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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: 2003/02/23 09:11:40 $
00007   Version:   $Revision: 1.10 $
00008 
00009   Copyright (c) 2002 Insight 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 
00025 namespace itk {
00026 namespace Statistics {
00027 
00054 template< class TKdTree >
00055 class ITK_EXPORT KdTreeBasedKmeansEstimator: 
00056     public Object
00057 {
00058 public:
00060   typedef KdTreeBasedKmeansEstimator Self ;
00061   typedef Object Superclass;
00062   typedef SmartPointer<Self> Pointer;
00063   typedef SmartPointer<const Self> ConstPointer;
00064   
00066   itkNewMacro(Self);
00067  
00069   itkTypeMacro(KdTreeBasedKmeansEstimator, Obeject);
00070  
00072   typedef typename TKdTree::KdTreeNodeType KdTreeNodeType ;
00073   typedef typename TKdTree::MeasurementType MeasurementType ;
00074   typedef typename TKdTree::MeasurementVectorType MeasurementVectorType ;
00075   typedef typename TKdTree::InstanceIdentifier InstanceIdentifier ;
00076   typedef typename TKdTree::SampleType SampleType ;
00077   typedef typename KdTreeNodeType::CenteroidType CenteroidType ;
00078   itkStaticConstMacro(MeasurementVectorSize, unsigned int,
00079                       TKdTree::MeasurementVectorSize);
00082 //   typedef FixedArray< double, itkGetStaticConstMacro(MeasurementVectorSize) > ParameterType ;
00083   typedef FixedArray< double, itkGetStaticConstMacro(MeasurementVectorSize) > ParameterType ;
00084   typedef std::vector< ParameterType > InternalParametersType;
00085   typedef Array< double > ParametersType;
00086 
00088   void SetParameters(ParametersType& params)
00089   { m_Parameters = params ; }
00090 
00092   ParametersType& GetParameters() 
00093   { return m_Parameters ; }
00094 
00096   itkSetMacro( MaximumIteration, int );
00097   itkGetConstMacro( MaximumIteration, int ); 
00098  
00101   itkSetMacro( CenteroidPositionChangesThreshold, double );   
00102   itkGetConstMacro( CenteroidPositionChangesThreshold, double );   
00103   
00105   void SetKdTree(TKdTree* tree) 
00106   { m_KdTree = tree ; }
00107 
00108   TKdTree* GetKdTree() 
00109   { return m_KdTree ; }
00110 
00111   itkGetConstMacro( CurrentIteration, int) ;
00112   itkGetConstMacro( CenteroidPositionChanges, double) ;
00113 
00118   void StartOptimization() ;
00119 
00120   typedef itk::hash_map< InstanceIdentifier, unsigned int > ClusterLabelsType ;
00121 
00122   void SetUseClusterLabels(bool flag)
00123   { m_UseClusterLabels = flag ; }
00124 
00125   ClusterLabelsType* GetClusterLabels() 
00126   { return &m_ClusterLabels ; }
00127 
00128 protected:
00129   KdTreeBasedKmeansEstimator() ;
00130   virtual ~KdTreeBasedKmeansEstimator() {}
00131 
00132   void PrintSelf(std::ostream& os, Indent indent) const;
00133 
00134   void FillClusterLabels(KdTreeNodeType* node, int closestIndex) ;
00135 
00137   class CandidateVector
00138   {
00139   public:
00140     CandidateVector() {}
00141 
00142     struct Candidate
00143     {
00144       CenteroidType Centeroid ;
00145       CenteroidType WeightedCenteroid ;
00146       int Size ;
00147     } ; // end of struct
00148 
00149     virtual ~CandidateVector() {} 
00150 
00152     int Size()
00153     { return static_cast<int>( m_Candidates.size() ); }
00154 
00157     void SetCenteroids(InternalParametersType& centeroids)
00158     {
00159       m_Candidates.resize(centeroids.size()) ;
00160       for (unsigned int i = 0 ; i < centeroids.size() ; i++)
00161         {
00162           Candidate candidate ;
00163           candidate.Centeroid = centeroids[i] ;
00164           candidate.WeightedCenteroid.Fill(0.0) ;
