itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType > Class Template Reference

#include <itkRegularStepGradientDescentOptimizerv4.h>

Inheritance diagram for itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >:

Collaboration diagram for itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >:

Public Types
using	CompensatedSummationType = CompensatedSummation< InternalComputationValueType >
using	ConstPointer = SmartPointer< const Self >
using	DerivativeType = typename Superclass::DerivativeType
using	IndexRangeType = typename Superclass::IndexRangeType
using	InternalComputationValueType = TInternalComputationValueType
using	MeasureType = typename Superclass::MeasureType
using	ParametersType = typename Superclass::ParametersType
using	Pointer = SmartPointer< Self >
using	ScalesType = typename Superclass::ScalesType
using	Self = RegularStepGradientDescentOptimizerv4
using	Superclass = GradientDescentOptimizerv4Template< TInternalComputationValueType >
Public Types inherited from itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >
using	ConstPointer = SmartPointer< const Self >
using	DerivativeType = typename Superclass::DerivativeType
using	IndexRangeType = typename Superclass::IndexRangeType
using	InternalComputationValueType = TInternalComputationValueType
using	MeasureType = typename Superclass::MeasureType
using	ParametersType = typename Superclass::ParametersType
using	Pointer = SmartPointer< Self >
using	ScalesType = typename Superclass::ScalesType
using	Self = GradientDescentOptimizerv4Template
using	Superclass = GradientDescentOptimizerBasev4Template< TInternalComputationValueType >
Public Types inherited from itk::GradientDescentOptimizerBasev4Template< TInternalComputationValueType >
using	ConstPointer = SmartPointer< const Self >
using	ConvergenceMonitoringType = itk::Function::WindowConvergenceMonitoringFunction< TInternalComputationValueType >
using	DerivativeType = typename Superclass::DerivativeType
using	InternalComputationValueType = TInternalComputationValueType
using	MeasureType = typename Superclass::MeasureType
using	MetricType = typename Superclass::MetricType
using	MetricTypePointer = typename MetricType::Pointer
using	ParametersType = typename Superclass::ParametersType
using	Pointer = SmartPointer< Self >
using	ScalesType = typename Superclass::ScalesType
using	Self = GradientDescentOptimizerBasev4Template
using	StopConditionDescriptionType = typename Superclass::StopConditionDescriptionType
using	StopConditionReturnStringType = typename Superclass::StopConditionReturnStringType
using	Superclass = ObjectToObjectOptimizerBaseTemplate< TInternalComputationValueType >
using	IndexRangeType = ThreadedIndexedContainerPartitioner::IndexRangeType
Public Types inherited from itk::ObjectToObjectOptimizerBaseTemplate< TInternalComputationValueType >
using	ConstPointer = SmartPointer< const Self >
using	DerivativeType = typename MetricType::DerivativeType
using	MeasureType = typename MetricType::MeasureType
using	MetricType = ObjectToObjectMetricBaseTemplate< TInternalComputationValueType >
using	MetricTypePointer = typename MetricType::Pointer
using	NumberOfParametersType = typename MetricType::NumberOfParametersType
using	ParametersType = OptimizerParameters< TInternalComputationValueType >
using	Pointer = SmartPointer< Self >
using	ScalesEstimatorType = OptimizerParameterScalesEstimatorTemplate< TInternalComputationValueType >
using	ScalesType = OptimizerParameters< TInternalComputationValueType >
using	Self = ObjectToObjectOptimizerBaseTemplate
using	StopConditionDescriptionType = std::ostringstream
using	StopConditionReturnStringType = std::string
using	Superclass = Object
Public Types inherited from itk::Object
using	ConstPointer = SmartPointer< const Self >
using	Pointer = SmartPointer< Self >
using	Self = Object
using	Superclass = LightObject
Public Types inherited from itk::LightObject
using	ConstPointer = SmartPointer< const Self >
using	Pointer = SmartPointer< Self >
using	Self = LightObject

