go home Home | Main Page | Modules | Namespace List | Class Hierarchy | Alphabetical List | Data Structures | File List | Namespace Members | Data Fields | Globals | Related Pages
Data Structures | Public Types | Public Member Functions | Static Public Member Functions | Protected Types | Protected Member Functions | Protected Attributes | Private Attributes
elastix::AdaptiveStochasticGradientDescent< TElastix > Class Template Reference

#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.lumc.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 $a(k)$ at each iteration $k$ 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 $a(k)$ at each iteration $k$ 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 $a(k)$ at each iteration $k$ 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 ( $t_0$). 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 >:
Inheritance graph
[legend]

Data Structures

struct  SettingsType
 

Public Types

using ConstPointer = itk::SmartPointer< const Self >
 
using ITKBaseType = typename Superclass2::ITKBaseType
 
using Pointer = itk::SmartPointer< Self >
 
using Self = AdaptiveStochasticGradientDescent
 
using SizeValueType = itk::SizeValueType
 
using Superclass1 = AdaptiveStochasticGradientDescentOptimizer
 
using Superclass2 = OptimizerBase< TElastix >
 
- Public Types inherited from itk::AdaptiveStochasticGradientDescentOptimizer
using ConstPointer = SmartPointer< const Self >
 
using Pointer = SmartPointer< Self >
 
using Self = AdaptiveStochasticGradientDescentOptimizer
 
using Superclass = StandardGradientDescentOptimizer
 
- Public Types inherited from itk::StandardGradientDescentOptimizer
using ConstPointer = SmartPointer< const Self >
 
using Pointer = SmartPointer< Self >
 
using ScaledCostFunctionPointer = ScaledCostFunctionType::Pointer
 
using ScaledCostFunctionType = ScaledSingleValuedCostFunction
 
using ScalesType = NonLinearOptimizer::ScalesType
 
using Self = StandardGradientDescentOptimizer
 
enum  StopConditionType
 
using Superclass = GradientDescentOptimizer2
 
- Public Types inherited from itk::GradientDescentOptimizer2
using ConstPointer = SmartPointer< const Self >
 
using Pointer = SmartPointer< Self >
 
using ScaledCostFunctionPointer = ScaledCostFunctionType::Pointer
 
using ScaledCostFunctionType = ScaledSingleValuedCostFunction
 
using ScalesType = NonLinearOptimizer::ScalesType
 
using Self = GradientDescentOptimizer2
 
enum  StopConditionType { MaximumNumberOfIterations , MetricError , MinimumStepSize }
 
using Superclass = ScaledSingleValuedNonLinearOptimizer
 
- Public Types inherited from itk::ScaledSingleValuedNonLinearOptimizer
using ConstPointer = SmartPointer< const Self >
 
using Pointer = SmartPointer< Self >
 
using ScaledCostFunctionPointer = ScaledCostFunctionType::Pointer
 
using ScaledCostFunctionType = ScaledSingleValuedCostFunction
 
using ScalesType = NonLinearOptimizer::ScalesType
 
using Self = ScaledSingleValuedNonLinearOptimizer
 
using Superclass = SingleValuedNonLinearOptimizer
 
- Public Types inherited from elastix::OptimizerBase< TElastix >
using ElastixType = TElastix
 
using ITKBaseType = itk::Optimizer
 
using ParametersType = typename ITKBaseType::ParametersType
 
using RegistrationType = typename ElastixType::RegistrationBaseType
 
using Self = OptimizerBase
 
using Superclass = BaseComponentSE< TElastix >
 
- Public Types inherited from elastix::BaseComponentSE< TElastix >
using ConfigurationPointer = Configuration::Pointer
 
using ElastixType = TElastix
 
using RegistrationType = typename ElastixType::RegistrationBaseType
 
using Self = BaseComponentSE
 
using Superclass = BaseComponent
 

Public Member Functions

void AfterEachIteration () override
 
void AfterEachResolution () override
 
void AfterRegistration () override
 
void BeforeEachResolution () override
 
void BeforeRegistration () override
 
 elxClassNameMacro ("AdaptiveStochasticGradientDescent")
 
virtual bool GetAutomaticParameterEstimation () const
 
virtual const char * GetClassName () const
 
virtual const SizeValueTypeGetMaximumNumberOfSamplingAttempts ()
 
virtual double GetMaximumStepLength () const
 
 ITK_DISALLOW_COPY_AND_MOVE (AdaptiveStochasticGradientDescent)
 
void MetricErrorResponse (itk::ExceptionObject &err) override
 
void ResumeOptimization () override
 
virtual void SetAutomaticParameterEstimation (bool _arg)
 
virtual void SetMaximumNumberOfSamplingAttempts (SizeValueType _arg)
 
virtual void SetMaximumStepLength (double _arg)
 
void StartOptimization () 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
 
 ITK_DISALLOW_COPY_AND_MOVE (AdaptiveStochasticGradientDescentOptimizer)
 
