#include <BALL/QSAR/kernel.h>

Public Member Functions
Constructors and Destructors
	Kernel (Model *m, int k_type, double p1, double p2=-1)

	Kernel (Model *m, String f, String g)

	Kernel (Model *m, Eigen::VectorXd &w)

	Kernel (Model *m, const LinearModel &lm, int column)

	~Kernel ()

Friends
class	FeatureSelection

Accessors
void	calculateKernelMatrix (Eigen::MatrixXd &input, Eigen::MatrixXd &output)

void	calculateKernelMatrix (Eigen::MatrixXd &K, Eigen::MatrixXd &m1, Eigen::MatrixXd &m2, Eigen::MatrixXd &output)

void	calculateKernelVector (Eigen::MatrixXd &K, Eigen::VectorXd &m1, Eigen::MatrixXd &m2, Eigen::RowVectorXd &output)

void	gridSearch (double step_width, int steps, int recursions, int k, bool opt=0)

void	gridSearch (double step_width, int steps, bool first_rec, int k, double par1_start, double par2_start, bool opt)

void	calculateWeightedKernelMatrix (Eigen::MatrixXd &input, Eigen::MatrixXd &output)

void	calculateWeightedKernelMatrix (Eigen::MatrixXd &m1, Eigen::MatrixXd &m2, Eigen::MatrixXd &output)

void	calculateKernelMatrix1 (Eigen::MatrixXd &input, Eigen::MatrixXd &output)

void	calculateKernelMatrix2 (Eigen::MatrixXd &input, Eigen::MatrixXd &output)

void	calculateKernelMatrix3 (Eigen::MatrixXd &input, Eigen::MatrixXd &output)

void	calculateKernelMatrix4 (Eigen::MatrixXd &input, Eigen::MatrixXd &output)

void	calculateKernelMatrix1 (Eigen::MatrixXd &m1, Eigen::MatrixXd &m2, Eigen::MatrixXd &output)

void	calculateKernelMatrix2 (Eigen::MatrixXd &m1, Eigen::MatrixXd &m2, Eigen::MatrixXd &output)

void	calculateKernelMatrix3 (Eigen::MatrixXd &m1, Eigen::MatrixXd &m2, Eigen::MatrixXd &output)

void	calculateKernelMatrix4 (Eigen::MatrixXd &m1, Eigen::MatrixXd &m2, Eigen::MatrixXd &output)

Attributes
int	type

double	par1

double	par2

String	equation1

String	equation2

Model *	model_

Eigen::VectorXd	weights_

Detailed Description

Definition at line 32 of file QSAR/kernel.h.

Constructor & Destructor Documentation

BALL::QSAR::Kernel::Kernel	(	Model *	m,
		int	k_type,
		double	p1,
		double	p2 = `-1`
	)

BALL::QSAR::Kernel::Kernel	(	Model *	m,
		String	f,
		String	g
	)

BALL::QSAR::Kernel::Kernel	(	Model *	m,
		Eigen::VectorXd &	w
	)

BALL::QSAR::Kernel::Kernel	(	Model *	m,
		const LinearModel &	lm,
		int	column
	)

constructor for weighted distance kernel.

Parameters

column no of column of LinearModel.training_result that is to be used as weights vector

BALL::QSAR::Kernel::~Kernel ( )

Member Function Documentation

void BALL::QSAR::Kernel::calculateKernelMatrix	(	Eigen::MatrixXd &	input,
		Eigen::MatrixXd &	output
	)

calculates pairwise distances between all substances in Eigen::MatrixXd input and saves them to Eigen::MatrixXd output.
If Kernel.weights is not empty, function Kernel.calculateWeightedDistanceMatrix() is used
Else if: Kernel.f=="" and Kernel.g="", the distance between two substances a and b is calculated as $\sum_{i=1}^m (input_{ai} * input_{bi})^p$ , with m=#descriptors
Else: distance is calculated as $g(\sum_{i=1}^m f(input_{ai}, input_{bi}))$

void BALL::QSAR::Kernel::calculateKernelMatrix	(	Eigen::MatrixXd &	K,
		Eigen::MatrixXd &	m1,
		Eigen::MatrixXd &	m2,
		Eigen::MatrixXd &	output
	)

calculates pairwise distance between all substances of m1 and m2 and saves them to Eigen::MatrixXd output.
If Kernel.weights is not empty, function Kernel.calculateWeightedDistanceMatrix() is used
Esle if: Kernel.f=="" and Kernel.g="", the distance between two substances a and b is calculated as $\sum_{i=1}^m (m1_{ai} * m2_{bi})^p$ , with m=#descriptors
Else: distance is calculated as $g(\sum_{i=1}^m f(m1_{ai}, m2_{bi}))$

void BALL::QSAR::Kernel::calculateKernelMatrix1	(	Eigen::MatrixXd &	input,
		Eigen::MatrixXd &	output
	)

protected

void BALL::QSAR::Kernel::calculateKernelMatrix1	(	Eigen::MatrixXd &	m1,
		Eigen::MatrixXd &	m2,
		Eigen::MatrixXd &	output
	)

