#include <elxAdaptiveStochasticGradientDescent.h>

Detailed Description

template<class TElastix>
class elastix::AdaptiveStochasticGradientDescent< TElastix >

A gradient descent optimizer with an adaptive gain.

This class is a wrap around the AdaptiveStochasticGradientDescentOptimizer class. It takes care of setting parameters and printing progress information. For more information about the optimization method, please read the documentation of the AdaptiveStochasticGradientDescentOptimizer class.

This optimizer is very suitable to be used in combination with the Random image sampler, or with the RandomCoordinate image sampler, with the setting (NewSamplesEveryIteration "true"). Much effort has been spent on providing reasonable default values for all parameters, to simplify usage. In most registration problems, good results should be obtained without specifying any of the parameters described below (except the first of course, which defines the optimizer to use).

This optimization method is described in the following references:

[1] P. Cruz, "Almost sure convergence and asymptotical normality of a generalization of Kesten's stochastic approximation algorithm for multidimensional case." Technical Report, 2005. http://hdl.handle.net/2052/74

[2] S. Klein, J.P.W. Pluim, and M. Staring, M.A. Viergever, "Adaptive stochastic gradient descent optimization for image registration," International Journal of Computer Vision, vol. 81, no. 3, pp. 227-239, 2009. http://dx.doi.org/10.1007/s11263-008-0168-y

Acceleration in case of many transform parameters was proposed in the following paper:

[3] Y. Qiao, B. van Lew, B.P.F. Lelieveldt and M. Staring "Fast Automatic Step Size Estimation for Gradient Descent Optimization of Image Registration," IEEE Transactions on Medical Imaging, vol. 35, no. 2, pp. 391 - 403, February 2016. http://elastix.isi.uu.nl/marius/publications/2016_j_TMIa.php

The parameters used in this class are:

Parameters:

Optimizer: Select this optimizer as follows:
(Optimizer "AdaptiveStochasticGradientDescent")

MaximumNumberOfIterations: The maximum number of iterations in each resolution.
example: (MaximumNumberOfIterations 100 100 50)
Default/recommended value: 500. When you are in a hurry, you may go down to 250 for example. When you have plenty of time, and want to be absolutely sure of the best results, a setting of 2000 is reasonable. In general, 500 gives satisfactory results.

MaximumNumberOfSamplingAttempts: The maximum number of sampling attempts. Sometimes not enough corresponding samples can be drawn, upon which an exception is thrown. With this parameter it is possible to try to draw another set of samples.
example: (MaximumNumberOfSamplingAttempts 10 15 10)
Default value: 0, i.e. just fail immediately, for backward compatibility.

AutomaticParameterEstimation: When this parameter is set to "true", many other parameters are calculated automatically: SP_a, SP_alpha, SigmoidMax, SigmoidMin, and SigmoidScale. In the elastix.log file the actually chosen values for these parameters can be found.
example: (AutomaticParameterEstimation "true")
Default/recommended value: "true". The parameter can be specified for each resolution, or for all resolutions at once.

UseAdaptiveStepSizes: When this parameter is set to "true", the adaptive step size mechanism described in the documentation of itk::AdaptiveStochasticGradientDescentOptimizer is used. The parameter can be specified for each resolution, or for all resolutions at once.
example: (UseAdaptiveStepSizes "true")
Default/recommend value: "true", because it makes the registration more robust. In case of using a RandomCoordinate sampler, with (UseRandomSampleRegion "true"), the adaptive step size mechanism is turned off, no matter the user setting.

MaximumStepLength: Also called $\delta$ . This parameter can be considered as the maximum voxel displacement between two iterations. The larger this parameter, the more aggressive the optimization. The parameter can be specified for each resolution, or for all resolutions at once.
example: (MaximumStepLength 1.0)
Default: mean voxel spacing of fixed and moving image. This seems to work well in general. This parameter only has influence when AutomaticParameterEstimation is used.

SP_a: The gain at each iteration is defined by
$a(k) = SP\_a / (SP\_A + k + 1)^{SP\_alpha}$ .
SP_a can be defined for each resolution.
example: (SP_a 3200.0 3200.0 1600.0)
The default value is 400.0. Tuning this variable for you specific problem is recommended. Alternatively set the AutomaticParameterEstimation to "true". In that case, you do not need to specify SP_a. SP_a has no influence when AutomaticParameterEstimation is used.

SP_A: The gain at each iteration is defined by
$a(k) = SP\_a / (SP\_A + k + 1)^{SP\_alpha}$ .
SP_A can be defined for each resolution.
example: (SP_A 50.0 50.0 100.0)
The default/recommended value for this particular optimizer is 20.0.

SP_alpha: The gain at each iteration is defined by
$a(k) = SP\_a / (SP\_A + k + 1)^{SP\_alpha}$ .
SP_alpha can be defined for each resolution.
example: (SP_alpha 0.602 0.602 0.602)
The default/recommended value for this particular optimizer is 1.0. Alternatively set the AutomaticParameterEstimation to "true". In that case, you do not need to specify SP_alpha. SP_alpha has no influence when AutomaticParameterEstimation is used.

SigmoidMax: The maximum of the sigmoid function ( $f_{max}$ ). Must be larger than 0. The parameter can be specified for each resolution, or for all resolutions at once.
example: (SigmoidMax 1.0)
Default/recommended value: 1.0. This parameter has no influence when AutomaticParameterEstimation is used. In that case, always a value 1.0 is used.

SigmoidMin: The minimum of the sigmoid function ( $f_{min}$ ). Must be smaller than 0. The parameter can be specified for each resolution, or for all resolutions at once.
example: (SigmoidMin -0.8)
Default value: -0.8. This parameter has no influence when AutomaticParameterEstimation is used. In that case, the value is automatically determined, depending on the images, metric etc.

SigmoidScale: The scale/width of the sigmoid function ( $\omega$ ). The parameter can be specified for each resolution, or for all resolutions at once.
example: (SigmoidScale 0.00001)
Default value: 1e-8. This parameter has no influence when AutomaticParameterEstimation is used. In that case, the value is automatically determined, depending on the images, metric etc.

SigmoidInitialTime: the initial time input for the sigmoid ( ). Must be larger than 0.0. The parameter can be specified for each resolution, or for all resolutions at once.
example: (SigmoidInitialTime 0.0 5.0 5.0)
Default value: 0.0. When increased, the optimization starts with smaller steps, leaving the possibility to increase the steps when necessary. If set to 0.0, the method starts with with the largest step allowed.

NumberOfGradientMeasurements: Number of gradients N to estimate the average square magnitudes of the exact gradient and the approximation error. The parameter can be specified for each resolution, or for all resolutions at once.
example: (NumberOfGradientMeasurements 10)
Default value: 0, which means that the value is automatically estimated. In principle, the more the better, but the slower. In practice N=10 is usually sufficient. But the automatic estimation achieved by N=0 also works good. The parameter has only influence when AutomaticParameterEstimation is used.

