itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree > Class Template Reference

fast k-means algorithm implementation using k-d tree structure More...

#include <itkKdTreeBasedKmeansEstimator.h>

Inheritance diagram for itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >:

Collaboration diagram for itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >:

List of all members.

Classes
class	CandidateVector
Public Types
typedef KdTreeNodeType::CentroidType	CentroidType
typedef KdTreeNodeType::CentroidType	CentroidType
typedef itk::hash_map < InstanceIdentifier, unsigned int >	ClusterLabelsType
typedef itk::hash_map < InstanceIdentifier, unsigned int >	ClusterLabelsType
typedef SmartPointer< const Self >	ConstPointer
typedef SmartPointer< const Self >	ConstPointer
typedef DistanceToCentroidMembershipFunctionType::Pointer	DistanceToCentroidMembershipFunctionPointer
typedef DistanceToCentroidMembershipFunction < MeasurementVectorType >	DistanceToCentroidMembershipFunctionType
typedef TKdTree::InstanceIdentifier	InstanceIdentifier
typedef TKdTree::InstanceIdentifier	InstanceIdentifier
typedef std::vector < ParameterType >	InternalParametersType
typedef std::vector < ParameterType >	InternalParametersType
typedef TKdTree::KdTreeNodeType	KdTreeNodeType
typedef TKdTree::KdTreeNodeType	KdTreeNodeType
typedef TKdTree::MeasurementType	MeasurementType
typedef TKdTree::MeasurementType	MeasurementType
typedef unsigned int	MeasurementVectorSizeType
typedef unsigned int	MeasurementVectorSizeType
typedef TKdTree::MeasurementVectorType	MeasurementVectorType
typedef TKdTree::MeasurementVectorType	MeasurementVectorType
typedef MembershipFunctionType::ConstPointer	MembershipFunctionPointer
typedef MembershipFunctionBase < MeasurementVectorType >	MembershipFunctionType
typedef MembershipFunctionVectorObjectType::Pointer	MembershipFunctionVectorObjectPointer
typedef SimpleDataObjectDecorator < MembershipFunctionVectorType >	MembershipFunctionVectorObjectType
typedef std::vector < MembershipFunctionPointer >	MembershipFunctionVectorType
typedef Array< double >	ParametersType
typedef Array< double >	ParametersType
typedef Array< double >	ParameterType
typedef Array< double >	ParameterType
typedef SmartPointer< Self >	Pointer
typedef SmartPointer< Self >	Pointer
typedef TKdTree::SampleType	SampleType
typedef TKdTree::SampleType	SampleType
typedef KdTreeBasedKmeansEstimator	Self
typedef KdTreeBasedKmeansEstimator	Self
typedef Object	Superclass
typedef Object	Superclass
Public Member Functions
virtual LightObject::Pointer	CreateAnother () const
virtual void	DebugOff () const
virtual void	DebugOn () const
virtual void	Delete ()
virtual const double &	GetCentroidPositionChanges ()
virtual double	GetCentroidPositionChanges () const
virtual double	GetCentroidPositionChangesThreshold () const
ClusterLabelsType *	GetClusterLabels ()
Command *	GetCommand (unsigned long tag)
virtual int	GetCurrentIteration () const
virtual const int &	GetCurrentIteration ()
bool	GetDebug () const
TKdTree *	GetKdTree ()
virtual const MeasurementVectorSizeType &	GetMeasurementVectorSize ()
virtual MeasurementVectorSizeType	GetMeasurementVectorSize () const
MetaDataDictionary &	GetMetaDataDictionary (void)
const MetaDataDictionary &	GetMetaDataDictionary (void) const
virtual unsigned long	GetMTime () const
virtual const char *	GetNameOfClass () const
virtual const char *	GetNameOfClass () const
const MembershipFunctionVectorObjectType *	GetOutput () const
ParametersType &	GetParameters ()
virtual int	GetReferenceCount () const
virtual bool	GetUseClusterLabels () const
bool	HasObserver (const EventObject &event) const
void	InvokeEvent (const EventObject &)
void	InvokeEvent (const EventObject &) const
virtual void	Modified () const
void	Print (std::ostream &os, Indent indent=0) const
virtual void	Register () const
void	RemoveAllObservers ()
void	RemoveObserver (unsigned long tag)
virtual void	SetCentroidPositionChangesThreshold (double _arg)
void	SetDebug (bool debugFlag) const
void	SetMetaDataDictionary (const MetaDataDictionary &rhs)
void	SetParameters (ParametersType &params)
virtual void	SetReferenceCount (int)
void	SetUseClusterLabels (bool flag)
virtual void	SetUseClusterLabels (bool _arg)
void	StartOptimization ()
void	StartOptimization ()
virtual void	UnRegister () const
virtual void	SetMaximumIteration (int _arg)
virtual const int &	GetMaximumIteration ()
virtual void	SetCentroidPositionChangesThreshold (double _arg)
virtual const double &	GetCentroidPositionChangesThreshold ()
void	SetKdTree (TKdTree *tree)
virtual void	SetParameters (ParametersType _arg)
virtual ParametersType	GetParameters () const
virtual void	SetMaximumIteration (int _arg)
virtual int	GetMaximumIteration () const
void	SetKdTree (TKdTree *tree)
const TKdTree *	GetKdTree () const
unsigned long	AddObserver (const EventObject &event, Command *)
unsigned long	AddObserver (const EventObject &event, Command *) const
Static Public Member Functions
static void	BreakOnError ()
static Pointer	New ()
static Pointer	New ()
static void	SetGlobalWarningDisplay (bool flag)
static bool	GetGlobalWarningDisplay ()
static void	GlobalWarningDisplayOn ()
static void	GlobalWarningDisplayOff ()
Protected Types
typedef int	InternalReferenceCountType
Protected Member Functions
void	CopyParameters (InternalParametersType &source, InternalParametersType &target)
void	CopyParameters (ParametersType &source, InternalParametersType &target)
void	CopyParameters (InternalParametersType &source, ParametersType &target)
void	CopyParameters (InternalParametersType &source, InternalParametersType &target)
void	CopyParameters (ParametersType &source, InternalParametersType &target)
void	CopyParameters (InternalParametersType &source, ParametersType &target)
void	FillClusterLabels (KdTreeNodeType *node, int closestIndex)
void	FillClusterLabels (KdTreeNodeType *node, int closestIndex)
void	Filter (KdTreeNodeType *node, std::vector< int > validIndexes, MeasurementVectorType &lowerBound, MeasurementVectorType &upperBound)
void	Filter (KdTreeNodeType *node, std::vector< int > validIndexes, MeasurementVectorType &lowerBound, MeasurementVectorType &upperBound)
int	GetClosestCandidate (ParameterType &measurements, std::vector< int > &validIndexes)
int	GetClosestCandidate (ParameterType &measurements, std::vector< int > &validIndexes)
void	GetPoint (ParameterType &point, MeasurementVectorType measurements)
double	GetSumOfSquaredPositionChanges (InternalParametersType &previous, InternalParametersType &current)
double	GetSumOfSquaredPositionChanges (InternalParametersType &previous, InternalParametersType &current)
bool	IsFarther (ParameterType &pointA, ParameterType &pointB, MeasurementVectorType &lowerBound, MeasurementVectorType &upperBound)
bool	IsFarther (ParameterType &pointA, ParameterType &pointB, MeasurementVectorType &lowerBound, MeasurementVectorType &upperBound)
	KdTreeBasedKmeansEstimator ()
	KdTreeBasedKmeansEstimator ()
bool	PrintObservers (std::ostream &os, Indent indent) const
void	PrintPoint (ParameterType &point)
void	PrintPoint (ParameterType &point)
void	PrintSelf (std::ostream &os, Indent indent) const
void	PrintSelf (std::ostream &os, Indent indent) const
virtual	~KdTreeBasedKmeansEstimator ()
virtual	~KdTreeBasedKmeansEstimator ()
void	GetPoint (ParameterType &point, MeasurementVectorType measurements)
virtual void	PrintHeader (std::ostream &os, Indent indent) const
virtual void	PrintTrailer (std::ostream &os, Indent indent) const
Protected Attributes
InternalReferenceCountType	m_ReferenceCount
SimpleFastMutexLock	m_ReferenceCountLock

