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Review/Statistics/itkKdTreeBasedKmeansEstimator.h

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00001 /*=========================================================================
00002 
00003   Program:   Insight Segmentation & Registration Toolkit
00004   Module:    $RCSfile: itkKdTreeBasedKmeansEstimator.h,v $
00005   Language:  C++
00006   Date:      $Date: 2009-05-02 05:43:56 $
00007   Version:   $Revision: 1.1 $
00008 
00009   Copyright (c) Insight Software Consortium. All rights reserved.
00010   See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details.
00011 
00012      This software is distributed WITHOUT ANY WARRANTY; without even 
00013      the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR 
00014      PURPOSE.  See the above copyright notices for more information.
00015 
00016 =========================================================================*/
00017 #ifndef __itkKdTreeBasedKmeansEstimator_h
00018 #define __itkKdTreeBasedKmeansEstimator_h
00019 
00020 #include <vector>
00021 #include "itk_hash_map.h"
00022 
00023 #include "itkObject.h"
00024 #include "itkMeasurementVectorTraits.h"
00025 #include "itkEuclideanDistanceMetric.h"
00026 #include "itkDistanceToCentroidMembershipFunction.h"
00027 #include "itkSimpleDataObjectDecorator.h"
00028 
00029 namespace itk {
00030 namespace Statistics {
00031 
00069 template< class TKdTree >
00070 class ITK_EXPORT KdTreeBasedKmeansEstimator: 
00071     public Object
00072 {
00073 public:
00075   typedef KdTreeBasedKmeansEstimator  Self;
00076   typedef Object                      Superclass;
00077   typedef SmartPointer<Self>          Pointer;
00078   typedef SmartPointer<const Self>    ConstPointer;
00079 
00081   itkNewMacro(Self);
00082 
00084   itkTypeMacro(KdTreeBasedKmeansEstimator, Obeject);
00085 
00087   typedef typename TKdTree::KdTreeNodeType        KdTreeNodeType;
00088   typedef typename TKdTree::MeasurementType       MeasurementType;
00089   typedef typename TKdTree::MeasurementVectorType MeasurementVectorType;
00090   typedef typename TKdTree::InstanceIdentifier    InstanceIdentifier;
00091   typedef typename TKdTree::SampleType            SampleType;
00092   typedef typename KdTreeNodeType::CentroidType   CentroidType;
00093 
00094 
00096   typedef unsigned int MeasurementVectorSizeType;
00097 
00100   typedef Array< double >              ParameterType;
00101   typedef std::vector< ParameterType > InternalParametersType;
00102   typedef Array< double >              ParametersType;
00103 
00104 
00107   typedef DistanceToCentroidMembershipFunction< MeasurementVectorType > 
00108                                                    DistanceToCentroidMembershipFunctionType;
00109 
00110   typedef typename DistanceToCentroidMembershipFunctionType::Pointer 
00111                                                    DistanceToCentroidMembershipFunctionPointer;
00112 
00113   typedef MembershipFunctionBase< MeasurementVectorType > MembershipFunctionType;
00114   typedef typename MembershipFunctionType::ConstPointer   MembershipFunctionPointer;
00115   typedef std::vector< MembershipFunctionPointer >        MembershipFunctionVectorType;
00116   typedef SimpleDataObjectDecorator<
00117     MembershipFunctionVectorType >                        MembershipFunctionVectorObjectType;
00118   typedef typename 
00119     MembershipFunctionVectorObjectType::Pointer           MembershipFunctionVectorObjectPointer;
00120 
00123   const MembershipFunctionVectorObjectType * GetOutput() const; 
00124 
00126   itkSetMacro( Parameters, ParametersType );
00127   itkGetConstMacro( Parameters, ParametersType );
00129 
00131   itkSetMacro( MaximumIteration, int );
00132   itkGetConstMacro( MaximumIteration, int ); 
00134 
00137   itkSetMacro( CentroidPositionChangesThreshold, double );   
00138   itkGetConstMacro( CentroidPositionChangesThreshold, double );   
00139 
00141   void SetKdTree(TKdTree* tree);
00142   const TKdTree* GetKdTree() const;
00144 
00146   itkGetConstMacro( MeasurementVectorSize, MeasurementVectorSizeType );
00147 
00148   itkGetConstMacro( CurrentIteration, int);
00149   itkGetConstMacro( CentroidPositionChanges, double);
00150 
00155   void StartOptimization();
00156 
00157   typedef itk::hash_map< InstanceIdentifier, unsigned int > ClusterLabelsType;
00158 
00159   itkSetMacro( UseClusterLabels, bool );
00160   itkGetConstMacro( UseClusterLabels, bool );
00161 
00162 protected:
00163   KdTreeBasedKmeansEstimator();
00164   virtual ~KdTreeBasedKmeansEstimator() {}
00165 
00166   void PrintSelf(std::ostream& os, Indent indent) const;
00167 