NumberOfJacobianMeasurements: The number of voxels M where the Jacobian is measured, which is used to estimate the covariance matrix. The parameter can be specified for each resolution, or for all resolutions at once.
example: (NumberOfJacobianMeasurements 5000 10000 20000)
Default value: M = max( 1000, nrofparams ), with nrofparams the number of transform parameters. This is a rather crude rule of thumb, which seems to work in practice. In principle, the more the better, but the slower. The parameter has only influence when AutomaticParameterEstimation is used.

NumberOfSamplesForExactGradient: The number of image samples used to compute the 'exact' gradient. The samples are chosen on a uniform grid. The parameter can be specified for each resolution, or for all resolutions at once.
example: (NumberOfSamplesForExactGradient 100000)
Default/recommended: 100000. This works in general. If the image is smaller, the number of samples is automatically reduced. In principle, the more the better, but the slower. The parameter has only influence when AutomaticParameterEstimation is used.

ASGDParameterEstimationMethod: The ASGD parameter estimation method used in this optimizer. The parameter can be specified for each resolution.
example: (ASGDParameterEstimationMethod "Original")
or (ASGDParameterEstimationMethod "DisplacementDistribution")
Default: Original.

MaximumDisplacementEstimationMethod: The suitable position selection method used only for displacement distribution estimation method. The parameter can be specified for each resolution.
example: (MaximumDisplacementEstimationMethod "2sigma")
or (MaximumDisplacementEstimationMethod "95percentile")
Default: 2sigma.

NoiseCompensation: Selects whether or not to use noise compensation. The parameter can be specified for each resolution, or for all resolutions at once.
example: (NoiseCompensation "true")
Default/recommended: true.

Todo:: : this class contains a lot of functional code, which actually does not belong here.

See also: AdaptiveStochasticGradientDescentOptimizer

Definition at line 193 of file elxAdaptiveStochasticGradientDescent.h.

Inheritance diagram for elastix::AdaptiveStochasticGradientDescent< TElastix >:

Data Structures
struct	SettingsType

Public Types
typedef Superclass2::ConfigurationPointer	ConfigurationPointer

typedef Superclass2::ConfigurationType	ConfigurationType

typedef itk::SmartPointer< const Self >	ConstPointer

typedef Superclass1::CostFunctionPointer	CostFunctionPointer

typedef Superclass1::CostFunctionType	CostFunctionType

typedef Superclass2::ElastixPointer	ElastixPointer

typedef Superclass2::ElastixType	ElastixType

typedef Superclass2::ITKBaseType	ITKBaseType

typedef Superclass1::ParametersType	ParametersType

typedef itk::SmartPointer< Self >	Pointer

typedef Superclass2::RegistrationPointer	RegistrationPointer

typedef Superclass2::RegistrationType	RegistrationType

typedef AdaptiveStochasticGradientDescent	Self

typedef itk::SizeValueType	SizeValueType

typedef Superclass1::StopConditionType	StopConditionType

typedef AdaptiveStochasticGradientDescentOptimizer	Superclass1

typedef OptimizerBase< TElastix >	Superclass2

Public Types inherited from itk::AdaptiveStochasticGradientDescentOptimizer
typedef SmartPointer< const Self >	ConstPointer

typedef Superclass::CostFunctionType	CostFunctionType

typedef Superclass::DerivativeType	DerivativeType

typedef Superclass::MeasureType	MeasureType

typedef Superclass::ParametersType	ParametersType

typedef SmartPointer< Self >	Pointer

typedef Superclass::ScaledCostFunctionPointer	ScaledCostFunctionPointer

typedef Superclass::ScaledCostFunctionType	ScaledCostFunctionType

typedef Superclass::ScalesType	ScalesType

typedef AdaptiveStochasticGradientDescentOptimizer	Self

typedef Superclass::StopConditionType	StopConditionType

typedef StandardGradientDescentOptimizer	Superclass

Public Types inherited from itk::StandardGradientDescentOptimizer
typedef SmartPointer< const Self >	ConstPointer

typedef Superclass::CostFunctionType	CostFunctionType

typedef Superclass::DerivativeType	DerivativeType

typedef Superclass::MeasureType	MeasureType

typedef Superclass::ParametersType	ParametersType

typedef SmartPointer< Self >	Pointer

typedef Superclass::ScaledCostFunctionPointer	ScaledCostFunctionPointer

typedef Superclass::ScaledCostFunctionType	ScaledCostFunctionType

typedef Superclass::ScalesType	ScalesType

typedef StandardGradientDescentOptimizer	Self

typedef Superclass::StopConditionType	StopConditionType

typedef GradientDescentOptimizer2	Superclass

Public Types inherited from itk::GradientDescentOptimizer2
typedef SmartPointer< const Self >	ConstPointer

typedef Superclass::CostFunctionType	CostFunctionType

typedef Superclass::DerivativeType	DerivativeType

typedef Superclass::MeasureType	MeasureType

typedef Superclass::ParametersType	ParametersType

typedef SmartPointer< Self >	Pointer

typedef Superclass::ScaledCostFunctionPointer	ScaledCostFunctionPointer

typedef Superclass::ScaledCostFunctionType	ScaledCostFunctionType

typedef Superclass::ScalesType	ScalesType

typedef GradientDescentOptimizer2	Self

enum	StopConditionType { MaximumNumberOfIterations , MetricError , MinimumStepSize }

typedef ScaledSingleValuedNonLinearOptimizer	Superclass

Public Types inherited from itk::ScaledSingleValuedNonLinearOptimizer
typedef SmartPointer< const Self >	ConstPointer

typedef Superclass::CostFunctionType	CostFunctionType

typedef Superclass::DerivativeType	DerivativeType

typedef Superclass::MeasureType	MeasureType

typedef Superclass::ParametersType	ParametersType

typedef SmartPointer< Self >	Pointer

typedef ScaledCostFunctionType::Pointer	ScaledCostFunctionPointer

typedef ScaledSingleValuedCostFunction	ScaledCostFunctionType

typedef NonLinearOptimizer::ScalesType	ScalesType

typedef ScaledSingleValuedNonLinearOptimizer	Self

typedef SingleValuedNonLinearOptimizer	Superclass

Public Types inherited from elastix::OptimizerBase< TElastix >
typedef Superclass::ConfigurationPointer	ConfigurationPointer

typedef Superclass::ConfigurationType	ConfigurationType

typedef Superclass::ElastixPointer	ElastixPointer

typedef Superclass::ElastixType	ElastixType

typedef itk::Optimizer	ITKBaseType

typedef ITKBaseType::ParametersType	ParametersType

typedef Superclass::RegistrationPointer	RegistrationPointer

typedef Superclass::RegistrationType	RegistrationType

typedef OptimizerBase	Self

typedef BaseComponentSE< TElastix >	Superclass

Public Types inherited from elastix::BaseComponentSE< TElastix >
typedef Configuration::Pointer	ConfigurationPointer

typedef Configuration	ConfigurationType

typedef itk::WeakPointer< ElastixType >	ElastixPointer

typedef TElastix	ElastixType

typedef RegistrationType *	RegistrationPointer

typedef ElastixType::RegistrationBaseType	RegistrationType

typedef BaseComponentSE	Self

typedef BaseComponent	Superclass

Public Member Functions
void	AfterEachIteration (void) override

void	AfterEachResolution (void) override

void	AfterRegistration (void) override

void	BeforeEachResolution (void) override

void	BeforeRegistration (void) override

	elxClassNameMacro ("AdaptiveStochasticGradientDescent")

