itk::AmoebaOptimizer Class Reference
[Numerics]

#include <itkAmoebaOptimizer.h>

Inheritance diagram for itk::AmoebaOptimizer:

[legend]Collaboration diagram for itk::AmoebaOptimizer:

[legend]List of all members.

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Detailed Description

Wrap of the vnl_amoeba algorithm.

AmoebaOptimizer is a wrapper around the vnl_amoeba algorithm which is an implementation of the Nelder-Meade downhill simplex problem. For most problems, it is a few times slower than a Levenberg-Marquardt algorithm but does not require derivatives of its cost function. It works by creating a simplex (n+1 points in ND space). The cost function is evaluated at each corner of the simplex. The simplex is then modified (by reflecting a corner about the opposite edge, by shrinking the entire simplex, by contracting one edge of the simplex, or by expanding the simplex) in searching for the minimum of the cost function.

The methods AutomaticInitialSimplex() and SetInitialSimplexDelta() control whether the optimizer defines the initial simplex automatically (by constructing a very small simplex around the initial position) or uses a user supplied simplex size.

AmoebaOptimizer can only minimize a function.

Optimizers

Definition at line 49 of file itkAmoebaOptimizer.h.

Public Types
typedef ReceptorMemberCommand< Self >	CommandType
typedef SmartPointer< const Self >	ConstPointer
typedef CostFunctionType::Pointer	CostFunctionPointer
typedef SingleValuedCostFunction	CostFunctionType
typedef CostFunctionType::DerivativeType	DerivativeType
typedef vnl_amoeba	InternalOptimizerType
typedef vnl_vector< double >	InternalParametersType
typedef CostFunctionType::MeasureType	MeasureType
typedef Superclass::ParametersType	ParametersType
typedef SmartPointer< Self >	Pointer
typedef Superclass::ScalesType	ScalesType
typedef AmoebaOptimizer	Self
typedef SingleValuedNonLinearVnlOptimizer	Superclass
Public Member Functions
virtual void	AutomaticInitialSimplexOff ()
virtual void	AutomaticInitialSimplexOn ()
virtual LightObject::Pointer	CreateAnother () const
virtual void	DebugOff () const
virtual void	DebugOn () const
virtual void	Delete ()
virtual bool	GetAutomaticInitialSimplex ()
virtual const ParametersType &	GetCachedCurrentPosition ()
virtual const DerivativeType &	GetCachedDerivative ()
Command *	GetCommand (unsigned long tag)
virtual const CostFunctionType *	GetCostFunction ()
virtual const ParametersType &	GetCurrentPosition ()
bool	GetDebug () const
virtual double	GetFunctionConvergenceTolerance ()
virtual const ParametersType &	GetInitialPosition ()
virtual ParametersType	GetInitialSimplexDelta ()
Methods to define whether the cost function will be maximized or *minimized By default the VNL amoeba optimizer is only a minimizer *Maximization is implemented here by notifying the CostFunctionAdaptor *which in its turn will multiply the function values and its derivative by **virtual const bool &	GetMaximize ()
virtual unsigned int	GetMaximumNumberOfIterations ()
const MetaDataDictionary &	GetMetaDataDictionary (void) const
MetaDataDictionary &	GetMetaDataDictionary (void)
bool	GetMinimize () const
virtual unsigned long	GetMTime () const
virtual const char *	GetNameOfClass () const
vnl_amoeba *	GetOptimizer (void)
virtual double	GetParametersConvergenceTolerance ()
virtual int	GetReferenceCount () const
virtual const ScalesType &	GetScales ()
MeasureType	GetValue (const ParametersType &parameters) const
MeasureType	GetValue () const
bool	HasObserver (const EventObject &event) const
void	InvokeEvent (const EventObject &) const
void	InvokeEvent (const EventObject &)
virtual void	MaximizeOff ()
virtual void	MaximizeOn ()
void	MinimizeOff ()
void	MinimizeOn ()
virtual void	Modified () const
void	Print (std::ostream &os, Indent indent=0) const
virtual void	Register () const
void	RemoveAllObservers ()
void	RemoveObserver (unsigned long tag)
Set Get the mode which determines how the amoeba algorithm *defines the initial simplex Default is *AutomaticInitialSimplexOn If AutomaticInitialSimplex is the *initial simplex is created with a default size If *AutomaticInitialSimplex is then InitialSimplexDelta will be *used to define the initial setting the ith corner of the *simplex as *[x0[0], x0[1],..., x0[i]+InitialSimplexDelta[i],...,*x0[d-1]] virtual void	SetAutomaticInitialSimplex (bool _arg)
virtual void	SetCostFunction (CostFunctionType *costFunction)
