ITK  5.2.0
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itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree > Class Template Reference

#include <itkKdTreeBasedKmeansEstimator.h>

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

Classes

class  CandidateVector
 

Public Types

using CentroidType = typename KdTreeNodeType::CentroidType
 
using ConstPointer = SmartPointer< const Self >
 
using DistanceToCentroidMembershipFunctionPointer = typename DistanceToCentroidMembershipFunctionType::Pointer
 
using DistanceToCentroidMembershipFunctionType = DistanceToCentroidMembershipFunction< MeasurementVectorType >
 
using InstanceIdentifier = typename TKdTree::InstanceIdentifier
 
using InternalParametersType = std::vector< ParameterType >
 
using KdTreeNodeType = typename TKdTree::KdTreeNodeType
 
using MeasurementType = typename TKdTree::MeasurementType
 
using MeasurementVectorSizeType = unsigned int
 
using MeasurementVectorType = typename TKdTree::MeasurementVectorType
 
using MembershipFunctionPointer = typename MembershipFunctionType::ConstPointer
 
using MembershipFunctionType = MembershipFunctionBase< MeasurementVectorType >
 
using MembershipFunctionVectorObjectPointer = typename MembershipFunctionVectorObjectType::Pointer
 
using MembershipFunctionVectorObjectType = SimpleDataObjectDecorator< MembershipFunctionVectorType >
 
using MembershipFunctionVectorType = std::vector< MembershipFunctionPointer >
 
using ParametersType = Array< double >
 
using ParameterType = Array< double >
 
using Pointer = SmartPointer< Self >
 
using SampleType = typename TKdTree::SampleType
 
using Self = KdTreeBasedKmeansEstimator
 
using Superclass = Object
 
- Public Types inherited from itk::Object
using ConstPointer = SmartPointer< const Self >
 
using Pointer = SmartPointer< Self >
 
using Self = Object
 
using Superclass = LightObject
 
- Public Types inherited from itk::LightObject
using ConstPointer = SmartPointer< const Self >
 
using Pointer = SmartPointer< Self >
 
using Self = LightObject
 

Public Member Functions

virtual ::itk::LightObject::Pointer CreateAnother () const
 
virtual const char * GetNameOfClass () const
 
const MembershipFunctionVectorObjectTypeGetOutput () const
 
- Public Member Functions inherited from itk::Object
unsigned long AddObserver (const EventObject &event, Command *)
 
unsigned long AddObserver (const EventObject &event, Command *) const
 
virtual void DebugOff () const
 
virtual void DebugOn () const
 
CommandGetCommand (unsigned long tag)
 
bool GetDebug () const
 
MetaDataDictionaryGetMetaDataDictionary ()
 
const MetaDataDictionaryGetMetaDataDictionary () const
 
virtual ModifiedTimeType GetMTime () const
 
virtual const TimeStampGetTimeStamp () const
 
bool HasObserver (const EventObject &event) const
 
void InvokeEvent (const EventObject &)
 
void InvokeEvent (const EventObject &) const
 
virtual void Modified () const
 
void Register () const override
 
void RemoveAllObservers ()
 
void RemoveObserver (unsigned long tag)
 
void SetDebug (bool debugFlag) const
 
void SetReferenceCount (int) override
 
void UnRegister () const noexcept override
 
void SetMetaDataDictionary (const MetaDataDictionary &rhs)
 
void SetMetaDataDictionary (MetaDataDictionary &&rrhs)
 
virtual void SetObjectName (std::string _arg)
 
virtual const std::string & GetObjectName () const
 
- Public Member Functions inherited from itk::LightObject
virtual void Delete ()
 
virtual int GetReferenceCount () const
 
 itkCloneMacro (Self)
 
void Print (std::ostream &os, Indent indent=0) const
 

Static Public Member Functions

static Pointer New ()
 
- Static Public Member Functions inherited from itk::Object
static bool GetGlobalWarningDisplay ()
 
static void GlobalWarningDisplayOff ()
 
static void GlobalWarningDisplayOn ()
 
static Pointer New ()
 
static void SetGlobalWarningDisplay (bool flag)
 