Public Member Functions
virtual ::itk::LightObject::Pointer	CreateAnother () const
virtual const char *	GetNameOfClass () const
Public Member Functions inherited from itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >
virtual void	SetLearningRate (TInternalComputationValueType _arg)
virtual const TInternalComputationValueType &	GetLearningRate () const
virtual void	SetMaximumStepSizeInPhysicalUnits (TInternalComputationValueType _arg)
virtual const TInternalComputationValueType &	GetMaximumStepSizeInPhysicalUnits () const
virtual void	SetDoEstimateLearningRateAtEachIteration (bool _arg)
virtual const bool &	GetDoEstimateLearningRateAtEachIteration () const
virtual void	DoEstimateLearningRateAtEachIterationOn ()
virtual void	DoEstimateLearningRateAtEachIterationOff ()
virtual void	SetDoEstimateLearningRateOnce (bool _arg)
virtual const bool &	GetDoEstimateLearningRateOnce () const
virtual void	DoEstimateLearningRateOnceOn ()
virtual void	DoEstimateLearningRateOnceOff ()
virtual void	SetMinimumConvergenceValue (TInternalComputationValueType _arg)
virtual void	SetConvergenceWindowSize (SizeValueType _arg)
virtual const TInternalComputationValueType &	GetConvergenceValue () const
virtual void	SetReturnBestParametersAndValue (bool _arg)
virtual const bool &	GetReturnBestParametersAndValue () const
virtual void	ReturnBestParametersAndValueOn ()
virtual void	ReturnBestParametersAndValueOff ()
void	StopOptimization () override
void	ResumeOptimization () override
Public Member Functions inherited from itk::GradientDescentOptimizerBasev4Template< TInternalComputationValueType >
virtual const DerivativeType &	GetGradient () const
virtual const StopConditionObjectToObjectOptimizerEnum &	GetStopCondition () const
void	SetNumberOfIterations (const SizeValueType numberOfIterations) override
SizeValueType	GetNumberOfIterations () const override
SizeValueType	GetCurrentIteration () const override
const StopConditionReturnStringType	GetStopConditionDescription () const override
virtual void	ModifyGradientByScales ()
virtual void	ModifyGradientByLearningRate ()
Public Member Functions inherited from itk::ObjectToObjectOptimizerBaseTemplate< TInternalComputationValueType >
virtual void	SetMetric (MetricType *_arg)
virtual MetricType *	GetModifiableMetric ()
virtual const MetricType *	GetMetric () const
virtual const MeasureType &	GetCurrentMetricValue () const
virtual const MeasureType &	GetValue () const
virtual void	SetScales (const ScalesType &scales)
virtual const ScalesType &	GetScales () const
virtual const bool &	GetScalesAreIdentity () const
virtual void	SetWeights (ScalesType _arg)
virtual const ScalesType &	GetWeights () const
virtual const bool &	GetWeightsAreIdentity () const
bool	GetScalesInitialized () const
virtual void	SetScalesEstimator (ScalesEstimatorType *_arg)
virtual void	SetDoEstimateScales (bool _arg)
virtual const bool &	GetDoEstimateScales () const
virtual void	DoEstimateScalesOn ()
virtual void	DoEstimateScalesOff ()
virtual void	SetNumberOfWorkUnits (ThreadIdType number)
virtual const ThreadIdType &	GetNumberOfWorkUnits () const
virtual const ParametersType &	GetCurrentPosition () const
Public Member Functions inherited from itk::Object
unsigned long	AddObserver (const EventObject &event, Command *)
unsigned long	AddObserver (const EventObject &event, Command *) const
unsigned long	AddObserver (const EventObject &event, std::function< void(const EventObject &)> function) const
virtual void	DebugOff () const
virtual void	DebugOn () const
Command *	GetCommand (unsigned long tag)
bool	GetDebug () const
MetaDataDictionary &	GetMetaDataDictionary ()
const MetaDataDictionary &	GetMetaDataDictionary () const
virtual ModifiedTimeType	GetMTime () const
virtual const TimeStamp &	GetTimeStamp () const
bool	HasObserver (const EventObject &event) const
void	InvokeEvent (const EventObject &)
void	InvokeEvent (const EventObject &) const
virtual void	Modified () const
void	Register () const override
void	RemoveAllObservers ()
void	RemoveObserver (unsigned long tag)
void	SetDebug (bool debugFlag) const
void	SetReferenceCount (int) override
void	UnRegister () const noexcept override
void	SetMetaDataDictionary (const MetaDataDictionary &rhs)
void	SetMetaDataDictionary (MetaDataDictionary &&rrhs)
virtual void	SetObjectName (std::string _arg)
virtual const std::string &	GetObjectName () const
Public Member Functions inherited from itk::LightObject
Pointer	Clone () const
virtual void	Delete ()
virtual int	GetReferenceCount () const
void	Print (std::ostream &os, Indent indent=0) const