virtual bool	GetAutomaticParameterEstimation () const

virtual const char *	GetClassName () const

virtual const SizeValueType &	GetMaximumNumberOfSamplingAttempts ()

virtual double	GetMaximumStepLength () const

void	MetricErrorResponse (itk::ExceptionObject &err) override

void	ResumeOptimization (void) override

virtual void	SetAutomaticParameterEstimation (bool _arg)

virtual void	SetMaximumNumberOfSamplingAttempts (SizeValueType _arg)

virtual void	SetMaximumStepLength (double _arg)

void	StartOptimization (void) override

Public Member Functions inherited from itk::AdaptiveStochasticGradientDescentOptimizer
virtual const char *	GetClassName () const

virtual double	GetSigmoidMax () const

virtual double	GetSigmoidMin () const

virtual double	GetSigmoidScale () const

virtual bool	GetUseAdaptiveStepSizes () const

virtual void	SetSigmoidMax (double _arg)

virtual void	SetSigmoidMin (double _arg)

virtual void	SetSigmoidScale (double _arg)

virtual void	SetUseAdaptiveStepSizes (bool _arg)

Public Member Functions inherited from itk::StandardGradientDescentOptimizer
void	AdvanceOneStep (void) override

virtual const char *	GetClassName () const

virtual double	GetCurrentTime () const

virtual double	GetInitialTime () const

virtual double	GetParam_a () const

virtual double	GetParam_A () const

virtual double	GetParam_alpha () const

virtual void	ResetCurrentTimeToInitialTime (void)

virtual void	SetInitialTime (double _arg)

virtual void	SetParam_a (double _arg)

virtual void	SetParam_A (double _arg)

virtual void	SetParam_alpha (double _arg)

void	StartOptimization (void) override

Public Member Functions inherited from itk::GradientDescentOptimizer2
virtual void	AdvanceOneStep (void)

virtual const char *	GetClassName () const

virtual unsigned int	GetCurrentIteration () const

virtual const DerivativeType &	GetGradient ()

virtual const double &	GetLearningRate ()

virtual const unsigned long &	GetNumberOfIterations ()

virtual const DerivativeType &	GetSearchDirection ()

virtual const StopConditionType &	GetStopCondition ()

virtual const double &	GetValue ()

virtual void	MetricErrorResponse (ExceptionObject &err)

virtual void	ResumeOptimization (void)

virtual void	SetLearningRate (double _arg)

virtual void	SetNumberOfIterations (unsigned long _arg)

virtual void	SetUseOpenMP (bool _arg)

void	StartOptimization (void) override

virtual void	StopOptimization (void)

Public Member Functions inherited from itk::ScaledSingleValuedNonLinearOptimizer
virtual const char *	GetClassName () const

const ParametersType &	GetCurrentPosition (void) const override

virtual bool	GetMaximize () const

virtual const ScaledCostFunctionType *	GetScaledCostFunction ()

virtual const ParametersType &	GetScaledCurrentPosition ()

bool	GetUseScales (void) const

virtual void	InitializeScales (void)

virtual void	MaximizeOff ()

virtual void	MaximizeOn ()

void	SetCostFunction (CostFunctionType *costFunction) override

virtual void	SetMaximize (bool _arg)

virtual void	SetUseScales (bool arg)

Public Member Functions inherited from elastix::OptimizerBase< TElastix >
void	AfterRegistrationBase (void) override

void	BeforeEachResolutionBase () override

virtual ITKBaseType *	GetAsITKBaseType (void)

virtual const ITKBaseType *	GetAsITKBaseType (void) const

virtual const char *	GetClassName () const

virtual void	SetCurrentPositionPublic (const ParametersType &param)

virtual void	SetSinusScales (double amplitude, double frequency, unsigned long numberOfParameters)

Public Member Functions inherited from elastix::BaseComponentSE< TElastix >
ConfigurationType *	GetConfiguration (void) const

ElastixType *	GetElastix (void) const

RegistrationPointer	GetRegistration (void) const

	ITK_DISALLOW_COPY_AND_ASSIGN (BaseComponentSE)

void	SetConfiguration (ConfigurationType *_arg)

void	SetElastix (ElastixType *_arg)

Public Member Functions inherited from elastix::BaseComponent
virtual void	AfterEachIteration (void)

virtual void	AfterEachIterationBase (void)

virtual void	AfterEachResolution (void)

virtual void	AfterEachResolutionBase (void)

virtual void	AfterRegistration (void)

virtual void	AfterRegistrationBase (void)

virtual int	BeforeAll (void)

virtual int	BeforeAllBase (void)

virtual void	BeforeEachResolution (void)

virtual void	BeforeEachResolutionBase (void)

virtual void	BeforeRegistration (void)

virtual void	BeforeRegistrationBase (void)

virtual const char *	elxGetClassName (void) const

const char *	GetComponentLabel (void) const

	ITK_DISALLOW_COPY_AND_ASSIGN (BaseComponent)

	itkTypeMacroNoParent (BaseComponent)

void	SetComponentLabel (const char *label, unsigned int idx)

Static Public Member Functions
static Pointer	New ()

Static Public Member Functions inherited from itk::AdaptiveStochasticGradientDescentOptimizer
static Pointer	New ()

Static Public Member Functions inherited from itk::StandardGradientDescentOptimizer
static Pointer	New ()

Static Public Member Functions inherited from itk::GradientDescentOptimizer2
static Pointer	New ()

Static Public Member Functions inherited from itk::ScaledSingleValuedNonLinearOptimizer
static Pointer	New ()

Static Public Member Functions inherited from elastix::BaseComponent
static std::string	ConvertSecondsToDHMS (const double totalSeconds, const unsigned int precision)

static void	InitializeElastixExecutable ()

static bool	IsElastixLibrary ()

Protected Types
typedef AdvancedTransformType::Pointer	AdvancedTransformPointer

typedef itk::AdvancedTransform< CoordinateRepresentationType, itkGetStaticConstMacro(FixedImageDimension), itkGetStaticConstMacro(MovingImageDimension) >	AdvancedTransformType

typedef itk::ComputeDisplacementDistribution< FixedImageType, TransformType >	ComputeDisplacementDistributionType

typedef itk::ComputeJacobianTerms< FixedImageType, TransformType >	ComputeJacobianTermsType

typedef TransformType::ScalarType	CoordinateRepresentationType

typedef FixedImageType::IndexType	FixedImageIndexType

typedef FixedImageType::PointType	FixedImagePointType

typedef FixedImageType::RegionType	FixedImageRegionType

typedef RegistrationType::FixedImageType	FixedImageType

typedef ImageGridSamplerType::Pointer	ImageGridSamplerPointer

typedef itk::ImageGridSampler< FixedImageType >	ImageGridSamplerType

typedef ImageRandomCoordinateSamplerType::Pointer	ImageRandomCoordinateSamplerPointer

typedef itk::ImageRandomCoordinateSampler< FixedImageType >	ImageRandomCoordinateSamplerType

