Base class of all Kernel functions. More...

#include <shark/Models/Kernels/AbstractKernelFunction.h>

Inheritance diagram for shark::AbstractKernelFunction< InputTypeT >:

Public Types
enum	Feature { HAS_FIRST_PARAMETER_DERIVATIVE = 1, HAS_FIRST_INPUT_DERIVATIVE = 2, IS_NORMALIZED = 4, SUPPORTS_VARIABLE_INPUT_SIZE = 8 }
	enumerations of kerneland metric features (flags) More...

typedef base_type::InputType	InputType
	Input type of the Kernel. More...

typedef base_type::BatchInputType	BatchInputType
	batch input type of the kernel More...

typedef base_type::ConstInputReference	ConstInputReference
	Const references to InputType. More...

typedef base_type::ConstBatchInputReference	ConstBatchInputReference
	Const references to BatchInputType. More...

typedef TypedFlags< Feature >	Features
	This statement declares the member m_features. See Core/Flags.h for details. More...

typedef TypedFeatureNotAvailableException< Feature >	FeatureNotAvailableException

Public Types inherited from shark::AbstractMetric< InputTypeT >
typedef InputTypeT	InputType
	Input type of the Kernel. More...

typedef Batch< InputTypeT >::type	BatchInputType
	batch input type of the kernel More...

typedef ConstProxyReference< InputType const >::type	ConstInputReference
	Const references to InputType. More...

typedef ConstProxyReference< BatchInputType const >::type	ConstBatchInputReference
	Const references to BatchInputType. More...

Public Types inherited from shark::IParameterizable<>
typedef RealVector	ParameterVectorType

Public Member Functions
	AbstractKernelFunction ()

const Features &	features () const

virtual void	updateFeatures ()

bool	hasFirstParameterDerivative () const

bool	hasFirstInputDerivative () const

bool	isNormalized () const

bool	supportsVariableInputSize () const

virtual boost::shared_ptr< State >	createState () const
	Creates an internal state of the kernel. More...

virtual double	eval (ConstInputReference x1, ConstInputReference x2) const
	Evaluates the kernel function. More...

double	operator() (ConstInputReference x1, ConstInputReference x2) const
	Convenience operator which evaluates the kernel function. More...

virtual void	eval (ConstBatchInputReference batchX1, ConstBatchInputReference batchX2, RealMatrix &result, State &state) const =0
	Evaluates the subset of the KernelGram matrix which is defined by X1(rows) and X2 (columns). More...

virtual void	eval (ConstBatchInputReference batchX1, ConstBatchInputReference batchX2, RealMatrix &result) const
	Evaluates the subset of the KernelGram matrix which is defined by X1(rows) and X2 (columns). More...

RealMatrix	operator() (ConstBatchInputReference batchX1, ConstBatchInputReference batchX2) const
	Evaluates the subset of the KernelGram matrix which is defined by X1(rows) and X2 (columns). More...

virtual void	weightedParameterDerivative (ConstBatchInputReference batchX1, ConstBatchInputReference batchX2, RealMatrix const &coefficients, State const &state, RealVector &gradient) const
	Computes the gradient of the parameters as a weighted sum over the gradient of all elements of the batch. More...

virtual void	weightedInputDerivative (ConstBatchInputReference batchX1, ConstBatchInputReference batchX2, RealMatrix const &coefficientsX2, State const &state, BatchInputType &gradient) const
	Calculates the derivative of the inputs X1 (only x1!). More...

virtual double	featureDistanceSqr (ConstInputReference x1, ConstInputReference x2) const
	Computes the squared distance in the kernel induced feature space. More...

virtual RealMatrix	featureDistanceSqr (ConstBatchInputReference batchX1, ConstBatchInputReference batchX2) const
	Computes the squared distance in the kernel induced feature space. More...

Public Member Functions inherited from shark::AbstractMetric< InputTypeT >
	AbstractMetric ()

virtual	~AbstractMetric ()

virtual void	read (InArchive &archive)
	From ISerializable, reads a metric from an archive. More...

virtual void	write (OutArchive &archive) const
	From ISerializable, writes a metric to an archive. More...

double	featureDistance (ConstInputReference x1, ConstInputReference x2) const
	Computes the distance in the kernel induced feature space. More...

Public Member Functions inherited from shark::INameable
virtual	~INameable ()

virtual std::string	name () const
	returns the name of the object More...

