Kernel expansion with missing features support For a choice of kernel, see Kernels. More...

#include <shark/Models/Kernels/MissingFeaturesKernelExpansion.h>

Inheritance diagram for shark::MissingFeaturesKernelExpansion< InputType >:

Public Types
typedef Base::KernelType	KernelType

typedef Base::BatchInputType	BatchInputType

typedef Base::BatchOutputType	BatchOutputType

Public Types inherited from shark::KernelExpansion< InputType >
typedef AbstractKernelFunction< InputType >	KernelType

typedef base_type::BatchInputType	BatchInputType

typedef base_type::BatchOutputType	BatchOutputType

Public Types inherited from shark::AbstractModel< InputType, RealVector >
enum	Feature

typedef InputType	InputType
	Defines the input type of the model.

typedef RealVector	OutputType
	Defines the output type of the model.

typedef AbstractModel< InputType, RealVector, RealVector >	ModelBaseType
	Defines the BaseType used by the model (this type). Useful for creating derived models.

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

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

typedef TypedFlags< Feature >	Features

typedef TypedFeatureNotAvailableException< Feature >	FeatureNotAvailableException

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

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

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

virtual void	eval (BatchInputType const &patterns, BatchOutputType &outputs) const
	Override eval(...) in the base class.

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

template<class InputTypeT >
double	computeNorm (const RealVector &alpha, const RealVector &scalingCoefficient, InputTypeT const &missingness) const

double	computeNorm (const RealVector &alpha, const RealVector &scalingCoefficient) const

void	setScalingCoefficients (const RealVector &scalingCoefficients)

void	setClassifierNorm (double classifierNorm)


	MissingFeaturesKernelExpansion (KernelType *kernel)

	MissingFeaturesKernelExpansion (KernelType *kernel, Data< InputType > const &basis, bool offset)

Public Member Functions inherited from shark::KernelExpansion< InputType >
	KernelExpansion ()

	KernelExpansion (KernelType *kernel)

	KernelExpansion (KernelType *kernel, Data< InputType > const &basis, bool offset, std::size_t outputs=1)

void	setStructure (KernelType *kernel, Data< InputType > const &basis, bool offset, std::size_t outputs=1)

Shape	outputShape () const
	dimensionality of the output RealVector

Shape	inputShape () const
	Returns the expected shape of the input.

KernelType const *	kernel () const

KernelType *	kernel ()

void	setKernel (KernelType *kernel)

bool	hasOffset () const

RealMatrix &	alpha ()

RealMatrix const &	alpha () const

double &	alpha (std::size_t example, std::size_t cls)

double const &	alpha (std::size_t example, std::size_t cls) const

RealVector &	offset ()

RealVector const &	offset () const

double &	offset (std::size_t cls)

double const &	offset (std::size_t cls) const

Data< InputType > const &	basis () const

Data< InputType > &	basis ()

void	sparsify ()

RealVector	parameterVector () const
	Return the parameter vector.

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

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

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

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

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

virtual	~AbstractModel ()

const Features &	features () const

virtual void	updateFeatures ()

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

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

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

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

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

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

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

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

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

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

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

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

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 ()

Protected Attributes
RealVector	m_scalingCoefficients
	The scaling coefficients.

double	m_classifierNorm
	The norm of classifier(w)

Protected Attributes inherited from shark::KernelExpansion< InputType >
KernelType *	mep_kernel
	kernel function used in the expansion

Data< InputType >	m_basis
	"support" basis vectors

RealMatrix	m_alpha
	kernel coefficients

RealVector	m_b
	offset or bias term

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

Detailed Description

template<class InputType>
class shark::MissingFeaturesKernelExpansion< InputType >

Kernel expansion with missing features support For a choice of kernel, see Kernels.

Definition at line 45 of file MissingFeaturesKernelExpansion.h.

Member Typedef Documentation

◆ BatchInputType

template<class InputType >

typedef Base::BatchInputType shark::MissingFeaturesKernelExpansion< InputType >::BatchInputType

Definition at line 51 of file MissingFeaturesKernelExpansion.h.

◆ BatchOutputType

template<class InputType >

typedef Base::BatchOutputType shark::MissingFeaturesKernelExpansion< InputType >::BatchOutputType

Definition at line 52 of file MissingFeaturesKernelExpansion.h.