---

Detailed Description

template<class TKdTree>
class itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >

fast k-means algorithm implementation using k-d tree structure

It returns k mean vectors that are centroids of k-clusters using pre-generated k-d tree. k-d tree generation is done by the WeightedCentroidKdTreeGenerator. The tree construction needs to be done only once. The resulting k-d tree's non-terminal nodes that have their children nodes have vector sums of measurement vectors that belong to the nodes and the number of measurement vectors in addition to the typical node boundary information and pointers to children nodes. Instead of reassigning every measurement vector to the nearest cluster centroid and recalculating centroid, it maintain a set of cluster centroid candidates and using pruning algorithm that utilizes k-d tree, it updates the means of only relevant candidates at each iterations. It would be faster than traditional implementation of k-means algorithm. However, the k-d tree consumes a large amount of memory. The tree construction time and pruning algorithm's performance are important factors to the whole process's performance. If users want to use k-d tree for some purpose other than k-means estimation, they can use the KdTreeGenerator instead of the WeightedCentroidKdTreeGenerator. It will save the tree construction time and memory usage.

Note: There is a second implementation of k-means algorithm in ITK under the While the Kd tree based implementation is more time efficient, the GLA/LBG based algorithm is more memory efficient.

Recent API changes: The static const macro to get the length of a measurement vector, MeasurementVectorSize has been removed to allow the length of a measurement vector to be specified at run time. It is now obtained from the KdTree set as input. You may query this length using the function GetMeasurementVectorSize().

