Training of Epsilon-SVMs for regression. More...

#include <shark/Algorithms/Trainers/EpsilonSvmTrainer.h>

Inheritance diagram for shark::EpsilonSvmTrainer< InputType, CacheType >:

Public Types
typedef CacheType	QpFloatType

typedef KernelMatrix< InputType, QpFloatType >	KernelMatrixType

typedef BlockMatrix2x2< KernelMatrixType >	BlockMatrixType

typedef CachedMatrix< BlockMatrixType >	CachedBlockMatrixType

typedef PrecomputedMatrix< BlockMatrixType >	PrecomputedBlockMatrixType

typedef AbstractModel< InputType, RealVector >	ModelType

typedef AbstractKernelFunction< InputType >	KernelType

Public Types inherited from shark::AbstractSvmTrainer< InputType, RealVector, KernelExpansion< InputType > >
typedef AbstractKernelFunction< InputType >	KernelType

Public Types inherited from shark::AbstractTrainer< Model, LabelTypeT >
typedef Model	ModelType

typedef ModelType::InputType	InputType

typedef LabelTypeT	LabelType

typedef LabeledData< InputType, LabelType >	DatasetType

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

Public Member Functions
	EpsilonSvmTrainer (KernelType *kernel, double C, double epsilon, bool unconstrained=false)

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

double	epsilon () const

void	setEpsilon (double epsilon)

RealVector	parameterVector () const
	get the hyper-parameter vector More...

void	setParameterVector (RealVector const &newParameters)
	set the vector of hyper-parameters More...

size_t	numberOfParameters () const
	return the number of hyper-parameters More...

void	train (KernelExpansion< InputType > &svm, LabeledData< InputType, RealVector > const &dataset)

Public Member Functions inherited from shark::AbstractSvmTrainer< InputType, RealVector, KernelExpansion< InputType > >
	AbstractSvmTrainer (KernelType *kernel, double C, bool offset, bool unconstrained=false)

	AbstractSvmTrainer (KernelType *kernel, double negativeC, double positiveC, bool offset, bool unconstrained=false)

double	C () const
	Return the value of the regularization parameter C. More...

void	setC (double C)
	Set the value of the regularization parameter C. More...

RealVector const &	regularizationParameters () const

void	setRegularizationParameters (RealVector const &regularizers)
	Set the value of the regularization parameter C. More...

KernelType *	kernel ()

KernelType const *	kernel () const

void	setKernel (KernelType *kernel)

bool	isUnconstrained () const

bool	trainOffset () const

std::size_t	cacheSize () const

void	setCacheSize (std::size_t size)

RealVector	parameterVector () const
	get the hyper-parameter vector More...

void	setParameterVector (RealVector const &newParameters)
	set the vector of hyper-parameters More...

size_t	numberOfParameters () const
	return the number of hyper-parameters More...

Public Member Functions inherited from shark::AbstractTrainer< Model, LabelTypeT >
virtual void	train (ModelType &model, DatasetType const &dataset)=0
	Core of the Trainer interface. More...

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

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

virtual void	read (InArchive &archive)
	Read the component from the supplied archive. More...

virtual void	write (OutArchive &archive) const
	Write the component to the supplied archive. 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 ()

Public Member Functions inherited from shark::QpConfig
	QpConfig (bool precomputedFlag=false, bool sparsifyFlag=true)
	Constructor. More...

QpStoppingCondition &	stoppingCondition ()
	Read/write access to the stopping condition. More...

QpStoppingCondition const &	stoppingCondition () const
	Read access to the stopping condition. More...

QpSolutionProperties &	solutionProperties ()
	Access to the solution properties. More...

bool &	precomputeKernel ()
	Flag for using a precomputed kernel matrix. More...

bool const &	precomputeKernel () const
	Flag for using a precomputed kernel matrix. More...

bool &	sparsify ()
	Flag for sparsifying the model after training. More...

bool const &	sparsify () const
	Flag for sparsifying the model after training. More...

bool &	shrinking ()
	Flag for shrinking in the decomposition solver. More...

bool const &	shrinking () const
	Flag for shrinking in the decomposition solver. More...

bool &	s2do ()
	Flag for S2DO (instead of SMO) More...

bool const &	s2do () const
	Flag for S2DO (instead of SMO) More...

unsigned int &	verbosity ()
	Verbosity level of the solver. More...

unsigned int const &	verbosity () const
	Verbosity level of the solver. More...

unsigned long long const &	accessCount () const
	Number of kernel accesses. More...

void	setMinAccuracy (double a)

void	setMaxIterations (unsigned long long i)

void	setTargetValue (double v)

void	setMaxSeconds (double s)

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

Additional Inherited Members
Protected Attributes inherited from shark::AbstractSvmTrainer< InputType, RealVector, KernelExpansion< InputType > >
KernelType *	m_kernel

RealVector	m_regularizers
	Vector of regularization parameters. More...

bool	m_trainOffset

bool	m_unconstrained
	Is log(C) stored internally as a parameter instead of C? If yes, then we get rid of the constraint C > 0 on the level of the parameter interface. More...

std::size_t	m_cacheSize
	Number of values in the kernel cache. The size of the cache in bytes is the size of one entry (4 for float, 8 for double) times this number. More...

