Weighted sum of kernel functions. More...

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

Inheritance diagram for shark::WeightedSumKernel< InputType >:

Classes
struct	tBase
	structure describing a single m_base kernel More...

Public Types
typedef base_type::BatchInputType	BatchInputType

typedef base_type::ConstInputReference	ConstInputReference

typedef base_type::ConstBatchInputReference	ConstBatchInputReference

Public Types inherited from shark::AbstractKernelFunction< InputTypeT >
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.

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

typedef base_type::ConstInputReference	ConstInputReference
	Const references to InputType.

typedef base_type::ConstBatchInputReference	ConstBatchInputReference
	Const references to BatchInputType.

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

typedef TypedFeatureNotAvailableException< Feature >	FeatureNotAvailableException

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

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

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

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

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

Public Member Functions
	WeightedSumKernel (std::vector< AbstractKernelFunction< InputType > * > const &base)

std::string	name () const
	From INameable: return the class name.

bool	isAdaptive (std::size_t index) const
	Check whether m_base kernel index is adaptive.

void	setAdaptive (std::size_t index, bool b=true)
	Set adaptivity of m_base kernel index.

void	setAdaptiveAll (bool b=true)
	Set adaptivity of all m_base kernels.

double	weight (std::size_t index)
	Get the weight of a kernel.

void	setAdaptiveWeights (bool b)

RealVector	parameterVector () const

boost::shared_ptr< State >	createState () const
	creates the internal state of the kernel

void	setParameterVector (RealVector const &newParameters)

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

double	eval (ConstInputReference x1, ConstInputReference x2) const

void	eval (ConstBatchInputReference batchX1, ConstBatchInputReference batchX2, RealMatrix &result) const

void	eval (ConstBatchInputReference batchX1, ConstBatchInputReference batchX2, RealMatrix &result, State &state) const

void	weightedParameterDerivative (ConstBatchInputReference batchX1, ConstBatchInputReference batchX2, RealMatrix const &coefficients, State const &state, RealVector &gradient) const

void	weightedInputDerivative (ConstBatchInputReference batchX1, ConstBatchInputReference batchX2, RealMatrix const &coefficientsX2, State const &state, BatchInputType &gradient) const

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

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

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

const Features &	features () const

virtual void	updateFeatures ()

bool	hasFirstParameterDerivative () const

bool	hasFirstInputDerivative () const

bool	isNormalized () const

bool	supportsVariableInputSize () const

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

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

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).

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).

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

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.

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!).

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

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

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

virtual	~AbstractMetric ()

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

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

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

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

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

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

	BOOST_SERIALIZATION_SPLIT_MEMBER ()

Protected Member Functions
void	updateNumberOfParameters ()

template<class T >
void	weightedInputDerivativeImpl (ConstBatchInputReference batchX1, ConstBatchInputReference batchX2, RealMatrix const &coefficientsX2, State const &state, BatchInputType &gradient, typename boost::enable_if< boost::is_same< T, RealMatrix > >::type *dummy=0) const

template<class T >
void	weightedInputDerivativeImpl (ConstBatchInputReference batchX1, ConstBatchInputReference batchX2, RealMatrix const &coefficientsX2, State const &state, BatchInputType &gradient, typename boost::disable_if< boost::is_same< T, RealMatrix > >::type *dummy=0) const

Protected Attributes
std::vector< tBase >	m_base
	collection of m_base kernels

double	m_weightsum
	sum of all weights

std::size_t	m_numParameters
	total number of parameters

bool	m_adaptWeights
	whether the weights should be adapted

Protected Attributes inherited from shark::AbstractKernelFunction< InputTypeT >
Features	m_features

Detailed Description

template<class InputType = RealVector>
class shark::WeightedSumKernel< InputType >

Weighted sum of kernel functions.

For a set of positive definite kernels \( k_1, \dots, k_n \) with positive coeffitients \( w_1, \dots, w_n \) the sum

\[ \tilde k(x_1, x_2) := \sum_{i=1}^{n} w_i \cdot k_i(x_1, x_2) \]

is again a positive definite kernel function. Internally, the weights are represented as \( w_i = \exp(\xi_i) \) to allow for unconstrained optimization.