See also:

ImageKmeansModelEstimator

WeightedCentroidKdTreeGenerator, KdTree

Definition at line 67 of file Numerics/Statistics/itkKdTreeBasedKmeansEstimator.h.

---

Member Typedef Documentation

template<class TKdTree >

typedef KdTreeNodeType::CentroidType itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::CentroidType

Definition at line 89 of file Numerics/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef KdTreeNodeType::CentroidType itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::CentroidType

Definition at line 92 of file Review/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef itk::hash_map< InstanceIdentifier, unsigned int > itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::ClusterLabelsType

Definition at line 157 of file Review/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef itk::hash_map< InstanceIdentifier, unsigned int > itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::ClusterLabelsType

Definition at line 145 of file Numerics/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef SmartPointer<const Self> itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::ConstPointer

Reimplemented from itk::Object.

Definition at line 75 of file Numerics/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef SmartPointer<const Self> itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::ConstPointer

Reimplemented from itk::Object.

Definition at line 78 of file Review/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef DistanceToCentroidMembershipFunctionType::Pointer itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::DistanceToCentroidMembershipFunctionPointer

Definition at line 111 of file Review/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef DistanceToCentroidMembershipFunction< MeasurementVectorType > itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::DistanceToCentroidMembershipFunctionType

Typedef requried to generate dataobject decorated output that can be plugged into SampleClassifierFilter

Definition at line 108 of file Review/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef TKdTree::InstanceIdentifier itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::InstanceIdentifier

Definition at line 87 of file Numerics/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef TKdTree::InstanceIdentifier itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::InstanceIdentifier

Definition at line 90 of file Review/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef std::vector< ParameterType > itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::InternalParametersType

Definition at line 101 of file Review/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef std::vector< ParameterType > itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::InternalParametersType

Definition at line 98 of file Numerics/Statistics/itkKdTreeBasedKmeansEstimator.h.

typedef int itk::LightObject::InternalReferenceCountType [protected, inherited]

Define the type of the reference count according to the target. This allows the use of atomic operations

Definition at line 139 of file itkLightObject.h.

template<class TKdTree >

typedef TKdTree::KdTreeNodeType itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::KdTreeNodeType

Types for the KdTree data structure

Definition at line 81 of file Numerics/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef TKdTree::KdTreeNodeType itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::KdTreeNodeType

Types for the KdTree data structure

Definition at line 84 of file Review/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef TKdTree::MeasurementType itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::MeasurementType

Definition at line 88 of file Review/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef TKdTree::MeasurementType itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::MeasurementType

Definition at line 85 of file Numerics/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef unsigned int itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::MeasurementVectorSizeType

Typedef for the length of a measurement vector

Definition at line 93 of file Numerics/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef unsigned int itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::MeasurementVectorSizeType

Typedef for the length of a measurement vector

Definition at line 96 of file Review/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef TKdTree::MeasurementVectorType itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::MeasurementVectorType

Definition at line 89 of file Review/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef TKdTree::MeasurementVectorType itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::MeasurementVectorType

Definition at line 86 of file Numerics/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef MembershipFunctionType::ConstPointer itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::MembershipFunctionPointer

Definition at line 114 of file Review/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef MembershipFunctionBase< MeasurementVectorType > itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::MembershipFunctionType

Definition at line 113 of file Review/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef MembershipFunctionVectorObjectType::Pointer itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::MembershipFunctionVectorObjectPointer

Definition at line 119 of file Review/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef SimpleDataObjectDecorator< MembershipFunctionVectorType > itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::MembershipFunctionVectorObjectType

Definition at line 117 of file Review/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef std::vector< MembershipFunctionPointer > itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::MembershipFunctionVectorType

Definition at line 115 of file Review/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef Array< double > itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::ParametersType

Definition at line 99 of file Numerics/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef Array< double > itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::ParametersType

Definition at line 102 of file Review/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef Array< double > itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::ParameterType

Parameters type. It defines a position in the optimization search space.

Definition at line 100 of file Review/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef Array< double > itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::ParameterType

Parameters type. It defines a position in the optimization search space.

Definition at line 97 of file Numerics/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef SmartPointer<Self> itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::Pointer

Reimplemented from itk::Object.

Definition at line 74 of file Numerics/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef SmartPointer<Self> itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::Pointer

Reimplemented from itk::Object.

Definition at line 77 of file Review/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef TKdTree::SampleType itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SampleType

Definition at line 91 of file Review/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef TKdTree::SampleType itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SampleType

Definition at line 88 of file Numerics/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef KdTreeBasedKmeansEstimator itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::Self

Standard Self typedef.

Reimplemented from itk::Object.

Definition at line 75 of file Review/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef KdTreeBasedKmeansEstimator itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::Self

Standard "Self" typedef.

Reimplemented from itk::Object.

Definition at line 72 of file Numerics/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef Object itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::Superclass

Reimplemented from itk::Object.

Definition at line 73 of file Numerics/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef Object itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::Superclass

Reimplemented from itk::Object.