Protected Attributes inherited from shark::QpConfig
QpStoppingCondition	m_stoppingcondition
	conditions for when to stop the QP solver More...

QpSolutionProperties	m_solutionproperties
	properties of the approximate solution found by the solver More...

bool	m_precomputedKernelMatrix
	should the solver use a precomputed kernel matrix? More...

bool	m_sparsify
	should the trainer sparsify the model after training? More...

bool	m_shrinking
	should shrinking be used? More...

bool	m_s2do
	should S2DO be used instead of SMO? More...

unsigned int	m_verbosity
	verbosity level (currently unused) More...

unsigned long long	m_accessCount
	kernel access count More...

Detailed Description

template<class InputType, class CacheType = float>
class shark::EpsilonSvmTrainer< InputType, CacheType >

Training of Epsilon-SVMs for regression.

The Epsilon-SVM is a support vector machine variant for regression problems. Given are data tuples \( (x_i, y_i) \) with x-component denoting input and y-component denoting a real-valued label (see the tutorial on label conventions; the implementation uses RealVector), a kernel function k(x, x'), a regularization constant C > 0, and a loss insensitivity parameter \( \varepsilon \). Let H denote the kernel induced reproducing kernel Hilbert space of k, and let \( \phi \) denote the corresponding feature map. Then the SVM regression function is of the form

\[ (x) = \langle w, \phi(x) \rangle + b \]

with coefficients w and b given by the (primal) optimization problem

\[ \min \frac{1}{2} \|w\|^2 + C \sum_i L(y_i, f(x_i)), \]

where

\[ L(y, f(x)) = \max\{0, |y - f(x)| - \varepsilon \} \]

is the \( \varepsilon \) insensitive absolute loss.

Definition at line 79 of file EpsilonSvmTrainer.h.

Member Typedef Documentation

◆ BlockMatrixType

template<class InputType, class CacheType = float>

typedef BlockMatrix2x2< KernelMatrixType > shark::EpsilonSvmTrainer< InputType, CacheType >::BlockMatrixType

Definition at line 86 of file EpsilonSvmTrainer.h.

◆ CachedBlockMatrixType

template<class InputType, class CacheType = float>

typedef CachedMatrix< BlockMatrixType > shark::EpsilonSvmTrainer< InputType, CacheType >::CachedBlockMatrixType

Definition at line 87 of file EpsilonSvmTrainer.h.

◆ KernelMatrixType

template<class InputType, class CacheType = float>

typedef KernelMatrix< InputType, QpFloatType > shark::EpsilonSvmTrainer< InputType, CacheType >::KernelMatrixType

Definition at line 85 of file EpsilonSvmTrainer.h.

◆ KernelType

template<class InputType, class CacheType = float>

typedef AbstractKernelFunction<InputType> shark::EpsilonSvmTrainer< InputType, CacheType >::KernelType

Definition at line 91 of file EpsilonSvmTrainer.h.

◆ ModelType

template<class InputType, class CacheType = float>

typedef AbstractModel<InputType, RealVector> shark::EpsilonSvmTrainer< InputType, CacheType >::ModelType

Definition at line 90 of file EpsilonSvmTrainer.h.

◆ PrecomputedBlockMatrixType

template<class InputType, class CacheType = float>

typedef PrecomputedMatrix< BlockMatrixType > shark::EpsilonSvmTrainer< InputType, CacheType >::PrecomputedBlockMatrixType

Definition at line 88 of file EpsilonSvmTrainer.h.

◆ QpFloatType

template<class InputType, class CacheType = float>

typedef CacheType shark::EpsilonSvmTrainer< InputType, CacheType >::QpFloatType

Definition at line 83 of file EpsilonSvmTrainer.h.

Constructor & Destructor Documentation

◆ EpsilonSvmTrainer()

template<class InputType, class CacheType = float>

shark::EpsilonSvmTrainer< InputType, CacheType >::EpsilonSvmTrainer	(	KernelType *	kernel,
		double	C,
		double	epsilon,
		bool	unconstrained = `false`
	)

inline

Constructor

Parameters

kernel	kernel function to use for training and prediction
C	regularization parameter - always the 'true' value of C, even when unconstrained is set
epsilon	Loss insensitivity parameter.
unconstrained	when a C-value is given via setParameter, should it be piped through the exp-function before using it in the solver?