Static Public Member Functions
static Pointer	New ()
Static Public Member Functions inherited from itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >
static Pointer	New ()
Static Public Member Functions inherited from itk::Object
static bool	GetGlobalWarningDisplay ()
static void	GlobalWarningDisplayOff ()
static void	GlobalWarningDisplayOn ()
static Pointer	New ()
static void	SetGlobalWarningDisplay (bool val)
Static Public Member Functions inherited from itk::LightObject
static void	BreakOnError ()
static Pointer	New ()
TInternalComputationValueType	m_RelaxationFactor
TInternalComputationValueType	m_MinimumStepLength
TInternalComputationValueType	m_GradientMagnitudeTolerance
MeasureType	m_CurrentLearningRateRelaxation
virtual void	SetMinimumStepLength (TInternalComputationValueType _arg)
virtual const TInternalComputationValueType &	GetMinimumStepLength () const
virtual void	SetRelaxationFactor (TInternalComputationValueType _arg)
virtual const TInternalComputationValueType &	GetRelaxationFactor () const
virtual void	SetGradientMagnitudeTolerance (TInternalComputationValueType _arg)
virtual const TInternalComputationValueType &	GetGradientMagnitudeTolerance () const
virtual void	SetCurrentLearningRateRelaxation (MeasureType _arg)
virtual const MeasureType &	GetCurrentLearningRateRelaxation () const
void	StartOptimization (bool doOnlyInitialization=false) override
void	EstimateLearningRate () override
double	GetCurrentStepLength () const
void	AdvanceOneStep () override
void	ModifyGradientByScalesOverSubRange (const IndexRangeType &subrange) override
void	ModifyGradientByLearningRateOverSubRange (const IndexRangeType &subrange) override
	RegularStepGradientDescentOptimizerv4 ()
	~RegularStepGradientDescentOptimizerv4 () override=default
void	PrintSelf (std::ostream &os, Indent indent) const override