Definition at line 76 of file Review/Statistics/itkKdTreeBasedKmeansEstimator.h.

---

Constructor & Destructor Documentation

template<class TKdTree >

itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::KdTreeBasedKmeansEstimator

(

)

[protected]

template<class TKdTree >

virtual itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::~KdTreeBasedKmeansEstimator

(

)

[inline, protected, virtual]

Definition at line 155 of file Numerics/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::KdTreeBasedKmeansEstimator

(

)

[protected]

template<class TKdTree >

virtual itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::~KdTreeBasedKmeansEstimator

(

)

[inline, protected, virtual]

Definition at line 164 of file Review/Statistics/itkKdTreeBasedKmeansEstimator.h.

---

Member Function Documentation

unsigned long itk::Object::AddObserver	(	const EventObject &	event,
		Command *
	)			[inherited]

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::Command to execute. It returns an unsigned long tag which can be used later to remove the event or retrieve the command. The memory for the Command becomes the responsibility of this object, so don't pass the same instance of a command to two different objects

unsigned long itk::Object::AddObserver	(	const EventObject &	event,
		Command *
	)			const [inherited]

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::Command to execute. It returns an unsigned long tag which can be used later to remove the event or retrieve the command. The memory for the Command becomes the responsibility of this object, so don't pass the same instance of a command to two different objects

static void itk::LightObject::BreakOnError

(

)

[static, inherited]

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

template<class TKdTree >

void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::CopyParameters	(	InternalParametersType &	source,
		InternalParametersType &	target
	)			[protected]

copies the source parameters (k-means) to the target

template<class TKdTree >

void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::CopyParameters	(	ParametersType &	source,
		InternalParametersType &	target
	)			[protected]

copies the source parameters (k-means) to the target

template<class TKdTree >

void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::CopyParameters	(	InternalParametersType &	source,
		ParametersType &	target
	)			[protected]

copies the source parameters (k-means) to the target

template<class TKdTree >

void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::CopyParameters	(	InternalParametersType &	source,
		InternalParametersType &	target
	)			[protected]

copies the source parameters (k-means) to the target

template<class TKdTree >

void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::CopyParameters	(	ParametersType &	source,
		InternalParametersType &	target
	)			[protected]

copies the source parameters (k-means) to the target

template<class TKdTree >

void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::CopyParameters	(	InternalParametersType &	source,
		ParametersType &	target
	)			[protected]

copies the source parameters (k-means) to the target

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.

Reimplemented in itk::BSplineDeformableTransform< TScalarType, NDimensions, VSplineOrder >, itk::CreateObjectFunction< T >, itk::TransformFactoryBase, itk::AnalyzeImageIOFactory, itk::BioRadImageIOFactory, itk::BMPImageIOFactory, itk::Brains2MaskImageIOFactory, itk::DICOMImageIO2Factory, itk::DicomImageIOFactory, itk::GDCMImageIOFactory, itk::GE4ImageIOFactory, itk::GE5ImageIOFactory, itk::GEAdwImageIOFactory, itk::GiplImageIOFactory, itk::JPEGImageIOFactory, itk::LSMImageIOFactory, itk::MetaImageIOFactory, itk::NiftiImageIOFactory, itk::NrrdImageIOFactory, itk::PNGImageIOFactory, itk::RawImageIOFactory< TPixel, VImageDimension >, itk::SiemensVisionImageIOFactory, itk::StimulateImageIOFactory, itk::TIFFImageIOFactory, itk::VTKImageIOFactory, itk::Bruker2DSEQImageIOFactory, itk::MatlabTransformIOFactory, itk::MINC2ImageIOFactory, itk::MRCImageIOFactory, itk::PhilipsRECImageIOFactory, itk::TxtTransformIOFactory, itk::VoxBoCUBImageIOFactory, itk::VTKImageIO2Factory, and itk::SpatialObjectFactoryBase.

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.

template<class TKdTree >

void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::FillClusterLabels	(	KdTreeNodeType *	node,
		int	closestIndex
	)			[protected]

template<class TKdTree >

void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::FillClusterLabels	(	KdTreeNodeType *	node,
		int	closestIndex
	)			[protected]

template<class TKdTree >

void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::Filter	(	KdTreeNodeType *	node,
		std::vector< int >	validIndexes,
		MeasurementVectorType &	lowerBound,
		MeasurementVectorType &	upperBound
	)			[protected]

recursive pruning algorithm. the "validIndexes" vector contains only the indexes of the surviving candidates for the "node"

template<class TKdTree >

void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::Filter	(	KdTreeNodeType *	node,
		std::vector< int >	validIndexes,
		MeasurementVectorType &	lowerBound,
		MeasurementVectorType &	upperBound
	)			[protected]

recursive pruning algorithm. the validIndexes vector contains only the indexes of the surviving candidates for the node

template<class TKdTree >

virtual double itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetCentroidPositionChanges

(

)

const [virtual]

template<class TKdTree >

virtual const double& itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetCentroidPositionChanges

(

)

[virtual]

template<class TKdTree >

virtual double itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetCentroidPositionChangesThreshold

(

)

const [virtual]

template<class TKdTree >

virtual const double& itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetCentroidPositionChangesThreshold

(

)

[virtual]

Set/Get the termination threshold for the squared sum of changes in centroid postions after one iteration

template<class TKdTree >

int itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetClosestCandidate	(	ParameterType &	measurements,
		std::vector< int > &	validIndexes
	)			[protected]

get the index of the closest candidate to the "measurements" measurement vector

template<class TKdTree >

int itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetClosestCandidate	(	ParameterType &	measurements,
		std::vector< int > &	validIndexes
	)			[protected]

get the index of the closest candidate to the measurements measurement vector

template<class TKdTree >

ClusterLabelsType* itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetClusterLabels

(

)

[inline]

Definition at line 150 of file Numerics/Statistics/itkKdTreeBasedKmeansEstimator.h.

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.

template<class TKdTree >

virtual int itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetCurrentIteration

(

)

const [virtual]

template<class TKdTree >

virtual const int& itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetCurrentIteration

(

)

[virtual]

bool itk::Object::GetDebug

(

)

const [inherited]

Get the value of the debug flag.

static bool itk::Object::GetGlobalWarningDisplay

(

)

[static, inherited]

This is a global flag that controls whether any debug, warning or error messages are displayed.

template<class TKdTree >

TKdTree* itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetKdTree

(

)

[inline]

Definition at line 130 of file Numerics/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

const TKdTree* itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetKdTree

(

)

const

Set/Get the pointer to the KdTree

template<class TKdTree >

virtual int itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetMaximumIteration

(

)

const [virtual]

Set/Get maximum iteration limit.

template<class TKdTree >

virtual const int& itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetMaximumIteration

(

)

[virtual]

Set/Get maximum iteration limit.

template<class TKdTree >

virtual MeasurementVectorSizeType itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetMeasurementVectorSize

(

)

const [virtual]

Get the length of measurement vectors in the KdTree

template<class TKdTree >

virtual const MeasurementVectorSizeType& itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetMeasurementVectorSize

(

)

[virtual]

Get the length of measurement vectors in the KdTree

MetaDataDictionary& itk::Object::GetMetaDataDictionary

(

void

)

[inherited]

Returns:

A reference to this objects MetaDataDictionary.

Warning:

This reference may be changed.

const MetaDataDictionary& itk::Object::GetMetaDataDictionary

(

void

)

const [inherited]

Returns:

A constant reference to this objects MetaDataDictionary.

virtual unsigned long itk::Object::GetMTime

(

)

const [virtual, inherited]

Return this objects modified time.

Reimplemented in itk::ImageRegistrationMethod< TFixedImage, TMovingImage >, itk::ImageToSpatialObjectRegistrationMethod< TFixedImage, TMovingSpatialObject >, itk::MultiResolutionImageRegistrationMethod< TFixedImage, TMovingImage >, itk::PointSetToImageRegistrationMethod< TFixedPointSet, TMovingImage >, itk::PointSetToPointSetRegistrationMethod< TFixedPointSet, TMovingPointSet >, itk::DeformationFieldSource< TOutputImage >, itk::InverseDeformationFieldImageFilter< TInputImage, TOutputImage >, itk::ResampleImageFilter< TInputImage, TOutputImage, TInterpolatorPrecisionType >, itk::VectorResampleImageFilter< TInputImage, TOutputImage, TInterpolatorPrecisionType >, itk::BoundingBox< TPointIdentifier, VPointDimension, TCoordRep, TPointsContainer >, itk::ImageAdaptor< TImage, TAccessor >, itk::ResampleImageFilter< TInputImage, TOutputImage, TInterpolatorPrecisionType >, itk::TransformToDeformationFieldSource< TOutputImage, TTransformPrecisionType >, itk::ImageSpatialObject< TDimension, TPixelType >, itk::MeshSpatialObject< TMesh >, itk::SceneSpatialObject< TSpaceDimension >, itk::SpatialObject< TDimension >, itk::ImageAdaptor< TImage, Accessor::AsinPixelAccessor< TImage::PixelType, TOutputPixelType > >, itk::ImageAdaptor< TImage, Accessor::SqrtPixelAccessor< TImage::PixelType, TOutputPixelType > >, itk::ImageAdaptor< TImage, Accessor::TanPixelAccessor< TImage::PixelType, TOutputPixelType > >, itk::ImageAdaptor< TImage, Accessor::CosPixelAccessor< TImage::PixelType, TOutputPixelType > >, itk::ImageAdaptor< TImage, Accessor::VectorToRGBPixelAccessor< TImage::PixelType::ValueType > >, itk::ImageAdaptor< TImage, Accessor::RGBToVectorPixelAccessor< TImage::PixelType::ComponentType > >, itk::ImageAdaptor< TImage, Accessor::ComplexToModulusPixelAccessor< TImage::PixelType, TOutputPixelType > >, itk::ImageAdaptor< TImage, Accessor::AbsPixelAccessor< TImage::PixelType, TOutputPixelType > >, itk::ImageAdaptor< TImage, Accessor::SinPixelAccessor< TImage::PixelType, TOutputPixelType > >, itk::ImageAdaptor< TImage, Accessor::LogPixelAccessor< TImage::PixelType, TOutputPixelType > >, itk::ImageAdaptor< TImage, Accessor::ComplexToPhasePixelAccessor< TImage::PixelType, TOutputPixelType > >, itk::ImageAdaptor< VectorImage< TPixelType, Dimension >, Accessor::VectorImageToImagePixelAccessor< TPixelType > >, itk::ImageAdaptor< TImage, Accessor::Log10PixelAccessor< TImage::PixelType, TOutputPixelType > >, itk::ImageAdaptor< TImage, Accessor::AtanPixelAccessor< TImage::PixelType, TOutputPixelType > >, itk::ImageAdaptor< TImage, Accessor::ComplexToRealPixelAccessor< TImage::PixelType, TOutputPixelType > >, itk::ImageAdaptor< TImage, Accessor::ComplexToImaginaryPixelAccessor< TImage::PixelType, TOutputPixelType > >, itk::ImageAdaptor< TImage, Accessor::ExpNegativePixelAccessor< TImage::PixelType, TOutputPixelType > >, itk::ImageAdaptor< TImage, Accessor::ExpPixelAccessor< TImage::PixelType, TOutputPixelType > >, itk::ImageAdaptor< TImage, Accessor::AcosPixelAccessor< TImage::PixelType, TOutputPixelType > >, itk::ImageAdaptor< TImage, Accessor::RGBToLuminancePixelAccessor< TImage::PixelType, TOutputPixelType > >, itk::ImageAdaptor< TImage, Accessor::AddPixelAccessor< TImage::PixelType > >, itk::ImageSpatialObject< TDimension, unsigned char >, itk::SpatialObject< 3 >, and itk::SpatialObject< ::itk::GetMeshDimension< TMesh >::PointDimension >.