typedef ImageRandomSamplerBaseType::Pointer	ImageRandomSamplerBasePointer

typedef itk::ImageRandomSamplerBase< FixedImageType >	ImageRandomSamplerBaseType

typedef ImageSampleContainerType::Pointer	ImageSampleContainerPointer

typedef ImageGridSamplerType::ImageSampleContainerType	ImageSampleContainerType

typedef ImageSamplerBaseType::Pointer	ImageSamplerBasePointer

typedef itk::ImageSamplerBase< FixedImageType >	ImageSamplerBaseType

typedef RegistrationType::ITKBaseType	itkRegistrationType

typedef TransformType::JacobianType	JacobianType

typedef JacobianType::ValueType	JacobianValueType

typedef RegistrationType::MovingImageType	MovingImageType

typedef AdvancedTransformType::NonZeroJacobianIndicesType	NonZeroJacobianIndicesType

typedef ProgressCommand::Pointer	ProgressCommandPointer

typedef ProgressCommand	ProgressCommandType

typedef RandomGeneratorType::Pointer	RandomGeneratorPointer

typedef itk::Statistics::MersenneTwisterRandomVariateGenerator	RandomGeneratorType

typedef std::vector< SettingsType >	SettingsVectorType

typedef JacobianType	TransformJacobianType

typedef itkRegistrationType::TransformType	TransformType

Protected Member Functions
	AdaptiveStochasticGradientDescent ()

virtual void	AddRandomPerturbation (ParametersType &parameters, double sigma)

virtual void	AutomaticParameterEstimation (void)

virtual void	AutomaticParameterEstimationOriginal (void)

virtual void	AutomaticParameterEstimationUsingDisplacementDistribution (void)

virtual void	CheckForAdvancedTransform (void)

virtual void	GetScaledDerivativeWithExceptionHandling (const ParametersType &parameters, DerivativeType &derivative)

	itkStaticConstMacro (FixedImageDimension, unsigned int, FixedImageType::ImageDimension)

	itkStaticConstMacro (MovingImageDimension, unsigned int, MovingImageType::ImageDimension)

virtual void	PrintSettingsVector (const SettingsVectorType &settings) const

virtual void	SampleGradients (const ParametersType &mu0, double perturbationSigma, double &gg, double &ee)

	~AdaptiveStochasticGradientDescent () override

Protected Member Functions inherited from itk::AdaptiveStochasticGradientDescentOptimizer
	AdaptiveStochasticGradientDescentOptimizer ()

void	UpdateCurrentTime (void) override

	~AdaptiveStochasticGradientDescentOptimizer () override

Protected Member Functions inherited from itk::StandardGradientDescentOptimizer
virtual double	Compute_a (double k) const

	StandardGradientDescentOptimizer ()

virtual void	UpdateCurrentTime (void)

	~StandardGradientDescentOptimizer () override

Protected Member Functions inherited from itk::GradientDescentOptimizer2
	GradientDescentOptimizer2 ()

void	PrintSelf (std::ostream &os, Indent indent) const override

	~GradientDescentOptimizer2 () override

Protected Member Functions inherited from itk::ScaledSingleValuedNonLinearOptimizer
virtual void	GetScaledDerivative (const ParametersType &parameters, DerivativeType &derivative) const

virtual MeasureType	GetScaledValue (const ParametersType &parameters) const

virtual void	GetScaledValueAndDerivative (const ParametersType &parameters, MeasureType &value, DerivativeType &derivative) const

void	PrintSelf (std::ostream &os, Indent indent) const override

	ScaledSingleValuedNonLinearOptimizer ()

void	SetCurrentPosition (const ParametersType &param) override

virtual void	SetScaledCurrentPosition (const ParametersType &parameters)

	~ScaledSingleValuedNonLinearOptimizer () override

Protected Member Functions inherited from elastix::OptimizerBase< TElastix >
virtual bool	GetNewSamplesEveryIteration (void) const

	OptimizerBase ()

virtual void	SelectNewSamples (void)

	~OptimizerBase () override

Protected Member Functions inherited from elastix::BaseComponentSE< TElastix >
	BaseComponentSE ()=default

	~BaseComponentSE () override=default

Protected Member Functions inherited from elastix::BaseComponent
	BaseComponent ()=default

virtual	~BaseComponent ()=default

Protected Attributes
AdvancedTransformPointer	m_AdvancedTransform

SizeValueType	m_NumberOfGradientMeasurements

SizeValueType	m_NumberOfJacobianMeasurements

SizeValueType	m_NumberOfSamplesForExactGradient

RandomGeneratorPointer	m_RandomGenerator

SettingsVectorType	m_SettingsVector

double	m_SigmoidScaleFactor

Protected Attributes inherited from itk::AdaptiveStochasticGradientDescentOptimizer
DerivativeType	m_PreviousGradient

Protected Attributes inherited from itk::StandardGradientDescentOptimizer
double	m_CurrentTime

bool	m_UseConstantStep

Protected Attributes inherited from itk::GradientDescentOptimizer2
unsigned long	m_CurrentIteration

DerivativeType	m_Gradient

double	m_LearningRate

unsigned long	m_NumberOfIterations

DerivativeType	m_SearchDirection

bool	m_Stop

StopConditionType	m_StopCondition

double	m_Value

Protected Attributes inherited from itk::ScaledSingleValuedNonLinearOptimizer
ScaledCostFunctionPointer	m_ScaledCostFunction

ParametersType	m_ScaledCurrentPosition

Protected Attributes inherited from elastix::BaseComponentSE< TElastix >
ConfigurationPointer	m_Configuration {}

ElastixPointer	m_Elastix {}

RegistrationPointer	m_Registration {}

Private Member Functions
	AdaptiveStochasticGradientDescent (const Self &)

void	operator= (const Self &)

Member Typedef Documentation

◆ AdvancedTransformPointer

template<class TElastix >

typedef AdvancedTransformType::Pointer elastix::AdaptiveStochasticGradientDescent< TElastix >::AdvancedTransformPointer

protected

Definition at line 338 of file elxAdaptiveStochasticGradientDescent.h.

◆ AdvancedTransformType

template<class TElastix >

typedef itk::AdvancedTransform< CoordinateRepresentationType, itkGetStaticConstMacro( FixedImageDimension ), itkGetStaticConstMacro( MovingImageDimension ) > elastix::AdaptiveStochasticGradientDescent< TElastix >::AdvancedTransformType

protected

Definition at line 337 of file elxAdaptiveStochasticGradientDescent.h.

◆ ComputeDisplacementDistributionType

template<class TElastix >

typedef itk::ComputeDisplacementDistribution< FixedImageType, TransformType > elastix::AdaptiveStochasticGradientDescent< TElastix >::ComputeDisplacementDistributionType

protected

Definition at line 305 of file elxAdaptiveStochasticGradientDescent.h.

◆ ComputeJacobianTermsType

template<class TElastix >

typedef itk::ComputeJacobianTerms< FixedImageType, TransformType > elastix::AdaptiveStochasticGradientDescent< TElastix >::ComputeJacobianTermsType

protected

Definition at line 299 of file elxAdaptiveStochasticGradientDescent.h.