virtual void	SetCostFunction (SingleValuedCostFunction *costFunction)
void	SetDebug (bool debugFlag) const
virtual void	SetFunctionConvergenceTolerance (double tol)
virtual void	SetInitialPosition (const ParametersType &param)
Set Get the deltas that are used to define the initial simplex *when AutomaticInitialSimplex is off *virtual void	SetInitialSimplexDelta (ParametersType _arg)
virtual void	SetMaximize (bool _arg)
Set Get the maximum number of iterations The optimization algorithm will *terminate after the maximum number of iterations has been reached *The default value is *virtual void	SetMaximumNumberOfIterations (unsigned int n)
void	SetMetaDataDictionary (const MetaDataDictionary &rhs)
void	SetMinimize (bool v)
The optimization algorithm will terminate when the simplex *diameter and the difference in cost function at the corners of *the simplex falls below user specified thresholds The simplex *diameter threshold is set via method *	SetParametersConvergenceTolerance () with the default value being *1e-8.The cost function convergence threshold is set via method *SetFunctionConvergenceTolerance() with the default value being *1e-4.*/virtual void SetParametersConvergenceTolerance(double tol)
virtual void	SetReferenceCount (int)
void	SetScales (const ScalesType &scales)
void	StartOptimization (void)
virtual void	UnRegister () const
Static Public Member Functions
static void	BreakOnError ()
static bool	GetGlobalWarningDisplay ()
static void	GlobalWarningDisplayOff ()
static void	GlobalWarningDisplayOn ()
static Pointer	New ()
This is a global flag that controls whether any warning *or error messages are displayed *static void	SetGlobalWarningDisplay (bool flag)
Public Attributes
Allow people to add remove invoke observers(callbacks) to any ITK *object.This is an implementation of the subject/observer design *pattern.An observer is added by specifying an event to respond to *and an itk unsigned lon	AddObserver )(const EventObject &event, Command *) const
This is a global flag that controls whether any	debug
Set Get the mode which determines how the amoeba algorithm *defines the initial simplex Default is *AutomaticInitialSimplexOn If AutomaticInitialSimplex is the *initial simplex is created with a default size If *AutomaticInitialSimplex is	off
Set Get the mode which determines how the amoeba algorithm *defines the initial simplex Default is *AutomaticInitialSimplexOn If AutomaticInitialSimplex is	on
Return Cached Values These method have the advantage of not triggering a *recomputation of the metric but it has the disadvantage of returning *a value that may not be the one corresponding to the current parameters For *GUI update this method is a good	option
Return Cached Values These method have the advantage of not triggering a *recomputation of the metric but it has the disadvantage of returning *a value that may not be the one corresponding to the current parameters For *GUI update	purposes
Set Get the mode which determines how the amoeba algorithm *defines the initial simplex Default is *AutomaticInitialSimplexOn If AutomaticInitialSimplex is the *initial simplex is created with a default size If *AutomaticInitialSimplex is then InitialSimplexDelta will be *used to define the initial	simplex
Return Cached Values These method have the advantage of not triggering a *recomputation of the metric	value
Protected Types
typedef Superclass::CostFunctionAdaptorType	CostFunctionAdaptorType
Protected Member Functions
	AmoebaOptimizer ()
CostFunctionAdaptorType *	GetCostFunctionAdaptor (void)
const CostFunctionAdaptorType *	GetCostFunctionAdaptor (void) const
CostFunctionAdaptorType *	GetNonConstCostFunctionAdaptor (void) const
bool	PrintObservers (std::ostream &os, Indent indent) const
void	PrintSelf (std::ostream &os, Indent indent) const
virtual void	PrintTrailer (std::ostream &os, Indent indent) const
void	SetCostFunctionAdaptor (CostFunctionAdaptorType *adaptor)
virtual void	SetCurrentPosition (const ParametersType &param)
virtual	~AmoebaOptimizer ()
Protected Attributes
CostFunctionPointer	m_CostFunction
int	m_ReferenceCount
SimpleFastMutexLock	m_ReferenceCountLock
bool	m_ScalesInitialized
Methods invoked by virtual Print() to print information about the object *including superclasses.Typically not called by the user(use Print()*instead) but used in the hierarchical print process to combine the *output of several classes.*/virtual void PrintSelf(std voi	PrintHeader )(std::ostream &os, Indent indent) const

