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// -*- Mode: C++; tab-width: 2; -*-
// vi: set ts=2:
//
//
#ifndef BALL_QSAR_KPLSMODEL_H
#define BALL_QSAR_KPLSMODEL_H
#ifndef BALL_QSAR_KERNELMODEL_H
#include <BALL/QSAR/kernelModel.h>
#endif
#ifndef BALL_QSAR_NONLINEARMODEL_H
#include <BALL/QSAR/nonlinearModel.h>
#endif
#ifndef BALL_QSAR_LATENTVARIABLEMODEL_H
#include <BALL/QSAR/latentVariableModel.h>
#endif
namespace BALL
{
namespace QSAR
{
/** kernel partial-least-squares class */
class BALL_EXPORT KPLSModel : public KernelModel, public LatentVariableModel
{
public:
/** @name Constructors and Destructors
*/
//@{
KPLSModel(const QSARData& q, int k_type, double p1, double p2=-1);
KPLSModel(const QSARData& q, Eigen::VectorXd& w);
KPLSModel(const QSARData& q, String s1, String s2);
KPLSModel(const QSARData& q, const LinearModel& lm, int column);
~KPLSModel();
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
//@}
/** @name Accessors
*/
//@{
void train();
/** tries to find the optimal number of latente variables (=PLS components) */
bool optimizeParameters(int k, int no_steps);
/** returns the number of latente variables (=PLS components) */
int getNoComponents();
void setNoComponents(int d);
/** returns a pointer to the PLS Y-scores matrix U */
const Eigen::MatrixXd* getU();
void setParameters(vector<double>& v);
vector<double> getParameters() const;
//@}
protected:
/** @name Attributes
*/
Eigen::MatrixXd U_;
int no_components_;
//@}
};
}
}
#endif // KPLSMODEL
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