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:

[legend]Collaboration diagram for itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >:

[legend]List of all members.

Public Types
typedef KdTreeBasedKmeansEstimator	Self
typedef Object	Superclass
typedef SmartPointer< Self >	Pointer
typedef SmartPointer< const Self >	ConstPointer
typedef TKdTree::KdTreeNodeType	KdTreeNodeType
typedef TKdTree::MeasurementType	MeasurementType
typedef TKdTree::MeasurementVectorType	MeasurementVectorType
typedef TKdTree::InstanceIdentifier	InstanceIdentifier
typedef TKdTree::SampleType	SampleType
typedef KdTreeNodeType::CenteroidType	CenteroidType
typedef itk::hash_map< InstanceIdentifier, unsigned int >	ClusterLabelsType
typedef FixedArray< double, itkGetStaticConstMacro(MeasurementVectorSize)	ParameterType )
typedef std::vector< ParameterType >	InternalParametersType
typedef Array< double >	ParametersType
Public Methods
virtual const char *	GetClassName () const
	itkStaticConstMacro (MeasurementVectorSize, unsigned int, TKdTree::MeasurementVectorSize)
void	SetParameters (ParametersType &params)
ParametersType &	GetParameters ()
void	SetKdTree (TKdTree *tree)
TKdTree *	GetKdTree ()
virtual int	GetCurrentIteration () const
virtual double	GetCenteroidPositionChanges () const
void	StartOptimization ()
void	SetUseClusterLabels (bool flag)
ClusterLabelsType *	GetClusterLabels ()
virtual void	SetMaximumIteration (int _arg)
virtual int	GetMaximumIteration () const
virtual void	SetCenteroidPositionChangesThreshold (double _arg)
virtual double	GetCenteroidPositionChangesThreshold () const
Static Public Methods
Pointer	New ()
Protected Methods
	KdTreeBasedKmeansEstimator ()
virtual	~KdTreeBasedKmeansEstimator ()
void	PrintSelf (std::ostream &os, Indent indent) const
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	PrintPoint (ParameterType &point)
void	GetPoint (ParameterType &point, MeasurementVectorType &measurements)

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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 centeroids of k-clusters using pre-generated k-d tree. k-d tree generation is done by the WeightedCenteroidKdTreeGenerator. 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 centeroid and recalculating centeroid, it maintain a set of cluster centeroid 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 WeightedCenteroidKdTreeGenerator. It will save the tree construction time and memory usage.

See also:

WeightedCenteroidKdTreeGenerator, KdTree

Definition at line 55 of file itkKdTreeBasedKmeansEstimator.h.

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Member Typedef Documentation

template<class TKdTree> typedef KdTreeNodeType::CenteroidType itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::CenteroidType

Definition at line 77 of file itkKdTreeBasedKmeansEstimator.h.

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

Definition at line 124 of file itkKdTreeBasedKmeansEstimator.h.

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

Reimplemented from itk::Object. Definition at line 63 of file itkKdTreeBasedKmeansEstimator.h.

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

Definition at line 75 of file itkKdTreeBasedKmeansEstimator.h.

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

Parameters type. It defines a position in the optimization search space. Definition at line 85 of file itkKdTreeBasedKmeansEstimator.h.

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

Types for the KdTree data structure Definition at line 72 of file itkKdTreeBasedKmeansEstimator.h.

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

Definition at line 73 of file itkKdTreeBasedKmeansEstimator.h.

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

Definition at line 74 of file itkKdTreeBasedKmeansEstimator.h.

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

Parameters type. It defines a position in the optimization search space. Definition at line 86 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree> typedef FixedArray< double, itkGetStaticConstMacro(MeasurementVectorSize) itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::ParameterType)

Parameters type. It defines a position in the optimization search space. Definition at line 84 of file itkKdTreeBasedKmeansEstimator.h.

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

Reimplemented from itk::Object. Definition at line 62 of file itkKdTreeBasedKmeansEstimator.h.

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

Definition at line 76 of file itkKdTreeBasedKmeansEstimator.h.

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

Standard "Self" typedef. Reimplemented from itk::Object. Definition at line 60 of file itkKdTreeBasedKmeansEstimator.h.

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

Reimplemented from itk::Object. Definition at line 61 of file itkKdTreeBasedKmeansEstimator.h.

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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 134 of file itkKdTreeBasedKmeansEstimator.h.

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Member Function Documentation

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 (  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, InternalParametersType &    target )  [protected]

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

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> virtual double itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetCenteroidPositionChanges (    )  const [virtual]

template<class TKdTree> virtual double itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetCenteroidPositionChangesThreshold (    )  const [virtual]

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

template<class TKdTree> virtual const char* itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetClassName (    )  const [virtual]

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

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 129 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree> virtual int itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetCurrentIteration (    )  const [virtual]

template<class TKdTree> TKdTree* itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetKdTree (    )  [inline]

Definition at line 112 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree> virtual int itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetMaximumIteration (    )  const [virtual]

Set/Get maximum iteration limit.

template<class TKdTree> ParametersType& itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetParameters (  void    )  [inline]

Get current position of the optimization. Definition at line 94 of file 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 253 of file itkKdTreeBasedKmeansEstimator.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 centeroid. This is the primary termination condition for this algorithm. If the return value is less than the value that was set by the SetCenteroidPositionChangesThreshold method.

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> itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::itkStaticConstMacro (  MeasurementVectorSize   , unsigned    int, TKdTree::MeasurementVectorSize    )

template<class TKdTree> Pointer itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::New (    )  [static]

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

template<class TKdTree> void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::PrintPoint (  ParameterType &    point )  [inline, protected]

Definition at line 263 of file itkKdTreeBasedKmeansEstimator.h.

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> virtual void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SetCenteroidPositionChangesThreshold (  double    _arg )  [virtual]

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

template<class TKdTree> void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SetKdTree (  TKdTree *    tree )  [inline]

Set/Get the pointer to the KdTree Definition at line 109 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree> virtual void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SetMaximumIteration (  int    _arg )  [virtual]

Set/Get maximum iteration limit.

template<class TKdTree> void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SetParameters (  ParametersType &    params )  [inline]

Set the position to initialize the optimization. Definition at line 90 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree> void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SetUseClusterLabels (  bool    flag )  [inline]

Definition at line 126 of file 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 centeroid positions)

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

• itkKdTreeBasedKmeansEstimator.h

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