◆ ConfigurationPointer

template<class TElastix >

typedef Superclass2::ConfigurationPointer elastix::AdaptiveStochasticGradientDescent< TElastix >::ConfigurationPointer

Definition at line 228 of file elxAdaptiveStochasticGradientDescent.h.

◆ ConfigurationType

template<class TElastix >

typedef Superclass2::ConfigurationType elastix::AdaptiveStochasticGradientDescent< TElastix >::ConfigurationType

Definition at line 227 of file elxAdaptiveStochasticGradientDescent.h.

◆ ConstPointer

template<class TElastix >

typedef itk::SmartPointer< const Self > elastix::AdaptiveStochasticGradientDescent< TElastix >::ConstPointer

Definition at line 204 of file elxAdaptiveStochasticGradientDescent.h.

◆ CoordinateRepresentationType

template<class TElastix >

typedef TransformType::ScalarType elastix::AdaptiveStochasticGradientDescent< TElastix >::CoordinateRepresentationType

protected

Definition at line 333 of file elxAdaptiveStochasticGradientDescent.h.

◆ CostFunctionPointer

template<class TElastix >

typedef Superclass1::CostFunctionPointer elastix::AdaptiveStochasticGradientDescent< TElastix >::CostFunctionPointer

Definition at line 221 of file elxAdaptiveStochasticGradientDescent.h.

◆ CostFunctionType

template<class TElastix >

typedef Superclass1::CostFunctionType elastix::AdaptiveStochasticGradientDescent< TElastix >::CostFunctionType

Typedef's inherited from Superclass1.

Definition at line 220 of file elxAdaptiveStochasticGradientDescent.h.

◆ ElastixPointer

template<class TElastix >

typedef Superclass2::ElastixPointer elastix::AdaptiveStochasticGradientDescent< TElastix >::ElastixPointer

Definition at line 226 of file elxAdaptiveStochasticGradientDescent.h.

◆ ElastixType

template<class TElastix >

typedef Superclass2::ElastixType elastix::AdaptiveStochasticGradientDescent< TElastix >::ElastixType

Typedef's inherited from Superclass2.

Definition at line 225 of file elxAdaptiveStochasticGradientDescent.h.

◆ FixedImageIndexType

template<class TElastix >

typedef FixedImageType::IndexType elastix::AdaptiveStochasticGradientDescent< TElastix >::FixedImageIndexType

protected

Definition at line 293 of file elxAdaptiveStochasticGradientDescent.h.

◆ FixedImagePointType

template<class TElastix >

typedef FixedImageType::PointType elastix::AdaptiveStochasticGradientDescent< TElastix >::FixedImagePointType

protected

Definition at line 294 of file elxAdaptiveStochasticGradientDescent.h.

◆ FixedImageRegionType

template<class TElastix >

typedef FixedImageType::RegionType elastix::AdaptiveStochasticGradientDescent< TElastix >::FixedImageRegionType

protected

Definition at line 292 of file elxAdaptiveStochasticGradientDescent.h.

◆ FixedImageType

template<class TElastix >

typedef RegistrationType::FixedImageType elastix::AdaptiveStochasticGradientDescent< TElastix >::FixedImageType

protected

Protected typedefs

Definition at line 289 of file elxAdaptiveStochasticGradientDescent.h.

◆ ImageGridSamplerPointer

template<class TElastix >

typedef ImageGridSamplerType::Pointer elastix::AdaptiveStochasticGradientDescent< TElastix >::ImageGridSamplerPointer

protected

Definition at line 318 of file elxAdaptiveStochasticGradientDescent.h.

◆ ImageGridSamplerType

template<class TElastix >

typedef itk::ImageGridSampler< FixedImageType > elastix::AdaptiveStochasticGradientDescent< TElastix >::ImageGridSamplerType

protected

Definition at line 317 of file elxAdaptiveStochasticGradientDescent.h.

◆ ImageRandomCoordinateSamplerPointer

template<class TElastix >

typedef ImageRandomCoordinateSamplerType::Pointer elastix::AdaptiveStochasticGradientDescent< TElastix >::ImageRandomCoordinateSamplerPointer

protected

Definition at line 316 of file elxAdaptiveStochasticGradientDescent.h.

◆ ImageRandomCoordinateSamplerType

template<class TElastix >

typedef itk::ImageRandomCoordinateSampler< FixedImageType > elastix::AdaptiveStochasticGradientDescent< TElastix >::ImageRandomCoordinateSamplerType

protected

Definition at line 314 of file elxAdaptiveStochasticGradientDescent.h.

◆ ImageRandomSamplerBasePointer

template<class TElastix >

typedef ImageRandomSamplerBaseType::Pointer elastix::AdaptiveStochasticGradientDescent< TElastix >::ImageRandomSamplerBasePointer

protected

Definition at line 312 of file elxAdaptiveStochasticGradientDescent.h.

◆ ImageRandomSamplerBaseType

template<class TElastix >

typedef itk::ImageRandomSamplerBase< FixedImageType > elastix::AdaptiveStochasticGradientDescent< TElastix >::ImageRandomSamplerBaseType

protected

Definition at line 310 of file elxAdaptiveStochasticGradientDescent.h.

◆ ImageSampleContainerPointer

template<class TElastix >

typedef ImageSampleContainerType::Pointer elastix::AdaptiveStochasticGradientDescent< TElastix >::ImageSampleContainerPointer

protected

Definition at line 321 of file elxAdaptiveStochasticGradientDescent.h.

◆ ImageSampleContainerType

template<class TElastix >

typedef ImageGridSamplerType::ImageSampleContainerType elastix::AdaptiveStochasticGradientDescent< TElastix >::ImageSampleContainerType

protected

Definition at line 320 of file elxAdaptiveStochasticGradientDescent.h.

◆ ImageSamplerBasePointer

template<class TElastix >

typedef ImageSamplerBaseType::Pointer elastix::AdaptiveStochasticGradientDescent< TElastix >::ImageSamplerBasePointer

protected

Definition at line 309 of file elxAdaptiveStochasticGradientDescent.h.

◆ ImageSamplerBaseType

template<class TElastix >

typedef itk::ImageSamplerBase< FixedImageType > elastix::AdaptiveStochasticGradientDescent< TElastix >::ImageSamplerBaseType

protected

Samplers:

Definition at line 308 of file elxAdaptiveStochasticGradientDescent.h.

◆ ITKBaseType

template<class TElastix >

typedef Superclass2::ITKBaseType elastix::AdaptiveStochasticGradientDescent< TElastix >::ITKBaseType

Definition at line 231 of file elxAdaptiveStochasticGradientDescent.h.

◆ itkRegistrationType

template<class TElastix >

typedef RegistrationType::ITKBaseType elastix::AdaptiveStochasticGradientDescent< TElastix >::itkRegistrationType

protected

Definition at line 295 of file elxAdaptiveStochasticGradientDescent.h.

◆ JacobianType

template<class TElastix >

typedef TransformType::JacobianType elastix::AdaptiveStochasticGradientDescent< TElastix >::JacobianType

protected

Definition at line 297 of file elxAdaptiveStochasticGradientDescent.h.