Additional Inherited Members
Protected Member Functions inherited from itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >
	GradientDescentOptimizerv4Template ()
	~GradientDescentOptimizerv4Template () override=default
Protected Member Functions inherited from itk::GradientDescentOptimizerBasev4Template< TInternalComputationValueType >
	GradientDescentOptimizerBasev4Template ()
	~GradientDescentOptimizerBasev4Template () override=default
Protected Member Functions inherited from itk::ObjectToObjectOptimizerBaseTemplate< TInternalComputationValueType >
	ObjectToObjectOptimizerBaseTemplate ()
	~ObjectToObjectOptimizerBaseTemplate () override
Protected Member Functions inherited from itk::Object
	Object ()
	~Object () override
bool	PrintObservers (std::ostream &os, Indent indent) const
virtual void	SetTimeStamp (const TimeStamp &timeStamp)
Protected Member Functions inherited from itk::LightObject
virtual LightObject::Pointer	InternalClone () const
	LightObject ()
virtual void	PrintHeader (std::ostream &os, Indent indent) const
virtual void	PrintTrailer (std::ostream &os, Indent indent) const
virtual	~LightObject ()
Protected Attributes inherited from itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >
TInternalComputationValueType	m_LearningRate
TInternalComputationValueType	m_MinimumConvergenceValue
TInternalComputationValueType	m_ConvergenceValue
MeasureType	m_CurrentBestValue
ParametersType	m_BestParameters
bool	m_ReturnBestParametersAndValue { false }
DerivativeType	m_PreviousGradient
Protected Attributes inherited from itk::GradientDescentOptimizerBasev4Template< TInternalComputationValueType >
bool	m_DoEstimateLearningRateAtEachIteration
bool	m_DoEstimateLearningRateOnce
TInternalComputationValueType	m_MaximumStepSizeInPhysicalUnits
bool	m_UseConvergenceMonitoring
SizeValueType	m_ConvergenceWindowSize
ConvergenceMonitoringType::Pointer	m_ConvergenceMonitoring
DomainThreader< ThreadedIndexedContainerPartitioner, Self >::Pointer	m_ModifyGradientByScalesThreader
DomainThreader< ThreadedIndexedContainerPartitioner, Self >::Pointer	m_ModifyGradientByLearningRateThreader
bool	m_Stop { false }
StopConditionObjectToObjectOptimizerEnum	m_StopCondition
StopConditionDescriptionType	m_StopConditionDescription
DerivativeType	m_Gradient
Protected Attributes inherited from itk::ObjectToObjectOptimizerBaseTemplate< TInternalComputationValueType >
MetricTypePointer	m_Metric
ThreadIdType	m_NumberOfWorkUnits
SizeValueType	m_CurrentIteration
SizeValueType	m_NumberOfIterations
MeasureType	m_CurrentMetricValue
ScalesType	m_Scales
ScalesType	m_Weights
bool	m_ScalesAreIdentity
ScalesEstimatorType::Pointer	m_ScalesEstimator
bool	m_WeightsAreIdentity
bool	m_DoEstimateScales
Protected Attributes inherited from itk::LightObject
std::atomic< int >	m_ReferenceCount

Detailed Description

template<typename TInternalComputationValueType = double>
class itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >

Regular Step Gradient descent optimizer.

This optimizer is a variant of gradient descent that attempts to prevent it from taking steps that are too large. At each iteration, this optimizer will take a step along the direction of the metric derivative. Each time the direction of the derivative abruptly changes, the optimizer assumes that a local extrema has been passed and reacts by reducing the step length by a relaxation factor that is set to 0.5 by default. The default value for the initial step length is 1, and this value can only be changed manually via SetLearningRate() since this optimizer does not use the ScaleEstimator to automatically estimate the learning rate. Also note that unlike the previous version of ReuglarStepGradientDescentOptimizer, ITKv4 does not have a "maximize/minimize" option to modify the effect of the metric derivative. The assigned metric is assumed to return a parameter derivative result that "improves" the optimization.

Examples

Examples/RegistrationITKv4/ImageRegistration1.cxx, Examples/RegistrationITKv4/ImageRegistration12.cxx, Examples/RegistrationITKv4/ImageRegistration13.cxx, Examples/RegistrationITKv4/ImageRegistration3.cxx, Examples/RegistrationITKv4/ImageRegistration4.cxx, Examples/RegistrationITKv4/ImageRegistration5.cxx, Examples/RegistrationITKv4/ImageRegistration6.cxx, Examples/RegistrationITKv4/ImageRegistration7.cxx, Examples/RegistrationITKv4/ImageRegistration8.cxx, Examples/RegistrationITKv4/ImageRegistration9.cxx, Examples/RegistrationITKv4/MultiResImageRegistration1.cxx, Examples/RegistrationITKv4/MultiStageImageRegistration1.cxx, Examples/RegistrationITKv4/MultiStageImageRegistration2.cxx, and SphinxExamples/src/Registration/Common/Perform2DTranslationRegistrationWithMeanSquares/Code.cxx.