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 () 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
 
 ITK_DISALLOW_COPY_AND_MOVE (StandardGradientDescentOptimizer)
 
virtual void ResetCurrentTimeToInitialTime ()
 
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 () override
 
- Public Member Functions inherited from itk::GradientDescentOptimizer2
virtual void AdvanceOneStep ()
 
virtual const char * GetClassName () const
 
virtual unsigned int GetCurrentIteration () const
 
virtual const DerivativeType & GetGradient ()
 
virtual const doubleGetLearningRate ()
 
virtual const unsigned long & GetNumberOfIterations ()
 
virtual const DerivativeType & GetSearchDirection ()
 
virtual const StopConditionTypeGetStopCondition ()
 
virtual const doubleGetValue ()
 
 ITK_DISALLOW_COPY_AND_MOVE (GradientDescentOptimizer2)
 
virtual void MetricErrorResponse (ExceptionObject &err)
 
virtual void ResumeOptimization ()
 
virtual void SetLearningRate (double _arg)
 
virtual void SetNumberOfIterations (unsigned long _arg)
 
virtual void SetUseOpenMP (bool _arg)
 
void StartOptimization () override
 
virtual void StopOptimization ()
 
- Public Member Functions inherited from itk::ScaledSingleValuedNonLinearOptimizer
virtual const char * GetClassName () const
 
const ParametersType & GetCurrentPosition () const override
 
virtual bool GetMaximize () const
 
virtual const ScaledCostFunctionTypeGetScaledCostFunction ()
 
virtual const ParametersType & GetScaledCurrentPosition ()
 
bool GetUseScales () const
 
virtual void InitializeScales ()
 
 ITK_DISALLOW_COPY_AND_MOVE (ScaledSingleValuedNonLinearOptimizer)
 
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 () override
 
void BeforeEachResolutionBase () override
 
ITKBaseTypeGetAsITKBaseType ()
 
const ITKBaseTypeGetAsITKBaseType () const
 
virtual const char * GetClassName () const
 
 ITK_DISALLOW_COPY_AND_MOVE (OptimizerBase)
 
virtual void SetCurrentPositionPublic (const ParametersType &param)
 
virtual void SetSinusScales (double amplitude, double frequency, unsigned long numberOfParameters)
 
- Public Member Functions inherited from elastix::BaseComponentSE< TElastix >
void AddTargetCellToIterationInfo (const char *const name)
 
ConfigurationGetConfiguration () const
 
ElastixTypeGetElastix () const
 
xl::xoutbaseGetIterationInfoAt (const char *const name)
 
RegistrationTypeGetRegistration () const
 
 ITK_DISALLOW_COPY_AND_MOVE (BaseComponentSE)
 
int RemoveTargetCellFromIterationInfo (const char *const name)
 
void SetConfiguration (Configuration *_arg)
 
void SetElastix (ElastixType *_arg)
 
- Public Member Functions inherited from elastix::BaseComponent
virtual void AfterEachIteration ()
 
virtual void AfterEachIterationBase ()
 
virtual void AfterEachResolution ()
 
virtual void AfterEachResolutionBase ()
 
virtual void AfterRegistration ()
 
virtual void AfterRegistrationBase ()
 
virtual int BeforeAll ()
 
virtual int BeforeAllBase ()
 
virtual void BeforeEachResolution ()
 
virtual void BeforeEachResolutionBase ()
 
virtual void BeforeRegistration ()
 
virtual void BeforeRegistrationBase ()
 
virtual const char * elxGetClassName () const
 
const char * GetComponentLabel () const
 
 ITK_DISALLOW_COPY_AND_MOVE (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
template<typename TBaseComponent >
static auto AsITKBaseType (TBaseComponent *const baseComponent) -> decltype(baseComponent->GetAsITKBaseType())
 
static void InitializeElastixExecutable ()
 
static bool IsElastixLibrary ()
 

