BALL
1.4.1
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00001 /* libsvmModel.h 00002 * 00003 * Copyright (C) 2009 Marcel Schumann 00004 * 00005 * This file is part of QuEasy -- A Toolbox for Automated QSAR Model 00006 * Construction and Validation. 00007 * QuEasy is free software; you can redistribute it and/or modify 00008 * it under the terms of the GNU General Public License as published by 00009 * the Free Software Foundation; either version 3 of the License, or (at 00010 * your option) any later version. 00011 * 00012 * QuEasy is distributed in the hope that it will be useful, but 00013 * WITHOUT ANY WARRANTY; without even the implied warranty of 00014 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU 00015 * General Public License for more details. 00016 * 00017 * You should have received a copy of the GNU General Public License 00018 * along with this program; if not, see <http://www.gnu.org/licenses/>. 00019 */ 00020 00021 // -*- Mode: C++; tab-width: 2; -*- 00022 // vi: set ts=2: 00023 // 00024 // 00025 00026 #ifndef LIBSVMMODELH 00027 #define LIBSVMMODELH 00028 00029 #ifndef SVRMODEL 00030 #include <BALL/QSAR/svrModel.h> 00031 #endif 00032 00033 #include <fstream> 00034 #include <svm.h> 00035 00036 00037 namespace BALL 00038 { 00039 namespace QSAR 00040 { 00041 class BALL_EXPORT LibsvmModel : public SVRModel 00042 { 00043 public: 00047 LibsvmModel(const QSARData& q, int k_type, double p1, double p2=-1); 00048 00049 virtual ~LibsvmModel(); 00051 00052 00056 void train(); 00057 00058 //RowVector predict(const vector<double>& substance, bool transform=1); 00059 00060 void setParameters(vector<double>& v); 00061 00062 virtual vector<double> getParameters() const; 00064 00065 00066 private: 00067 00068 // part of libsvm; unfortunately defined in svm.C instead of svm.h, so that we need this hack ... 00069 struct svm_model 00070 { 00071 svm_parameter param; // parameter 00072 int nr_class; // number of classes, = 2 in regression/one class svm 00073 int l; // total #SV 00074 svm_node **SV; // SVs (SV[l]) 00075 double **sv_coef; // coefficients for SVs in decision functions (sv_coef[k-1][l]) 00076 double *rho; // constants in decision functions (rho[k*(k-1)/2]) 00077 double *probA; // pariwise probability information 00078 double *probB; 00079 00080 // for classification only 00081 int *label; // label of each class (label[k]) 00082 int *nSV; // number of SVs for each class (nSV[k]) 00083 // nSV[0] + nSV[1] + ... + nSV[k-1] = l 00084 // XXX 00085 int free_sv; // 1 if svm_model is created by svm_load_model 00086 // 0 if svm_model is created by svm_train 00087 }; 00088 00089 struct svm_problem* createProblem(int response_id); 00090 00091 void createParameters(); 00092 00093 struct svm_model* svm_train_result_; 00094 00095 struct svm_parameter parameters_; 00096 00097 struct svm_node* x_space_; 00098 00100 bool use_nu_; 00101 00103 bool use_shrinking_; 00104 00105 double nu_; 00106 double p_; 00107 double eps_; 00108 double C_; 00109 00110 00111 }; 00112 } 00113 } 00114 00115 00116 00117 #endif // LIBSVMMODELH