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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:

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

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List of all members.

Public Types

typedef KdTreeBasedKmeansEstimator Self
typedef Object Superclass
typedef SmartPointer< SelfPointer
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< ParameterTypeInternalParametersType
typedef Array< double > ParametersType

Public Methods

virtual const char * GetClassName () const
 itkStaticConstMacro (MeasurementVectorSize, unsigned int, TKdTree::MeasurementVectorSize)
void SetParameters (ParametersType &params)
ParametersTypeGetParameters ()
void SetKdTree (TKdTree *tree)
TKdTree * GetKdTree ()
virtual int GetCurrentIteration () const
virtual double GetCenteroidPositionChanges () const
void StartOptimization ()
void SetUseClusterLabels (bool flag)
ClusterLabelsTypeGetClusterLabels ()
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)

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.


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.


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.


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)


The documentation for this class was generated from the following file:
Generated at Fri May 21 01:53:06 2004 for ITK by doxygen 1.2.15 written by Dimitri van Heesch, © 1997-2000