Generic solver skeleton for linear multi-class SVM problems. More...

#include <shark/Algorithms/QP/QpMcLinear.h>

Inheritance diagram for shark::QpMcLinear< InputT >:

Public Types
enum	CoordinateSelectionStrategy { UNIFORM , ACF }

typedef LabeledData< InputT, unsigned int >	DatasetType

typedef LabeledData< InputT, unsignedint >::const_element_reference	ElementType

typedef Batch< InputT >::const_reference	InputReferenceType

Public Member Functions
	QpMcLinear (const DatasetType &dataset, std::size_t dim, std::size_t classes, std::size_t strategy=ACF, bool shrinking=false)
	Constructor.

RealMatrix	solve (random::rng_type &rng, double C, QpStoppingCondition &stop, QpSolutionProperties *prop=NULL, bool verbose=false)
	Solve the SVM training problem.

Protected Member Functions
void	add_scaled (RealMatrix &w, RealVector const &mu, InputReferenceType x)

virtual double	calcGradient (RealVector &gradient, RealVector wx, blas::dense_vector_adaptor< double const > const &alpha, double C, unsigned int y)=0
	Compute the gradient from the inner products of the weight vectors with the current sample.

virtual void	updateWeightVectors (RealMatrix &w, RealVector const &mu, std::size_t index)=0
	Update the weight vectors (primal variables) after a step on the dual variables.

virtual double	solveSub (double epsilon, RealVector &gradient, double q, double C, unsigned int y, blas::dense_vector_adaptor< double > &alpha, RealVector &mu)=0
	Solve the sub-problem posed by a single training example.

Protected Attributes
DataView< const DatasetType >	m_data
	view on training data

RealVector	m_xSquared
	diagonal entries of the quadratic matrix

std::size_t	m_dim
	input space dimension

std::size_t	m_classes
	number of classes

std::size_t	m_strategy
	strategy for coordinate selection

bool	m_shrinking
	apply shrinking or not?

Detailed Description

template<class InputT>
class shark::QpMcLinear< InputT >

Generic solver skeleton for linear multi-class SVM problems.

Definition at line 54 of file QpMcLinear.h.

Member Typedef Documentation

◆ DatasetType

template<class InputT >

typedef LabeledData<InputT, unsigned int> shark::QpMcLinear< InputT >::DatasetType

Definition at line 57 of file QpMcLinear.h.

◆ ElementType

template<class InputT >

typedef LabeledData<InputT,unsignedint>::const_element_reference shark::QpMcLinear< InputT >::ElementType

Definition at line 58 of file QpMcLinear.h.

◆ InputReferenceType

template<class InputT >

typedef Batch<InputT>::const_reference shark::QpMcLinear< InputT >::InputReferenceType

Definition at line 59 of file QpMcLinear.h.

Member Enumeration Documentation

◆ CoordinateSelectionStrategy

template<class InputT >

enum shark::QpMcLinear::CoordinateSelectionStrategy

Enumerator
UNIFORM
ACF

Definition at line 61 of file QpMcLinear.h.

Constructor & Destructor Documentation

◆ QpMcLinear()

template<class InputT >

shark::QpMcLinear< InputT >::QpMcLinear	(	const DatasetType &	dataset,
		std::size_t	dim,
		std::size_t	classes,
		std::size_t	strategy = `ACF`,
		bool	shrinking = `false`
	)

inline

Constructor.

Parameters

dataset	training data
dim	problem dimension
classes	number of classes in the problem
strategy	coordinate selection strategy
shrinking	flag turning shrinking on and off

Definition at line 73 of file QpMcLinear.h.

References shark::QpMcLinear< InputT >::m_data, shark::QpMcLinear< InputT >::m_dim, shark::QpMcLinear< InputT >::m_xSquared, and SHARK_ASSERT.

Member Function Documentation

◆ add_scaled()

template<class InputT >

void shark::QpMcLinear< InputT >::add_scaled	(	RealMatrix &	w,
		RealVector const &	mu,
		InputReferenceType	x
	)

inlineprotected

Definition at line 343 of file QpMcLinear.h.

References shark::QpMcLinear< InputT >::m_classes.

Referenced by shark::QpMcLinearWW< InputT >::updateWeightVectors(), shark::QpMcLinearLLW< InputT >::updateWeightVectors(), shark::QpMcLinearATS< InputT >::updateWeightVectors(), shark::QpMcLinearMMR< InputT >::updateWeightVectors(), shark::QpMcLinearCS< InputT >::updateWeightVectors(), shark::QpMcLinearADM< InputT >::updateWeightVectors(), shark::QpMcLinearATM< InputT >::updateWeightVectors(), and shark::QpMcLinearReinforced< InputT >::updateWeightVectors().

◆ calcGradient()

template<class InputT >

virtual double shark::QpMcLinear< InputT >::calcGradient	(	RealVector &	gradient,
		RealVector	wx,
		blas::dense_vector_adaptor< double const > const &	alpha,
		double	C,
		unsigned int	y
	)

protectedpure virtual

Compute the gradient from the inner products of the weight vectors with the current sample.

Parameters

gradient	gradient vector to be filled in. The vector is correctly sized.
wx	inner products of weight vectors with the current sample; wx(c) = <w_c, x>
alpha	variables corresponding to the current sample
C	upper bound on the variables
y	label of the current sample

Returns: The function must return the violation of the KKT conditions.