This variant of the weighted sum kernel eleminates one redundant degree of freedom by fixing the first kernel weight to 1.0.

The result of the kernel evaluation is devided by the sum of the kernel weights, so that in total, this amounts to fixing the sum of the of the weights to one.

Definition at line 64 of file WeightedSumKernel.h.

Member Typedef Documentation

◆ BatchInputType

template<class InputType = RealVector>

typedef base_type::BatchInputType shark::WeightedSumKernel< InputType >::BatchInputType

Definition at line 85 of file WeightedSumKernel.h.

◆ ConstBatchInputReference

template<class InputType = RealVector>

typedef base_type::ConstBatchInputReference shark::WeightedSumKernel< InputType >::ConstBatchInputReference

Definition at line 87 of file WeightedSumKernel.h.

◆ ConstInputReference

template<class InputType = RealVector>

typedef base_type::ConstInputReference shark::WeightedSumKernel< InputType >::ConstInputReference

Definition at line 86 of file WeightedSumKernel.h.

Constructor & Destructor Documentation

◆ WeightedSumKernel()

template<class InputType = RealVector>

shark::WeightedSumKernel< InputType >::WeightedSumKernel ( std::vector< AbstractKernelFunction< InputType > * > const & base )

inline

Definition at line 89 of file WeightedSumKernel.h.

References shark::AbstractKernelFunction< InputType >::HAS_FIRST_INPUT_DERIVATIVE, shark::AbstractKernelFunction< InputType >::HAS_FIRST_PARAMETER_DERIVATIVE, shark::AbstractKernelFunction< InputTypeT >::hasFirstInputDerivative(), shark::AbstractKernelFunction< InputTypeT >::hasFirstParameterDerivative(), shark::WeightedSumKernel< InputType >::m_adaptWeights, shark::WeightedSumKernel< InputType >::m_base, shark::AbstractKernelFunction< InputTypeT >::m_features, shark::WeightedSumKernel< InputType >::m_numParameters, shark::WeightedSumKernel< InputType >::m_weightsum, SHARK_ASSERT, and SHARK_RUNTIME_CHECK.

Member Function Documentation

◆ createState()

template<class InputType = RealVector>

boost::shared_ptr< State > shark::WeightedSumKernel< InputType >::createState ( ) const

inlinevirtual

creates the internal state of the kernel

Reimplemented from shark::AbstractKernelFunction< InputTypeT >.

Definition at line 178 of file WeightedSumKernel.h.

References shark::WeightedSumKernel< InputType >::m_base.

◆ eval() [1/3]

template<class InputType = RealVector>

void shark::WeightedSumKernel< InputType >::eval	(	ConstBatchInputReference	batchX1,
		ConstBatchInputReference	batchX2,
		RealMatrix &	result
	)		const

inline

Evaluate the kernel according to the equation: \( k(x, y) = \frac{\sum_i \exp(w_i) k_i(x, y)}{sum_i exp(w_i)} \) for two batches of inputs.

Definition at line 226 of file WeightedSumKernel.h.

References shark::batchSize(), shark::WeightedSumKernel< InputType >::m_base, and shark::WeightedSumKernel< InputType >::m_weightsum.

◆ eval() [2/3]

template<class InputType = RealVector>

void shark::WeightedSumKernel< InputType >::eval	(	ConstBatchInputReference	batchX1,
		ConstBatchInputReference	batchX2,
		RealMatrix &	result,
		State &	state
	)		const

inline

Evaluate the kernel according to the equation: \( k(x, y) = \frac{\sum_i \exp(w_i) k_i(x, y)}{sum_i exp(w_i)} \) for two batches of inputs. (see the documentation of numberOfIntermediateValues for the workings of the intermediates)

Definition at line 244 of file WeightedSumKernel.h.

