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

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

Public Types
typedef CacheType	QpFloatType

typedef RegularizedKernelMatrix< InputType, QpFloatType >	KernelMatrixType

typedef CachedMatrix< KernelMatrixType >	CachedMatrixType

typedef PrecomputedMatrix< KernelMatrixType >	PrecomputedMatrixType

typedef AbstractModel< InputType, RealVector >	ModelType

typedef AbstractKernelFunction< InputType >	KernelType

Public Types inherited from shark::AbstractSvmTrainer< InputType, unsigned int >
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< VectorType >
typedef VectorType	ParameterVectorType

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

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

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

void	train (KernelClassifier< InputType > &svm, LabeledData< InputType, unsigned int > const &dataset)
	Train the C-SVM.

Public Member Functions inherited from shark::AbstractSvmTrainer< InputType, unsigned int >
	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.

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

RealVector const &	regularizationParameters () const

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

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

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

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

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

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

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

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

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

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 ()

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

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

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

QpSolutionProperties &	solutionProperties ()
	Access to the solution properties.

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

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

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

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

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

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

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

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

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

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

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

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< VectorType >
virtual	~IParameterizable ()

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

RealVector	m_regularizers
	Vector of regularization parameters.

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.

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.

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

QpSolutionProperties	m_solutionproperties
	properties of the approximate solution found by the solver

bool	m_precomputedKernelMatrix
	should the solver use a precomputed kernel matrix?

bool	m_sparsify
	should the trainer sparsify the model after training?

bool	m_shrinking
	should shrinking be used?

bool	m_s2do
	should S2DO be used instead of SMO?

unsigned int	m_verbosity
	verbosity level (currently unused)

unsigned long long	m_accessCount
	kernel access count

Detailed Description

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

Definition at line 936 of file CSvmTrainer.h.

Member Typedef Documentation

◆ CachedMatrixType

template<class InputType , class CacheType = float>

typedef CachedMatrix< KernelMatrixType > shark::SquaredHingeCSvmTrainer< InputType, CacheType >::CachedMatrixType

Definition at line 942 of file CSvmTrainer.h.

◆ KernelMatrixType

template<class InputType , class CacheType = float>

typedef RegularizedKernelMatrix<InputType, QpFloatType> shark::SquaredHingeCSvmTrainer< InputType, CacheType >::KernelMatrixType

Definition at line 941 of file CSvmTrainer.h.

◆ KernelType

template<class InputType , class CacheType = float>

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

Definition at line 946 of file CSvmTrainer.h.

◆ ModelType

template<class InputType , class CacheType = float>

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

Definition at line 945 of file CSvmTrainer.h.

◆ PrecomputedMatrixType

template<class InputType , class CacheType = float>

typedef PrecomputedMatrix< KernelMatrixType > shark::SquaredHingeCSvmTrainer< InputType, CacheType >::PrecomputedMatrixType

Definition at line 943 of file CSvmTrainer.h.

◆ QpFloatType

template<class InputType , class CacheType = float>

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

Definition at line 939 of file CSvmTrainer.h.

Constructor & Destructor Documentation

◆ SquaredHingeCSvmTrainer() [1/2]

template<class InputType , class CacheType = float>

shark::SquaredHingeCSvmTrainer< InputType, CacheType >::SquaredHingeCSvmTrainer	(	KernelType *	kernel,
		double	C,
		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
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 953 of file CSvmTrainer.h.

◆ SquaredHingeCSvmTrainer() [2/2]

template<class InputType , class CacheType = float>

shark::SquaredHingeCSvmTrainer< InputType, CacheType >::SquaredHingeCSvmTrainer	(	KernelType *	kernel,
		double	negativeC,
		double	positiveC,
		bool	unconstrained = `false`
	)

inline

Constructor

Parameters

kernel	kernel function to use for training and prediction
negativeC	regularization parameter of the negative class (label 0)
positiveC	regularization parameter of the positive class (label 1)
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 962 of file CSvmTrainer.h.

Member Function Documentation

◆ name()

template<class InputType , class CacheType = float>

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

inlinevirtual

From INameable: return the class name.

Reimplemented from shark::INameable.

Definition at line 967 of file CSvmTrainer.h.

◆ train()

template<class InputType , class CacheType = float>

void shark::SquaredHingeCSvmTrainer< InputType, CacheType >::train	(	KernelClassifier< InputType > &	svm,
		LabeledData< InputType, unsigned int > const &	dataset
	)

inline

Train the C-SVM.

Definition at line 971 of file CSvmTrainer.h.

References shark::Classifier< Model >::decisionFunction(), shark::LabeledData< InputT, LabelT >::element(), shark::RegularizedKernelMatrix< InputType, CacheType >::getAccessCount(), shark::LabeledData< InputT, LabelT >::inputs(), shark::QpConfig::m_accessCount, shark::AbstractSvmTrainer< InputType, unsigned int >::m_kernel, shark::AbstractSvmTrainer< InputType, unsigned int >::m_regularizers, shark::LabeledData< InputT, LabelT >::numberOfElements(), shark::QpConfig::precomputeKernel(), and shark::QpConfig::sparsify().

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

include/shark/Algorithms/Trainers/CSvmTrainer.h

Public Types

Public Member Functions

Additional Inherited Members

Detailed Description

Member Typedef Documentation

◆ CachedMatrixType

◆ KernelMatrixType

◆ KernelType

◆ ModelType

◆ PrecomputedMatrixType

◆ QpFloatType

Constructor & Destructor Documentation

◆ SquaredHingeCSvmTrainer() [1/2]

◆ SquaredHingeCSvmTrainer() [2/2]

Member Function Documentation

◆ name()

◆ train()