Definition at line 47 of file itkRegularStepGradientDescentOptimizerv4.h.

Member Typedef Documentation

CompensatedSummationType

template<typename TInternalComputationValueType = double>

using itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::CompensatedSummationType = CompensatedSummation<InternalComputationValueType>

Compensated summation type.

Definition at line 79 of file itkRegularStepGradientDescentOptimizerv4.h.

ConstPointer

template<typename TInternalComputationValueType = double>

using itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::ConstPointer = SmartPointer<const Self>

Definition at line 57 of file itkRegularStepGradientDescentOptimizerv4.h.

DerivativeType

template<typename TInternalComputationValueType = double>

using itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::DerivativeType = typename Superclass::DerivativeType

Derivative type.

Definition at line 70 of file itkRegularStepGradientDescentOptimizerv4.h.

IndexRangeType

template<typename TInternalComputationValueType = double>

using itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::IndexRangeType = typename Superclass::IndexRangeType

Definition at line 74 of file itkRegularStepGradientDescentOptimizerv4.h.

InternalComputationValueType

template<typename TInternalComputationValueType = double>

using itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::InternalComputationValueType = TInternalComputationValueType

It should be possible to derive the internal computation type from the class object.

Definition at line 67 of file itkRegularStepGradientDescentOptimizerv4.h.

MeasureType

template<typename TInternalComputationValueType = double>

using itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::MeasureType = typename Superclass::MeasureType

Metric type over which this class is templated.

Definition at line 73 of file itkRegularStepGradientDescentOptimizerv4.h.

ParametersType

template<typename TInternalComputationValueType = double>

using itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::ParametersType = typename Superclass::ParametersType

Definition at line 76 of file itkRegularStepGradientDescentOptimizerv4.h.

Pointer

template<typename TInternalComputationValueType = double>

using itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::Pointer = SmartPointer<Self>

Definition at line 56 of file itkRegularStepGradientDescentOptimizerv4.h.

ScalesType

template<typename TInternalComputationValueType = double>

using itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::ScalesType = typename Superclass::ScalesType

Definition at line 75 of file itkRegularStepGradientDescentOptimizerv4.h.

Self

template<typename TInternalComputationValueType = double>

using itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::Self = RegularStepGradientDescentOptimizerv4

Standard class type aliases.

Definition at line 54 of file itkRegularStepGradientDescentOptimizerv4.h.

Superclass

template<typename TInternalComputationValueType = double>

using itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::Superclass = GradientDescentOptimizerv4Template<TInternalComputationValueType>

Definition at line 55 of file itkRegularStepGradientDescentOptimizerv4.h.

Constructor & Destructor Documentation

RegularStepGradientDescentOptimizerv4()

template<typename TInternalComputationValueType = double>

itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::RegularStepGradientDescentOptimizerv4 ( )

protected

Default constructor.

~RegularStepGradientDescentOptimizerv4()

template<typename TInternalComputationValueType = double>

itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::~RegularStepGradientDescentOptimizerv4 ( )

overrideprotecteddefault

Destructor.

Member Function Documentation

AdvanceOneStep()

template<typename TInternalComputationValueType = double>

void itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::AdvanceOneStep ( )

overrideprotectedvirtual

Advance one Step following the gradient direction. Includes transform update.

Reimplemented from itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >.

CreateAnother()

template<typename TInternalComputationValueType = double>

virtual::itk::LightObject::Pointer itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::CreateAnother ( ) const

virtual

Create an object from an instance, potentially deferring to a factory. This method allows you to create an instance of an object that is exactly the same type as the referring object. This is useful in cases where an object has been cast back to a base class.