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Member Typedef Documentation

typedef ReceptorMemberCommand< Self > itk::SingleValuedNonLinearVnlOptimizer::CommandType [inherited]

Command observer that will interact with the ITK-VNL cost-function adaptor in order to generate iteration events. This will allow to overcome the limitation of VNL optimizers not offering callbacks for every iteration Definition at line 48 of file itkSingleValuedNonLinearVnlOptimizer.h.

typedef SmartPointer<const Self> itk::AmoebaOptimizer::ConstPointer

Reimplemented from itk::SingleValuedNonLinearVnlOptimizer. Definition at line 57 of file itkAmoebaOptimizer.h.

typedef Superclass::CostFunctionAdaptorType itk::AmoebaOptimizer::CostFunctionAdaptorType [protected]

Reimplemented from itk::SingleValuedNonLinearVnlOptimizer. Definition at line 132 of file itkAmoebaOptimizer.h.

typedef CostFunctionType::Pointer itk::SingleValuedNonLinearOptimizer::CostFunctionPointer [inherited]

Reimplemented in itk::FRPROptimizer, itk::LBFGSBOptimizer, itk::OnePlusOneEvolutionaryOptimizer, itk::PowellOptimizer, and itk::RegularStepGradientDescentOptimizer. Definition at line 56 of file itkSingleValuedNonLinearOptimizer.h.

typedef SingleValuedCostFunction itk::SingleValuedNonLinearOptimizer::CostFunctionType [inherited]

Type of the Cost Function Reimplemented in itk::FRPROptimizer, itk::LBFGSBOptimizer, itk::OnePlusOneEvolutionaryOptimizer, itk::PowellOptimizer, and itk::RegularStepGradientDescentOptimizer. Definition at line 55 of file itkSingleValuedNonLinearOptimizer.h.

typedef CostFunctionType::DerivativeType itk::SingleValuedNonLinearOptimizer::DerivativeType [inherited]

Derivative type. It defines a type used to return the cost function derivative. Definition at line 64 of file itkSingleValuedNonLinearOptimizer.h.

typedef vnl_amoeba itk::AmoebaOptimizer::InternalOptimizerType

Internal optimizer type. Definition at line 73 of file itkAmoebaOptimizer.h.

typedef vnl_vector<double> itk::AmoebaOptimizer::InternalParametersType

InternalParameters typedef. Definition at line 70 of file itkAmoebaOptimizer.h.

typedef CostFunctionType::MeasureType itk::SingleValuedNonLinearOptimizer::MeasureType [inherited]

Measure type. It defines a type used to return the cost function value. Reimplemented in itk::LBFGSBOptimizer. Definition at line 60 of file itkSingleValuedNonLinearOptimizer.h.

typedef Superclass::ParametersType itk::AmoebaOptimizer::ParametersType

Parameters type. It defines a position in the optimization search space. Reimplemented from itk::SingleValuedNonLinearOptimizer. Definition at line 63 of file itkAmoebaOptimizer.h.

typedef SmartPointer<Self> itk::AmoebaOptimizer::Pointer

Reimplemented from itk::SingleValuedNonLinearVnlOptimizer. Definition at line 56 of file itkAmoebaOptimizer.h.

typedef Superclass::ScalesType itk::NonLinearOptimizer::ScalesType [inherited]

Scale type. This array defines scale to be applied to parameters before being evaluated in the cost function. This allows to map to a more convenient space. In particular this is used to normalize parameter spaces in which some parameters have a different dynamic range. Reimplemented from itk::Optimizer. Definition at line 52 of file itkNonLinearOptimizer.h.

typedef AmoebaOptimizer itk::AmoebaOptimizer::Self

Standard "Self" typedef. Reimplemented from itk::SingleValuedNonLinearVnlOptimizer. Definition at line 54 of file itkAmoebaOptimizer.h.

typedef SingleValuedNonLinearVnlOptimizer itk::AmoebaOptimizer::Superclass

Reimplemented from itk::SingleValuedNonLinearVnlOptimizer. Definition at line 55 of file itkAmoebaOptimizer.h.