Public Member Functions inherited from shark::IParameterizable<>
virtual	~IParameterizable ()

virtual ParameterVectorType	parameterVector () const
	Return the parameter vector. More...

virtual void	setParameterVector (ParameterVectorType const &newParameters)
	Set the parameter vector. More...

virtual std::size_t	numberOfParameters () const
	Return the number of parameters. More...

Public Member Functions inherited from shark::ISerializable
virtual	~ISerializable ()
	Virtual d'tor. More...

void	load (InArchive &archive, unsigned int version)
	Versioned loading of components, calls read(...). More...

void	save (OutArchive &archive, unsigned int version) const
	Versioned storing of components, calls write(...). More...

	BOOST_SERIALIZATION_SPLIT_MEMBER ()

Protected Attributes
Features	m_features

Detailed Description

template<class InputTypeT>
class shark::AbstractKernelFunction< InputTypeT >

Base class of all Kernel functions.

: A (Mercer) kernel is a symmetric positive definite function of two parameters. It is (currently) used in two contexts in Shark, namely for kernel methods such as support vector machines (SVMs), and for radial basis function networks.

: In Shark a kernel function class represents a parametric family of such kernel functions: The AbstractKernelFunction interface inherits the IParameterizable interface.

Definition at line 65 of file AbstractKernelFunction.h.

Member Typedef Documentation

◆ BatchInputType

template<class InputTypeT>

typedef base_type::BatchInputType shark::AbstractKernelFunction< InputTypeT >::BatchInputType

batch input type of the kernel

Definition at line 74 of file AbstractKernelFunction.h.

◆ ConstBatchInputReference

template<class InputTypeT>

typedef base_type::ConstBatchInputReference shark::AbstractKernelFunction< InputTypeT >::ConstBatchInputReference

Const references to BatchInputType.

Definition at line 78 of file AbstractKernelFunction.h.

◆ ConstInputReference

template<class InputTypeT>

typedef base_type::ConstInputReference shark::AbstractKernelFunction< InputTypeT >::ConstInputReference

Const references to InputType.

Definition at line 76 of file AbstractKernelFunction.h.

◆ FeatureNotAvailableException

template<class InputTypeT>

typedef TypedFeatureNotAvailableException<Feature> shark::AbstractKernelFunction< InputTypeT >::FeatureNotAvailableException

Definition at line 91 of file AbstractKernelFunction.h.

◆ Features

template<class InputTypeT>

typedef TypedFlags<Feature> shark::AbstractKernelFunction< InputTypeT >::Features

This statement declares the member m_features. See Core/Flags.h for details.

Definition at line 91 of file AbstractKernelFunction.h.

◆ InputType

template<class InputTypeT>

typedef base_type::InputType shark::AbstractKernelFunction< InputTypeT >::InputType

Input type of the Kernel.

Definition at line 72 of file AbstractKernelFunction.h.

Member Enumeration Documentation

◆ Feature

template<class InputTypeT>

enum shark::AbstractKernelFunction::Feature

enumerations of kerneland metric features (flags)

Enumerator
HAS_FIRST_PARAMETER_DERIVATIVE	is the kernel differentiable w.r.t. its parameters?
HAS_FIRST_INPUT_DERIVATIVE	is the kernel differentiable w.r.t. its inputs?
IS_NORMALIZED	does k(x, x) = 1 hold for all inputs x?
SUPPORTS_VARIABLE_INPUT_SIZE	Input arguments must have same size, but not the same size in different calls to eval.

Definition at line 83 of file AbstractKernelFunction.h.

Constructor & Destructor Documentation

◆ AbstractKernelFunction()

template<class InputTypeT>

shark::AbstractKernelFunction< InputTypeT >::AbstractKernelFunction ( )

inline

Definition at line 80 of file AbstractKernelFunction.h.

Member Function Documentation

◆ createState()

template<class InputTypeT>

virtual boost::shared_ptr<State> shark::AbstractKernelFunction< InputTypeT >::createState ( ) const

inlinevirtual

Creates an internal state of the kernel.

The state is needed when the derivatives are to be calculated. Eval can store a state which is then reused to speed up the calculations of the derivatives. This also allows eval to be evaluated in parallel!

