itkweightedcentroidkdtreegenerator.txx

InsightToolkit-1.4.0(有大量的优化算法程序)

/*=========================================================================

  Program:   Insight Segmentation & Registration Toolkit
  Module:    $RCSfile: itkWeightedCentroidKdTreeGenerator.txx,v $
  Language:  C++
  Date:      $Date: 2003/09/10 14:29:48 $
  Version:   $Revision: 1.2 $

  Copyright (c) Insight Software Consortium. All rights reserved.
  See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details.

     This software is distributed WITHOUT ANY WARRANTY; without even 
     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR 
     PURPOSE.  See the above copyright notices for more information.

=========================================================================*/
#ifndef __itkWeightedCentroidKdTreeGenerator_txx
#define __itkWeightedCentroidKdTreeGenerator_txx

namespace itk{ 
namespace Statistics{

template< class TSample >
WeightedCentroidKdTreeGenerator< TSample >
::WeightedCentroidKdTreeGenerator()
{
}

template< class TSample >
void
WeightedCentroidKdTreeGenerator< TSample >
::PrintSelf(std::ostream& os, Indent indent) const
{
  Superclass::PrintSelf(os,indent);
}

template< class TSample >
inline typename KdTreeGenerator< TSample >::KdTreeNodeType*
WeightedCentroidKdTreeGenerator< TSample >
::GenerateNonterminalNode(int beginIndex,
                          int endIndex,
                          MeasurementVectorType &lowerBound,
                          MeasurementVectorType &upperBound,
                          int level)
{
  MeasurementType dimensionLowerBound ;
  MeasurementType dimensionUpperBound ;
  MeasurementType partitionValue ;
  unsigned int partitionDimension = 0 ;
  KdTreeNodeType* left ;
  KdTreeNodeType* right ;
  int i, j ;
  MeasurementType spread ;
  MeasurementType maxSpread ;
  int medianIndex ;
  SubsamplePointer subsample = this->GetSubsample() ;

  // calculates the weighted centroid which is the vector sum
  // of all the associated instances.
  typename KdTreeNodeType::CentroidType weightedCentroid ;
  MeasurementVectorType tempVector ;
  weightedCentroid.Fill(NumericTraits< MeasurementType >::Zero) ;
  for (i = beginIndex ; i < endIndex ; i++)
    {
    tempVector = subsample->GetMeasurementVectorByIndex(i) ;
    for(j = 0 ; j < (int)MeasurementVectorSize ; j++)
      {
      weightedCentroid[j] += tempVector[j] ;
      }
    }

  // find most widely spread dimension
  FindSampleBoundAndMean< SubsampleType >(this->GetSubsample(), 
                                          beginIndex, endIndex, 
                                          m_TempLowerBound, m_TempUpperBound,
                                          m_TempMean) ;

  maxSpread = NumericTraits< MeasurementType >::NonpositiveMin() ;
  for (i = 0 ; i < (int)MeasurementVectorSize ; i++)
    {
    spread = m_TempUpperBound[i] - m_TempLowerBound[i] ;
    if (spread >= maxSpread)
      {
      maxSpread = spread ;
      partitionDimension = i ;
      }
    }

  // find median and partition this node using the quick select algorithm
  medianIndex = (endIndex - beginIndex) / 2 ;
  partitionValue = 
    QuickSelect< SubsampleType >(this->GetSubsample(), 
                                 partitionDimension, 
                                 beginIndex, endIndex, medianIndex, 
                                 m_TempMean[partitionDimension]) ;
  medianIndex += beginIndex - 1 ;

  dimensionLowerBound = lowerBound[partitionDimension] ;
  dimensionUpperBound = upperBound[partitionDimension] ;

  upperBound[partitionDimension] = partitionValue ;
  left = GenerateTreeLoop(beginIndex, medianIndex, lowerBound, upperBound, level + 1);
  upperBound[partitionDimension] = dimensionUpperBound ;

  lowerBound[partitionDimension] = partitionValue ;
  right = GenerateTreeLoop(medianIndex, endIndex, lowerBound, upperBound, level + 1) ;
  lowerBound[partitionDimension] = dimensionLowerBound ;

  return new KdTreeWeightedCentroidNonterminalNode< TSample >
    (partitionDimension, partitionValue,
     left, right, weightedCentroid, endIndex - beginIndex) ;
}

} // end of namespace Statistics 
} // end of namespace itk

#endif