Reimplemented from itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >.

EstimateLearningRate()

template<typename TInternalComputationValueType = double>

void itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::EstimateLearningRate ( )

overridevirtual

Estimate the learning rate based on the current gradient.

Reimplemented from itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >.

GetCurrentLearningRateRelaxation()

template<typename TInternalComputationValueType = double>

virtual const MeasureType& itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::GetCurrentLearningRateRelaxation ( ) const

virtual

Minimum step length (learning rate) value for convergence checking. When the local minima is passed by taking a large step, the step length is adjusted (decreased) by the relaxation factor, so that smaller steps are taken towards the minimum point (convergence). When the step length value reaches a small value, it would be treated as converged.

The default value is set to 1e-4 to pass all tests.

GetCurrentStepLength()

template<typename TInternalComputationValueType = double>

double itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::GetCurrentStepLength

(

)

const

Get current gradient step value.

GetGradientMagnitudeTolerance()

template<typename TInternalComputationValueType = double>

virtual const TInternalComputationValueType& itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::GetGradientMagnitudeTolerance ( ) const

virtual

Minimum step length (learning rate) value for convergence checking. When the local minima is passed by taking a large step, the step length is adjusted (decreased) by the relaxation factor, so that smaller steps are taken towards the minimum point (convergence). When the step length value reaches a small value, it would be treated as converged.

The default value is set to 1e-4 to pass all tests.

GetMinimumStepLength()

template<typename TInternalComputationValueType = double>

virtual const TInternalComputationValueType& itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::GetMinimumStepLength ( ) const

virtual

Minimum step length (learning rate) value for convergence checking. When the local minima is passed by taking a large step, the step length is adjusted (decreased) by the relaxation factor, so that smaller steps are taken towards the minimum point (convergence). When the step length value reaches a small value, it would be treated as converged.

The default value is set to 1e-4 to pass all tests.

GetNameOfClass()

template<typename TInternalComputationValueType = double>

virtual const char* itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::GetNameOfClass ( ) const

virtual

Run-time type information (and related methods).

Reimplemented from itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >.

GetRelaxationFactor()

template<typename TInternalComputationValueType = double>

virtual const TInternalComputationValueType& itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::GetRelaxationFactor ( ) const

virtual

Minimum step length (learning rate) value for convergence checking. When the local minima is passed by taking a large step, the step length is adjusted (decreased) by the relaxation factor, so that smaller steps are taken towards the minimum point (convergence). When the step length value reaches a small value, it would be treated as converged.

The default value is set to 1e-4 to pass all tests.

ModifyGradientByLearningRateOverSubRange()

template<typename TInternalComputationValueType = double>

void itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::ModifyGradientByLearningRateOverSubRange ( const IndexRangeType &  subrange )

overrideprotectedvirtual

Minimum step length (learning rate) value for convergence checking. When the local minima is passed by taking a large step, the step length is adjusted (decreased) by the relaxation factor, so that smaller steps are taken towards the minimum point (convergence). When the step length value reaches a small value, it would be treated as converged.

The default value is set to 1e-4 to pass all tests.

Reimplemented from itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >.

ModifyGradientByScalesOverSubRange()

template<typename TInternalComputationValueType = double>

void itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::ModifyGradientByScalesOverSubRange ( const IndexRangeType &  subrange )

overrideprotectedvirtual

Modify the input gradient over a given index range.

Reimplemented from itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >.

New()

template<typename TInternalComputationValueType = double>

static Pointer itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::New ( )

static

New macro for creation of through a Smart Pointer.

PrintSelf()

template<typename TInternalComputationValueType = double>

void itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::PrintSelf ( std::ostream &  os, Indent  indent  ) const

overrideprotectedvirtual

Minimum step length (learning rate) value for convergence checking. When the local minima is passed by taking a large step, the step length is adjusted (decreased) by the relaxation factor, so that smaller steps are taken towards the minimum point (convergence). When the step length value reaches a small value, it would be treated as converged.