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Constructor & Destructor Documentation

itk::AmoebaOptimizer::AmoebaOptimizer (   )  [protected]

virtual itk::AmoebaOptimizer::~AmoebaOptimizer (   )  [protected, virtual]

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Member Function Documentation

virtual void itk::AmoebaOptimizer::AutomaticInitialSimplexOff (   )  [virtual]

virtual void itk::AmoebaOptimizer::AutomaticInitialSimplexOn (   )  [virtual]

static void itk::LightObject::BreakOnError (   )  [static, inherited]

This method is called when itkExceptionMacro executes. It allows the debugger to break on error.

virtual LightObject::Pointer itk::Object::CreateAnother (   )  const [virtual, inherited]

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::LightObject.

virtual void itk::Object::DebugOff (   )  const [virtual, inherited]

Turn debugging output off.

virtual void itk::Object::DebugOn (   )  const [virtual, inherited]

Turn debugging output on.

virtual void itk::LightObject::Delete (   )  [virtual, inherited]

Delete an itk object. This method should always be used to delete an object when the new operator was used to create it. Using the C delete method will not work with reference counting.

virtual bool itk::AmoebaOptimizer::GetAutomaticInitialSimplex (   )  [virtual]

virtual const ParametersType& itk::SingleValuedNonLinearVnlOptimizer::GetCachedCurrentPosition (   )  [virtual, inherited]

virtual const DerivativeType& itk::SingleValuedNonLinearVnlOptimizer::GetCachedDerivative (   )  [virtual, inherited]

Command* itk::Object::GetCommand (  unsigned long  tag  )  [inherited]

Get the command associated with the given tag. NOTE: This returns a pointer to a Command, but it is safe to asign this to a Command::Pointer. Since Command inherits from LightObject, at this point in the code, only a pointer or a reference to the Command can be used.

virtual const CostFunctionType* itk::SingleValuedNonLinearOptimizer::GetCostFunction (   )  [virtual, inherited]

Get the cost function.

CostFunctionAdaptorType* itk::SingleValuedNonLinearVnlOptimizer::GetCostFunctionAdaptor (  void   )  [protected, inherited]

const CostFunctionAdaptorType* itk::SingleValuedNonLinearVnlOptimizer::GetCostFunctionAdaptor (  void   )  const [protected, inherited]

virtual const ParametersType& itk::Optimizer::GetCurrentPosition (   )  [virtual, inherited]

Get current position of the optimization.

bool itk::Object::GetDebug (   )  const [inherited]

Get the value of the debug flag.

virtual double itk::AmoebaOptimizer::GetFunctionConvergenceTolerance (   )  [virtual]

static bool itk::Object::GetGlobalWarningDisplay (   )  [static, inherited]

virtual const ParametersType& itk::Optimizer::GetInitialPosition (   )  [virtual, inherited]

Get the position to initialize the optimization.

virtual ParametersType itk::AmoebaOptimizer::GetInitialSimplexDelta (   )  [virtual]

Methods to define whether the cost function will be maximized or* minimized By default the VNL amoeba optimizer is only a minimizer* Maximization is implemented here by notifying the CostFunctionAdaptor* which in its turn will multiply the function values and its derivative by* * virtual const bool& itk::SingleValuedNonLinearVnlOptimizer::GetMaximize (   )  [virtual, inherited]

virtual unsigned int itk::AmoebaOptimizer::GetMaximumNumberOfIterations (   )  [virtual]

const MetaDataDictionary& itk::Object::GetMetaDataDictionary (  void   )  const [inherited]

Returns: A constant reference to this objects MetaDataDictionary.