- Static Public Member Functions inherited from itk::LightObject
static void BreakOnError ()
 
static Pointer New ()
 
using ClusterLabelsType = std::unordered_map< InstanceIdentifier, unsigned int >
 
int m_CurrentIteration { 0 }
 
int m_MaximumIteration { 100 }
 
double m_CentroidPositionChanges { 0.0 }
 
double m_CentroidPositionChangesThreshold { 0.0 }
 
TKdTree::Pointer m_KdTree
 
EuclideanDistanceMetric< ParameterType >::Pointer m_DistanceMetric
 
ParametersType m_Parameters
 
CandidateVector m_CandidateVector
 
ParameterType m_TempVertex
 
bool m_UseClusterLabels { false }
 
bool m_GenerateClusterLabels { false }
 
ClusterLabelsType m_ClusterLabels
 
MeasurementVectorSizeType m_MeasurementVectorSize { 0 }
 
MembershipFunctionVectorObjectPointer m_MembershipFunctionsObject
 
virtual void SetParameters (ParametersType _arg)
 
virtual ParametersType GetParameters () const
 
virtual void SetMaximumIteration (int _arg)
 
virtual int GetMaximumIteration () const
 
virtual void SetCentroidPositionChangesThreshold (double _arg)
 
virtual double GetCentroidPositionChangesThreshold () const
 
void SetKdTree (TKdTree *tree)
 
const TKdTree * GetKdTree () const
 
virtual MeasurementVectorSizeType GetMeasurementVectorSize () const
 
virtual int GetCurrentIteration () const
 
virtual double GetCentroidPositionChanges () const
 
void StartOptimization ()
 
virtual void SetUseClusterLabels (bool _arg)
 
virtual bool GetUseClusterLabels () const
 
 KdTreeBasedKmeansEstimator ()
 
 ~KdTreeBasedKmeansEstimator () override=default
 
void PrintSelf (std::ostream &os, Indent indent) const override
 
void FillClusterLabels (KdTreeNodeType *node, int closestIndex)
 
double GetSumOfSquaredPositionChanges (InternalParametersType &previous, InternalParametersType &current)
 
int GetClosestCandidate (ParameterType &measurements, std::vector< int > &validIndexes)
 
bool IsFarther (ParameterType &pointA, ParameterType &pointB, MeasurementVectorType &lowerBound, MeasurementVectorType &upperBound)
 
void Filter (KdTreeNodeType *node, std::vector< int > validIndexes, MeasurementVectorType &lowerBound, MeasurementVectorType &upperBound)
 
void CopyParameters (InternalParametersType &source, InternalParametersType &target)
 
void CopyParameters (ParametersType &source, InternalParametersType &target)
 
void CopyParameters (InternalParametersType &source, ParametersType &target)
 
void GetPoint (ParameterType &point, MeasurementVectorType measurements)
 
void PrintPoint (ParameterType &point)
 

Additional Inherited Members

- Protected Member Functions inherited from itk::Object
 Object ()
 
 ~Object () override
 
bool PrintObservers (std::ostream &os, Indent indent) const
 
virtual void SetTimeStamp (const TimeStamp &time)
 
- Protected Member Functions inherited from itk::LightObject
virtual LightObject::Pointer InternalClone () const
 
 LightObject ()
 
virtual void PrintHeader (std::ostream &os, Indent indent) const
 
virtual void PrintTrailer (std::ostream &os, Indent indent) const
 
virtual ~LightObject ()
 
- Protected Attributes inherited from itk::LightObject
std::atomic< int > m_ReferenceCount
 

Detailed Description

template<typename 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
Examples
Examples/Statistics/KdTreeBasedKMeansClustering.cxx, and Examples/Statistics/ScalarImageKmeansModelEstimator.cxx.

Definition at line 74 of file itkKdTreeBasedKmeansEstimator.h.