The default value is set to 1e-4 to pass all tests.

Reimplemented from itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >.

SetCurrentLearningRateRelaxation()

template<typename TInternalComputationValueType = double>

virtual void itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::SetCurrentLearningRateRelaxation ( MeasureType  _arg )

virtual

Set/Get current scale for learning rate.

SetGradientMagnitudeTolerance()

template<typename TInternalComputationValueType = double>

virtual void itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::SetGradientMagnitudeTolerance ( TInternalComputationValueType  _arg )

virtual

Set/Get gradient magnitude tolerance value for convergence checking.

SetMinimumStepLength()

template<typename TInternalComputationValueType = double>

virtual void itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::SetMinimumStepLength ( TInternalComputationValueType  _arg )

virtual

Minimum step length (learning rate) value for convergence checking. When the local minima is passed by taking a large step, the step length is adjusted (decreased) by the relaxation factor, so that smaller steps are taken towards the minimum point (convergence). When the step length value reaches a small value, it would be treated as converged.

The default value is set to 1e-4 to pass all tests.

SetRelaxationFactor()

template<typename TInternalComputationValueType = double>

virtual void itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::SetRelaxationFactor ( TInternalComputationValueType  _arg )

virtual

Set/Get relaxation factor value.

StartOptimization()

template<typename TInternalComputationValueType = double>

void itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::StartOptimization ( bool  doOnlyInitialization = false )

overridevirtual

Start and run the optimization.

Reimplemented from itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >.

Member Data Documentation

m_CurrentLearningRateRelaxation

template<typename TInternalComputationValueType = double>

MeasureType itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::m_CurrentLearningRateRelaxation

private

Minimum step length (learning rate) value for convergence checking. When the local minima is passed by taking a large step, the step length is adjusted (decreased) by the relaxation factor, so that smaller steps are taken towards the minimum point (convergence). When the step length value reaches a small value, it would be treated as converged.

The default value is set to 1e-4 to pass all tests.

Definition at line 152 of file itkRegularStepGradientDescentOptimizerv4.h.

m_GradientMagnitudeTolerance

template<typename TInternalComputationValueType = double>

TInternalComputationValueType itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::m_GradientMagnitudeTolerance

private

Minimum step length (learning rate) value for convergence checking. When the local minima is passed by taking a large step, the step length is adjusted (decreased) by the relaxation factor, so that smaller steps are taken towards the minimum point (convergence). When the step length value reaches a small value, it would be treated as converged.

The default value is set to 1e-4 to pass all tests.

Definition at line 150 of file itkRegularStepGradientDescentOptimizerv4.h.

m_MinimumStepLength

template<typename TInternalComputationValueType = double>

TInternalComputationValueType itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::m_MinimumStepLength

private

Minimum step length (learning rate) value for convergence checking. When the local minima is passed by taking a large step, the step length is adjusted (decreased) by the relaxation factor, so that smaller steps are taken towards the minimum point (convergence). When the step length value reaches a small value, it would be treated as converged.

The default value is set to 1e-4 to pass all tests.

Definition at line 148 of file itkRegularStepGradientDescentOptimizerv4.h.

m_RelaxationFactor

template<typename TInternalComputationValueType = double>

TInternalComputationValueType itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >::m_RelaxationFactor

private

Minimum step length (learning rate) value for convergence checking. When the local minima is passed by taking a large step, the step length is adjusted (decreased) by the relaxation factor, so that smaller steps are taken towards the minimum point (convergence). When the step length value reaches a small value, it would be treated as converged.

The default value is set to 1e-4 to pass all tests.

Definition at line 146 of file itkRegularStepGradientDescentOptimizerv4.h.

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The documentation for this class was generated from the following file:

• itkRegularStepGradientDescentOptimizerv4.h