/usr/include/BALL/QSAR/gpModel.h is in libball1.4-dev 1.4.1+20111206-3.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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 | /* gpModel.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 GPMODEL
#define GPMODEL
#ifndef KMODEL
#include <BALL/QSAR/kernelModel.h>
#endif
#ifndef NLMODEL
#include <BALL/QSAR/nonlinearModel.h>
#endif
namespace BALL
{
namespace QSAR
{ /** class for gaussian process regression */
class BALL_EXPORT GPModel : public KernelModel
{
public:
/** @name Constructors and Destructors
*/
//@{
GPModel(const QSARData& q, int k_type, double p1, double p2=-1);
GPModel(const QSARData& q, Vector<double>& w);
/** constructor that sets KernelModel.f to s1 and KernelModel.g to s2 */
GPModel(const QSARData& q, String s1, String s2);
GPModel(const QSARData& q, const LinearModel& lm, int column);
~GPModel();
//@}
/** @name Accessors
*/
//@{
void train();
Vector<double> predict(const vector<double>& substance, bool transform=1);
/** calculates standart error for the last prediction as \f$ \sqrt{k(x_*,x_*)-\sum_{i=1}^n \sum_{j=1}^n k(x_*,x_i)*k(x_*,x_j)-L_{ij} } \f$*/
double calculateStdErr();
void setParameters(vector<double>& v);
vector<double> getParameters() const;
//@}
private:
/** @name Attributes
*/
//@{
Matrix<double> L_;
/** the last predicted substance */
Vector<double> input_;
Vector<double> K_t_;
double lambda_;
//@}
};
}
}
#endif // GPMODEL
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