Protected Types

using AdvancedTransformPointer = typename AdvancedTransformType::Pointer
 
using AdvancedTransformType = itk::AdvancedTransform< CoordinateRepresentationType, Self::FixedImageDimension, Self::MovingImageDimension >
 
using ComputeDisplacementDistributionType = itk::ComputeDisplacementDistribution< FixedImageType, TransformType >
 
using ComputeJacobianTermsType = itk::ComputeJacobianTerms< FixedImageType, TransformType >
 
using CoordinateRepresentationType = typename TransformType::ScalarType
 
using FixedImageIndexType = typename FixedImageType::IndexType
 
using FixedImagePointType = typename FixedImageType::PointType
 
using FixedImageRegionType = typename FixedImageType::RegionType
 
using FixedImageType = typename RegistrationType::FixedImageType
 
using ImageGridSamplerPointer = typename ImageGridSamplerType::Pointer
 
using ImageGridSamplerType = itk::ImageGridSampler< FixedImageType >
 
using ImageRandomCoordinateSamplerPointer = typename ImageRandomCoordinateSamplerType::Pointer
 
using ImageRandomCoordinateSamplerType = itk::ImageRandomCoordinateSampler< FixedImageType >
 
using ImageRandomSamplerBasePointer = typename ImageRandomSamplerBaseType::Pointer
 
using ImageRandomSamplerBaseType = itk::ImageRandomSamplerBase< FixedImageType >
 
using ImageSampleContainerPointer = typename ImageSampleContainerType::Pointer
 
using ImageSampleContainerType = typename ImageGridSamplerType::ImageSampleContainerType
 
using ImageSamplerBasePointer = typename ImageSamplerBaseType::Pointer
 
using ImageSamplerBaseType = itk::ImageSamplerBase< FixedImageType >
 
using itkRegistrationType = typename RegistrationType::ITKBaseType
 
using JacobianType = typename TransformType::JacobianType
 
using JacobianValueType = typename JacobianType::ValueType
 
using MovingImageType = typename RegistrationType::MovingImageType
 
using NonZeroJacobianIndicesType = typename AdvancedTransformType::NonZeroJacobianIndicesType
 
using ProgressCommandPointer = typename ProgressCommand::Pointer
 
using ProgressCommandType = ProgressCommand
 
using RandomGeneratorPointer = typename RandomGeneratorType::Pointer
 
using RandomGeneratorType = itk::Statistics::MersenneTwisterRandomVariateGenerator
 
using SettingsVectorType = typename std::vector< SettingsType >
 
using TransformJacobianType = JacobianType
 
using TransformType = typename itkRegistrationType::TransformType
 

Protected Member Functions

 AdaptiveStochasticGradientDescent ()
 
virtual void AddRandomPerturbation (ParametersType &parameters, double sigma)
 
virtual void AutomaticParameterEstimation ()
 
virtual void AutomaticParameterEstimationOriginal ()
 
virtual void AutomaticParameterEstimationUsingDisplacementDistribution ()
 
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=default
 
- Protected Member Functions inherited from itk::AdaptiveStochasticGradientDescentOptimizer
 AdaptiveStochasticGradientDescentOptimizer ()
 
void UpdateCurrentTime () override
 
 ~AdaptiveStochasticGradientDescentOptimizer () override=default
 
- Protected Member Functions inherited from itk::StandardGradientDescentOptimizer
virtual double Compute_a (double k) const
 
 StandardGradientDescentOptimizer ()
 
virtual void UpdateCurrentTime ()
 
 ~StandardGradientDescentOptimizer () override=default
 
- Protected Member Functions inherited from itk::GradientDescentOptimizer2
 GradientDescentOptimizer2 ()
 
void PrintSelf (std::ostream &os, Indent indent) const override
 
 ~GradientDescentOptimizer2 () override=default
 
- 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=default
 
- Protected Member Functions inherited from elastix::OptimizerBase< TElastix >
virtual bool GetNewSamplesEveryIteration () const
 
 OptimizerBase ()=default
 
virtual void SelectNewSamples ()
 
 ~OptimizerBase () override=default
 
- 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 { 0.0 }
 
bool m_UseConstantStep { false }
 
- Protected Attributes inherited from itk::GradientDescentOptimizer2
DerivativeType m_Gradient
 
DerivativeType m_SearchDirection
 
StopConditionType m_StopCondition { MaximumNumberOfIterations }
 
- Protected Attributes inherited from itk::ScaledSingleValuedNonLinearOptimizer
ScaledCostFunctionPointer m_ScaledCostFunction
 