Member Typedef Documentation

◆ CentroidType

template<typename TKdTree >
using itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::CentroidType = typename KdTreeNodeType::CentroidType

Definition at line 95 of file itkKdTreeBasedKmeansEstimator.h.

◆ ClusterLabelsType

template<typename TKdTree >
using itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::ClusterLabelsType = std::unordered_map<InstanceIdentifier, unsigned int>

current number of iteration

Definition at line 158 of file itkKdTreeBasedKmeansEstimator.h.

◆ ConstPointer

template<typename TKdTree >
using itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::ConstPointer = SmartPointer<const Self>

Definition at line 81 of file itkKdTreeBasedKmeansEstimator.h.

◆ DistanceToCentroidMembershipFunctionPointer

Definition at line 110 of file itkKdTreeBasedKmeansEstimator.h.

◆ DistanceToCentroidMembershipFunctionType

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

Definition at line 108 of file itkKdTreeBasedKmeansEstimator.h.

◆ InstanceIdentifier

template<typename TKdTree >
using itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::InstanceIdentifier = typename TKdTree::InstanceIdentifier

Definition at line 93 of file itkKdTreeBasedKmeansEstimator.h.

◆ InternalParametersType

template<typename TKdTree >
using itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::InternalParametersType = std::vector<ParameterType>

Definition at line 103 of file itkKdTreeBasedKmeansEstimator.h.

◆ KdTreeNodeType

template<typename TKdTree >
using itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::KdTreeNodeType = typename TKdTree::KdTreeNodeType

Types for the KdTree data structure

Definition at line 90 of file itkKdTreeBasedKmeansEstimator.h.

◆ MeasurementType

template<typename TKdTree >
using itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::MeasurementType = typename TKdTree::MeasurementType

Definition at line 91 of file itkKdTreeBasedKmeansEstimator.h.

◆ MeasurementVectorSizeType

template<typename TKdTree >
using itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::MeasurementVectorSizeType = unsigned int

Typedef for the length of a measurement vector

Definition at line 98 of file itkKdTreeBasedKmeansEstimator.h.

◆ MeasurementVectorType

template<typename TKdTree >
using itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::MeasurementVectorType = typename TKdTree::MeasurementVectorType

Definition at line 92 of file itkKdTreeBasedKmeansEstimator.h.

◆ MembershipFunctionPointer

Definition at line 113 of file itkKdTreeBasedKmeansEstimator.h.

◆ MembershipFunctionType

Definition at line 112 of file itkKdTreeBasedKmeansEstimator.h.

◆ MembershipFunctionVectorObjectPointer

Definition at line 116 of file itkKdTreeBasedKmeansEstimator.h.

◆ MembershipFunctionVectorObjectType

Definition at line 115 of file itkKdTreeBasedKmeansEstimator.h.

◆ MembershipFunctionVectorType

template<typename TKdTree >
using itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::MembershipFunctionVectorType = std::vector<MembershipFunctionPointer>

Definition at line 114 of file itkKdTreeBasedKmeansEstimator.h.

◆ ParametersType

template<typename TKdTree >
using itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::ParametersType = Array<double>

Definition at line 104 of file itkKdTreeBasedKmeansEstimator.h.

◆ ParameterType

template<typename TKdTree >
using itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::ParameterType = Array<double>

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

Definition at line 102 of file itkKdTreeBasedKmeansEstimator.h.

◆ Pointer

template<typename TKdTree >
using itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::Pointer = SmartPointer<Self>

Definition at line 80 of file itkKdTreeBasedKmeansEstimator.h.

◆ SampleType

template<typename TKdTree >
using itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SampleType = typename TKdTree::SampleType

Definition at line 94 of file itkKdTreeBasedKmeansEstimator.h.

◆ Self

template<typename TKdTree >
using itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::Self = KdTreeBasedKmeansEstimator

Standard Self type alias.

Definition at line 78 of file itkKdTreeBasedKmeansEstimator.h.

◆ Superclass

template<typename TKdTree >
using itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::Superclass = Object

Definition at line 79 of file itkKdTreeBasedKmeansEstimator.h.

