text: // --------------------------------------------------------------------------
  //                   OpenMS -- Open-Source Mass Spectrometry
  // --------------------------------------------------------------------------
  // Copyright The OpenMS Team -- Eberhard Karls University Tuebingen,
  // ETH Zurich, and Freie Universitaet Berlin 2002-2018.
  //
  // This software is released under a three-clause BSD license:
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  //  * 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.
  //  * Neither the name of any author or any participating institution
  //    may be used to endorse or promote products derived from this software
  //    without specific prior written permission.
  // For a full list of authors, refer to the file AUTHORS.
  // --------------------------------------------------------------------------
  // 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 ANY OF THE AUTHORS OR THE CONTRIBUTING
  // INSTITUTIONS 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,
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  //
  // --------------------------------------------------------------------------
  // $Maintainer: Timo Sachsenberg $
  // $Authors: $
  // --------------------------------------------------------------------------
  
  #pragma once
  
  #include <OpenMS/DATASTRUCTURES/Matrix.h>
  
  namespace OpenMS
  {
  
    /**
        @brief Trained Local Linear %Map (LLM) model for peak intensity prediction
  
        This class offers a model for predictions of peptide peak heights
        (referred to as intensities) by a Local Linear %Map (LLM) model and
        is the basis of PeakIntensityPredictor.
  
        A general introduction to the Peak Intensity Predictor (PIP)
        can be found in the <A HREF="tutorial_pip.html">PIP Tutorial</A>.
  
        The model trained needs two files for storing the position of the
        codebook vectors and the linear mappings (codebooks.data, linearMapping.data)
        This is the default model used by PeakIntensityPredictor.
  */
    class OPENMS_DLLAPI LocalLinearMap
    {
  
  public:
  
      /**
      @brief Define parameters needed by the Local Linear %Map (LLM) model
  
      Parameters xdim and ydim define the size of the two dimensional
      grid structure. Parameter radius gives the width of the Gaussian
      neighborhood function.
  */
      struct OPENMS_DLLAPI LLMParam
      {
        UInt xdim;           /**< size of first coordinate */
        UInt ydim;           /**< size of second coordinate */
        double radius;           /**< width of Gaussian neighborhood function */
      };
  
      /// default constructor
      LocalLinearMap();
      /// destructor
      virtual ~LocalLinearMap();
  
      ///return parameters of the LocalLinearMap model
      const LLMParam & getLLMParam() const;
      ///return position of the codebook vectors (18-dim)
      const Matrix<double> & getCodebooks() const;
      ///return linear mappings of the codebooks
      const Matrix<double> & getMatrixA() const;
      ///return linear bias
      const std::vector<double> & getVectorWout() const;
      ///return coordinates of codebook vectors on the 2-d grid
      const Matrix<UInt> & getCord() const;
      ///calculate and return normalized amino acid index variables from string representation of peptide
      void normalizeVector(std::vector<double> & aaIndexVariables);
      ///calculate neighborhood function based on distance of prototypes to winner prototype on two-dimensional grid structure and neighborhood width.
      std::vector<double> neigh(const Matrix<UInt> & cord, Size win, double radius);
  
  private:
  
      LLMParam param_;                                            ///<parameters of the model
      Matrix<double> code_;                           ///<codebook vectors
      Matrix<double> A_;                              ///<linear mappings
      std::vector<double> wout_;              ///<linear bias
      Matrix<UInt> cord_;                                     ///<coordinates of codebooks on grid
  
      /// needed to store prototype coordinates
      Matrix<UInt> genCord_(Size xdim, Size ydim);
      ///calculate distance between two prototypes
      double dist_(const Matrix<UInt> & u, const Matrix<UInt> & v, Size a, Size b);
  
      ///Copy constructor not implemented => private
      LocalLinearMap(LocalLinearMap & rhs);
      ///Assignment operator not implemented => private
      LocalLinearMap & operator=(const LocalLinearMap & llm);
  
    };
  
  
  }
  