Referenced by itk::SpatialObject< ::itk::GetMeshDimension< TMesh >::PointDimension >::GetObjectMTime().

template<class TKdTree >

virtual const char* itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetNameOfClass

(

)

const [virtual]

Run-time type information (and related methods).

Reimplemented from itk::Object.

template<class TKdTree >

virtual const char* itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetNameOfClass

(

)

const [virtual]

Run-time type information (and related methods).

Reimplemented from itk::Object.

template<class TKdTree >

const MembershipFunctionVectorObjectType* itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetOutput

(

)

const

Output Membership function vector containing the membership functions with the final optimized paramters

template<class TKdTree >

virtual ParametersType itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetParameters

(

)

const [virtual]

Set the position to initialize the optimization.

template<class TKdTree >

ParametersType& itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetParameters

(

void

)

[inline]

Get current position of the optimization.

Definition at line 106 of file Numerics/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetPoint	(	ParameterType &	point,
		MeasurementVectorType	measurements
	)			[inline, protected]

imports the "measurements" measurement vector data to the "point"

Definition at line 279 of file Numerics/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetPoint	(	ParameterType &	point,
		MeasurementVectorType	measurements
	)			[protected]

imports the measurements measurement vector data to the point

virtual int itk::LightObject::GetReferenceCount

(

)

const [inline, virtual, inherited]

Gets the reference count on this object.

Definition at line 106 of file itkLightObject.h.

template<class TKdTree >

double itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetSumOfSquaredPositionChanges	(	InternalParametersType &	previous,
		InternalParametersType &	current
	)			[protected]

gets the sum of squared difference between the previous position and current postion of all centroid. This is the primary termination condition for this algorithm. If the return value is less than the value that was set by the SetCentroidPositionChangesThreshold method.

template<class TKdTree >

double itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetSumOfSquaredPositionChanges	(	InternalParametersType &	previous,
		InternalParametersType &	current
	)			[protected]

gets the sum of squared difference between the previous position and current postion of all centroid. This is the primary termination condition for this algorithm. If the return value is less than the value that was set by the SetCentroidPositionChangesThreshold method.

template<class TKdTree >

virtual bool itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetUseClusterLabels

(

)

const [virtual]

static void itk::Object::GlobalWarningDisplayOff

(

)

[inline, static, inherited]

This is a global flag that controls whether any debug, warning or error messages are displayed.

Definition at line 100 of file itkObject.h.

References itk::Object::SetGlobalWarningDisplay().

static void itk::Object::GlobalWarningDisplayOn

(

)

[inline, static, inherited]

This is a global flag that controls whether any debug, warning or error messages are displayed.

