/usr/include/BALL/QSAR/nBModel.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 | /* nBModel.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 NBMODEL
#define NBMODEL
#ifndef BAYESMODEL
#include <BALL/QSAR/bayesModel.h>
#endif
#ifndef STATISTICS
#include <BALL/QSAR/statistics.h>
#endif
namespace BALL
{
namespace QSAR
{
/** class for Naive Bayes */
class BALL_EXPORT NBModel : public BayesModel
{
public:
/** @name Constructors and Destructors
*/
//@{
NBModel(const QSARData& q);
~NBModel();
//@}
/** @name Accessors
*/
//@{
void train();
Vector<double> predict(const vector<double>& substance, bool transform=1);
void saveToFile(string filename);
void readFromFile(string filename);
vector<double> getParameters() const;
void setParameters(vector<double>& v);
bool isTrained();
vector<double> calculateProbabilities(int activitiy_index, int feature_index, double feature_value);
int getNoResponseVariables();
//@}
private:
/** @name Attributes
*/
//@{
uint discretization_steps_;
/** the minmum (row1) and maximum (row2) of each feature. */
Matrix<double> min_max_;
/** One probability Matrix for each modelled activity and each class. \n
Each Matrix stores in each cell the probability for a feature lying within a specific range to be in a specific class */
vector<vector<Matrix<double> > > probabilities_;
//@}
};
}
}
#endif //NBMODEL
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