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// -*- Mode: C++; tab-width: 2; -*-
// vi: set ts=2:
//
//
#ifndef BALL_QSAR_ALLMODEL_H
#define BALL_QSAR_ALLMODEL_H
#ifndef BALL_QSAR_NONLINEARMODEL_H
#include <BALL/QSAR/nonlinearModel.h>
#endif
namespace BALL
{
namespace QSAR
{
/** class for automated lazy learning (ALL-QSAR) */
class BALL_EXPORT ALLModel : public NonLinearModel
{
public:
/** @name Constructors and Destructors
*/
//@{
ALLModel(const QSARData& q, double kw=4);
~ALLModel();
//@}
/** @name Accessors
*/
//@{
void setKw(double kw);
virtual Eigen::VectorXd predict(const vector<double>& substance, bool transform=1);
/** automated lazy learning does not have a seperate training step */
void train(){};
/** Tries to find the optimal kernel width for the current data. Therefore some training data must have been read by the connected QSARData object before running this method. */
virtual bool optimizeParameters(int d, int no_steps);
/** returns the current kernel width */
double getKw();
virtual void setParameters(vector<double>& v);
virtual vector<double> getParameters() const;
void saveToFile(string filename);
void readFromFile(string filename);
//@}
protected:
/** @name Accessors
*/
//@{
virtual void calculateWeights(Eigen::MatrixXd& dist, Eigen::VectorXd& w);
/** calculates weighted Eigen::MatrixXd X^T*X */
void calculateXX(Eigen::VectorXd& w, Eigen::MatrixXd& res);
/** calculates weighted Eigen::MatrixXd X^T*Y */
void calculateXY(Eigen::VectorXd& w, Eigen::MatrixXd& res);
/** calculates pairwise euclidean distance between all substances of m1 and m2 and saves them to Eigen::MatrixXd output */
void calculateEuclDistanceMatrix(Eigen::MatrixXd& m1, Eigen::MatrixXd& m2, Eigen::MatrixXd& output);
//@}
/** @name Attributes
*/
//@{
/** kernel width */
double kw_;
double lambda_;
//@}
};
}
}
#endif // ALLMODEL
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