shark::LinearRegression Class Reference

Linear Regression. More...

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

Inheritance diagram for shark::LinearRegression:

Public Member Functions
SHARK_EXPORT_SYMBOL	LinearRegression (double regularization=0.0)
std::string	name () const
	From INameable: return the class name.
double	regularization () const
void	setRegularization (double regularization)
RealVector	parameterVector () const
	Return the parameter vector.
void	setParameterVector (const RealVector &param)
	Set the parameter vector.
size_t	numberOfParameters () const
	Return the number of parameters.
SHARK_EXPORT_SYMBOL void	train (LinearModel<> &model, LabeledData< RealVector, RealVector > const &dataset)
Public Member Functions inherited from shark::AbstractTrainer< LinearModel<> >
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::IParameterizable< VectorType >
virtual	~IParameterizable ()

Protected Attributes
double	m_regularization

Additional Inherited Members
Public Types inherited from shark::AbstractTrainer< LinearModel<> >
typedef LinearModel<>	ModelType
typedef ModelType::InputType	InputType
typedef typename Model::OutputType	LabelType
typedef LabeledData< InputType, LabelType >	DatasetType
Public Types inherited from shark::IParameterizable< VectorType >
typedef VectorType	ParameterVectorType

Detailed Description

Linear Regression.

Linear Regression builds an affine linear model \( f(x) = A x + b \) minimizing the squared error from a dataset of pairs of vectors (x, y). That is, the error \( \sum_i (f(x_i) - y_i)^2 \) is minimized. The solution to this problem is found analytically.

Definition at line 60 of file LinearRegression.h.

Constructor & Destructor Documentation

LinearRegression()

SHARK_EXPORT_SYMBOL shark::LinearRegression::LinearRegression

(

double

regularization = 0.0

)

Member Function Documentation

name()

std::string shark::LinearRegression::name ( ) const

inlinevirtual

From INameable: return the class name.

Reimplemented from shark::INameable.

Definition at line 66 of file LinearRegression.h.

numberOfParameters()

size_t shark::LinearRegression::numberOfParameters ( ) const

inlinevirtual

Return the number of parameters.

Reimplemented from shark::IParameterizable< VectorType >.

Definition at line 86 of file LinearRegression.h.

parameterVector()

RealVector shark::LinearRegression::parameterVector ( ) const

inlinevirtual

Return the parameter vector.

Reimplemented from shark::IParameterizable< VectorType >.

Definition at line 77 of file LinearRegression.h.

References m_regularization.

regularization()

double shark::LinearRegression::regularization ( ) const

inline

Definition at line 69 of file LinearRegression.h.

References m_regularization.

Referenced by setRegularization().

setParameterVector()

void shark::LinearRegression::setParameterVector ( const RealVector &  newParameters )

inlinevirtual

Set the parameter vector.

Reimplemented from shark::IParameterizable< VectorType >.

Definition at line 82 of file LinearRegression.h.

References m_regularization, and SIZE_CHECK.

setRegularization()

void shark::LinearRegression::setRegularization ( double  regularization )

inline

Definition at line 72 of file LinearRegression.h.

References m_regularization, RANGE_CHECK, and regularization().

train()

SHARK_EXPORT_SYMBOL void shark::LinearRegression::train	(	LinearModel<> &	model,
		LabeledData< RealVector, RealVector > const &	dataset
	)

Referenced by main().

Member Data Documentation

m_regularization

double shark::LinearRegression::m_regularization

protected

Definition at line 92 of file LinearRegression.h.

Referenced by parameterVector(), regularization(), setParameterVector(), and setRegularization().

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

• include/shark/Algorithms/Trainers/LinearRegression.h