"Diagonal" linear model for data normalization. More...

#include <shark/Models/Normalizer.h>

Inheritance diagram for shark::Normalizer< VectorType >:

Public Types
typedef base_type::BatchInputType	BatchInputType

typedef base_type::BatchOutputType	BatchOutputType

Public Types inherited from shark::AbstractModel< InputTypeT, OutputTypeT, ParameterVectorType >
enum	Feature { HAS_FIRST_PARAMETER_DERIVATIVE = 1 , HAS_FIRST_INPUT_DERIVATIVE = 4 }

typedef InputTypeT	InputType
	Defines the input type of the model.

typedef OutputTypeT	OutputType
	Defines the output type of the model.

typedef AbstractModel< InputTypeT, OutputTypeT, ParameterVectorType >	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
	Normalizer (std::size_t dimension=0, bool hasOffset=false)
	Construction from dimension.

	Normalizer (VectorType const &diagonal, VectorType const &offset=VectorType())
	Construction from matrix and and optional offset vector.

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

boost::shared_ptr< State >	createState () const
	derivative storage object (empty for this model)

bool	hasOffset () const
	check for the presence of an offset term

Shape	inputShape () const
	obtain the input dimension

Shape	outputShape () const
	obtain the output dimension

VectorType const &	diagonal () const
	return the diagonal of the matrix

VectorType const &	offset () const
	return the offset vector

VectorType	parameterVector () const
	obtain the parameter vector

void	setParameterVector (VectorType const &newParameters)
	overwrite the parameter vector

std::size_t	numberOfParameters () const
	return the number of parameter

void	setStructure (VectorType const &diagonal, VectorType const &offset=VectorType())
	overwrite structure and parameters

void	setStructure (std::size_t dimension, bool hasOffset=false)
	overwrite structure and parameters

void	eval (BatchInputType const &input, BatchOutputType &output) const
	Evaluate the model: output = matrix * input + offset.

void	eval (BatchInputType const &input, BatchOutputType &output, State &state) const
	Evaluate the model: output = matrix * input + offset.

void	read (InArchive &archive)
	from ISerializable

void	write (OutArchive &archive) const
	from ISerializable

Public Member Functions inherited from shark::AbstractModel< InputTypeT, OutputTypeT, ParameterVectorType >
	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, ParameterVectorType &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, ParameterVectorType &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
VectorType	m_A
	matrix A (see class documentation)

VectorType	m_b
	vector b (see class documentation)

Protected Attributes inherited from shark::AbstractModel< InputTypeT, OutputTypeT, ParameterVectorType >
Features	m_features

Detailed Description

template<class VectorType = RealVector>
class shark::Normalizer< VectorType >

"Diagonal" linear model for data normalization.

: The Normalizer is a restricted and often more efficient variant of the LinearModel class. It restricts the linear model in two respects: (1) input and output dimension must agree, (2) computations are independent for each component. This is useful mostly for data normalization (therefore the name). The model's operation is of the form \( x \mapsto A x + b \) where A is a diagonal matrix. This reduces memory requirements to linear, which is why there is no sparse version of this model (as opposed to the more general linear model). Also, the addition of b is optional.

Definition at line 59 of file Normalizer.h.