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Numerics/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: 2009-03-04 15:23:51 $
00007   Version:   $Revision: 1.19 $
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 
00026 namespace itk {
00027 namespace Statistics {
00028 
00066 template< class TKdTree >
00067 class ITK_EXPORT KdTreeBasedKmeansEstimator: 
00068     public Object
00069 {
00070 public:
00072   typedef KdTreeBasedKmeansEstimator Self;
00073   typedef Object                     Superclass;
00074   typedef SmartPointer<Self>         Pointer;
00075   typedef SmartPointer<const Self>   ConstPointer;
00076 
00078   itkNewMacro(Self);
00079 
00081   itkTypeMacro(KdTreeBasedKmeansEstimator, Obeject);
00082 
00084   typedef typename TKdTree::KdTreeNodeType        KdTreeNodeType;
00085   typedef typename TKdTree::MeasurementType       MeasurementType;
00086   typedef typename TKdTree::MeasurementVectorType MeasurementVectorType;
00087   typedef typename TKdTree::InstanceIdentifier    InstanceIdentifier;
00088   typedef typename TKdTree::SampleType            SampleType;
00089   typedef typename KdTreeNodeType::CentroidType   CentroidType;
00090 
00091 
00093   typedef unsigned int MeasurementVectorSizeType;
00094 
00097   typedef Array< double >              ParameterType;
00098   typedef std::vector< ParameterType > InternalParametersType;
00099   typedef Array< double >              ParametersType;
00100 
00102   void SetParameters(ParametersType& params)
00103     { m_Parameters = params; }
00104 
00106   ParametersType& GetParameters() 
00107     { return m_Parameters; }
00108 
00110   itkSetMacro( MaximumIteration, int );
00111   itkGetConstReferenceMacro( MaximumIteration, int ); 
00113 
00116   itkSetMacro( CentroidPositionChangesThreshold, double );   
00117   itkGetConstReferenceMacro( CentroidPositionChangesThreshold, double );   
00119 
00121   void SetKdTree(TKdTree* tree) 
00122     { 
00123     m_KdTree = tree; 
00124     m_MeasurementVectorSize = tree->GetMeasurementVectorSize();
00125     m_DistanceMetric->SetMeasurementVectorSize( m_MeasurementVectorSize );
00126     MeasurementVectorTraits::SetLength( m_TempVertex, m_MeasurementVectorSize );
00127     }
00129 
00130   TKdTree* GetKdTree() 
00131     { return m_KdTree.GetPointer(); }
00132 
00134   itkGetConstReferenceMacro( MeasurementVectorSize, MeasurementVectorSizeType );
00135 
00136   itkGetConstReferenceMacro( CurrentIteration, int);
00137   itkGetConstReferenceMacro( CentroidPositionChanges, double);
00138 
00143   void StartOptimization();
00144 
00145   typedef itk::hash_map< InstanceIdentifier, unsigned int > ClusterLabelsType;
00146 
00147   void SetUseClusterLabels(bool flag)
00148     { m_UseClusterLabels = flag; }
00149 
00150   ClusterLabelsType* GetClusterLabels() 
00151     { return &m_ClusterLabels; }
00152 
00153 protected:
00154   KdTreeBasedKmeansEstimator();
00155   virtual ~KdTreeBasedKmeansEstimator() {}
00156 
00157   void PrintSelf(std::ostream& os, Indent indent) const;
00158 
00159   void FillClusterLabels(KdTreeNodeType* node, int closestIndex);
00160 
00163   class CandidateVector
00164     {
00165     public:
00166     CandidateVector() {}
00167 
00168     struct Candidate
00169       {
00170       CentroidType Centroid;
00171       CentroidType WeightedCentroid;
00172       int Size;
00173       }; // end of struct
00174 
00175     virtual ~CandidateVector() {} 
00176 
00178     int Size() const
00179       { return static_cast<int>( m_Candidates.size() ); }
00180 
00183     void SetCentroids(InternalParametersType& centroids)
00184       {
00185       this->m_MeasurementVectorSize = MeasurementVectorTraits::GetLength( centroids[0] );
00186       m_Candidates.resize(centroids.size());
00187       for (unsigned int i = 0; i < centroids.size(); i++)
00188         {
00189         Candidate candidate;