00165           candidate.Size = 0 ;
00166           m_Candidates[i] = candidate ;
00167         }
00168     }
00169 
00171     void GetCenteroids(InternalParametersType& centeroids)
00172     {
00173       unsigned int i ;
00174       centeroids.resize(this->Size()) ;
00175       for (i = 0 ; i < (unsigned int)this->Size() ; i++)
00176         {
00177           centeroids[i] = m_Candidates[i].Centeroid ;
00178         }
00179     }
00180 
00183     void UpdateCenteroids()
00184     {
00185       unsigned int i, j ;
00186       for (i = 0 ; i < (unsigned int)this->Size() ; i++)
00187         {
00188           if (m_Candidates[i].Size > 0)
00189             {
00190               for (j = 0 ; j < MeasurementVectorSize ; j++)
00191                 {
00192                   m_Candidates[i].Centeroid[j] = 
00193                     m_Candidates[i].WeightedCenteroid[j] / 
00194                     double(m_Candidates[i].Size) ;
00195                 }
00196             }
00197         }
00198     }
00199 
00201     Candidate& operator[](int index)
00202     { return m_Candidates[index] ; }
00203     
00204 
00205   private:
00207     std::vector< Candidate > m_Candidates ;
00208   } ; // end of class
00209 
00215   double GetSumOfSquaredPositionChanges(InternalParametersType &previous, 
00216                                         InternalParametersType &current) ;
00217 
00220   int GetClosestCandidate(ParameterType &measurements, 
00221                           std::vector< int > &validIndexes) ;
00222 
00224   bool IsFarther(ParameterType &pointA,
00225                  ParameterType &pointB,
00226                  MeasurementVectorType &lowerBound,
00227                  MeasurementVectorType &upperBound) ;
00228 
00231   void Filter(KdTreeNodeType* node, 
00232               std::vector< int > validIndexes,
00233               MeasurementVectorType &lowerBound, 
00234               MeasurementVectorType &upperBound) ;
00235 
00237   void CopyParameters(InternalParametersType &source, InternalParametersType &target) ;
00238 
00240   void CopyParameters(ParametersType &source, InternalParametersType &target) ;
00241 
00243   void CopyParameters(InternalParametersType &source, ParametersType &target) ;
00244 
00246   void GetPoint(ParameterType &point, 
00247                 MeasurementVectorType &measurements)
00248   {
00249     for (unsigned int i = 0 ; i < MeasurementVectorSize ; i++)
00250       {
00251         point[i] = measurements[i] ;
00252       }
00253   }
00254 
00255   void PrintPoint(ParameterType &point)
00256   {
00257     std::cout << "[ " ;
00258     for (unsigned int i = 0 ; i < MeasurementVectorSize ; i++)
00259       {
00260         std::cout << point[i] << " " ;
00261       }
00262     std::cout << "]" ;
00263   }
00264 
00265 private:
00267   int m_CurrentIteration ;
00269   int m_MaximumIteration ;
00271   double m_CenteroidPositionChanges ;
00274   double m_CenteroidPositionChangesThreshold ;
00276   TKdTree* m_KdTree ;
00278   typename EuclideanDistance< ParameterType >::Pointer m_DistanceMetric ;
00279 
00281   ParametersType m_Parameters ;
00282 
00283   CandidateVector m_CandidateVector ;
00284   
00285   ParameterType m_TempVertex ;
00286 
00287   bool m_UseClusterLabels ;
00288   bool m_GenerateClusterLabels ;
00289   ClusterLabelsType m_ClusterLabels ;
00290 } ; // end of class
00291 
00292 } // end of namespace Statistics
00293 } // end of namespace itk
00294 
00295 #ifndef ITK_MANUAL_INSTANTIATION
00296 #include "itkKdTreeBasedKmeansEstimator.txx"
00297 #endif
00298 
00299 
00300 #endif

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