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.

template<class TKdTree >

bool itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::IsFarther	(	ParameterType &	pointA,
		ParameterType &	pointB,
		MeasurementVectorType &	lowerBound,
		MeasurementVectorType &	upperBound
	)			[protected]

returns true if the pointA is farther than pointB to the boundary

template<class TKdTree >

bool itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::IsFarther	(	ParameterType &	pointA,
		ParameterType &	pointB,
		MeasurementVectorType &	lowerBound,
		MeasurementVectorType &	upperBound
	)			[protected]

returns true if the pointA is farther than pointB to the boundary

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.

Reimplemented in itk::NormalizeImageFilter< TInputImage, TOutputImage >, itk::ImageAdaptor< TImage, TAccessor >, itk::MiniPipelineSeparableImageFilter< TInputImage, TOutputImage, TFilter >, itk::GrayscaleDilateImageFilter< TInputImage, TOutputImage, TKernel >, itk::GrayscaleErodeImageFilter< TInputImage, TOutputImage, TKernel >, itk::GrayscaleMorphologicalClosingImageFilter< TInputImage, TOutputImage, TKernel >, itk::GrayscaleMorphologicalOpeningImageFilter< TInputImage, TOutputImage, TKernel >, itk::MorphologicalGradientImageFilter< TInputImage, TOutputImage, TKernel >, itk::ImageAdaptor< TImage, Accessor::AsinPixelAccessor< TImage::PixelType, TOutputPixelType > >, itk::ImageAdaptor< TImage, Accessor::SqrtPixelAccessor< TImage::PixelType, TOutputPixelType > >, itk::ImageAdaptor< TImage, Accessor::TanPixelAccessor< TImage::PixelType, TOutputPixelType > >, itk::ImageAdaptor< TImage, Accessor::CosPixelAccessor< TImage::PixelType, TOutputPixelType > >, itk::ImageAdaptor< TImage, Accessor::VectorToRGBPixelAccessor< TImage::PixelType::ValueType > >, itk::ImageAdaptor< TImage, Accessor::RGBToVectorPixelAccessor< TImage::PixelType::ComponentType > >, itk::ImageAdaptor< TImage, Accessor::ComplexToModulusPixelAccessor< TImage::PixelType, TOutputPixelType > >, itk::ImageAdaptor< TImage, Accessor::AbsPixelAccessor< TImage::PixelType, TOutputPixelType > >, itk::ImageAdaptor< TImage, Accessor::SinPixelAccessor< TImage::PixelType, TOutputPixelType > >, itk::ImageAdaptor< TImage, Accessor::LogPixelAccessor< TImage::PixelType, TOutputPixelType > >, itk::ImageAdaptor< TImage, Accessor::ComplexToPhasePixelAccessor< TImage::PixelType, TOutputPixelType > >, itk::ImageAdaptor< VectorImage< TPixelType, Dimension >, Accessor::VectorImageToImagePixelAccessor< TPixelType > >, itk::ImageAdaptor< TImage, Accessor::Log10PixelAccessor< TImage::PixelType, TOutputPixelType > >, itk::ImageAdaptor< TImage, Accessor::AtanPixelAccessor< TImage::PixelType, TOutputPixelType > >, itk::ImageAdaptor< TImage, Accessor::ComplexToRealPixelAccessor< TImage::PixelType, TOutputPixelType > >, itk::ImageAdaptor< TImage, Accessor::ComplexToImaginaryPixelAccessor< TImage::PixelType, TOutputPixelType > >, itk::ImageAdaptor< TImage, Accessor::ExpNegativePixelAccessor< TImage::PixelType, TOutputPixelType > >, itk::ImageAdaptor< TImage, Accessor::ExpPixelAccessor< TImage::PixelType, TOutputPixelType > >, itk::ImageAdaptor< TImage, Accessor::AcosPixelAccessor< TImage::PixelType, TOutputPixelType > >, itk::ImageAdaptor< TImage, Accessor::RGBToLuminancePixelAccessor< TImage::PixelType, TOutputPixelType > >, itk::ImageAdaptor< TImage, Accessor::AddPixelAccessor< TImage::PixelType > >, and itk::MiniPipelineSeparableImageFilter< TInputImage, TOutputImage, RankImageFilter< TInputImage, TInputImage, FlatStructuringElement< ::itk::GetImageDimension< TInputImage >::ImageDimension > > >.

Referenced by itk::NarrowBandImageFilterBase< TInputImage, Image< TOutputPixelType,::itk::GetImageDimension< TInputImage >::ImageDimension > >::InsertNarrowBandNode(), itk::MatrixOffsetTransformBase< TScalarType, 3, 3 >::SetCenter(), itk::MatrixOffsetTransformBase< TScalarType, 3, 3 >::SetMatrix(), itk::NarrowBandImageFilterBase< TInputImage, Image< TOutputPixelType,::itk::GetImageDimension< TInputImage >::ImageDimension > >::SetNarrowBand(), itk::NarrowBandImageFilterBase< TInputImage, Image< TOutputPixelType,::itk::GetImageDimension< TInputImage >::ImageDimension > >::SetNarrowBandInnerRadius(), itk::NarrowBandImageFilterBase< TInputImage, Image< TOutputPixelType,::itk::GetImageDimension< TInputImage >::ImageDimension > >::SetNarrowBandTotalRadius(), itk::MatrixOffsetTransformBase< TScalarType, 3, 3 >::SetOffset(), itk::ThresholdLabelerImageFilter< TInputImage, TOutputImage >::SetRealThresholds(), itk::ThresholdLabelerImageFilter< TInputImage, TOutputImage >::SetThresholds(), itk::Statistics::GoodnessOfFitFunctionBase< TInputHistogram >::SetTotalObservedScale(), and itk::MatrixOffsetTransformBase< TScalarType, 3, 3 >::SetTranslation().