◆ JacobianValueType

template<class TElastix >

typedef JacobianType::ValueType elastix::AdaptiveStochasticGradientDescent< TElastix >::JacobianValueType

protected

Definition at line 300 of file elxAdaptiveStochasticGradientDescent.h.

◆ MovingImageType

template<class TElastix >

typedef RegistrationType::MovingImageType elastix::AdaptiveStochasticGradientDescent< TElastix >::MovingImageType

protected

Definition at line 290 of file elxAdaptiveStochasticGradientDescent.h.

◆ NonZeroJacobianIndicesType

template<class TElastix >

typedef AdvancedTransformType::NonZeroJacobianIndicesType elastix::AdaptiveStochasticGradientDescent< TElastix >::NonZeroJacobianIndicesType

protected

Definition at line 340 of file elxAdaptiveStochasticGradientDescent.h.

◆ ParametersType

template<class TElastix >

typedef Superclass1::ParametersType elastix::AdaptiveStochasticGradientDescent< TElastix >::ParametersType

Typedef for the ParametersType.

Definition at line 235 of file elxAdaptiveStochasticGradientDescent.h.

◆ Pointer

template<class TElastix >

typedef itk::SmartPointer< Self > elastix::AdaptiveStochasticGradientDescent< TElastix >::Pointer

Definition at line 203 of file elxAdaptiveStochasticGradientDescent.h.

◆ ProgressCommandPointer

template<class TElastix >

typedef ProgressCommand::Pointer elastix::AdaptiveStochasticGradientDescent< TElastix >::ProgressCommandPointer

protected

Definition at line 327 of file elxAdaptiveStochasticGradientDescent.h.

◆ ProgressCommandType

template<class TElastix >

typedef ProgressCommand elastix::AdaptiveStochasticGradientDescent< TElastix >::ProgressCommandType

protected

Definition at line 326 of file elxAdaptiveStochasticGradientDescent.h.

◆ RandomGeneratorPointer

template<class TElastix >

typedef RandomGeneratorType::Pointer elastix::AdaptiveStochasticGradientDescent< TElastix >::RandomGeneratorPointer

protected

Definition at line 325 of file elxAdaptiveStochasticGradientDescent.h.

◆ RandomGeneratorType

template<class TElastix >

typedef itk::Statistics::MersenneTwisterRandomVariateGenerator elastix::AdaptiveStochasticGradientDescent< TElastix >::RandomGeneratorType

protected

Other protected typedefs

Definition at line 324 of file elxAdaptiveStochasticGradientDescent.h.

◆ RegistrationPointer

template<class TElastix >

typedef Superclass2::RegistrationPointer elastix::AdaptiveStochasticGradientDescent< TElastix >::RegistrationPointer

Definition at line 230 of file elxAdaptiveStochasticGradientDescent.h.

◆ RegistrationType

template<class TElastix >

typedef Superclass2::RegistrationType elastix::AdaptiveStochasticGradientDescent< TElastix >::RegistrationType

Definition at line 229 of file elxAdaptiveStochasticGradientDescent.h.

◆ Self

template<class TElastix >

typedef AdaptiveStochasticGradientDescent elastix::AdaptiveStochasticGradientDescent< TElastix >::Self

Standard ITK.

Definition at line 200 of file elxAdaptiveStochasticGradientDescent.h.

◆ SettingsVectorType

template<class TElastix >

typedef std::vector< SettingsType > elastix::AdaptiveStochasticGradientDescent< TElastix >::SettingsVectorType

protected

Definition at line 302 of file elxAdaptiveStochasticGradientDescent.h.

◆ SizeValueType

template<class TElastix >

typedef itk::SizeValueType elastix::AdaptiveStochasticGradientDescent< TElastix >::SizeValueType

Definition at line 232 of file elxAdaptiveStochasticGradientDescent.h.

◆ StopConditionType

template<class TElastix >

typedef Superclass1::StopConditionType elastix::AdaptiveStochasticGradientDescent< TElastix >::StopConditionType

Definition at line 222 of file elxAdaptiveStochasticGradientDescent.h.

◆ Superclass1

template<class TElastix >

typedef AdaptiveStochasticGradientDescentOptimizer elastix::AdaptiveStochasticGradientDescent< TElastix >::Superclass1

Definition at line 201 of file elxAdaptiveStochasticGradientDescent.h.

◆ Superclass2

template<class TElastix >

typedef OptimizerBase< TElastix > elastix::AdaptiveStochasticGradientDescent< TElastix >::Superclass2

Definition at line 202 of file elxAdaptiveStochasticGradientDescent.h.

◆ TransformJacobianType

template<class TElastix >

typedef JacobianType elastix::AdaptiveStochasticGradientDescent< TElastix >::TransformJacobianType

protected

Typedefs for support of sparse Jacobians and AdvancedTransforms.

Definition at line 330 of file elxAdaptiveStochasticGradientDescent.h.

◆ TransformType

template<class TElastix >

typedef itkRegistrationType::TransformType elastix::AdaptiveStochasticGradientDescent< TElastix >::TransformType

protected

Definition at line 296 of file elxAdaptiveStochasticGradientDescent.h.

Constructor & Destructor Documentation

◆ AdaptiveStochasticGradientDescent() [1/2]

template<class TElastix >

elastix::AdaptiveStochasticGradientDescent< TElastix >::AdaptiveStochasticGradientDescent ( )

protected

◆ ~AdaptiveStochasticGradientDescent()

template<class TElastix >

elastix::AdaptiveStochasticGradientDescent< TElastix >::~AdaptiveStochasticGradientDescent ( )

inlineoverrideprotected

Definition at line 343 of file elxAdaptiveStochasticGradientDescent.h.

◆ AdaptiveStochasticGradientDescent() [2/2]

template<class TElastix >

elastix::AdaptiveStochasticGradientDescent< TElastix >::AdaptiveStochasticGradientDescent ( const Self & )

private

Member Function Documentation

◆ AddRandomPerturbation()

template<class TElastix >

virtual void elastix::AdaptiveStochasticGradientDescent< TElastix >::AddRandomPerturbation	(	ParametersType &	parameters,
		double	sigma
	)

protectedvirtual

Helper function that adds a random perturbation delta to the input parameters, with delta ~ sigma * N(0,I). Used by SampleGradients.

◆ AfterEachIteration()

template<class TElastix >

void elastix::AdaptiveStochasticGradientDescent< TElastix >::AfterEachIteration ( void )

overridevirtual

Reimplemented from elastix::BaseComponent.

◆ AfterEachResolution()

template<class TElastix >

void elastix::AdaptiveStochasticGradientDescent< TElastix >::AfterEachResolution ( void )

overridevirtual

Reimplemented from elastix::BaseComponent.

◆ AfterRegistration()

template<class TElastix >

void elastix::AdaptiveStochasticGradientDescent< TElastix >::AfterRegistration ( void )

overridevirtual

Reimplemented from elastix::BaseComponent.

