libsvmModel.h

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/* libsvmModel.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 LIBSVMMODELH
#define LIBSVMMODELH

#ifndef SVRMODEL
#include <BALL/QSAR/svrModel.h>
#endif

#include <fstream>
#include <svm.h>


namespace BALL
{
  namespace QSAR
  {
    class BALL_EXPORT LibsvmModel : public SVRModel
    {
      public:
        LibsvmModel(const QSARData& q, int k_type, double p1, double p2=-1);

        virtual ~LibsvmModel();
        
        
        void train();
        
        //RowVector predict(const vector<double>& substance, bool transform=1);
        
        void setParameters(vector<double>& v);
        
        virtual vector<double> getParameters() const;
        
        
      private:

        // part of libsvm; unfortunately defined in svm.C instead of svm.h, so that we need this hack ...
        struct svm_model
        {
          svm_parameter param;  // parameter
          int nr_class;   // number of classes, = 2 in regression/one class svm
          int l;      // total #SV
          svm_node **SV;    // SVs (SV[l])
          double **sv_coef; // coefficients for SVs in decision functions (sv_coef[k-1][l])
          double *rho;    // constants in decision functions (rho[k*(k-1)/2])
          double *probA;          // pariwise probability information
          double *probB;

          // for classification only
          int *label;   // label of each class (label[k])
          int *nSV;   // number of SVs for each class (nSV[k])
          // nSV[0] + nSV[1] + ... + nSV[k-1] = l
          // XXX
          int free_sv;    // 1 if svm_model is created by svm_load_model
                  // 0 if svm_model is created by svm_train
        };
        
        struct svm_problem* createProblem(int response_id);
        
        void createParameters();
        
        struct svm_model* svm_train_result_;
        
        struct svm_parameter parameters_;
        
        struct svm_node* x_space_;
        
        bool use_nu_;
        
        bool use_shrinking_;
        
        double nu_;
        double p_;
        double eps_;
        double C_;
        
        
    };
  }
}



#endif // LIBSVMMODELH