MetaDataDictionary& itk::Object::GetMetaDataDictionary (  void   )  [inherited]

Returns: A reference to this objects MetaDataDictionary. Warning: This reference may be changed.

bool itk::SingleValuedNonLinearVnlOptimizer::GetMinimize (   )  const [inline, inherited]

Definition at line 72 of file itkSingleValuedNonLinearVnlOptimizer.h.

virtual unsigned long itk::Object::GetMTime (   )  const [virtual, inherited]

Return this objects modified time. Reimplemented in itk::ImageRegistrationMethod< TFixedImage, TMovingImage >, itk::DeformationFieldSource< TOutputImage >, itk::InverseDeformationFieldImageFilter< TInputImage, TOutputImage >, itk::ResampleImageFilter< TInputImage, TOutputImage, TInterpolatorPrecisionType >, itk::VectorResampleImageFilter< TInputImage, TOutputImage, TInterpolatorPrecisionType >, itk::BoundingBox< TPointIdentifier, VPointDimension, TCoordRep, TPointsContainer >, itk::SceneSpatialObject< SpaceDimension >, and itk::SceneSpatialObject< NDimensions >.

virtual const char* itk::AmoebaOptimizer::GetNameOfClass (   )  const [virtual]

Run-time type information (and related methods). Reimplemented from itk::SingleValuedNonLinearVnlOptimizer.

CostFunctionAdaptorType* itk::SingleValuedNonLinearVnlOptimizer::GetNonConstCostFunctionAdaptor (  void   )  const [protected, inherited]

The purpose of this method is to get around the lack of const-correctness in VNL cost-functions and optimizers

vnl_amoeba* itk::AmoebaOptimizer::GetOptimizer (  void   )

Method for getting access to the internal optimizer.

virtual double itk::AmoebaOptimizer::GetParametersConvergenceTolerance (   )  [virtual]

virtual int itk::LightObject::GetReferenceCount (   )  const [inline, virtual, inherited]

Gets the reference count on this object. Definition at line 98 of file itkLightObject.h.

virtual const ScalesType& itk::Optimizer::GetScales (   )  [virtual, inherited]

Get current parameters scaling.

MeasureType itk::SingleValuedNonLinearOptimizer::GetValue (  const ParametersType &  parameters  )  const [inherited]

Get the cost function value at the given parameters. Reimplemented in itk::SPSAOptimizer.

MeasureType itk::AmoebaOptimizer::GetValue (   )  const

Return Current Value Reimplemented from itk::SingleValuedNonLinearVnlOptimizer.

static void itk::Object::GlobalWarningDisplayOff (   )  [inline, static, inherited]

Definition at line 100 of file itkObject.h. References itk::Object::SetGlobalWarningDisplay().

static void itk::Object::GlobalWarningDisplayOn (   )  [inline, static, inherited]

Definition at line 98 of file itkObject.h. References itk::Object::SetGlobalWarningDisplay().

bool itk::Object::HasObserver (  const EventObject &  event  )  const [inherited]

Return true if an observer is registered for this event.

void itk::Object::InvokeEvent (  const EventObject &   )  const [inherited]

Call Execute on all the Commands observing this event id. The actions triggered by this call doesn't modify this object.

void itk::Object::InvokeEvent (  const EventObject &   )  [inherited]

Call Execute on all the Commands observing this event id.

virtual void itk::SingleValuedNonLinearVnlOptimizer::MaximizeOff (   )  [virtual, inherited]

virtual void itk::SingleValuedNonLinearVnlOptimizer::MaximizeOn (   )  [virtual, inherited]

void itk::SingleValuedNonLinearVnlOptimizer::MinimizeOff (  void   )  [inline, inherited]

Definition at line 78 of file itkSingleValuedNonLinearVnlOptimizer.h.

void itk::SingleValuedNonLinearVnlOptimizer::MinimizeOn (  void   )  [inline, inherited]

Definition at line 76 of file itkSingleValuedNonLinearVnlOptimizer.h.

virtual void itk::Object::Modified (   )  const [virtual, inherited]

Update the modification time for this object. Many filters rely on the modification time to determine if they need to recompute their data. Referenced by itk::MatrixOffsetTransformBase< TScalarType, 3, 3 >::SetCenter(), itk::HistogramAlgorithmBase< TInputHistogram >::SetInputHistogram(), itk::MatrixOffsetTransformBase< TScalarType, 3, 3 >::SetMatrix(), itk::MatrixOffsetTransformBase< TScalarType, 3, 3 >::SetOffset(), itk::ThresholdLabelerImageFilter< TInputImage, TOutputImage >::SetRealThresholds(), itk::CollidingFrontsImageFilter< TInputImage, TOutputImage >::SetSeedPoints1(), itk::CollidingFrontsImageFilter< TInputImage, TOutputImage >::SetSeedPoints2(), itk::NonUniformBSpline< TDimension >::SetSplineOrder(), itk::ThresholdLabelerImageFilter< TInputImage, TOutputImage >::SetThresholds(), itk::Statistics::GoodnessOfFitFunctionBase< typename ComponentType::HistogramType >::SetTotalObservedScale(), and itk::MatrixOffsetTransformBase< TScalarType, 3, 3 >::SetTranslation().