◆ AutomaticParameterEstimation()

template<class TElastix >

virtual void elastix::AdaptiveStochasticGradientDescent< TElastix >::AutomaticParameterEstimation ( void )

protectedvirtual

Select different method to estimate some reasonable values for the parameters SP_a, SP_alpha (=1), SigmoidMin, SigmoidMax (=1), and SigmoidScale.

◆ AutomaticParameterEstimationOriginal()

template<class TElastix >

virtual void elastix::AdaptiveStochasticGradientDescent< TElastix >::AutomaticParameterEstimationOriginal ( void )

protectedvirtual

Original estimation method to get the reasonable values for the parameters SP_a, SP_alpha (=1), SigmoidMin, SigmoidMax (=1), and SigmoidScale.

◆ AutomaticParameterEstimationUsingDisplacementDistribution()

template<class TElastix >

virtual void elastix::AdaptiveStochasticGradientDescent< TElastix >::AutomaticParameterEstimationUsingDisplacementDistribution ( void )

protectedvirtual

Estimates some reasonable values for the parameters using displacement distribution SP_a, SP_alpha (=1)

◆ BeforeEachResolution()

template<class TElastix >

void elastix::AdaptiveStochasticGradientDescent< TElastix >::BeforeEachResolution ( void )

overridevirtual

Reimplemented from elastix::BaseComponent.

◆ BeforeRegistration()

template<class TElastix >

void elastix::AdaptiveStochasticGradientDescent< TElastix >::BeforeRegistration ( void )

overridevirtual

Methods invoked by elastix, in which parameters can be set and progress information can be printed.

Reimplemented from elastix::BaseComponent.

◆ CheckForAdvancedTransform()

template<class TElastix >

virtual void elastix::AdaptiveStochasticGradientDescent< TElastix >::CheckForAdvancedTransform ( void )

protectedvirtual

Check if the transform is an advanced transform. Called by Initialize.

◆ elxClassNameMacro()

template<class TElastix >

elastix::AdaptiveStochasticGradientDescent< TElastix >::elxClassNameMacro ( "AdaptiveStochasticGradientDescent< TElastix >" )

Name of this class. Use this name in the parameter file to select this specific optimizer. example: (Optimizer "AdaptiveStochasticGradientDescent")

◆ GetAutomaticParameterEstimation()

template<class TElastix >

virtual bool elastix::AdaptiveStochasticGradientDescent< TElastix >::GetAutomaticParameterEstimation ( ) const

virtual

◆ GetClassName()

template<class TElastix >

virtual const char * elastix::AdaptiveStochasticGradientDescent< TElastix >::GetClassName ( ) const

virtual

Run-time type information (and related methods).

Reimplemented from itk::AdaptiveStochasticGradientDescentOptimizer.

◆ GetMaximumNumberOfSamplingAttempts()

template<class TElastix >

virtual const SizeValueType & elastix::AdaptiveStochasticGradientDescent< TElastix >::GetMaximumNumberOfSamplingAttempts ( )

virtual

Get the MaximumNumberOfSamplingAttempts.

◆ GetMaximumStepLength()

template<class TElastix >

virtual double elastix::AdaptiveStochasticGradientDescent< TElastix >::GetMaximumStepLength ( ) const

virtual

◆ GetScaledDerivativeWithExceptionHandling()

template<class TElastix >

virtual void elastix::AdaptiveStochasticGradientDescent< TElastix >::GetScaledDerivativeWithExceptionHandling	(	const ParametersType &	parameters,
		DerivativeType &	derivative
	)

protectedvirtual

Helper function, which calls GetScaledValueAndDerivative and does some exception handling. Used by SampleGradients.

◆ itkStaticConstMacro() [1/2]

template<class TElastix >

elastix::AdaptiveStochasticGradientDescent< TElastix >::itkStaticConstMacro	(	FixedImageDimension	,
		unsigned int	,
		FixedImageType::ImageDimension
	)

protected

◆ itkStaticConstMacro() [2/2]

template<class TElastix >

elastix::AdaptiveStochasticGradientDescent< TElastix >::itkStaticConstMacro	(	MovingImageDimension	,
		unsigned int	,
		MovingImageType::ImageDimension
	)

protected

◆ MetricErrorResponse()

template<class TElastix >

void elastix::AdaptiveStochasticGradientDescent< TElastix >::MetricErrorResponse ( itk::ExceptionObject & err )

override

Stop optimization and pass on exception.

◆ New()

template<class TElastix >

static Pointer elastix::AdaptiveStochasticGradientDescent< TElastix >::New ( )

static

Method for creation through the object factory.

◆ operator=()

template<class TElastix >

void elastix::AdaptiveStochasticGradientDescent< TElastix >::operator= ( const Self & )

private

◆ PrintSettingsVector()

template<class TElastix >

virtual void elastix::AdaptiveStochasticGradientDescent< TElastix >::PrintSettingsVector ( const SettingsVectorType & settings ) const

protectedvirtual

Print the contents of the settings vector to elxout.

◆ ResumeOptimization()

template<class TElastix >

void elastix::AdaptiveStochasticGradientDescent< TElastix >::ResumeOptimization ( void )

overridevirtual

If automatic gain estimation is desired, then estimate SP_a, SP_alpha SigmoidScale, SigmoidMax, SigmoidMin. After that call Superclass' implementation.

Reimplemented from itk::GradientDescentOptimizer2.

◆ SampleGradients()

template<class TElastix >

virtual void elastix::AdaptiveStochasticGradientDescent< TElastix >::SampleGradients	(	const ParametersType &	mu0,
		double	perturbationSigma,
		double &	gg,
		double &	ee
	)

protectedvirtual

Measure some derivatives, exact and approximated. Returns the squared magnitude of the gradient and approximation error. Needed for the automatic parameter estimation. Gradients are measured at position mu_n, which are generated according to: mu_n - mu_0 ~ N(0, perturbationSigma^2 I ); gg = g^T g, etc.

◆ SetAutomaticParameterEstimation()

template<class TElastix >

virtual void elastix::AdaptiveStochasticGradientDescent< TElastix >::SetAutomaticParameterEstimation ( bool _arg )

virtual

Set/Get whether automatic parameter estimation is desired. If true, make sure to set the maximum step length.

The following parameters are automatically determined: SP_a, SP_alpha (=1), SigmoidMin, SigmoidMax (=1), SigmoidScale. A usually suitable value for SP_A is 20, which is the default setting, if not specified by the user.

◆ SetMaximumNumberOfSamplingAttempts()

template<class TElastix >

virtual void elastix::AdaptiveStochasticGradientDescent< TElastix >::SetMaximumNumberOfSamplingAttempts ( SizeValueType _arg )

virtual

Set the MaximumNumberOfSamplingAttempts.

◆ SetMaximumStepLength()

template<class TElastix >

virtual void elastix::AdaptiveStochasticGradientDescent< TElastix >::SetMaximumStepLength ( double _arg )

virtual

Set/Get maximum step length.

◆ StartOptimization()

template<class TElastix >

void elastix::AdaptiveStochasticGradientDescent< TElastix >::StartOptimization ( void )

override

Check if any scales are set, and set the UseScales flag on or off; after that call the superclass' implementation.

