Super class of all linear SVM trainers. More...

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

Inheritance diagram for shark::AbstractLinearSvmTrainer< InputType >:

Public Types
typedef LinearClassifier< InputType >	ModelType

Public Types inherited from shark::AbstractTrainer< LinearClassifier< InputType >, unsigned int >
typedef LinearClassifier< InputType >	ModelType

typedef ModelType::InputType	InputType

typedef unsigned int	LabelType

typedef LabeledData< InputType, LabelType >	DatasetType

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

Public Member Functions
	AbstractLinearSvmTrainer (double C, bool offset, bool unconstrained)

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...

bool	isUnconstrained () const
	Is the regularization parameter provided in logarithmic (unconstrained) form as a parameter? More...

bool	trainOffset () const

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< LinearClassifier< InputType >, unsigned int >
virtual void	train (ModelType &model, DatasetType const &dataset)=0
	Core of the Trainer interface. 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::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 ()

Protected Attributes
double	m_C
	Regularization parameter. The exact meaning depends on the sub-class, but the value is always positive, and higher implies a less regular solution. More...

bool	m_trainOffset
	Is the SVM trained with or without bias? More...

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...

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 shark::AbstractLinearSvmTrainer< InputType >

Super class of all linear SVM trainers.

: This class is analogous to the AbstractSvmTrainer class, but for training of linear SVMs. It represents the regularization parameter of the SVM. The class also manages meta-information of the training process, like the stopping criterion and information on the actual solution.

Definition at line 320 of file AbstractSvmTrainer.h.

Member Typedef Documentation

◆ ModelType

template<class InputType >

typedef LinearClassifier<InputType> shark::AbstractLinearSvmTrainer< InputType >::ModelType

Definition at line 327 of file AbstractSvmTrainer.h.

Constructor & Destructor Documentation

◆ AbstractLinearSvmTrainer()

template<class InputType >

shark::AbstractLinearSvmTrainer< InputType >::AbstractLinearSvmTrainer	(	double	C,
		bool	offset,
		bool	unconstrained
	)

inline

Constructor

Parameters

C	regularization parameter - always the 'true' value of C, even when unconstrained is set
offset	train svm with offset - this is not supported for all SVM solvers.
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 333 of file AbstractSvmTrainer.h.

References SHARK_RUNTIME_CHECK.

Member Function Documentation

◆ C()

template<class InputType >

double shark::AbstractLinearSvmTrainer< InputType >::C ( ) const

inline

Return the value of the regularization parameter C.

Definition at line 340 of file AbstractSvmTrainer.h.

◆ isUnconstrained()

template<class InputType >

bool shark::AbstractLinearSvmTrainer< InputType >::isUnconstrained ( ) const

inline

Is the regularization parameter provided in logarithmic (unconstrained) form as a parameter?

Definition at line 350 of file AbstractSvmTrainer.h.

◆ numberOfParameters()

template<class InputType >

size_t shark::AbstractLinearSvmTrainer< InputType >::numberOfParameters ( ) const

inlinevirtual

Return the number of hyper-parameters.

Reimplemented from shark::IParameterizable<>.

Definition at line 372 of file AbstractSvmTrainer.h.

References shark::QpConfig::m_solutionproperties, shark::QpConfig::m_stoppingcondition, and shark::QpConfig::m_verbosity.

◆ parameterVector()

template<class InputType >

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

inlinevirtual

Get the hyper-parameter vector.

Reimplemented from shark::IParameterizable<>.

Definition at line 357 of file AbstractSvmTrainer.h.

◆ setC()

template<class InputType >

void shark::AbstractLinearSvmTrainer< InputType >::setC ( double C )

inline

Set the value of the regularization parameter C.

Definition at line 344 of file AbstractSvmTrainer.h.

References SHARK_RUNTIME_CHECK.

◆ setParameterVector()

template<class InputType >

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

inlinevirtual

Set the vector of hyper-parameters.

Reimplemented from shark::IParameterizable<>.

Definition at line 365 of file AbstractSvmTrainer.h.

References SHARK_ASSERT.

◆ trainOffset()

template<class InputType >

bool shark::AbstractLinearSvmTrainer< InputType >::trainOffset ( ) const

inline

Definition at line 353 of file AbstractSvmTrainer.h.

Member Data Documentation

◆ m_C

template<class InputType >

double shark::AbstractLinearSvmTrainer< InputType >::m_C

protected

Regularization parameter. The exact meaning depends on the sub-class, but the value is always positive, and higher implies a less regular solution.

Definition at line 380 of file AbstractSvmTrainer.h.

◆ m_trainOffset

template<class InputType >

bool shark::AbstractLinearSvmTrainer< InputType >::m_trainOffset

protected

Is the SVM trained with or without bias?

Definition at line 381 of file AbstractSvmTrainer.h.

◆ m_unconstrained

template<class InputType >

bool shark::AbstractLinearSvmTrainer< InputType >::m_unconstrained

protected

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.

Definition at line 382 of file AbstractSvmTrainer.h.

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

include/shark/Algorithms/Trainers/AbstractSvmTrainer.h

Public Types

Public Member Functions

Protected Attributes

Detailed Description

template<class InputType> class shark::AbstractLinearSvmTrainer< InputType >

Member Typedef Documentation

◆ ModelType

Constructor & Destructor Documentation

◆ AbstractLinearSvmTrainer()

Member Function Documentation

◆ C()

◆ isUnconstrained()

◆ numberOfParameters()

◆ parameterVector()

◆ setC()

◆ setParameterVector()

◆ trainOffset()

Member Data Documentation

◆ m_C

◆ m_trainOffset

◆ m_unconstrained

template<class InputType>
class shark::AbstractLinearSvmTrainer< InputType >