static Pointer itk::AmoebaOptimizer::New (   )  [static]

Method for creation through the object factory. Reimplemented from itk::SingleValuedNonLinearOptimizer.

void itk::LightObject::Print (  std::ostream &  os, Indent  indent = 0 )  const [inherited]

Cause the object to print itself out.

bool itk::Object::PrintObservers (  std::ostream &  os, Indent  indent )  const [protected, inherited]

void itk::AmoebaOptimizer::PrintSelf (  std::ostream &  os, Indent  indent )  const [protected, virtual]

Print out internal state Reimplemented from itk::SingleValuedNonLinearVnlOptimizer.

virtual void itk::LightObject::PrintTrailer (  std::ostream &  os, Indent  indent )  const [protected, virtual, inherited]

virtual void itk::Object::Register (   )  const [virtual, inherited]

Increase the reference count (mark as used by another object). Reimplemented from itk::LightObject.

void itk::Object::RemoveAllObservers (   )  [inherited]

Remove all observers .

void itk::Object::RemoveObserver (  unsigned long  tag  )  [inherited]

Remove the observer with this tag value.

Set Get the mode which determines how the amoeba algorithm* defines the initial simplex Default is* AutomaticInitialSimplexOn If AutomaticInitialSimplex is the* initial simplex is created with a default size If* AutomaticInitialSimplex is then InitialSimplexDelta will be* used to define the initial setting the ith corner of the* simplex as* [x0[0], x0[1], ..., x0[i]+InitialSimplexDelta[i], ..., * x0[d-1]] virtual void itk::AmoebaOptimizer::SetAutomaticInitialSimplex (  bool  _arg  )  [virtual]

virtual void itk::SingleValuedNonLinearOptimizer::SetCostFunction (  CostFunctionType *  costFunction  )  [virtual, inherited]

Set the cost function.

virtual void itk::AmoebaOptimizer::SetCostFunction (  SingleValuedCostFunction *  costFunction  )  [virtual]

Plug in a Cost Function into the optimizer Implements itk::SingleValuedNonLinearVnlOptimizer.

void itk::SingleValuedNonLinearVnlOptimizer::SetCostFunctionAdaptor (  CostFunctionAdaptorType *  adaptor  )  [protected, inherited]

virtual void itk::Optimizer::SetCurrentPosition (  const ParametersType &  param  )  [protected, virtual, inherited]

Set the current position.

void itk::Object::SetDebug (  bool  debugFlag  )  const [inherited]

Set the value of the debug flag. A non-zero value turns debugging on.

virtual void itk::AmoebaOptimizer::SetFunctionConvergenceTolerance (  double  tol  )  [virtual]

This is a global flag that controls whether any warning* or error messages are displayed* static void itk::Object::SetGlobalWarningDisplay (  bool  flag  )  [static, inherited]

Referenced by itk::Object::GlobalWarningDisplayOff(), and itk::Object::GlobalWarningDisplayOn().

virtual void itk::Optimizer::SetInitialPosition (  const ParametersType &  param  )  [virtual, inherited]

Set the position to initialize the optimization.

Set Get the deltas that are used to define the initial simplex* when AutomaticInitialSimplex is off* virtual void itk::AmoebaOptimizer::SetInitialSimplexDelta (  ParametersType  _arg  )  [virtual]

virtual void itk::SingleValuedNonLinearVnlOptimizer::SetMaximize (  bool  _arg  )  [virtual, inherited]

Set Get the maximum number of iterations The optimization algorithm will* terminate after the maximum number of iterations has been reached* The default value is* virtual void itk::AmoebaOptimizer::SetMaximumNumberOfIterations (  unsigned int  n  )  [virtual]

void itk::Object::SetMetaDataDictionary (  const MetaDataDictionary &  rhs  )  [inherited]

Returns: Set the MetaDataDictionary

void itk::SingleValuedNonLinearVnlOptimizer::SetMinimize (  bool  v  )  [inline, inherited]

Definition at line 74 of file itkSingleValuedNonLinearVnlOptimizer.h.