References shark::batchSize(), shark::WeightedSumKernel< InputType >::m_base, shark::WeightedSumKernel< InputType >::m_weightsum, and shark::State::toState().

◆ eval() [3/3]

template<class InputType = RealVector>

double shark::WeightedSumKernel< InputType >::eval	(	ConstInputReference	x1,
		ConstInputReference	x2
	)		const

inline

Evaluate the weighted sum kernel (according to the following equation:) \( k(x, y) = \frac{\sum_i \exp(w_i) k_i(x, y)}{sum_i exp(w_i)} \)

Definition at line 214 of file WeightedSumKernel.h.

References shark::WeightedSumKernel< InputType >::m_base, and shark::WeightedSumKernel< InputType >::m_weightsum.

Referenced by main().

◆ isAdaptive()

template<class InputType = RealVector>

bool shark::WeightedSumKernel< InputType >::isAdaptive ( std::size_t index ) const

inline

Check whether m_base kernel index is adaptive.

Definition at line 131 of file WeightedSumKernel.h.

References shark::WeightedSumKernel< InputType >::m_base.

Referenced by main(), and shark::WeightedSumKernel< InputType >::weightedParameterDerivative().

◆ name()

template<class InputType = RealVector>

std::string shark::WeightedSumKernel< InputType >::name ( ) const

inlinevirtual

From INameable: return the class name.

Reimplemented from shark::INameable.

Definition at line 127 of file WeightedSumKernel.h.

◆ numberOfParameters()

template<class InputType = RealVector>

std::size_t shark::WeightedSumKernel< InputType >::numberOfParameters ( ) const

inlinevirtual

Return the number of parameters.

Reimplemented from shark::IParameterizable< VectorType >.

Definition at line 208 of file WeightedSumKernel.h.

References shark::WeightedSumKernel< InputType >::m_numParameters.

Referenced by main(), shark::WeightedSumKernel< InputType >::parameterVector(), shark::WeightedSumKernel< InputType >::setParameterVector(), and shark::WeightedSumKernel< InputType >::weightedParameterDerivative().

◆ parameterVector()

template<class InputType = RealVector>

RealVector shark::WeightedSumKernel< InputType >::parameterVector ( ) const

inlinevirtual

return the parameter vector. The first N-1 entries are the (log-encoded) kernel weights, the sub-kernel's parameters are stacked behind each other after that.

Reimplemented from shark::IParameterizable< VectorType >.

Definition at line 158 of file WeightedSumKernel.h.

References shark::WeightedSumKernel< InputType >::m_base, shark::WeightedSumKernel< InputType >::numberOfParameters(), and shark::WeightedSumKernel< InputType >::weight().

Referenced by main().

◆ read()

template<class InputType = RealVector>

void shark::WeightedSumKernel< InputType >::read ( InArchive & archive )

inlinevirtual

From ISerializable, reads a metric from an archive.

Reimplemented from shark::AbstractMetric< InputTypeT >.

Definition at line 319 of file WeightedSumKernel.h.

References shark::WeightedSumKernel< InputType >::m_base, shark::WeightedSumKernel< InputType >::m_numParameters, and shark::WeightedSumKernel< InputType >::m_weightsum.

◆ setAdaptive()

template<class InputType = RealVector>

void shark::WeightedSumKernel< InputType >::setAdaptive	(	std::size_t	index,
		bool	b = `true`
	)

inline

Set adaptivity of m_base kernel index.

Definition at line 135 of file WeightedSumKernel.h.

References shark::WeightedSumKernel< InputType >::m_base, and shark::WeightedSumKernel< InputType >::updateNumberOfParameters().

Referenced by main().

◆ setAdaptiveAll()

template<class InputType = RealVector>

void shark::WeightedSumKernel< InputType >::setAdaptiveAll ( bool b = true )

inline

Set adaptivity of all m_base kernels.

Definition at line 140 of file WeightedSumKernel.h.

References shark::WeightedSumKernel< InputType >::m_base, and shark::WeightedSumKernel< InputType >::updateNumberOfParameters().