ParametersType m_ScaledCurrentPosition
 
- Protected Attributes inherited from elastix::BaseComponentSE< TElastix >
ConfigurationPointer m_Configuration {}
 
itk::WeakPointer< TElastix > m_Elastix {}
 
RegistrationTypem_Registration {}
 

Private Attributes

 elxOverrideGetSelfMacro
 
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
 

Member Typedef Documentation

◆ AdvancedTransformPointer

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::AdvancedTransformPointer = typename AdvancedTransformType::Pointer
protected

Definition at line 336 of file elxAdaptiveStochasticGradientDescent.h.

◆ AdvancedTransformType

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::AdvancedTransformType = itk::AdvancedTransform<CoordinateRepresentationType, Self::FixedImageDimension, Self::MovingImageDimension>
protected

Definition at line 334 of file elxAdaptiveStochasticGradientDescent.h.

◆ ComputeDisplacementDistributionType

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::ComputeDisplacementDistributionType = itk::ComputeDisplacementDistribution<FixedImageType, TransformType>
protected

Definition at line 309 of file elxAdaptiveStochasticGradientDescent.h.

◆ ComputeJacobianTermsType

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::ComputeJacobianTermsType = itk::ComputeJacobianTerms<FixedImageType, TransformType>
protected

Definition at line 301 of file elxAdaptiveStochasticGradientDescent.h.

◆ ConstPointer

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::ConstPointer = itk::SmartPointer<const Self>

Definition at line 205 of file elxAdaptiveStochasticGradientDescent.h.

◆ CoordinateRepresentationType

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::CoordinateRepresentationType = typename TransformType::ScalarType
protected

Definition at line 333 of file elxAdaptiveStochasticGradientDescent.h.

◆ FixedImageIndexType

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::FixedImageIndexType = typename FixedImageType::IndexType
protected

Definition at line 296 of file elxAdaptiveStochasticGradientDescent.h.

◆ FixedImagePointType

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::FixedImagePointType = typename FixedImageType::PointType
protected

Definition at line 297 of file elxAdaptiveStochasticGradientDescent.h.

◆ FixedImageRegionType

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::FixedImageRegionType = typename FixedImageType::RegionType
protected

Definition at line 295 of file elxAdaptiveStochasticGradientDescent.h.

◆ FixedImageType

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::FixedImageType = typename RegistrationType::FixedImageType
protected

Protected typedefs

Definition at line 292 of file elxAdaptiveStochasticGradientDescent.h.

◆ ImageGridSamplerPointer

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::ImageGridSamplerPointer = typename ImageGridSamplerType::Pointer
protected

Definition at line 319 of file elxAdaptiveStochasticGradientDescent.h.

◆ ImageGridSamplerType

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::ImageGridSamplerType = itk::ImageGridSampler<FixedImageType>
protected

Definition at line 318 of file elxAdaptiveStochasticGradientDescent.h.

◆ ImageRandomCoordinateSamplerPointer

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::ImageRandomCoordinateSamplerPointer = typename ImageRandomCoordinateSamplerType::Pointer
protected

Definition at line 317 of file elxAdaptiveStochasticGradientDescent.h.

◆ ImageRandomCoordinateSamplerType

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::ImageRandomCoordinateSamplerType = itk::ImageRandomCoordinateSampler<FixedImageType>
protected

Definition at line 316 of file elxAdaptiveStochasticGradientDescent.h.

◆ ImageRandomSamplerBasePointer

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::ImageRandomSamplerBasePointer = typename ImageRandomSamplerBaseType::Pointer
protected

Definition at line 315 of file elxAdaptiveStochasticGradientDescent.h.

◆ ImageRandomSamplerBaseType

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::ImageRandomSamplerBaseType = itk::ImageRandomSamplerBase<FixedImageType>
protected

Definition at line 314 of file elxAdaptiveStochasticGradientDescent.h.

◆ ImageSampleContainerPointer

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::ImageSampleContainerPointer = typename ImageSampleContainerType::Pointer
protected

Definition at line 321 of file elxAdaptiveStochasticGradientDescent.h.

◆ ImageSampleContainerType

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::ImageSampleContainerType = typename ImageGridSamplerType::ImageSampleContainerType
protected

Definition at line 320 of file elxAdaptiveStochasticGradientDescent.h.