protected

void BALL::QSAR::Kernel::calculateKernelMatrix2	(	Eigen::MatrixXd &	input,
		Eigen::MatrixXd &	output
	)

protected

void BALL::QSAR::Kernel::calculateKernelMatrix2	(	Eigen::MatrixXd &	m1,
		Eigen::MatrixXd &	m2,
		Eigen::MatrixXd &	output
	)

protected

void BALL::QSAR::Kernel::calculateKernelMatrix3	(	Eigen::MatrixXd &	input,
		Eigen::MatrixXd &	output
	)

protected

void BALL::QSAR::Kernel::calculateKernelMatrix3	(	Eigen::MatrixXd &	m1,
		Eigen::MatrixXd &	m2,
		Eigen::MatrixXd &	output
	)

protected

void BALL::QSAR::Kernel::calculateKernelMatrix4	(	Eigen::MatrixXd &	input,
		Eigen::MatrixXd &	output
	)

protected

void BALL::QSAR::Kernel::calculateKernelMatrix4	(	Eigen::MatrixXd &	m1,
		Eigen::MatrixXd &	m2,
		Eigen::MatrixXd &	output
	)

protected

void BALL::QSAR::Kernel::calculateKernelVector	(	Eigen::MatrixXd &	K,
		Eigen::VectorXd &	m1,
		Eigen::MatrixXd &	m2,
		Eigen::RowVectorXd &	output
	)

transforms test data 'input' into the kernel-saves and saves it to matrix 'output'

void BALL::QSAR::Kernel::calculateWeightedKernelMatrix	(	Eigen::MatrixXd &	input,
		Eigen::MatrixXd &	output
	)

protected

calculates pairwise distances between all substances in Eigen::MatrixXd input, weighted by the contribution of every descriptor (as encoded in Kernel.weights), and saves them to Eigen::MatrixXd output.
Distance between two substances a and b is calculated as $\sum_{i=1}^m w_i * (input_{ai}- input_{bi})^2$ , with m=#descriptors

void BALL::QSAR::Kernel::calculateWeightedKernelMatrix	(	Eigen::MatrixXd &	m1,
		Eigen::MatrixXd &	m2,
		Eigen::MatrixXd &	output
	)

protected

calculates pairwise distances between all substances of m1 and m2, weighted by the contribution of every descriptor (as encoded in Kernel.weights), and saves them to Eigen::MatrixXd output.
Distance between two substances a and b is calculated as $\sum_{i=1}^m w_i * (m1_{ai}- m2_{bi})^2$ , with m=#descriptors

void BALL::QSAR::Kernel::gridSearch	(	double	step_width,
		int	steps,
		int	recursions,
		int	k,
		bool	opt = `0`
	)

grid search for the best kernel parameters.
Grid search is done locally around the current kernel parameter value(s).

Parameters

opt	if ==1, Model.optitimizeParameters() is used in each step of grid search, optimizing the parameter of the Model in addition to those of the kernel.
step_width	the size of each step to be made
steps	the number of steps for grid search
recursions	number of recursions of grid search; in each recursion the step width is decreased by factor of 10 and searching is done in 20 steps around the values of the best kernel parameters determined in last recursion

void BALL::QSAR::Kernel::gridSearch	(	double	step_width,
		int	steps,
		bool	first_rec,
		int	k,
		double	par1_start,
		double	par2_start,
		bool	opt
	)

protected

Friends And Related Function Documentation

friend class FeatureSelection

friend

Definition at line 155 of file QSAR/kernel.h.

Member Data Documentation

String BALL::QSAR::Kernel::equation1

Equation for distance function for calculation of kernel matrix.
Distance of two substances a and b is calculated as $dist(a,b)= g(\sum_{i=1}^m f(x1,x2))$ , with m=#descriptors
Use "x1" and "x2" in the String, e.g. "x1*x2"

Definition at line 100 of file QSAR/kernel.h.

String BALL::QSAR::Kernel::equation2

Equation for distance function for calculation of kernel matrix.
Distance of two substances a and b is calculated as $dist(a,b)= g(\sum_{i=1}^m f(x1,x2))$ , with m=#descriptors
g determines what is to be done with the calculated "sum" over all elements (use "sum" in String); e.g. "sum^0.5" => euclidean distance if f=="x1*x2"

Definition at line 105 of file QSAR/kernel.h.

Model* BALL::QSAR::Kernel::model_

protected

pointer to the model which uses this kernel

Definition at line 149 of file QSAR/kernel.h.

double BALL::QSAR::Kernel::par1

parameters for kernel functions set by the user

Definition at line 95 of file QSAR/kernel.h.

double BALL::QSAR::Kernel::par2

Definition at line 95 of file QSAR/kernel.h.

int BALL::QSAR::Kernel::type

specifies which kind of kernel is chosen:
1 = polynomial kernel
2 = radial basis function kernel
3 = sigmoid kernel
4 = individual kernel-function
5 = weighted distance kernel

Definition at line 92 of file QSAR/kernel.h.

Eigen::VectorXd BALL::QSAR::Kernel::weights_

protected

Definition at line 151 of file QSAR/kernel.h.

Public Member Functions

Friends

Accessors

Attributes

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Friends And Related Function Documentation

Member Data Documentation