Field Documentation

◆ m_AdvancedTransform

template<class TElastix >

AdvancedTransformPointer elastix::AdaptiveStochasticGradientDescent< TElastix >::m_AdvancedTransform

protected

The transform stored as AdvancedTransform

Definition at line 354 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_AutomaticParameterEstimation

template<class TElastix >

bool elastix::AdaptiveStochasticGradientDescent< TElastix >::m_AutomaticParameterEstimation

private

Definition at line 410 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_AutomaticParameterEstimationDone

template<class TElastix >

bool elastix::AdaptiveStochasticGradientDescent< TElastix >::m_AutomaticParameterEstimationDone

private

Definition at line 418 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_CurrentNumberOfSamplingAttempts

template<class TElastix >

SizeValueType elastix::AdaptiveStochasticGradientDescent< TElastix >::m_CurrentNumberOfSamplingAttempts

private

Definition at line 416 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_MaxBandCovSize

template<class TElastix >

SizeValueType elastix::AdaptiveStochasticGradientDescent< TElastix >::m_MaxBandCovSize

private

Private variables for band size estimation of covariance matrix.

Definition at line 421 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_MaximumNumberOfSamplingAttempts

template<class TElastix >

SizeValueType elastix::AdaptiveStochasticGradientDescent< TElastix >::m_MaximumNumberOfSamplingAttempts

private

Private variables for the sampling attempts.

Definition at line 415 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_MaximumStepLength

template<class TElastix >

double elastix::AdaptiveStochasticGradientDescent< TElastix >::m_MaximumStepLength

private

Definition at line 411 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_MaximumStepLengthRatio

template<class TElastix >

double elastix::AdaptiveStochasticGradientDescent< TElastix >::m_MaximumStepLengthRatio

private

Definition at line 412 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_NumberOfBandStructureSamples

template<class TElastix >

SizeValueType elastix::AdaptiveStochasticGradientDescent< TElastix >::m_NumberOfBandStructureSamples

private

Definition at line 422 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_NumberOfGradientMeasurements

template<class TElastix >

SizeValueType elastix::AdaptiveStochasticGradientDescent< TElastix >::m_NumberOfGradientMeasurements

protected

Some options for automatic parameter estimation.

Definition at line 349 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_NumberOfJacobianMeasurements

template<class TElastix >

SizeValueType elastix::AdaptiveStochasticGradientDescent< TElastix >::m_NumberOfJacobianMeasurements

protected

Definition at line 350 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_NumberOfSamplesForExactGradient

template<class TElastix >

SizeValueType elastix::AdaptiveStochasticGradientDescent< TElastix >::m_NumberOfSamplesForExactGradient

protected

Definition at line 351 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_OriginalButSigmoidToDefault

template<class TElastix >

bool elastix::AdaptiveStochasticGradientDescent< TElastix >::m_OriginalButSigmoidToDefault

private

Definition at line 426 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_PreviousErrorAtIteration

template<class TElastix >

SizeValueType elastix::AdaptiveStochasticGradientDescent< TElastix >::m_PreviousErrorAtIteration

private

Definition at line 417 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_RandomGenerator

template<class TElastix >

RandomGeneratorPointer elastix::AdaptiveStochasticGradientDescent< TElastix >::m_RandomGenerator

protected

RandomGenerator for AddRandomPerturbation.

Definition at line 357 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_SettingsVector

template<class TElastix >

SettingsVectorType elastix::AdaptiveStochasticGradientDescent< TElastix >::m_SettingsVector

protected

Variable to store the automatically determined settings for each resolution.

Definition at line 346 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_SigmoidScaleFactor

template<class TElastix >

double elastix::AdaptiveStochasticGradientDescent< TElastix >::m_SigmoidScaleFactor

protected

Definition at line 359 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_UseNoiseCompensation

template<class TElastix >

bool elastix::AdaptiveStochasticGradientDescent< TElastix >::m_UseNoiseCompensation

private

The flag of using noise compensation.

Definition at line 425 of file elxAdaptiveStochasticGradientDescent.h.

Private Attributes
bool	m_AutomaticParameterEstimation

bool	m_AutomaticParameterEstimationDone

SizeValueType	m_CurrentNumberOfSamplingAttempts

SizeValueType	m_MaxBandCovSize

SizeValueType	m_MaximumNumberOfSamplingAttempts

double	m_MaximumStepLength

double	m_MaximumStepLengthRatio

SizeValueType	m_NumberOfBandStructureSamples

bool	m_OriginalButSigmoidToDefault

SizeValueType	m_PreviousErrorAtIteration

bool	m_UseNoiseCompensation

Detailed Description

Data Structures

Public Types

Public Member Functions

Static Public Member Functions

Protected Types

Protected Member Functions

Protected Attributes

Private Member Functions

Private Attributes

Member Typedef Documentation

◆ AdvancedTransformPointer

◆ AdvancedTransformType

◆ ComputeDisplacementDistributionType

◆ ComputeJacobianTermsType

◆ ConfigurationPointer

◆ ConfigurationType

◆ ConstPointer

◆ CoordinateRepresentationType

◆ CostFunctionPointer

◆ CostFunctionType

◆ ElastixPointer

◆ ElastixType

◆ FixedImageIndexType

◆ FixedImagePointType

◆ FixedImageRegionType

◆ FixedImageType

◆ ImageGridSamplerPointer

◆ ImageGridSamplerType

◆ ImageRandomCoordinateSamplerPointer

◆ ImageRandomCoordinateSamplerType

◆ ImageRandomSamplerBasePointer

◆ ImageRandomSamplerBaseType

◆ ImageSampleContainerPointer

◆ ImageSampleContainerType

◆ ImageSamplerBasePointer

◆ ImageSamplerBaseType

◆ ITKBaseType

◆ itkRegistrationType

◆ JacobianType

◆ JacobianValueType

◆ MovingImageType

◆ NonZeroJacobianIndicesType

◆ ParametersType

◆ Pointer

◆ ProgressCommandPointer

◆ ProgressCommandType

◆ RandomGeneratorPointer

◆ RandomGeneratorType

◆ RegistrationPointer

◆ RegistrationType

◆ Self

◆ SettingsVectorType

◆ SizeValueType

◆ StopConditionType

◆ Superclass1

◆ Superclass2

◆ TransformJacobianType

◆ TransformType

Constructor & Destructor Documentation

◆ AdaptiveStochasticGradientDescent() [1/2]

◆ ~AdaptiveStochasticGradientDescent()

◆ AdaptiveStochasticGradientDescent() [2/2]

Member Function Documentation

◆ AddRandomPerturbation()

◆ AfterEachIteration()

◆ AfterEachResolution()

◆ AfterRegistration()

◆ AutomaticParameterEstimation()

◆ AutomaticParameterEstimationOriginal()

◆ AutomaticParameterEstimationUsingDisplacementDistribution()

◆ BeforeEachResolution()

◆ BeforeRegistration()

◆ CheckForAdvancedTransform()

◆ elxClassNameMacro()

◆ GetAutomaticParameterEstimation()

◆ GetClassName()

◆ GetMaximumNumberOfSamplingAttempts()

◆ GetMaximumStepLength()

◆ GetScaledDerivativeWithExceptionHandling()