template<class TKdTree >

static Pointer itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::New

(

)

[static]

Method for creation through the object factory.

Reimplemented from itk::Object.

template<class TKdTree >

static Pointer itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::New

(

)

[static]

Method for creation through the object factory.

Reimplemented from itk::Object.

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

Cause the object to print itself out.

Referenced by itk::WeakPointer< ProcessObject >::Print().

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

Methods invoked by 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.

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

template<class TKdTree >

void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::PrintPoint

(

ParameterType &

point

)

[inline, protected]

Definition at line 288 of file Numerics/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::PrintPoint

(

ParameterType &

point

)

[protected]

template<class TKdTree >

void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::PrintSelf	(	std::ostream &	os,
		Indent	indent
	)			const [protected, virtual]

Methods invoked by 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.

Reimplemented from itk::Object.

template<class TKdTree >

void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::PrintSelf	(	std::ostream &	os,
		Indent	indent
	)			const [protected, virtual]

Methods invoked by 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.

Reimplemented from itk::Object.

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

Methods invoked by 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 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.

template<class TKdTree >

virtual void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SetCentroidPositionChangesThreshold

(

double

_arg

)

[virtual]

Set/Get the termination threshold for the squared sum of changes in centroid postions after one iteration

template<class TKdTree >

virtual void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SetCentroidPositionChangesThreshold

(

double

_arg

)

[virtual]

Set/Get the termination threshold for the squared sum of changes in centroid postions after one iteration

void itk::Object::SetDebug

(

bool

debugFlag

)

const [inherited]

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

static void itk::Object::SetGlobalWarningDisplay

(

bool

flag

)

[static, inherited]

This is a global flag that controls whether any debug, warning or error messages are displayed.

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

template<class TKdTree >

void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SetKdTree

(

TKdTree *

tree

)

Set/Get the pointer to the KdTree

template<class TKdTree >

void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SetKdTree

(

TKdTree *

tree

)

[inline]

Set/Get the pointer to the KdTree

Definition at line 121 of file Numerics/Statistics/itkKdTreeBasedKmeansEstimator.h.

References itk::Statistics::MeasurementVectorTraits::SetLength().

template<class TKdTree >

virtual void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SetMaximumIteration

(

int

_arg

)

[virtual]

Set/Get maximum iteration limit.

template<class TKdTree >

virtual void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SetMaximumIteration

(

int

_arg

)

[virtual]

Set/Get maximum iteration limit.

void itk::Object::SetMetaDataDictionary

(

const MetaDataDictionary &

rhs

)

[inherited]

Returns:

Set the MetaDataDictionary

template<class TKdTree >

void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SetParameters

(

ParametersType &

params

)

[inline]

Set the position to initialize the optimization.

Definition at line 102 of file Numerics/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

virtual void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SetParameters

(

ParametersType

_arg

)

[virtual]

Set the position to initialize the optimization.

virtual void itk::Object::SetReferenceCount

(

int

)

[virtual, inherited]

Sets the reference count (use with care)

Reimplemented from itk::LightObject.

template<class TKdTree >

virtual void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SetUseClusterLabels

(

bool

_arg

)

[virtual]

template<class TKdTree >

void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SetUseClusterLabels

(

bool

flag

)

[inline]

Definition at line 147 of file Numerics/Statistics/itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::StartOptimization

(

)

Start optimization Optimization will stop when it meets either of two termination conditions, the maximum iteration limit or epsilon (minimal changes in squared sum of changes in centroid positions)

template<class TKdTree >

void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::StartOptimization

(

)

Start optimization Optimization will stop when it meets either of two termination conditions, the maximum iteration limit or epsilon (minimal changes in squared sum of changes in centroid positions)

virtual void itk::Object::UnRegister

(

)

const [virtual, inherited]

Decrease the reference count (release by another object).

Reimplemented from itk::LightObject.

---

Member Data Documentation

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

Number of uses of this object by other objects.

Definition at line 144 of file itkLightObject.h.

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

Mutex lock to protect modification to the reference count

Definition at line 147 of file itkLightObject.h.

---

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

• Numerics/Statistics/itkKdTreeBasedKmeansEstimator.h
• Review/Statistics/itkKdTreeBasedKmeansEstimator.h