/usr/include/BALL/QSAR/libsvmModel.h is in libball1.4-dev 1.4.1+20111206-3.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 | /* 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:
/** @name Constructors and Destructors
*/
//@{
LibsvmModel(const QSARData& q, int k_type, double p1, double p2=-1);
virtual ~LibsvmModel();
//@}
/** @name Accessors
*/
//@{
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_;
/** determines whether nu-SVR is to be used; else eps-SVR is applied */
bool use_nu_;
/** determines whether the libsvm shrinking heuristics is to be used */
bool use_shrinking_;
double nu_;
double p_;
double eps_;
double C_;
};
}
}
#endif // LIBSVMMODELH
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