McCloskey, Pasquale Domenico Colaianni $
// --------------------------------------------------------------------------
 
#pragma once
 
#include <OpenMS/config.h> // OPENMS_DLLAPI
#include <OpenMS/DATASTRUCTURES/DefaultParamHandler.h>
#include <OpenMS/KERNEL/MSChromatogram.h>
#include <OpenMS/KERNEL/MSSpectrum.h>
 
namespace OpenMS
{
  class OPENMS_DLLAPI EmgGradientDescent :
    public DefaultParamHandler
  {
public:
    EmgGradientDescent();
    ~EmgGradientDescent() override = default;
 
    void getDefaultParameters(Param& params);
 
    friend class EmgGradientDescent_friend;
 
    template <typename PeakContainerT>
    void fitEMGPeakModel(
      const PeakContainerT& input_peak,
      PeakContainerT& output_peak,
      const double left_pos = 0.0,
      const double right_pos = 0.0
    ) const;
 
    UInt estimateEmgParameters(
      const std::vector<double>& xs,
      const std::vector<double>& ys,
      double& best_h,
      double& best_mu,
      double& best_sigma,
      double& best_tau
    ) const;
 
    void applyEstimatedParameters(
      const std::vector<double>& xs,
      const double h,
      const double mu,
      const double sigma,
      const double tau,
      std::vector<double>& out_xs,
      std::vector<double>& out_ys
    ) const;
 
protected:
    void updateMembers_() override;
 
    void extractTrainingSet(
      const std::vector<double>& xs,
      const std::vector<double>& ys,
      std::vector<double>& TrX,
      std::vector<double>& TrY
    ) const;
 
    double computeMuMaxDistance(const std::vector<double>& xs) const;
 
    double computeInitialMean(
      const std::vector<double>& xs,
      const std::vector<double>& ys
    ) const;
 
private:
    void iRpropPlus(
      const double prev_diff_E_param,
      double& diff_E_param,
      double& param_lr,
      double& param_update,
      double& param,
      const double current_E,
      const double previous_E
    ) const;
 
    double Loss_function(
      const std::vector<double>& xs,
      const std::vector<double>& ys,
      const double h,
      const double mu,
      const double sigma,
      const double tau
    ) const;
 
    double E_wrt_h(
      const std::vector<double>& xs,
      const std::vector<double>& ys,
      const double h,
      const double mu,
      const double sigma,
      const double tau
    ) const;
 
    double E_wrt_mu(
      const std::vector<double>& xs,
      const std::vector<double>& ys,
      const double h,
      const double mu,
      const double sigma,
      const double tau
    ) const;
 
    double E_wrt_sigma(
      const std::vector<double>& xs,
      const std::vector<double>& ys,
      const double h,
      const double mu,
      const double sigma,
      const double tau
    ) const;
 
    double E_wrt_tau(
      const std::vector<double>& xs,
      const std::vector<double>& ys,
      const double h,
      const double mu,
      const double sigma,
      const double tau
    ) const;
 
    double compute_z(
      const double x,
      const double mu,
      const double sigma,
      const double tau
    ) const;
 
    double emg_point(
      const double x,
      const double h,
      const double mu,
      const double sigma,
      const double tau
    ) const;
 
    const double PI = OpenMS::Constants::PI;
 
    UInt print_debug_;
 
    UInt max_gd_iter_;
 
    bool compute_additional_points_;
  };
 
  class EmgGradientDescent_friend
  {
public:
    EmgGradientDescent_friend() = default;
    ~EmgGradientDescent_friend() = default;
 
    double Loss_function(
      const std::vector<double>& xs,
      const std::vector<double>& ys,
      const double h,
      const double mu,
      const double sigma,
      const double tau
    ) const
    {
      return emg_gd_.Loss_function(xs, ys, h, mu, sigma, tau);
    }
 
    double computeMuMaxDistance(const std::vector<double>& xs) const
    {
      return emg_gd_.computeMuMaxDistance(xs);
    }
 
    void extractTrainingSet(
      const std::vector<double>& xs,
      const std::vector<double>& ys,
      std::vector<double>& TrX,
      std::vector<double>& TrY
    ) const
    {
      emg_gd_.extractTrainingSet(xs, ys, TrX, TrY);
    }
 
    double computeInitialMean(
      const std::vector<double>& xs,
      const std::vector<double>& ys
    ) const
    {
      return emg_gd_.computeInitialMean(xs, ys);
    }
 
    void iRpropPlus(
      const double prev_diff_E_param,
      double& diff_E_param,
      double& param_lr,
      double& param_update,
      double& param,
      const double current_E,
      const double previous_E
    ) const
    {
      emg_gd_.iRpropPlus(
        prev_diff_E_param, diff_E_param, param_lr,
        param_update, param, current_E, previous_E
      );
    }
 
    double compute_z(
      const double x,
      const double mu,
      const double sigma,
      const double tau
    ) const
    {
      return emg_gd_.compute_z(x, mu, sigma, tau);
    }
 
    void applyEstimatedParameters(
      const std::vector<double>& xs,
      const double h,
      const double mu,
      const double sigma,
      const double tau,
      std::vector<double>& out_xs,
      std::vector<double>& out_ys
    ) const
    {
      emg_gd_.applyEstimatedParameters(xs, h, mu, sigma, tau, out_xs, out_ys);
    }
 
    double emg_point(
      const double x,
      const double h,
      const double mu,
      const double sigma,
      const double tau
    ) const
    {
      return emg_gd_.emg_point(x, h, mu, sigma, tau);
    }
 
    EmgGradientDescent emg_gd_;
  };
}