Definition at line 99 of file EpsilonSvmTrainer.h.

Member Function Documentation

◆ epsilon()

template<class InputType, class CacheType = float>

double shark::EpsilonSvmTrainer< InputType, CacheType >::epsilon ( ) const

inline

Definition at line 108 of file EpsilonSvmTrainer.h.

Referenced by shark::EpsilonSvmTrainer< InputType, CacheType >::setEpsilon().

◆ name()

template<class InputType, class CacheType = float>

std::string shark::EpsilonSvmTrainer< InputType, CacheType >::name ( ) const

inlinevirtual

From INameable: return the class name.

Reimplemented from shark::INameable.

Definition at line 105 of file EpsilonSvmTrainer.h.

◆ numberOfParameters()

template<class InputType, class CacheType = float>

size_t shark::EpsilonSvmTrainer< InputType, CacheType >::numberOfParameters ( ) const

inlinevirtual

return the number of hyper-parameters

Reimplemented from shark::IParameterizable<>.

Definition at line 128 of file EpsilonSvmTrainer.h.

References shark::AbstractSvmTrainer< InputType, RealVector, KernelExpansion< InputType > >::numberOfParameters().

◆ parameterVector()

template<class InputType, class CacheType = float>

RealVector shark::EpsilonSvmTrainer< InputType, CacheType >::parameterVector ( ) const

inlinevirtual

get the hyper-parameter vector

Reimplemented from shark::IParameterizable<>.

Definition at line 114 of file EpsilonSvmTrainer.h.

References shark::AbstractSvmTrainer< InputType, RealVector, KernelExpansion< InputType > >::m_unconstrained, and shark::AbstractSvmTrainer< InputType, RealVector, KernelExpansion< InputType > >::parameterVector().

◆ setEpsilon()

template<class InputType, class CacheType = float>

void shark::EpsilonSvmTrainer< InputType, CacheType >::setEpsilon ( double epsilon )

inline

Definition at line 110 of file EpsilonSvmTrainer.h.

References shark::EpsilonSvmTrainer< InputType, CacheType >::epsilon().

◆ setParameterVector()

template<class InputType, class CacheType = float>

void shark::EpsilonSvmTrainer< InputType, CacheType >::setParameterVector ( RealVector const & newParameters )

inlinevirtual

set the vector of hyper-parameters

Reimplemented from shark::IParameterizable<>.

Definition at line 120 of file EpsilonSvmTrainer.h.

References shark::AbstractSvmTrainer< InputType, RealVector, KernelExpansion< InputType > >::numberOfParameters(), shark::AbstractSvmTrainer< InputType, RealVector, KernelExpansion< InputType > >::setParameterVector(), and SHARK_ASSERT.

◆ train()

template<class InputType, class CacheType = float>

void shark::EpsilonSvmTrainer< InputType, CacheType >::train	(	KernelExpansion< InputType > &	svm,
		LabeledData< InputType, RealVector > const &	dataset
	)

inline

Definition at line 131 of file EpsilonSvmTrainer.h.

References shark::AbstractSvmTrainer< InputType, RealVector, KernelExpansion< InputType > >::C(), shark::LabeledData< InputT, LabelT >::element(), shark::LabeledData< InputT, LabelT >::inputs(), shark::labelDimension(), shark::AbstractSvmTrainer< InputType, RealVector, KernelExpansion< InputType > >::m_kernel, shark::QpConfig::m_shrinking, shark::QpConfig::precomputeKernel(), shark::KernelExpansion< InputType >::setStructure(), SHARK_RUNTIME_CHECK, shark::QpConfig::solutionProperties(), shark::QpSolver< Problem, SelectionStrategy >::solve(), shark::QpConfig::sparsify(), and shark::QpConfig::stoppingCondition().

Referenced by main().

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

include/shark/Algorithms/Trainers/EpsilonSvmTrainer.h

Public Types

Public Member Functions

Additional Inherited Members

Detailed Description

template<class InputType, class CacheType = float> class shark::EpsilonSvmTrainer< InputType, CacheType >

Member Typedef Documentation

◆ BlockMatrixType

◆ CachedBlockMatrixType

◆ KernelMatrixType

◆ KernelType

◆ ModelType

◆ PrecomputedBlockMatrixType

◆ QpFloatType

Constructor & Destructor Documentation

◆ EpsilonSvmTrainer()

Member Function Documentation

◆ epsilon()

◆ name()

◆ numberOfParameters()

◆ parameterVector()

◆ setEpsilon()

◆ setParameterVector()

◆ train()

template<class InputType, class CacheType = float>
class shark::EpsilonSvmTrainer< InputType, CacheType >