Training of one-class SVMs. More...

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

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

Public Types
typedef CacheType	QpFloatType

typedef AbstractModel< InputType, RealVector >	ModelType

typedef AbstractKernelFunction< InputType >	KernelType

typedef KernelMatrix< InputType, QpFloatType >	KernelMatrixType

typedef CachedMatrix< KernelMatrixType >	CachedMatrixType

typedef PrecomputedMatrix< KernelMatrixType >	PrecomputedMatrixType

Public Types inherited from shark::AbstractUnsupervisedTrainer< KernelExpansion< InputType > >
typedef KernelExpansion< InputType >	ModelType

typedef KernelExpansion< InputType > ::InputType	InputType

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

Public Member Functions
	OneClassSvmTrainer (KernelType *kernel, double nu)

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

double	nu () const

void	setNu (double nu)

KernelType *	kernel ()

const KernelType *	kernel () const

void	setKernel (KernelType *kernel)

double	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...

void	train (KernelExpansion< InputType > &svm, UnlabeledData< InputType > const &inputset)

Public Member Functions inherited from shark::AbstractUnsupervisedTrainer< KernelExpansion< InputType > >
virtual void	train (ModelType &model, const UnlabeledData< InputType > &inputset)=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 ()

Protected Member Functions
template<class MatrixType >
void	trainSVM (KernelExpansion< InputType > &svm, UnlabeledData< InputType > const &inputset)

Protected Attributes
KernelType *	m_kernel

double	m_nu

std::size_t	m_cacheSize

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::OneClassSvmTrainer< InputType, CacheType >

Training of one-class SVMs.

The one-class support vector machine is an unsupervised method for learning the high probability region of a distribution. Given are data points \( x_i, i \in \{1, \dots, m\} \), a kernel function k(x, x') and a regularization constant C > 0. Let H denote the kernel induced reproducing kernel Hilbert space of k, and let \( \phi \) denote the corresponding feature map. Then an estimate of a high probability region of the distribution generating the sample points is described by the set where the following function takes positive values:

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

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

\[ \min \frac{1}{2} \|w\|^2 + \frac{1}{\nu m} \sum_{i=1}^m \xi_i - \rho \]

\[ \text{s.t. } \langle w, \phi(x_i) \rangle + b \geq \rho - \xi_i; \xi_i \geq 0 \]

\( 0 \leq \nu, \rho \leq 1 \) are parameters of the method for controlling the smoothness of the solution and the amount of probability mass covered.

For more details refer to the paper:

Estimating the support of a high-dimensional distribution. B. Schölkopf, J. C. Platt, J. Shawe-Taylor, A. Smola, and R. C. Williamson, 1999.

Definition at line 84 of file OneClassSvmTrainer.h.

Member Typedef Documentation

◆ CachedMatrixType

template<class InputType, class CacheType = float>

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

Definition at line 94 of file OneClassSvmTrainer.h.

◆ KernelMatrixType

template<class InputType, class CacheType = float>

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

Definition at line 93 of file OneClassSvmTrainer.h.

◆ KernelType

template<class InputType, class CacheType = float>

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

Definition at line 90 of file OneClassSvmTrainer.h.

◆ ModelType

template<class InputType, class CacheType = float>

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

Definition at line 89 of file OneClassSvmTrainer.h.

◆ PrecomputedMatrixType

template<class InputType, class CacheType = float>

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

Definition at line 95 of file OneClassSvmTrainer.h.

◆ QpFloatType

template<class InputType, class CacheType = float>

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

Definition at line 88 of file OneClassSvmTrainer.h.

Constructor & Destructor Documentation

◆ OneClassSvmTrainer()

template<class InputType, class CacheType = float>

shark::OneClassSvmTrainer< InputType, CacheType >::OneClassSvmTrainer	(	KernelType *	kernel,
		double	nu
	)

inline

Definition at line 97 of file OneClassSvmTrainer.h.

Member Function Documentation

◆ CacheSize()

template<class InputType, class CacheType = float>

double shark::OneClassSvmTrainer< InputType, CacheType >::CacheSize ( ) const

inline

Definition at line 118 of file OneClassSvmTrainer.h.

References shark::OneClassSvmTrainer< InputType, CacheType >::m_cacheSize.

◆ kernel() [1/2]

template<class InputType, class CacheType = float>

KernelType* shark::OneClassSvmTrainer< InputType, CacheType >::kernel ( )

inline

Definition at line 111 of file OneClassSvmTrainer.h.

References shark::OneClassSvmTrainer< InputType, CacheType >::m_kernel.

Referenced by shark::OneClassSvmTrainer< InputType, CacheType >::setKernel().

◆ kernel() [2/2]

template<class InputType, class CacheType = float>

const KernelType* shark::OneClassSvmTrainer< InputType, CacheType >::kernel ( ) const

inline

Definition at line 113 of file OneClassSvmTrainer.h.

References shark::OneClassSvmTrainer< InputType, CacheType >::m_kernel.

◆ name()

template<class InputType, class CacheType = float>

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

inlinevirtual

From INameable: return the class name.

Reimplemented from shark::INameable.

Definition at line 104 of file OneClassSvmTrainer.h.

◆ nu()

template<class InputType, class CacheType = float>

double shark::OneClassSvmTrainer< InputType, CacheType >::nu ( ) const

inline

Definition at line 107 of file OneClassSvmTrainer.h.

References shark::OneClassSvmTrainer< InputType, CacheType >::m_nu.

Referenced by shark::OneClassSvmTrainer< InputType, CacheType >::setNu().

◆ numberOfParameters()

template<class InputType, class CacheType = float>

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

inlinevirtual

return the number of hyper-parameters

Reimplemented from shark::IParameterizable<>.