Reimplemented in shark::ModelKernel< InputType >, shark::WeightedSumKernel< InputType >, shark::PolynomialKernel< InputType >, shark::GaussianRbfKernel< InputType >, shark::ARDKernelUnconstrained< InputType >, shark::NormalizedKernel< InputType >, shark::DiscreteKernel, shark::MonomialKernel< InputType >, shark::PointSetKernel< InputType >, shark::ScaledKernel< InputType >, and shark::LinearKernel< InputType >.

Definition at line 112 of file AbstractKernelFunction.h.

Referenced by shark::calculateKernelMatrixParameterDerivative(), shark::ScaledKernel< InputType >::createState(), shark::AbstractKernelFunction< MultiTaskSample< InputTypeT > >::eval(), shark::KernelTargetAlignment< InputType, LabelType >::evalDerivative(), shark::CSvmDerivative< InputType, CacheType >::modelCSvmParameterDerivative(), and shark::CSvmDerivative< InputType, CacheType >::write().

◆ eval() [1/3]

template<class InputTypeT>

virtual double shark::AbstractKernelFunction< InputTypeT >::eval	(	ConstInputReference	x1,
		ConstInputReference	x2
	)		const

inlinevirtual

Evaluates the kernel function.

Definition at line 122 of file AbstractKernelFunction.h.

Referenced by shark::calculateKernelMatrixParameterDerivative(), shark::KernelMatrix< InputType, CacheType >::entry(), shark::ScaledKernel< InputType >::eval(), shark::PointSetKernel< InputType >::eval(), shark::NormalizedKernel< InputType >::eval(), shark::AbstractKernelFunction< MultiTaskSample< InputTypeT > >::eval(), shark::KernelTargetAlignment< InputType, LabelType >::evalDerivative(), shark::evalSkipMissingFeatures(), shark::AbstractKernelFunction< MultiTaskSample< InputTypeT > >::featureDistanceSqr(), shark::KHCTree< Container, CuttingAccuracy >::funct(), shark::AbstractKernelFunction< MultiTaskSample< InputTypeT > >::operator()(), shark::MergeBudgetMaintenanceStrategy< RealVector >::reduceBudget(), shark::KernelMatrix< InputType, CacheType >::row(), and shark::CSvmDerivative< InputType, CacheType >::write().

◆ eval() [2/3]

template<class InputTypeT>

virtual void shark::AbstractKernelFunction< InputTypeT >::eval	(	ConstBatchInputReference	batchX1,
		ConstBatchInputReference	batchX2,
		RealMatrix &	result,
		State &	state
	)		const

pure virtual

Evaluates the subset of the KernelGram matrix which is defined by X1(rows) and X2 (columns).

The result matrix is filled in with the values result(i,j) = kernel(x1[i], x2[j]); The State object is filled in with data used in subsequent derivative computations.

Implemented in shark::WeightedSumKernel< InputType >, shark::ModelKernel< InputType >, shark::PolynomialKernel< InputType >, shark::ARDKernelUnconstrained< InputType >, shark::ProductKernel< InputType >, shark::ProductKernel< MultiTaskSample< InputTypeT > >, shark::GaussianRbfKernel< InputType >, shark::DiscreteKernel, shark::MonomialKernel< InputType >, shark::ScaledKernel< InputType >, and shark::LinearKernel< InputType >.

◆ eval() [3/3]

template<class InputTypeT>

virtual void shark::AbstractKernelFunction< InputTypeT >::eval	(	ConstBatchInputReference	batchX1,
		ConstBatchInputReference	batchX2,
		RealMatrix &	result
	)		const

inlinevirtual

Evaluates the subset of the KernelGram matrix which is defined by X1(rows) and X2 (columns).

The result matrix is filled in with the values result(i,j) = kernel(x1[i], x2[j]);

Reimplemented in shark::ModelKernel< InputType >, shark::WeightedSumKernel< InputType >, shark::GaussianRbfKernel< InputType >, shark::PolynomialKernel< InputType >, shark::ProductKernel< InputType >, shark::ProductKernel< MultiTaskSample< InputTypeT > >, shark::ARDKernelUnconstrained< InputType >, shark::DiscreteKernel, shark::ScaledKernel< InputType >, shark::MonomialKernel< InputType >, and shark::LinearKernel< InputType >.

Definition at line 148 of file AbstractKernelFunction.h.

