Basic linear classifier. More...

#include <shark/Models/LinearClassifier.h>

Inheritance diagram for shark::LinearClassifier:

Collaboration diagram for shark::LinearClassifier:

Public Member Functions
	LinearClassifier ()

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

void	setStructure (std::size_t inputSize, std::size_t classes)

void	configure (PropertyTree const &node)
	Configures the component given a property tree. More...

std::size_t	inputSize () const

std::size_t	outputSize () const

std::size_t	numberOfClasses () const

RealVector	parameterVector () const
	Return the parameter vector. More...

void	setParameterVector (RealVector const &newParameters)
	Set the parameter vector. More...

std::size_t	numberOfParameters () const
	Return the number of parameters. More...

void	setInverseCholeskyMatrix (const RealMatrix &matrix)

const RealMatrix &	inverseCholeskyMatrix () const

void	importCovarianceMatrix (RealMatrix const &covariance)

void	setClassMean (std::size_t classID, RealVector const &mean)

ConstRealMatrixRow	classMean (std::size_t classID) const

boost::shared_ptr< State >	createState () const
	Creates an internal state of the model. More...

void	eval (BatchInputType const &patterns, BatchOutputType &outputs) const
	Standard interface for evaluating the response of the model to a batch of patterns. More...

void	eval (BatchInputType const &patterns, BatchOutputType &outputs, State &state) const
	Standard interface for evaluating the response of the model to a batch of patterns. More...

void	read (InArchive &archive)
	From ISerializable, reads a model from an archive. More...

void	write (OutArchive &archive) const
	From ISerializable, writes a model to an archive. More...

Data< RealVector >	softMembership (Data< InputType > const &patterns) const
	Return memberships to the different classes, the memberships sum to unity. More...

void	softMembership (BatchInputType const &patterns, Batch< RealVector >::type &output) const
	Compute memberships to the different classes, the memberships sum to unity. More...

Public Member Functions inherited from shark::AbstractModel< RealVector, unsigned int >
	AbstractModel ()

virtual	~AbstractModel ()

const Features &	features () const

virtual void	updateFeatures ()

bool	hasFirstParameterDerivative () const
	Returns true when the first parameter derivative is implemented. More...

bool	hasSecondParameterDerivative () const
	Returns true when the second parameter derivative is implemented. More...

bool	hasFirstInputDerivative () const
	Returns true when the first input derivative is implemented. More...

bool	hasSecondInputDerivative () const
	Returns true when the second parameter derivative is implemented. More...

bool	isSequential () const

virtual void	eval (InputType const &pattern, OutputType &output) const
	Standard interface for evaluating the response of the model to a single pattern. More...

Data< OutputType >	operator() (Data< InputType > const &patterns) const
	Model evaluation as an operator for a whole dataset. This is a convenience function. More...

OutputType	operator() (InputType const &pattern) const
	Model evaluation as an operator for a single pattern. This is a convenience function. More...

BatchOutputType	operator() (BatchInputType const &patterns) const
	Model evaluation as an operator for a single pattern. This is a convenience function. More...

virtual void	weightedParameterDerivative (BatchInputType const &pattern, BatchOutputType const &coefficients, State const &state, RealVector &derivative) const
	calculates the weighted sum of derivatives w.r.t the parameters. More...

virtual void	weightedParameterDerivative (BatchInputType const &pattern, BatchOutputType const &coefficients, Batch< RealMatrix >::type const &errorHessian, State const &state, RealVector &derivative, RealMatrix &hessian) const
	calculates the weighted sum of derivatives w.r.t the parameters More...

virtual void	weightedInputDerivative (BatchInputType const &pattern, BatchOutputType const &coefficients, State const &state, BatchInputType &derivative) const
	calculates the weighted sum of derivatives w.r.t the inputs More...

virtual void	weightedInputDerivative (BatchInputType const &pattern, BatchOutputType const &coefficients, typename Batch< RealMatrix >::type const &errorHessian, State const &state, RealMatrix &derivative, Batch< RealMatrix >::type &hessian) const
	calculates the weighted sum of derivatives w.r.t the inputs More...

virtual void	weightedDerivatives (BatchInputType const &patterns, BatchOutputType const &coefficients, State const &state, RealVector &parameterDerivative, BatchInputType &inputDerivative) const
	calculates weighted input and parameter derivative at the same time More...

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

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

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

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

Protected Attributes
RealMatrix	m_inverseCholesky

RealMatrix	m_means

RealMatrix	m_transformedMeans

RealVector	m_classBias

bool	m_changed

Protected Attributes inherited from shark::AbstractModel< RealVector, unsigned int >
Features	m_features

Additional Inherited Members
Public Types inherited from shark::AbstractModel< RealVector, unsigned int >
enum	Feature

typedef RealVector	InputType
	Defines the input type of the model. More...

typedef unsigned int	OutputType
	Defines the output type of the model. More...

typedef Batch< InputType >::type	BatchInputType
	defines the batch type of the input type. More...

typedef Batch< OutputType >::type	BatchOutputType
	defines the batch type of the output type More...

typedef TypedFlags< Feature >	Features

typedef TypedFeatureNotAvailableException < Feature >	FeatureNotAvailableException

Detailed Description

Basic linear classifier.

The LinearClassifier class is a multi class classifier model suited for linear discriminant analysis. For c classes \( 0, \dots, c-1 \) the model holds class mean vectors \( m_c \) and a shared data scatter matrix \( M \). It predicts the class of a vector x according to the rule \( \textrm{argmin}_{c} (x - m_c)^T M^{-1} (x - m_c) \). The output is an integer in the range [0,c-1] telling to which class the point belongs

As default, this class uses the class means and the inverse covariance matrix. So these are also the parameters the vector returned by parameterVector consists of. However there exists a convenience function importCovarianceMatrix which automatically inverts the covariance matrix for use of the class. Be warned, that this may take a lot of time

Definition at line 25 of file LinearClassifier.h.

Constructor & Destructor Documentation

shark::LinearClassifier::LinearClassifier ( )

inline

Definition at line 36 of file LinearClassifier.h.

Member Function Documentation

ConstRealMatrixRow shark::LinearClassifier::classMean ( std::size_t classID ) const

inline

Definition at line 72 of file LinearClassifier.h.

References m_means, numberOfClasses(), shark::blas::row(), and SIZE_CHECK.

void LinearClassifier::configure ( PropertyTree const & node )

virtual

Configures the component given a property tree.

Parameters

[in] node The root of the property tree.

Reimplemented from shark::IConfigurable.

Definition at line 51 of file LinearClassifier.cpp.

References setStructure().

boost::shared_ptr<State> shark::LinearClassifier::createState ( ) const

inlinevirtual

Creates an internal state of the model.

The state is needed when the derivatives are to be calculated. Eval can store a state which is then reused to speed up the calculations of the derivatives. This also allows eval to be evaluated in parallel!

Reimplemented from shark::AbstractModel< RealVector, unsigned int >.

Definition at line 77 of file LinearClassifier.h.