kernelModel.h

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/* kernelModel.h
 * 
 * Copyright (C) 2009 Marcel Schumann
 * 
 * This file is part of QuEasy -- A Toolbox for Automated QSAR Model
 * Construction and Validation.
 * QuEasy is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 3 of the License, or (at
 * your option) any later version.
 * 
 * QuEasy is distributed in the hope that it will be useful, but
 * WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
 * General Public License for more details.
 * 
 * You should have received a copy of the GNU General Public License
 * along with this program; if not, see <http://www.gnu.org/licenses/>.
 */

// -*- Mode: C++; tab-width: 2; -*-
// vi: set ts=2:
//
//

#ifndef KMODEL
#define KMODEL

#ifndef NLMODEL
#include <BALL/QSAR/nonlinearModel.h>
#endif

#ifndef LMODEL
#include <BALL/QSAR/linearModel.h>
#endif

#ifndef KERNEL
#include <BALL/QSAR/kernel.h>
#endif



namespace BALL
{
  namespace QSAR
  {
    class BALL_EXPORT KernelModel : public NonLinearModel
    {
      public:
        KernelModel(const QSARData& q, int k_type, double p1, double p2);
        
        KernelModel(const QSARData& q, String f, String g);
            
        KernelModel(const QSARData& q, Vector<double>& w);
        
        KernelModel(const QSARData& q, const LinearModel& lm, int column);
        
        ~KernelModel();
        
        virtual void saveToFile(string filename);
        
        virtual void readFromFile(string filename);
        
        virtual Vector<double> predict(const vector<double>& substance, bool transform);
        
        void operator=(const Model& m);
        
        Kernel* kernel;

        
      protected:
        
        Matrix<double> K_;
        
        //Matrix B;
        
        
        void calculateOffsets();
        
        void readKernelParametersFromFile(std::ifstream& in);
        void saveKernelParametersToFile(std::ofstream& out);
        void saveTrainingResult(std::ofstream& out);
        void readTrainingResult(std::ifstream& input, int no_substances, int no_y);
        
        friend class RegressionValidation;
        //}@
        
      
    };
  }
}


#endif // NLMODEL

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