Definition at line 141 of file OneClassSvmTrainer.h.

References shark::OneClassSvmTrainer< InputType, CacheType >::m_kernel, and shark::IParameterizable< VectorType >::numberOfParameters().

◆ parameterVector()

template<class InputType, class CacheType = float>

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

inlinevirtual

get the hyper-parameter vector

Reimplemented from shark::IParameterizable<>.

Definition at line 124 of file OneClassSvmTrainer.h.

References shark::OneClassSvmTrainer< InputType, CacheType >::m_kernel, and shark::IParameterizable< VectorType >::numberOfParameters().

◆ setCacheSize()

template<class InputType, class CacheType = float>

void shark::OneClassSvmTrainer< InputType, CacheType >::setCacheSize ( std::size_t size )

inline

Definition at line 120 of file OneClassSvmTrainer.h.

References shark::OneClassSvmTrainer< InputType, CacheType >::m_cacheSize.

◆ setKernel()

template<class InputType, class CacheType = float>

void shark::OneClassSvmTrainer< InputType, CacheType >::setKernel ( KernelType * kernel )

inline

Definition at line 115 of file OneClassSvmTrainer.h.

References shark::OneClassSvmTrainer< InputType, CacheType >::kernel(), and shark::OneClassSvmTrainer< InputType, CacheType >::m_kernel.

◆ setNu()

template<class InputType, class CacheType = float>

void shark::OneClassSvmTrainer< InputType, CacheType >::setNu ( double nu )

inline

Definition at line 109 of file OneClassSvmTrainer.h.

References shark::OneClassSvmTrainer< InputType, CacheType >::m_nu, and shark::OneClassSvmTrainer< InputType, CacheType >::nu().

◆ setParameterVector()

template<class InputType, class CacheType = float>

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

inlinevirtual

set the vector of hyper-parameters

Reimplemented from shark::IParameterizable<>.

Definition at line 133 of file OneClassSvmTrainer.h.

References shark::OneClassSvmTrainer< InputType, CacheType >::m_kernel, shark::IParameterizable< VectorType >::numberOfParameters(), shark::IParameterizable< VectorType >::setParameterVector(), and SHARK_ASSERT.

◆ train()

template<class InputType, class CacheType = float>

void shark::OneClassSvmTrainer< InputType, CacheType >::train	(	KernelExpansion< InputType > &	svm,
		UnlabeledData< InputType > const &	inputset
	)

inline

Definition at line 144 of file OneClassSvmTrainer.h.

References shark::OneClassSvmTrainer< InputType, CacheType >::m_kernel, shark::OneClassSvmTrainer< InputType, CacheType >::m_nu, shark::QpConfig::precomputeKernel(), shark::KernelExpansion< InputType >::setStructure(), SHARK_RUNTIME_CHECK, and shark::QpConfig::sparsify().

Referenced by main().

◆ trainSVM()

template<class InputType, class CacheType = float>

template<class MatrixType >

void shark::OneClassSvmTrainer< InputType, CacheType >::trainSVM	(	KernelExpansion< InputType > &	svm,
		UnlabeledData< InputType > const &	inputset
	)

inlineprotected

Definition at line 165 of file OneClassSvmTrainer.h.

Member Data Documentation

◆ m_cacheSize

template<class InputType, class CacheType = float>

std::size_t shark::OneClassSvmTrainer< InputType, CacheType >::m_cacheSize

protected

Definition at line 162 of file OneClassSvmTrainer.h.

Referenced by shark::OneClassSvmTrainer< InputType, CacheType >::CacheSize(), and shark::OneClassSvmTrainer< InputType, CacheType >::setCacheSize().

◆ m_kernel

template<class InputType, class CacheType = float>

KernelType* shark::OneClassSvmTrainer< InputType, CacheType >::m_kernel

protected

Definition at line 160 of file OneClassSvmTrainer.h.

Referenced by shark::OneClassSvmTrainer< InputType, CacheType >::kernel(), shark::OneClassSvmTrainer< InputType, CacheType >::numberOfParameters(), shark::OneClassSvmTrainer< InputType, CacheType >::parameterVector(), shark::OneClassSvmTrainer< InputType, CacheType >::setKernel(), shark::OneClassSvmTrainer< InputType, CacheType >::setParameterVector(), and shark::OneClassSvmTrainer< InputType, CacheType >::train().

◆ m_nu

template<class InputType, class CacheType = float>

double shark::OneClassSvmTrainer< InputType, CacheType >::m_nu

protected

Definition at line 161 of file OneClassSvmTrainer.h.

Referenced by shark::OneClassSvmTrainer< InputType, CacheType >::nu(), shark::OneClassSvmTrainer< InputType, CacheType >::setNu(), and shark::OneClassSvmTrainer< InputType, CacheType >::train().

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

include/shark/Algorithms/Trainers/OneClassSvmTrainer.h

Public Types

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

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

Member Typedef Documentation

◆ CachedMatrixType

◆ KernelMatrixType

◆ KernelType

◆ ModelType

◆ PrecomputedMatrixType

◆ QpFloatType

Constructor & Destructor Documentation

◆ OneClassSvmTrainer()

Member Function Documentation

◆ CacheSize()

◆ kernel() [1/2]

◆ kernel() [2/2]

◆ name()

◆ nu()

◆ numberOfParameters()

◆ parameterVector()

◆ setCacheSize()

◆ setKernel()

◆ setNu()

◆ setParameterVector()

◆ train()

◆ trainSVM()

Member Data Documentation

◆ m_cacheSize

◆ m_kernel

◆ m_nu

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