◆ featureDistanceSqr() [1/2]

template<class InputTypeT>

virtual double shark::AbstractKernelFunction< InputTypeT >::featureDistanceSqr	(	ConstInputReference	x1,
		ConstInputReference	x2
	)		const

inlinevirtual

Computes the squared distance in the kernel induced feature space.

Implements shark::AbstractMetric< InputTypeT >.

Reimplemented in shark::LinearKernel< InputType >.

Definition at line 196 of file AbstractKernelFunction.h.

Referenced by shark::KHCTree< Container, CuttingAccuracy >::calculateNormal().

◆ featureDistanceSqr() [2/2]

template<class InputTypeT>

virtual RealMatrix shark::AbstractKernelFunction< InputTypeT >::featureDistanceSqr	(	ConstBatchInputReference	x1,
		ConstBatchInputReference	x2
	)		const

inlinevirtual

Computes the squared distance in the kernel induced feature space.

Implements shark::AbstractMetric< InputTypeT >.

Reimplemented in shark::LinearKernel< InputType >.

Definition at line 208 of file AbstractKernelFunction.h.

◆ features()

template<class InputTypeT>

const Features& shark::AbstractKernelFunction< InputTypeT >::features ( ) const

inline

Definition at line 91 of file AbstractKernelFunction.h.

◆ hasFirstInputDerivative()

template<class InputTypeT>

bool shark::AbstractKernelFunction< InputTypeT >::hasFirstInputDerivative ( ) const

inline

Definition at line 96 of file AbstractKernelFunction.h.

Referenced by shark::AbstractKernelFunction< MultiTaskSample< InputTypeT > >::createState(), shark::NormalizedKernel< InputType >::NormalizedKernel(), and shark::ScaledKernel< InputType >::ScaledKernel().

◆ hasFirstParameterDerivative()

template<class InputTypeT>

bool shark::AbstractKernelFunction< InputTypeT >::hasFirstParameterDerivative ( ) const

inline

Definition at line 93 of file AbstractKernelFunction.h.

Referenced by shark::AbstractKernelFunction< MultiTaskSample< InputTypeT > >::createState(), shark::NegativeGaussianProcessEvidence< InputType, OutputType, LabelType >::NegativeGaussianProcessEvidence(), shark::NormalizedKernel< InputType >::NormalizedKernel(), shark::PointSetKernel< InputType >::PointSetKernel(), shark::RadiusMarginQuotient< InputType, CacheType >::RadiusMarginQuotient(), shark::ScaledKernel< InputType >::ScaledKernel(), and shark::SvmLogisticInterpretation< InputType >::SvmLogisticInterpretation().

◆ isNormalized()

template<class InputTypeT>

bool shark::AbstractKernelFunction< InputTypeT >::isNormalized ( ) const

inline

Definition at line 99 of file AbstractKernelFunction.h.

Referenced by shark::ProductKernel< MultiTaskSample< InputTypeT > >::addKernel(), and shark::AbstractKernelFunction< MultiTaskSample< InputTypeT > >::featureDistanceSqr().

◆ operator()() [1/2]

template<class InputTypeT>

double shark::AbstractKernelFunction< InputTypeT >::operator()	(	ConstInputReference	x1,
		ConstInputReference	x2
	)		const

inline

Convenience operator which evaluates the kernel function.

Definition at line 133 of file AbstractKernelFunction.h.

◆ operator()() [2/2]

template<class InputTypeT>

RealMatrix shark::AbstractKernelFunction< InputTypeT >::operator()	(	ConstBatchInputReference	batchX1,
		ConstBatchInputReference	batchX2
	)		const

inline

Evaluates the subset of the KernelGram matrix which is defined by X1(rows) and X2 (columns).

Convenience operator. The result matrix is filled in with the values result(i,j) = kernel(x1[i], x2[j]);

Definition at line 157 of file AbstractKernelFunction.h.

◆ supportsVariableInputSize()

template<class InputTypeT>

bool shark::AbstractKernelFunction< InputTypeT >::supportsVariableInputSize ( ) const

inline

Definition at line 102 of file AbstractKernelFunction.h.

Referenced by shark::evalSkipMissingFeatures().

◆ updateFeatures()

template<class InputTypeT>

virtual void shark::AbstractKernelFunction< InputTypeT >::updateFeatures ( )

inlinevirtual

Definition at line 91 of file AbstractKernelFunction.h.