Referenced by main().

◆ setAdaptiveWeights()

template<class InputType = RealVector>

void shark::WeightedSumKernel< InputType >::setAdaptiveWeights ( bool b )

inline

Definition at line 152 of file WeightedSumKernel.h.

References shark::WeightedSumKernel< InputType >::m_adaptWeights.

◆ setParameterVector()

template<class InputType = RealVector>

void shark::WeightedSumKernel< InputType >::setParameterVector ( RealVector const & newParameters )

inlinevirtual

set the parameter vector. The first N-1 entries are the (log-encoded) kernel weights, the sub-kernel's parameters are stacked behind each other after that.

Reimplemented from shark::IParameterizable< VectorType >.

Definition at line 188 of file WeightedSumKernel.h.

References shark::WeightedSumKernel< InputType >::m_base, shark::WeightedSumKernel< InputType >::m_weightsum, shark::WeightedSumKernel< InputType >::numberOfParameters(), and SIZE_CHECK.

Referenced by main().

◆ updateNumberOfParameters()

template<class InputType = RealVector>

void shark::WeightedSumKernel< InputType >::updateNumberOfParameters ( )

inlineprotected

Definition at line 347 of file WeightedSumKernel.h.

References shark::WeightedSumKernel< InputType >::m_adaptWeights, shark::WeightedSumKernel< InputType >::m_base, and shark::WeightedSumKernel< InputType >::m_numParameters.

Referenced by shark::WeightedSumKernel< InputType >::setAdaptive(), and shark::WeightedSumKernel< InputType >::setAdaptiveAll().

◆ weight()

template<class InputType = RealVector>

double shark::WeightedSumKernel< InputType >::weight ( std::size_t index )

inline

Get the weight of a kernel.

Definition at line 147 of file WeightedSumKernel.h.

References shark::WeightedSumKernel< InputType >::m_base, and RANGE_CHECK.

Referenced by shark::WeightedSumKernel< InputType >::parameterVector().

◆ weightedInputDerivative()

template<class InputType = RealVector>

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

inline

Input derivative, calculated according to the equation:
\( \frac{\partial k(x, y)}{\partial x} \frac{\sum_i \exp(w_i) \frac{\partial k_i(x, y)}{\partial x}} {\sum_i exp(w_i)} \) and summed up over all of the second batch

Definition at line 307 of file WeightedSumKernel.h.

References shark::batchSize(), and SIZE_CHECK.

◆ weightedInputDerivativeImpl() [1/2]

template<class InputType = RealVector>

template<class T >

void shark::WeightedSumKernel< InputType >::weightedInputDerivativeImpl	(	ConstBatchInputReference	batchX1,
		ConstBatchInputReference	batchX2,
		RealMatrix const &	coefficientsX2,
		State const &	state,
		BatchInputType &	gradient,
		typename boost::disable_if< boost::is_same< T, RealMatrix > >::type *	dummy = `0`
	)		const

inlineprotected

Definition at line 382 of file WeightedSumKernel.h.

References SHARKEXCEPTION.

◆ weightedInputDerivativeImpl() [2/2]

template<class InputType = RealVector>

template<class T >

void shark::WeightedSumKernel< InputType >::weightedInputDerivativeImpl	(	ConstBatchInputReference	batchX1,
		ConstBatchInputReference	batchX2,
		RealMatrix const &	coefficientsX2,
		State const &	state,
		BatchInputType &	gradient,
		typename boost::enable_if< boost::is_same< T, RealMatrix > >::type *	dummy = `0`
	)		const

inlineprotected

Definition at line 359 of file WeightedSumKernel.h.

References shark::WeightedSumKernel< InputType >::m_base, shark::WeightedSumKernel< InputType >::m_weightsum, and shark::State::toState().

◆ weightedParameterDerivative()

template<class InputType = RealVector>

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

inline

Definition at line 262 of file WeightedSumKernel.h.