◆ KernelType

template<class InputType >

typedef Base::KernelType shark::MissingFeaturesKernelExpansion< InputType >::KernelType

Definition at line 50 of file MissingFeaturesKernelExpansion.h.

Constructor & Destructor Documentation

◆ MissingFeaturesKernelExpansion() [1/3]

template<class InputType >

shark::MissingFeaturesKernelExpansion< InputType >::MissingFeaturesKernelExpansion ( )

inline

Constructors from the base class

Definition at line 55 of file MissingFeaturesKernelExpansion.h.

◆ MissingFeaturesKernelExpansion() [2/3]

template<class InputType >

shark::MissingFeaturesKernelExpansion< InputType >::MissingFeaturesKernelExpansion ( KernelType * kernel )

inline

Definition at line 58 of file MissingFeaturesKernelExpansion.h.

◆ MissingFeaturesKernelExpansion() [3/3]

template<class InputType >

shark::MissingFeaturesKernelExpansion< InputType >::MissingFeaturesKernelExpansion	(	KernelType *	kernel,
		Data< InputType > const &	basis,
		bool	offset
	)

inline

Definition at line 62 of file MissingFeaturesKernelExpansion.h.

Member Function Documentation

◆ computeNorm() [1/2]

template<class InputType >

double shark::MissingFeaturesKernelExpansion< InputType >::computeNorm	(	const RealVector &	alpha,
		const RealVector &	scalingCoefficient
	)		const

inline

Definition at line 153 of file MissingFeaturesKernelExpansion.h.

References shark::KernelExpansion< InputType >::alpha(), shark::evalSkipMissingFeatures(), shark::KernelExpansion< InputType >::m_basis, shark::KernelExpansion< InputType >::mep_kernel, SHARK_ASSERT, and SIZE_CHECK.

◆ computeNorm() [2/2]

template<class InputType >

template<class InputTypeT >

double shark::MissingFeaturesKernelExpansion< InputType >::computeNorm	(	const RealVector &	alpha,
		const RealVector &	scalingCoefficient,
		InputTypeT const &	missingness
	)		const

inline

Calculate norm of classifier, i.e., ||w||

formula: \( \sum_{i,j=1}^{n}\alpha_i\frac{y_i}{s_i}K\left(x_i,x_j)\right)\frac{y_j}{s_j}\alpha_j \) where \( s_i \) is scaling coefficient, and \( K \) is kernel function, \( K\left(x_i,x_j)\right) \) is taken only over features that are valid for both \( x_i \) and \( x_j \)

Definition at line 121 of file MissingFeaturesKernelExpansion.h.

References shark::KernelExpansion< InputType >::alpha(), shark::evalSkipMissingFeatures(), shark::KernelExpansion< InputType >::m_basis, shark::KernelExpansion< InputType >::mep_kernel, SHARK_ASSERT, and SIZE_CHECK.

Referenced by shark::MissingFeaturesKernelExpansion< InputType >::eval().

◆ createState()

template<class InputType >

boost::shared_ptr< State > shark::MissingFeaturesKernelExpansion< InputType >::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::KernelExpansion< InputType >.

Definition at line 71 of file MissingFeaturesKernelExpansion.h.

◆ eval() [1/2]

template<class InputType >

virtual void shark::MissingFeaturesKernelExpansion< InputType >::eval	(	BatchInputType const &	patterns,
		BatchOutputType &	outputs
	)		const

inlinevirtual

Override eval(...) in the base class.

Reimplemented from shark::KernelExpansion< InputType >.

Definition at line 76 of file MissingFeaturesKernelExpansion.h.

Referenced by shark::MissingFeaturesKernelExpansion< InputType >::eval().

◆ eval() [2/2]

template<class InputType >

void shark::MissingFeaturesKernelExpansion< InputType >::eval	(	BatchInputType const &	patterns,
		BatchOutputType &	outputs,
		State &	state
	)		const

inlinevirtual

Standard interface for evaluating the response of the model to a batch of patterns.

Parameters

patterns	the inputs of the model
outputs	the predictions or response of the model to every pattern
state	intermediate results stored by eval which can be reused for derivative computation.

Reimplemented from shark::KernelExpansion< InputType >.

Definition at line 110 of file MissingFeaturesKernelExpansion.h.

References shark::MissingFeaturesKernelExpansion< InputType >::eval().