00168   void FillClusterLabels(KdTreeNodeType* node, int closestIndex);
00169 
00171   class CandidateVector {
00172   public:
00173     CandidateVector() {}
00174 
00175     struct Candidate {
00176       CentroidType Centroid;
00177       CentroidType WeightedCentroid;
00178       int Size;
00179       }; // end of struct
00180 
00181     virtual ~CandidateVector() {} 
00182 
00184     int Size() const
00185       { 
00186       return static_cast<int>( m_Candidates.size() ); 
00187       }
00188 
00191     void SetCentroids(InternalParametersType& centroids)
00192       {
00193       this->m_MeasurementVectorSize = MeasurementVectorTraits::GetLength( centroids[0] );
00194       m_Candidates.resize(centroids.size());
00195       for (unsigned int i = 0; i < centroids.size(); i++)
00196         {
00197           Candidate candidate;
00198           candidate.Centroid = centroids[i];
00199           MeasurementVectorTraits::SetLength( candidate.WeightedCentroid, m_MeasurementVectorSize );
00200           candidate.WeightedCentroid.Fill(0.0);
00201           candidate.Size = 0;
00202           m_Candidates[i] = candidate;
00203         }
00204       }
00206 
00208     void GetCentroids(InternalParametersType& centroids)
00209       {
00210       unsigned int i;
00211       centroids.resize(this->Size());
00212       for (i = 0; i < (unsigned int)this->Size(); i++)
00213         {
00214         centroids[i] = m_Candidates[i].Centroid;
00215         }
00216       }
00218 
00221     void UpdateCentroids()
00222       {
00223       unsigned int i, j;
00224       for (i = 0; i < (unsigned int)this->Size(); i++)
00225         {
00226         if (m_Candidates[i].Size > 0)
00227           {
00228           for (j = 0; j < m_MeasurementVectorSize; j++)
00229             {
00230             m_Candidates[i].Centroid[j] = 
00231                     m_Candidates[i].WeightedCentroid[j] / 
00232                     double(m_Candidates[i].Size);
00233             }
00234           }
00235         }
00236       }
00238 
00240     Candidate& operator[](int index)
00241       { 
00242       return m_Candidates[index]; 
00243       }
00244 
00245   private:
00247     std::vector< Candidate > m_Candidates;
00248 
00250     MeasurementVectorSizeType m_MeasurementVectorSize;
00251     };// end of class
00252 
00258   double GetSumOfSquaredPositionChanges(InternalParametersType &previous, 
00259                                         InternalParametersType &current);
00260 
00263   int GetClosestCandidate(ParameterType &measurements, 
00264                           std::vector< int > &validIndexes);
00265 
00267   bool IsFarther(ParameterType &pointA,
00268                  ParameterType &pointB,
00269                  MeasurementVectorType &lowerBound,
00270                  MeasurementVectorType &upperBound);
00271 
00274   void Filter(KdTreeNodeType* node, 
00275               std::vector< int > validIndexes,
00276               MeasurementVectorType &lowerBound, 
00277               MeasurementVectorType &upperBound);
00278 
00280   void CopyParameters(InternalParametersType &source, InternalParametersType &target);
00281 
00283   void CopyParameters(ParametersType &source, InternalParametersType &target);
00284 
00286   void CopyParameters(InternalParametersType &source, ParametersType &target);
00287 
00289   void GetPoint(ParameterType &point, MeasurementVectorType measurements);
00290 
00291   void PrintPoint(ParameterType &point);
00292 
00293 private:
00295   int m_CurrentIteration;
00296 
00298   int m_MaximumIteration;
00299 
00301   double m_CentroidPositionChanges;
00302 
00305   double m_CentroidPositionChangesThreshold;
00306 
00308   typename TKdTree::Pointer m_KdTree;
00309 
00311   typename EuclideanDistanceMetric< ParameterType >::Pointer m_DistanceMetric;
00312 
00314   ParametersType                          m_Parameters;
00315 
00316   CandidateVector                         m_CandidateVector;
00317   
00318   ParameterType                           m_TempVertex;
00319 
00320   bool                                    m_UseClusterLabels;
00321   bool                                    m_GenerateClusterLabels;
00322   ClusterLabelsType                       m_ClusterLabels;
00323   MeasurementVectorSizeType               m_MeasurementVectorSize;
00324   MembershipFunctionVectorObjectPointer   m_MembershipFunctionsObject;
00325 
00326 }; // end of class
00327 
00328 } // end of namespace Statistics
00329 
00330 } // end of namespace itk
00331 
00332 #ifndef ITK_MANUAL_INSTANTIATION
00333 #include "itkKdTreeBasedKmeansEstimator.txx"
00334 #endif
00335 
00336 #endif
00337 

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