Constructor & Destructor Documentation

◆ KdTreeBasedKmeansEstimator()

template<typename TKdTree >
itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::KdTreeBasedKmeansEstimator ( )
protected

current number of iteration

◆ ~KdTreeBasedKmeansEstimator()

template<typename TKdTree >
itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::~KdTreeBasedKmeansEstimator ( )
overrideprotecteddefault

current number of iteration

Member Function Documentation

◆ CopyParameters() [1/3]

template<typename TKdTree >
void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::CopyParameters ( InternalParametersType source,
InternalParametersType target 
)
protected

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

◆ CopyParameters() [2/3]

template<typename TKdTree >
void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::CopyParameters ( InternalParametersType source,
ParametersType target 
)
protected

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

◆ CopyParameters() [3/3]

template<typename TKdTree >
void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::CopyParameters ( ParametersType source,
InternalParametersType target 
)
protected

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

◆ CreateAnother()

template<typename TKdTree >
virtual::itk::LightObject::Pointer itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::CreateAnother ( ) const
virtual

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

Reimplemented from itk::Object.

◆ FillClusterLabels()

template<typename TKdTree >
void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::FillClusterLabels ( KdTreeNodeType node,
int  closestIndex 
)
protected

current number of iteration

◆ Filter()

template<typename 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

◆ GetCentroidPositionChanges()

template<typename TKdTree >
virtual double itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetCentroidPositionChanges ( ) const
virtual

current number of iteration

◆ GetCentroidPositionChangesThreshold()

template<typename TKdTree >
virtual double itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetCentroidPositionChangesThreshold ( ) const
virtual

current number of iteration

◆ GetClosestCandidate()

template<typename 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

◆ GetCurrentIteration()

template<typename TKdTree >
virtual int itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetCurrentIteration ( ) const
virtual

current number of iteration

◆ GetKdTree()

template<typename TKdTree >
const TKdTree* itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetKdTree ( ) const

current number of iteration

◆ GetMaximumIteration()

template<typename TKdTree >
virtual int itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetMaximumIteration ( ) const
virtual

current number of iteration

◆ GetMeasurementVectorSize()

template<typename TKdTree >
virtual MeasurementVectorSizeType itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetMeasurementVectorSize ( ) const
virtual

Get the length of measurement vectors in the KdTree

◆ GetNameOfClass()

template<typename TKdTree >
virtual const char* itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetNameOfClass ( ) const
virtual

Run-time type information (and related methods).

Reimplemented from itk::Object.

◆ GetOutput()

template<typename TKdTree >
const MembershipFunctionVectorObjectType* itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetOutput ( ) const

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

◆ GetParameters()

template<typename TKdTree >
virtual ParametersType itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetParameters ( ) const
virtual

current number of iteration

◆ GetPoint()

template<typename TKdTree >
void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetPoint ( ParameterType point,
MeasurementVectorType  measurements 
)
protected

imports the measurements measurement vector data to the point

◆ GetSumOfSquaredPositionChanges()

template<typename TKdTree >
double itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetSumOfSquaredPositionChanges ( InternalParametersType previous,
InternalParametersType current 
)
protected

gets the sum of squared difference between the previous position and current position 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.

◆ GetUseClusterLabels()

template<typename TKdTree >
virtual bool itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetUseClusterLabels ( ) const
virtual

current number of iteration

◆ IsFarther()

template<typename 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

◆ New()

template<typename TKdTree >
static Pointer itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::New ( )
static

Method for creation through the object factory.

◆ PrintPoint()

template<typename TKdTree >
void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::PrintPoint ( ParameterType point)
protected

current number of iteration

◆ PrintSelf()

template<typename TKdTree >
void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::PrintSelf ( std::ostream &  os,
Indent  indent 
) const
overrideprotectedvirtual

current number of iteration

Reimplemented from itk::Object.