References shark::WeightedSumKernel< InputType >::isAdaptive(), shark::WeightedSumKernel< InputType >::m_adaptWeights, shark::WeightedSumKernel< InputType >::m_base, shark::WeightedSumKernel< InputType >::m_weightsum, shark::WeightedSumKernel< InputType >::numberOfParameters(), shark::sqr(), and shark::State::toState().

◆ write()

template<class InputType = RealVector>

void shark::WeightedSumKernel< InputType >::write ( OutArchive & archive ) const

inlinevirtual

From ISerializable, writes a metric to an archive.

The default implementation just saves the parameters.

Reimplemented from shark::AbstractMetric< InputTypeT >.

Definition at line 328 of file WeightedSumKernel.h.

References shark::WeightedSumKernel< InputType >::m_base, shark::WeightedSumKernel< InputType >::m_numParameters, and shark::WeightedSumKernel< InputType >::m_weightsum.

Member Data Documentation

◆ m_adaptWeights

template<class InputType = RealVector>

bool shark::WeightedSumKernel< InputType >::m_adaptWeights

protected

whether the weights should be adapted

Definition at line 396 of file WeightedSumKernel.h.

Referenced by shark::WeightedSumKernel< InputType >::setAdaptiveWeights(), shark::WeightedSumKernel< InputType >::updateNumberOfParameters(), shark::WeightedSumKernel< InputType >::weightedParameterDerivative(), and shark::WeightedSumKernel< InputType >::WeightedSumKernel().

◆ m_base

template<class InputType = RealVector>

std::vector<tBase> shark::WeightedSumKernel< InputType >::m_base

protected

collection of m_base kernels

Definition at line 393 of file WeightedSumKernel.h.

◆ m_numParameters

template<class InputType = RealVector>

std::size_t shark::WeightedSumKernel< InputType >::m_numParameters

protected

total number of parameters

Definition at line 395 of file WeightedSumKernel.h.

Referenced by shark::WeightedSumKernel< InputType >::numberOfParameters(), shark::WeightedSumKernel< InputType >::read(), shark::WeightedSumKernel< InputType >::updateNumberOfParameters(), shark::WeightedSumKernel< InputType >::WeightedSumKernel(), and shark::WeightedSumKernel< InputType >::write().

◆ m_weightsum

template<class InputType = RealVector>

double shark::WeightedSumKernel< InputType >::m_weightsum

protected

sum of all weights

Definition at line 394 of file WeightedSumKernel.h.

Referenced by shark::WeightedSumKernel< InputType >::eval(), shark::WeightedSumKernel< InputType >::eval(), shark::WeightedSumKernel< InputType >::eval(), shark::WeightedSumKernel< InputType >::read(), shark::WeightedSumKernel< InputType >::setParameterVector(), shark::WeightedSumKernel< InputType >::weightedInputDerivativeImpl(), shark::WeightedSumKernel< InputType >::weightedParameterDerivative(), shark::WeightedSumKernel< InputType >::WeightedSumKernel(), and shark::WeightedSumKernel< InputType >::write().

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

include/shark/Models/Kernels/WeightedSumKernel.h

Classes

Public Types

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

Member Typedef Documentation

◆ BatchInputType

◆ ConstBatchInputReference

◆ ConstInputReference

Constructor & Destructor Documentation

◆ WeightedSumKernel()

Member Function Documentation

◆ createState()

◆ eval() [1/3]

◆ eval() [2/3]

◆ eval() [3/3]

◆ isAdaptive()

◆ name()

◆ numberOfParameters()

◆ parameterVector()

◆ read()

◆ setAdaptive()

◆ setAdaptiveAll()

◆ setAdaptiveWeights()

◆ setParameterVector()

◆ updateNumberOfParameters()

◆ weight()

◆ weightedInputDerivative()

◆ weightedInputDerivativeImpl() [1/2]

◆ weightedInputDerivativeImpl() [2/2]

◆ weightedParameterDerivative()

◆ write()

Member Data Documentation

◆ m_adaptWeights

◆ m_base

◆ m_numParameters

◆ m_weightsum