The optimization algorithm will terminate when the simplex* diameter and the difference in cost function at the corners of* the simplex falls below user specified thresholds The simplex* diameter threshold is set via method* itk::AmoebaOptimizer::SetParametersConvergenceTolerance (   )

virtual void itk::Object::SetReferenceCount (  int   )  [virtual, inherited]

Sets the reference count (use with care) Reimplemented from itk::LightObject.

void itk::Optimizer::SetScales (  const ScalesType &  scales  )  [inherited]

Set current parameters scaling. Reimplemented in itk::LBFGSBOptimizer.

void itk::AmoebaOptimizer::StartOptimization (  void   )  [virtual]

Start optimization with an initial value. Reimplemented from itk::Optimizer.

virtual void itk::Object::UnRegister (   )  const [virtual, inherited]

Decrease the reference count (release by another object). Reimplemented from itk::LightObject.

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Member Data Documentation

Allow people to add remove invoke observers (callbacks) to any ITK * object. This is an implementation of the subject/observer design * pattern. An observer is added by specifying an event to respond to * and an itk unsigned lon itk::Object::AddObserver)(const EventObject &event, Command *) const [inherited]

This is a global flag that controls whether any itk::Object::debug [inherited]

Definition at line 94 of file itkObject.h.

CostFunctionPointer itk::SingleValuedNonLinearOptimizer::m_CostFunction [protected, inherited]

Definition at line 80 of file itkSingleValuedNonLinearOptimizer.h.

int itk::LightObject::m_ReferenceCount [mutable, protected, inherited]

Number of uses of this object by other objects. Definition at line 119 of file itkLightObject.h.

SimpleFastMutexLock itk::LightObject::m_ReferenceCountLock [mutable, protected, inherited]

Mutex lock to protect modification to the reference count Definition at line 122 of file itkLightObject.h.

bool itk::Optimizer::m_ScalesInitialized [protected, inherited]

Definition at line 90 of file itkOptimizer.h.

Set Get the mode which determines how the amoeba algorithm* defines the initial simplex Default is* AutomaticInitialSimplexOn If AutomaticInitialSimplex is the* initial simplex is created with a default size If* AutomaticInitialSimplex is itk::AmoebaOptimizer::off

Definition at line 88 of file itkAmoebaOptimizer.h.

Set Get the mode which determines how the amoeba algorithm* defines the initial simplex Default is* AutomaticInitialSimplexOn If AutomaticInitialSimplex is itk::AmoebaOptimizer::on

Definition at line 88 of file itkAmoebaOptimizer.h.

Return Cached Values These method have the advantage of not triggering a* recomputation of the metric but it has the disadvantage of returning* a value that may not be the one corresponding to the current parameters For* GUI update this method is a good itk::SingleValuedNonLinearVnlOptimizer::option [inherited]

Definition at line 83 of file itkSingleValuedNonLinearVnlOptimizer.h.

Methods invoked by virtual Print () to print information about the object * including superclasses. Typically not called by the user (use Print() * instead) but used in the hierarchical print process to combine the * output of several classes. */ virtual void PrintSelf(std voi itk::LightObject::PrintHeader)(std::ostream &os, Indent indent) const [protected, inherited]

Return Cached Values These method have the advantage of not triggering a* recomputation of the metric but it has the disadvantage of returning* a value that may not be the one corresponding to the current parameters For* GUI update itk::SingleValuedNonLinearVnlOptimizer::purposes [inherited]

Definition at line 83 of file itkSingleValuedNonLinearVnlOptimizer.h.

Set Get the mode which determines how the amoeba algorithm* defines the initial simplex Default is* AutomaticInitialSimplexOn If AutomaticInitialSimplex is the* initial simplex is created with a default size If* AutomaticInitialSimplex is then InitialSimplexDelta will be* used to define the initial itk::AmoebaOptimizer::simplex

Definition at line 88 of file itkAmoebaOptimizer.h.

Return Cached Values These method have the advantage of not triggering a* recomputation of the metric itk::SingleValuedNonLinearVnlOptimizer::value [inherited]

Definition at line 83 of file itkSingleValuedNonLinearVnlOptimizer.h.

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

• itkAmoebaOptimizer.h

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