◆ SetCentroidPositionChangesThreshold()

template<typename TKdTree >
virtual void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SetCentroidPositionChangesThreshold ( double  _arg)
virtual

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

◆ SetKdTree()

template<typename TKdTree >
void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SetKdTree ( TKdTree *  tree)

Set/Get the pointer to the KdTree

◆ SetMaximumIteration()

template<typename TKdTree >
virtual void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SetMaximumIteration ( int  _arg)
virtual

Set/Get maximum iteration limit.

◆ SetParameters()

template<typename TKdTree >
virtual void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SetParameters ( ParametersType  _arg)
virtual

Set the position to initialize the optimization.

◆ SetUseClusterLabels()

template<typename TKdTree >
virtual void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SetUseClusterLabels ( bool  _arg)
virtual

current number of iteration

◆ StartOptimization()

template<typename 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)

Member Data Documentation

◆ m_CandidateVector

template<typename TKdTree >
CandidateVector itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::m_CandidateVector
private

current number of iteration

Definition at line 331 of file itkKdTreeBasedKmeansEstimator.h.

◆ m_CentroidPositionChanges

template<typename TKdTree >
double itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::m_CentroidPositionChanges { 0.0 }
private

sum of squared centroid position changes at the current iteration

Definition at line 316 of file itkKdTreeBasedKmeansEstimator.h.

◆ m_CentroidPositionChangesThreshold

template<typename TKdTree >
double itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::m_CentroidPositionChangesThreshold { 0.0 }
private

threshold for the sum of squared centroid position changes. termination criterion

Definition at line 320 of file itkKdTreeBasedKmeansEstimator.h.

◆ m_ClusterLabels

template<typename TKdTree >
ClusterLabelsType itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::m_ClusterLabels
private

current number of iteration

Definition at line 337 of file itkKdTreeBasedKmeansEstimator.h.

◆ m_CurrentIteration

template<typename TKdTree >
int itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::m_CurrentIteration { 0 }
private

current number of iteration

Definition at line 310 of file itkKdTreeBasedKmeansEstimator.h.

◆ m_DistanceMetric

template<typename TKdTree >
EuclideanDistanceMetric<ParameterType>::Pointer itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::m_DistanceMetric
private

pointer to the euclidean distance function

Definition at line 326 of file itkKdTreeBasedKmeansEstimator.h.

◆ m_GenerateClusterLabels

template<typename TKdTree >
bool itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::m_GenerateClusterLabels { false }
private

current number of iteration

Definition at line 336 of file itkKdTreeBasedKmeansEstimator.h.

◆ m_KdTree

template<typename TKdTree >
TKdTree::Pointer itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::m_KdTree
private

pointer to the k-d tree

Definition at line 323 of file itkKdTreeBasedKmeansEstimator.h.

◆ m_MaximumIteration

template<typename TKdTree >
int itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::m_MaximumIteration { 100 }
private

maximum number of iteration. termination criterion

Definition at line 313 of file itkKdTreeBasedKmeansEstimator.h.

◆ m_MeasurementVectorSize

template<typename TKdTree >
MeasurementVectorSizeType itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::m_MeasurementVectorSize { 0 }
private

current number of iteration

Definition at line 338 of file itkKdTreeBasedKmeansEstimator.h.

◆ m_MembershipFunctionsObject

template<typename TKdTree >
MembershipFunctionVectorObjectPointer itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::m_MembershipFunctionsObject
private

current number of iteration

Definition at line 339 of file itkKdTreeBasedKmeansEstimator.h.

◆ m_Parameters

template<typename TKdTree >
ParametersType itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::m_Parameters
private

k-means

Definition at line 329 of file itkKdTreeBasedKmeansEstimator.h.

◆ m_TempVertex

template<typename TKdTree >
ParameterType itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::m_TempVertex
private

current number of iteration

Definition at line 333 of file itkKdTreeBasedKmeansEstimator.h.

◆ m_UseClusterLabels

template<typename TKdTree >
bool itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::m_UseClusterLabels { false }
private

current number of iteration

Definition at line 335 of file itkKdTreeBasedKmeansEstimator.h.


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