/usr/include/BALL/QSAR/gpModel.h is in libball1.4-dev 1.4.3~beta1-3.
This file is owned by root:root, with mode 0o644.
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// vi: set ts=2:
//
//
#ifndef BALL_QSAR_GPMODEL_H
#define BALL_QSAR_GPMODEL_H
#ifndef BALL_QSAR_KERNELMODEL_H
#include <BALL/QSAR/kernelModel.h>
#endif
#ifndef BALL_QSAR_NONLINEARMODEL_H
#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, Eigen::VectorXd& 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();
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
//@}
/** @name Accessors
*/
//@{
void train();
Eigen::VectorXd 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
*/
//@{
Eigen::MatrixXd L_;
/** the last predicted substance */
Eigen::VectorXd input_;
Eigen::RowVectorXd K_t_;
double lambda_;
//@}
};
}
}
#endif // GPMODEL
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