#include <BALL/QSAR/kernelModel.h>

Inheritance diagram for BALL::QSAR::KernelModel:

Public Member Functions
Constructors and Destructors
	KernelModel (const QSARData &q, int k_type, double p1, double p2)

	KernelModel (const QSARData &q, String f, String g)

	KernelModel (const QSARData &q, Vector< double > &w)

	KernelModel (const QSARData &q, const LinearModel &lm, int column)

	~KernelModel ()

virtual void	saveToFile (string filename)

virtual void	readFromFile (string filename)

virtual Vector< double >	predict (const vector< double > &substance, bool transform)

void	operator= (const Model &m)

Public Member Functions inherited from BALL::QSAR::NonLinearModel
	NonLinearModel (const QSARData &q)

	~NonLinearModel ()

Attributes
Kernel *	kernel

Matrix< double >	K_

Input and Output. The following methods can be used to implement the functions saveToFile() and readFromFile() in final classes derived from this base-class
resulting matrix with one column for each modeled activity and with one coefficient for each substance (i.e. one column for each column for Model.Y)
class	RegressionValidation

void	calculateOffsets ()

void	readKernelParametersFromFile (std::ifstream &in)

void	saveKernelParametersToFile (std::ofstream &out)

void	saveTrainingResult (std::ofstream &out)

void	readTrainingResult (std::ifstream &input, int no_substances, int no_y)

Additional Inherited Members
Public Attributes inherited from BALL::QSAR::RegressionModel
RegressionValidation *	validation

Public Attributes inherited from BALL::QSAR::Model
const QSARData *	data

Validation *	model_val

Protected Member Functions inherited from BALL::QSAR::NonLinearModel
Protected Member Functions inherited from BALL::QSAR::RegressionModel
void	readDescriptorInformationFromFile (std::ifstream &in, int no_descriptors, bool transformation, int no_coefficients)

void	saveDescriptorInformationToFile (std::ofstream &out)

Protected Attributes inherited from BALL::QSAR::RegressionModel
BALL::Matrix< double >	training_result_

Vector< double >	offsets_

Detailed Description

Definition at line 47 of file kernelModel.h.

Constructor & Destructor Documentation

BALL::QSAR::KernelModel::KernelModel	(	const QSARData &	q,
		int	k_type,
		double	p1,
		double	p2
	)

BALL::QSAR::KernelModel::KernelModel	(	const QSARData &	q,
		String	f,
		String	g
	)

BALL::QSAR::KernelModel::KernelModel	(	const QSARData &	q,
		Vector< double > &	w
	)

BALL::QSAR::KernelModel::KernelModel	(	const QSARData &	q,
		const LinearModel &	lm,
		int	column
	)

BALL::QSAR::KernelModel::~KernelModel ( )

Member Function Documentation

void BALL::QSAR::KernelModel::calculateOffsets ( )

protectedvirtual

do nothing for non-kernel models. KernelModel on the other hand overloads this function...

Reimplemented from BALL::QSAR::NonLinearModel.

void BALL::QSAR::KernelModel::operator= ( const Model & m )

virtual

copy constructur; creates a model with the same specifications as the given one (same model and kernel parameters). If the given model has been trained, the training result is copied as well. \n

Note, that the input data that has been read by m to m.descriptor_matrix_ and m.Y_ is NOT copied to new model, since the input data is not part of the specification of a model. If nevertheless, copying of the input data is desired, use function copyData() (afterwards).

Reimplemented from BALL::QSAR::Model.

virtual Vector<double> BALL::QSAR::KernelModel::predict	(	const vector< double > &	substance,
		bool	transform
	)

virtual

Predicts the activities of a given substance

Parameters

substance the substance which activity is to be predicted in form of a vecor containing the values for all descriptors (if neccessary, relevant descriptors will be selected automatically)

transform determines whether the values for each descriptor of the given substance should be transformed before prediction of activity.
If (transform==1): each descriptor value is transformed according to the centering of the respective column of QSARData.descriptor_matrix used to train this model.
If the substance to be predicted is part of the same input data (e.g. same SD-file) as the training data (as is the case during cross validation), transform should therefore be set to 0.