#include <itkMultipleValuedVnlCostFunctionAdaptor.h>

Detailed Description

This class is an Adaptor that allows to pass itk::MultipleValuedCostFunctions to vnl_optimizers expecting a vnl_cost_function.

This class returns a single valued.

Definition at line 37 of file itkMultipleValuedVnlCostFunctionAdaptor.h.

Inheritance diagram for itk::MultipleValuedVnlCostFunctionAdaptor:

Collaboration diagram for itk::MultipleValuedVnlCostFunctionAdaptor:

Public Types
using	DerivativeType = MultipleValuedCostFunction::DerivativeType

using	InternalDerivativeType = vnl_matrix<double>

using	InternalMeasureType = vnl_vector<double>

using	InternalParametersType = vnl_vector<double>

using	MeasureType = MultipleValuedCostFunction::MeasureType

using	ParametersType = MultipleValuedCostFunction::ParametersType

using	ScalesType = Array<double>

Public Member Functions
unsigned long	AddObserver (const EventObject &event, Command *) const

virtual void	compute (const InternalParametersType &x, InternalMeasureType ff, InternalDerivativeType g)

void	ConvertExternalToInternalGradient (const DerivativeType &input, InternalDerivativeType &output)

void	ConvertExternalToInternalMeasures (const MeasureType &input, InternalMeasureType &output)

void	f (const InternalParametersType &inparameters, InternalMeasureType &measures) override

const ParametersType &	GetCachedCurrentParameters () const

const DerivativeType &	GetCachedDerivative () const

const MeasureType &	GetCachedValue () const

const MultipleValuedCostFunction *	GetCostFunction () const

bool	GetUseGradient () const

void	gradf (const InternalParametersType &inparameters, InternalDerivativeType &gradient) override

	MultipleValuedVnlCostFunctionAdaptor (unsigned int spaceDimension, unsigned int numberOfValues)

void	SetCostFunction (MultipleValuedCostFunction *costFunction)

void	SetScales (const ScalesType &scales)

void	SetUseGradient (bool)

void	UseGradientOff ()

void	UseGradientOn ()

Protected Member Functions
void	ReportIteration (const EventObject &event) const

Private Member Functions
virtual const ScalesType &	GetInverseScales () const

Private Attributes
ParametersType	m_CachedCurrentParameters {}

DerivativeType	m_CachedDerivative {}

MeasureType	m_CachedValue {}

MultipleValuedCostFunction::Pointer	m_CostFunction {}

ScalesType	m_InverseScales {}

Object::Pointer	m_Reporter {}

bool	m_ScalesInitialized {}

Member Typedef Documentation

◆ DerivativeType

using itk::MultipleValuedVnlCostFunctionAdaptor::DerivativeType = MultipleValuedCostFunction::DerivativeType

Derivatives of the MultipleValuedCostFunction

Definition at line 56 of file itkMultipleValuedVnlCostFunctionAdaptor.h.

◆ InternalDerivativeType

using itk::MultipleValuedVnlCostFunctionAdaptor::InternalDerivativeType = vnl_matrix<double>

InternalGradientType type alias.

Definition at line 47 of file itkMultipleValuedVnlCostFunctionAdaptor.h.

◆ InternalMeasureType

using itk::MultipleValuedVnlCostFunctionAdaptor::InternalMeasureType = vnl_vector<double>

InternalMeasureType type alias.

Definition at line 44 of file itkMultipleValuedVnlCostFunctionAdaptor.h.

◆ InternalParametersType

using itk::MultipleValuedVnlCostFunctionAdaptor::InternalParametersType = vnl_vector<double>

InternalParametersType type alias.

Definition at line 41 of file itkMultipleValuedVnlCostFunctionAdaptor.h.

◆ MeasureType

using itk::MultipleValuedVnlCostFunctionAdaptor::MeasureType = MultipleValuedCostFunction::MeasureType

MeasureType of the MultipleValuedCostFunction

Definition at line 50 of file itkMultipleValuedVnlCostFunctionAdaptor.h.

◆ ParametersType

using itk::MultipleValuedVnlCostFunctionAdaptor::ParametersType = MultipleValuedCostFunction::ParametersType

Parameters of the MultipleValuedCostFunction

Definition at line 53 of file itkMultipleValuedVnlCostFunctionAdaptor.h.

◆ ScalesType

using itk::MultipleValuedVnlCostFunctionAdaptor::ScalesType = Array<double>

Scales type alias

Definition at line 59 of file itkMultipleValuedVnlCostFunctionAdaptor.h.

Constructor & Destructor Documentation

◆ MultipleValuedVnlCostFunctionAdaptor()

itk::MultipleValuedVnlCostFunctionAdaptor::MultipleValuedVnlCostFunctionAdaptor	(	unsigned int	spaceDimension,
		unsigned int	numberOfValues )

Constructor with size

Member Function Documentation

◆ AddObserver()

unsigned long itk::MultipleValuedVnlCostFunctionAdaptor::AddObserver	(	const EventObject &	event,
		Command *	) const

This AddObserver method allows to simulate that this class derives from an itkObject for the purpose of reporting iteration events. The goal of this method is to allow ITK-vnl optimizer adaptors to get iteration events despite the fact that VNL does not provide callbacks.

◆ compute()

virtual void itk::MultipleValuedVnlCostFunctionAdaptor::compute	(	const InternalParametersType &	x,
		InternalMeasureType *	ff,
		InternalDerivativeType *	g )

virtual

Delegate computation of value and gradient to the costFunction.

◆ ConvertExternalToInternalGradient()

void itk::MultipleValuedVnlCostFunctionAdaptor::ConvertExternalToInternalGradient	(	const DerivativeType &	input,
		InternalDerivativeType &	output )

Convert external derivative measures into internal type

◆ ConvertExternalToInternalMeasures()

void itk::MultipleValuedVnlCostFunctionAdaptor::ConvertExternalToInternalMeasures	(	const MeasureType &	input,
		InternalMeasureType &	output )

Convert external measures into internal type

◆ f()

void itk::MultipleValuedVnlCostFunctionAdaptor::f	(	const InternalParametersType &	inparameters,
		InternalMeasureType &	measures )

override

Delegate computation of the value to the CostFunction.

◆ GetCachedCurrentParameters()

const ParametersType & itk::MultipleValuedVnlCostFunctionAdaptor::GetCachedCurrentParameters ( ) const

◆ GetCachedDerivative()

const DerivativeType & itk::MultipleValuedVnlCostFunctionAdaptor::GetCachedDerivative ( ) const

◆ GetCachedValue()

const MeasureType & itk::MultipleValuedVnlCostFunctionAdaptor::GetCachedValue ( ) const

Return the value of the last evaluation to the value of the cost function. Note that this method DOES NOT triggers a computation of the function or the derivatives, it only returns previous values. Therefore the values here are only valid after you invoke the f() or gradf() methods.