/usr/include/BALL/QSAR/pcrModel.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 | /* pcrModel.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 PCRMODEL
#define PCRMODEL
#ifndef LMODEL
#include <BALL/QSAR/linearModel.h>
#endif
#ifndef QSAR_EXCEPTION
#include <BALL/QSAR/exception.h>
#endif
#include <BALL/QSAR/rrModel.h>
#include <BALL/QSAR/latentVariableModel.h>
namespace BALL
{
namespace QSAR
{
class BALL_EXPORT PCRModel : public LinearModel, public LatentVariableModel
{
public:
/** @name Constructors and Destructors
*/
//@{
/** constructur
@param q QSAR-wrapper object, from which the data for this model should be taken
@param frac_var the part of the variance that is to be explained by the latent variables; as many latent variables as necessary to achieve this are created */
PCRModel(const QSARData& q, double frac_var=0.99);
~PCRModel();
//@}
/** @name Accessors
*/
//@{
/** Starts principal component regression with the current data and saves the resulting linear combination of descriptors to training_result.*/
void train();
/** set the fraction of the variance that should be explained by the latente variables. */
void setFracVar(double frac_var);
void setParameters(vector<double>& v);
vector<double> getParameters() const;
/** calculates the first eigenvectors of the given matrix 'data' and saves them as columns of matrix 'output'
@param frac_var the fraction of the variance that is to the covered the selected eigenvectors */
static void calculateEigenvectors(const Matrix<double>& data, double frac_var, Matrix<double>& output);
//@}
protected:
/** @name Attributes
*/
//@{
/** fraction of the variance that is to be explained */
double frac_var_;
//@}
};
}
}
#endif // PCRMODEL
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