Implemented in shark::QpMcLinearWW< InputT >, shark::QpMcLinearLLW< InputT >, shark::QpMcLinearATS< InputT >, shark::QpMcLinearMMR< InputT >, shark::QpMcLinearCS< InputT >, shark::QpMcLinearADM< InputT >, shark::QpMcLinearATM< InputT >, and shark::QpMcLinearReinforced< InputT >.

Referenced by shark::QpMcLinear< InputT >::solve().

◆ solve()

template<class InputT >

RealMatrix shark::QpMcLinear< InputT >::solve	(	random::rng_type &	rng,
		double	C,
		QpStoppingCondition &	stop,
		QpSolutionProperties *	prop = `NULL`,
		bool	verbose = `false`
	)

inline

Solve the SVM training problem.

Parameters

rng	random number generator used by the algorithm
C	regularization constant of the SVM
stop	stopping condition(s)
prop	solution properties
verbose	if true, the solver prints status information and solution statistics

Definition at line 103 of file QpMcLinear.h.

◆ solveSub()

template<class InputT >

virtual double shark::QpMcLinear< InputT >::solveSub	(	double	epsilon,
		RealVector &	gradient,
		double	q,
		double	C,
		unsigned int	y,
		blas::dense_vector_adaptor< double > &	alpha,
		RealVector &	mu
	)

protectedpure virtual

Solve the sub-problem posed by a single training example.

Parameters

epsilon	accuracy (dual gradient) up to which the sub-problem should be solved
gradient	gradient of the objective function w.r.t. alpha
q	squared norm of the current sample
C	upper bound on the variables
y	label of the current sample
alpha	input: initial point; output: (near) optimal point
mu	step from initial point to final point

Returns: The function must return the gain of the step, i.e., the improvement of the objective function.

Implemented in shark::QpMcLinearWW< InputT >, shark::QpMcLinearLLW< InputT >, shark::QpMcLinearATS< InputT >, shark::QpMcLinearMMR< InputT >, shark::QpMcLinearCS< InputT >, shark::QpMcLinearADM< InputT >, shark::QpMcLinearATM< InputT >, and shark::QpMcLinearReinforced< InputT >.

Referenced by shark::QpMcLinear< InputT >::solve().

◆ updateWeightVectors()

template<class InputT >

virtual void shark::QpMcLinear< InputT >::updateWeightVectors	(	RealMatrix &	w,
		RealVector const &	mu,
		std::size_t	index
	)

protectedpure virtual

Update the weight vectors (primal variables) after a step on the dual variables.

Parameters

w	matrix of (dense) weight vectors (as rows)
mu	dual step on the variables corresponding to the current sample
index	current sample

Implemented in shark::QpMcLinearWW< InputT >, shark::QpMcLinearLLW< InputT >, shark::QpMcLinearATS< InputT >, shark::QpMcLinearMMR< InputT >, shark::QpMcLinearCS< InputT >, shark::QpMcLinearADM< InputT >, shark::QpMcLinearATM< InputT >, and shark::QpMcLinearReinforced< InputT >.

Referenced by shark::QpMcLinear< InputT >::solve().

Member Data Documentation

◆ m_classes

template<class InputT >

std::size_t shark::QpMcLinear< InputT >::m_classes

protected

number of classes

Definition at line 382 of file QpMcLinear.h.

◆ m_data

template<class InputT >

DataView<const DatasetType> shark::QpMcLinear< InputT >::m_data

protected

view on training data

Definition at line 379 of file QpMcLinear.h.

◆ m_dim

template<class InputT >

std::size_t shark::QpMcLinear< InputT >::m_dim

protected

input space dimension

Definition at line 381 of file QpMcLinear.h.

Referenced by shark::QpMcLinear< InputT >::QpMcLinear(), and shark::QpMcLinear< InputT >::solve().

◆ m_shrinking

template<class InputT >

bool shark::QpMcLinear< InputT >::m_shrinking

protected

apply shrinking or not?

Definition at line 384 of file QpMcLinear.h.

Referenced by shark::QpMcLinear< InputT >::solve().

◆ m_strategy

template<class InputT >

std::size_t shark::QpMcLinear< InputT >::m_strategy

protected

strategy for coordinate selection

Definition at line 383 of file QpMcLinear.h.

Referenced by shark::QpMcLinear< InputT >::solve().

◆ m_xSquared

template<class InputT >

RealVector shark::QpMcLinear< InputT >::m_xSquared

protected

diagonal entries of the quadratic matrix

Definition at line 380 of file QpMcLinear.h.

Referenced by shark::QpMcLinear< InputT >::QpMcLinear(), and shark::QpMcLinear< InputT >::solve().

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

include/shark/Algorithms/QP/QpMcLinear.h

Public Types

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

Member Typedef Documentation

◆ DatasetType

◆ ElementType

◆ InputReferenceType

Member Enumeration Documentation

◆ CoordinateSelectionStrategy

Constructor & Destructor Documentation

◆ QpMcLinear()

Member Function Documentation

◆ add_scaled()

◆ calcGradient()

◆ solve()

◆ solveSub()

◆ updateWeightVectors()

Member Data Documentation

◆ m_classes

◆ m_data

◆ m_dim

◆ m_shrinking

◆ m_strategy

◆ m_xSquared