LevMarqFitter1D.h

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#ifndef OPENMS_TRANSFORMATIONS_FEATUREFINDER_LEVMARQFITTER1D_H
#define OPENMS_TRANSFORMATIONS_FEATUREFINDER_LEVMARQFITTER1D_H

#include <OpenMS/TRANSFORMATIONS/FEATUREFINDER/Fitter1D.h>

#include <unsupported/Eigen/NonLinearOptimization>

#include <algorithm>

#include <OpenMS/DATASTRUCTURES/ListUtils.h>

namespace OpenMS
{

  class OPENMS_DLLAPI LevMarqFitter1D :
    public Fitter1D
  {

public:

    typedef std::vector<double> ContainerType;

    //TODO: This is copy and paste from TraceFitter.h. Make a generic wrapper for LM optimization
    class GenericFunctor
    {
    public:
      int inputs() const { return m_inputs; }
      int values() const { return m_values; }

      GenericFunctor(int dimensions, int num_data_points)
      : m_inputs(dimensions), m_values(num_data_points) {}

      virtual ~GenericFunctor() {}

      virtual int operator()(const Eigen::VectorXd &x, Eigen::VectorXd &fvec) = 0;

      // compute Jacobian matrix for the different parameters
      virtual int df(const Eigen::VectorXd &x, Eigen::MatrixXd &J) = 0;

    protected:
      const int m_inputs, m_values;
    };

    LevMarqFitter1D() :
      Fitter1D()
    {
      this->defaults_.setValue("max_iteration", 500, "Maximum number of iterations using by Levenberg-Marquardt algorithm.", ListUtils::create<String>("advanced"));
    }

    LevMarqFitter1D(const LevMarqFitter1D & source) :
      Fitter1D(source),
      max_iteration_(source.max_iteration_)
    {
    }

    virtual ~LevMarqFitter1D()
    {
    }

    virtual LevMarqFitter1D & operator=(const LevMarqFitter1D & source)
    {
      if (&source == this) return *this;

      Fitter1D::operator=(source);
      max_iteration_ = source.max_iteration_;

      return *this;
    }

protected:

    bool symmetric_;
    Int max_iteration_;

    void optimize_(Eigen::VectorXd& x_init, GenericFunctor& functor)
    {
      //TODO: this function is copy&paste from TraceFitter.h. Make a generic wrapper for
      //LM optimization
      int data_count = functor.values();
      int num_params = functor.inputs();

      // LM always expects N>=p, cause Jacobian be rectangular M x N with M>=N
      if (data_count < num_params) throw Exception::UnableToFit(__FILE__, __LINE__, OPENMS_PRETTY_FUNCTION, "UnableToFit-FinalSet", "Skipping feature, we always expects N>=p");

      Eigen::LevenbergMarquardt<GenericFunctor> lmSolver (functor);
      lmSolver.parameters.maxfev = max_iteration_;
      Eigen::LevenbergMarquardtSpace::Status status = lmSolver.minimize(x_init);

      //the states are poorly documented. after checking the source, we believe that
      //all states except NotStarted, Running and ImproperInputParameters are good
      //termination states.
      if (status <= Eigen::LevenbergMarquardtSpace::ImproperInputParameters)
      {
          throw Exception::UnableToFit(__FILE__, __LINE__, OPENMS_PRETTY_FUNCTION, "UnableToFit-FinalSet", "Could not fit the gaussian to the data: Error " + String(status));
      }
    }

    void updateMembers_()
    {
      Fitter1D::updateMembers_();
      max_iteration_ = this->param_.getValue("max_iteration");
    }

  };
}

#endif // OPENMS_TRANSFORMATIONS_FEATUREFINDER_LEVMARQFITTER1D_H