00190         candidate.Centroid = centroids[i];
00191         MeasurementVectorTraits::SetLength( candidate.WeightedCentroid, m_MeasurementVectorSize );
00192         candidate.WeightedCentroid.Fill(0.0);
00193         candidate.Size = 0;
00194         m_Candidates[i] = candidate;
00195         }
00196       }
00198 
00200     void GetCentroids(InternalParametersType& centroids)
00201       {
00202       unsigned int i;
00203       centroids.resize(this->Size());
00204       for (i = 0; i < (unsigned int)this->Size(); i++)
00205         {
00206         centroids[i] = m_Candidates[i].Centroid;
00207         }
00208       }
00210 
00213     void UpdateCentroids()
00214       {
00215       unsigned int i, j;
00216       for (i = 0; i < (unsigned int)this->Size(); i++)
00217         {
00218         if (m_Candidates[i].Size > 0)
00219           {
00220           for (j = 0; j < m_MeasurementVectorSize; j++)
00221             {
00222             m_Candidates[i].Centroid[j] = 
00223               m_Candidates[i].WeightedCentroid[j] / 
00224               double(m_Candidates[i].Size);
00225             }
00226           }
00227         }
00228       }
00230 
00232     Candidate& operator[](int index)
00233       { return m_Candidates[index]; }
00234 
00235   private:
00237     std::vector< Candidate > m_Candidates;
00238 
00240     MeasurementVectorSizeType m_MeasurementVectorSize;
00241   }; // end of class
00242 
00248   double GetSumOfSquaredPositionChanges(InternalParametersType &previous, 
00249                                         InternalParametersType &current);
00250 
00253   int GetClosestCandidate(ParameterType &measurements, 
00254                           std::vector< int > &validIndexes);
00255 
00257   bool IsFarther(ParameterType &pointA,
00258                  ParameterType &pointB,
00259                  MeasurementVectorType &lowerBound,
00260                  MeasurementVectorType &upperBound);
00261 
00264   void Filter(KdTreeNodeType* node, 
00265               std::vector< int > validIndexes,
00266               MeasurementVectorType &lowerBound, 
00267               MeasurementVectorType &upperBound);
00268 
00270   void CopyParameters(InternalParametersType &source, InternalParametersType &target);
00271 
00273   void CopyParameters(ParametersType &source, InternalParametersType &target);
00274 
00276   void CopyParameters(InternalParametersType &source, ParametersType &target);
00277 
00279   void GetPoint(ParameterType &point, MeasurementVectorType measurements)
00280     {
00281     for (unsigned int i = 0; i < m_MeasurementVectorSize; i++)
00282       {
00283       point[i] = measurements[i];
00284       }
00285     }
00287 
00288   void PrintPoint(ParameterType &point)
00289     {
00290     std::cout << "[ ";
00291     for (unsigned int i = 0; i < m_MeasurementVectorSize; i++)
00292       {
00293       std::cout << point[i] << " ";
00294       }
00295     std::cout << "]";
00296     }
00297 
00298 private:
00300   int m_CurrentIteration;
00301 
00303   int m_MaximumIteration;
00304 
00306   double m_CentroidPositionChanges;
00307 
00310   double m_CentroidPositionChangesThreshold;
00311 
00313   typename TKdTree::Pointer m_KdTree;
00314 
00316   typename EuclideanDistance< ParameterType >::Pointer m_DistanceMetric;
00317 
00319   ParametersType m_Parameters;
00320 
00321   CandidateVector m_CandidateVector;
00322   
00323   ParameterType m_TempVertex;
00324 
00325   bool m_UseClusterLabels;
00326   bool m_GenerateClusterLabels;
00327   ClusterLabelsType m_ClusterLabels;
00328   MeasurementVectorSizeType m_MeasurementVectorSize;
00329 }; // end of class
00330 
00331 } // end of namespace Statistics
00332 } // end of namespace itk
00333 
00334 #ifndef ITK_MANUAL_INSTANTIATION
00335 #include "itkKdTreeBasedKmeansEstimator.txx"
00336 #endif
00337 
00338 
00339 #endif
00340 

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