◆ ImageSamplerBasePointer

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::ImageSamplerBasePointer = typename ImageSamplerBaseType::Pointer
protected

Definition at line 313 of file elxAdaptiveStochasticGradientDescent.h.

◆ ImageSamplerBaseType

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::ImageSamplerBaseType = itk::ImageSamplerBase<FixedImageType>
protected

Samplers:

Definition at line 312 of file elxAdaptiveStochasticGradientDescent.h.

◆ ITKBaseType

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::ITKBaseType = typename Superclass2::ITKBaseType

Definition at line 227 of file elxAdaptiveStochasticGradientDescent.h.

◆ itkRegistrationType

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::itkRegistrationType = typename RegistrationType::ITKBaseType
protected

Definition at line 298 of file elxAdaptiveStochasticGradientDescent.h.

◆ JacobianType

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::JacobianType = typename TransformType::JacobianType
protected

Definition at line 300 of file elxAdaptiveStochasticGradientDescent.h.

◆ JacobianValueType

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::JacobianValueType = typename JacobianType::ValueType
protected

Definition at line 302 of file elxAdaptiveStochasticGradientDescent.h.

◆ MovingImageType

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::MovingImageType = typename RegistrationType::MovingImageType
protected

Definition at line 293 of file elxAdaptiveStochasticGradientDescent.h.

◆ NonZeroJacobianIndicesType

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::NonZeroJacobianIndicesType = typename AdvancedTransformType::NonZeroJacobianIndicesType
protected

Definition at line 337 of file elxAdaptiveStochasticGradientDescent.h.

◆ Pointer

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::Pointer = itk::SmartPointer<Self>

Definition at line 204 of file elxAdaptiveStochasticGradientDescent.h.

◆ ProgressCommandPointer

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::ProgressCommandPointer = typename ProgressCommand::Pointer
protected

Definition at line 327 of file elxAdaptiveStochasticGradientDescent.h.

◆ ProgressCommandType

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::ProgressCommandType = ProgressCommand
protected

Definition at line 326 of file elxAdaptiveStochasticGradientDescent.h.

◆ RandomGeneratorPointer

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::RandomGeneratorPointer = typename RandomGeneratorType::Pointer
protected

Definition at line 325 of file elxAdaptiveStochasticGradientDescent.h.

◆ RandomGeneratorType

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::RandomGeneratorType = itk::Statistics::MersenneTwisterRandomVariateGenerator
protected

Other protected typedefs

Definition at line 324 of file elxAdaptiveStochasticGradientDescent.h.

◆ Self

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::Self = AdaptiveStochasticGradientDescent

Standard ITK.

Definition at line 201 of file elxAdaptiveStochasticGradientDescent.h.

◆ SettingsVectorType

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::SettingsVectorType = typename std::vector<SettingsType>
protected

Definition at line 307 of file elxAdaptiveStochasticGradientDescent.h.

◆ SizeValueType

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::SizeValueType = itk::SizeValueType

Definition at line 228 of file elxAdaptiveStochasticGradientDescent.h.

◆ Superclass1

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::Superclass1 = AdaptiveStochasticGradientDescentOptimizer

Definition at line 202 of file elxAdaptiveStochasticGradientDescent.h.

◆ Superclass2

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::Superclass2 = OptimizerBase<TElastix>

Definition at line 203 of file elxAdaptiveStochasticGradientDescent.h.

◆ TransformJacobianType

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::TransformJacobianType = JacobianType
protected

Typedefs for support of sparse Jacobians and AdvancedTransforms.

Definition at line 330 of file elxAdaptiveStochasticGradientDescent.h.

◆ TransformType

template<class TElastix >
using elastix::AdaptiveStochasticGradientDescent< TElastix >::TransformType = typename itkRegistrationType::TransformType
protected

Definition at line 299 of file elxAdaptiveStochasticGradientDescent.h.

Constructor & Destructor Documentation

◆ AdaptiveStochasticGradientDescent()

template<class TElastix >
elastix::AdaptiveStochasticGradientDescent< TElastix >::AdaptiveStochasticGradientDescent ( )
protected

◆ ~AdaptiveStochasticGradientDescent()

template<class TElastix >
elastix::AdaptiveStochasticGradientDescent< TElastix >::~AdaptiveStochasticGradientDescent ( )
overrideprotecteddefault

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 ( )
overridevirtual

Reimplemented from elastix::BaseComponent.