◆ weightedInputDerivative()

template<class InputTypeT>

virtual void shark::AbstractKernelFunction< InputTypeT >::weightedInputDerivative	(	ConstBatchInputReference	batchX1,
		ConstBatchInputReference	batchX2,
		RealMatrix const &	coefficientsX2,
		State const &	state,
		BatchInputType &	gradient
	)		const

inlinevirtual

Calculates the derivative of the inputs X1 (only x1!).

The i-th row of the resulting matrix is a weighted sum of the form: c[i,0] * k'(x1[i], x2[0]) + c[i,1] * k'(x1[i], x2[1]) + ... + c[i,n] * k'(x1[i], x2[n]).

The default implementation throws a "not implemented" exception.

Reimplemented in shark::WeightedSumKernel< InputType >, shark::PolynomialKernel< InputType >, shark::ARDKernelUnconstrained< InputType >, shark::GaussianRbfKernel< InputType >, shark::MonomialKernel< InputType >, shark::ScaledKernel< InputType >, and shark::LinearKernel< InputType >.

Definition at line 182 of file AbstractKernelFunction.h.

Referenced by shark::ScaledKernel< InputType >::weightedInputDerivative().

◆ weightedParameterDerivative()

template<class InputTypeT>

virtual void shark::AbstractKernelFunction< InputTypeT >::weightedParameterDerivative	(	ConstBatchInputReference	batchX1,
		ConstBatchInputReference	batchX2,
		RealMatrix const &	coefficients,
		State const &	state,
		RealVector &	gradient
	)		const

inlinevirtual

Computes the gradient of the parameters as a weighted sum over the gradient of all elements of the batch.

The default implementation throws a "not implemented" exception.

Reimplemented in shark::WeightedSumKernel< InputType >, shark::ModelKernel< InputType >, shark::PolynomialKernel< InputType >, shark::ARDKernelUnconstrained< InputType >, shark::GaussianRbfKernel< InputType >, shark::MonomialKernel< InputType >, shark::ScaledKernel< InputType >, and shark::LinearKernel< InputType >.

Definition at line 166 of file AbstractKernelFunction.h.

Referenced by shark::calculateKernelMatrixParameterDerivative(), shark::KernelTargetAlignment< InputType, LabelType >::evalDerivative(), shark::ScaledKernel< InputType >::weightedParameterDerivative(), shark::PointSetKernel< InputType >::weightedParameterDerivative(), and shark::CSvmDerivative< InputType, CacheType >::write().

Member Data Documentation

◆ m_features

template<class InputTypeT>

Features shark::AbstractKernelFunction< InputTypeT >::m_features

protected

Definition at line 91 of file AbstractKernelFunction.h.

Referenced by shark::AbstractKernelFunction< MultiTaskSample< InputTypeT > >::hasFirstInputDerivative(), shark::AbstractKernelFunction< MultiTaskSample< InputTypeT > >::hasFirstParameterDerivative(), shark::AbstractKernelFunction< MultiTaskSample< InputTypeT > >::isNormalized(), and shark::AbstractKernelFunction< MultiTaskSample< InputTypeT > >::supportsVariableInputSize().

The documentation for this class was generated from the following file:

include/shark/Models/Kernels/AbstractKernelFunction.h

Public Types

Public Member Functions

Protected Attributes

Detailed Description

template<class InputTypeT> class shark::AbstractKernelFunction< InputTypeT >

Member Typedef Documentation

◆ BatchInputType

◆ ConstBatchInputReference

◆ ConstInputReference

◆ FeatureNotAvailableException

◆ Features

◆ InputType

Member Enumeration Documentation

◆ Feature

Constructor & Destructor Documentation

◆ AbstractKernelFunction()

Member Function Documentation

◆ createState()

◆ eval() [1/3]

◆ eval() [2/3]

◆ eval() [3/3]

◆ featureDistanceSqr() [1/2]

◆ featureDistanceSqr() [2/2]

◆ features()

◆ hasFirstInputDerivative()

◆ hasFirstParameterDerivative()

◆ isNormalized()

◆ operator()() [1/2]

◆ operator()() [2/2]

◆ supportsVariableInputSize()

◆ updateFeatures()

◆ weightedInputDerivative()

◆ weightedParameterDerivative()

Member Data Documentation

◆ m_features

template<class InputTypeT>
class shark::AbstractKernelFunction< InputTypeT >