◆ AfterEachResolution()

template<class TElastix >
void elastix::AdaptiveStochasticGradientDescent< TElastix >::AfterEachResolution ( )
overridevirtual

Reimplemented from elastix::BaseComponent.

◆ AfterRegistration()

template<class TElastix >
void elastix::AdaptiveStochasticGradientDescent< TElastix >::AfterRegistration ( )
overridevirtual

Reimplemented from elastix::BaseComponent.

◆ AutomaticParameterEstimation()

template<class TElastix >
virtual void elastix::AdaptiveStochasticGradientDescent< TElastix >::AutomaticParameterEstimation ( )
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 ( )
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 ( )
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 ( )
overridevirtual

Reimplemented from elastix::BaseComponent.

◆ BeforeRegistration()

template<class TElastix >
void elastix::AdaptiveStochasticGradientDescent< TElastix >::BeforeRegistration ( )
overridevirtual

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

Reimplemented from elastix::BaseComponent.

◆ 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.

◆ ITK_DISALLOW_COPY_AND_MOVE()

template<class TElastix >
elastix::AdaptiveStochasticGradientDescent< TElastix >::ITK_DISALLOW_COPY_AND_MOVE ( AdaptiveStochasticGradientDescent< TElastix >  )

◆ 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.

◆ 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 ( )
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 ( )
override

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

Field Documentation

◆ elxOverrideGetSelfMacro

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

Definition at line 405 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_AdvancedTransform

template<class TElastix >
AdvancedTransformPointer elastix::AdaptiveStochasticGradientDescent< TElastix >::m_AdvancedTransform
protected

The transform stored as AdvancedTransform

Definition at line 351 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_AutomaticParameterEstimation

template<class TElastix >
bool elastix::AdaptiveStochasticGradientDescent< TElastix >::m_AutomaticParameterEstimation
private

Definition at line 407 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_AutomaticParameterEstimationDone

template<class TElastix >
bool elastix::AdaptiveStochasticGradientDescent< TElastix >::m_AutomaticParameterEstimationDone
private

Definition at line 415 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_CurrentNumberOfSamplingAttempts

template<class TElastix >
SizeValueType elastix::AdaptiveStochasticGradientDescent< TElastix >::m_CurrentNumberOfSamplingAttempts
private

Definition at line 413 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 418 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 412 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_MaximumStepLength

template<class TElastix >
double elastix::AdaptiveStochasticGradientDescent< TElastix >::m_MaximumStepLength
private

Definition at line 408 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_MaximumStepLengthRatio

template<class TElastix >
double elastix::AdaptiveStochasticGradientDescent< TElastix >::m_MaximumStepLengthRatio
private

Definition at line 409 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_NumberOfBandStructureSamples

template<class TElastix >
SizeValueType elastix::AdaptiveStochasticGradientDescent< TElastix >::m_NumberOfBandStructureSamples
private

Definition at line 419 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 346 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_NumberOfJacobianMeasurements

template<class TElastix >
SizeValueType elastix::AdaptiveStochasticGradientDescent< TElastix >::m_NumberOfJacobianMeasurements
protected

Definition at line 347 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_NumberOfSamplesForExactGradient

template<class TElastix >
SizeValueType elastix::AdaptiveStochasticGradientDescent< TElastix >::m_NumberOfSamplesForExactGradient
protected

Definition at line 348 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_OriginalButSigmoidToDefault

template<class TElastix >
bool elastix::AdaptiveStochasticGradientDescent< TElastix >::m_OriginalButSigmoidToDefault
private

Definition at line 423 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_PreviousErrorAtIteration

template<class TElastix >
SizeValueType elastix::AdaptiveStochasticGradientDescent< TElastix >::m_PreviousErrorAtIteration
private

Definition at line 414 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_RandomGenerator

template<class TElastix >
RandomGeneratorPointer elastix::AdaptiveStochasticGradientDescent< TElastix >::m_RandomGenerator
protected

RandomGenerator for AddRandomPerturbation.

Definition at line 354 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 343 of file elxAdaptiveStochasticGradientDescent.h.

◆ m_SigmoidScaleFactor

template<class TElastix >
double elastix::AdaptiveStochasticGradientDescent< TElastix >::m_SigmoidScaleFactor
protected

Definition at line 356 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 422 of file elxAdaptiveStochasticGradientDescent.h.



Generated on 2023-01-13 for elastix by doxygen 1.9.6 elastix logo