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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: KdTreeBasedKmeansValidationApp.txx
Language: C++
Date: $Date$
Version: $Revision$
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 __KdTreeBasedKmeansValidationApp_txx
#define __KdTreeBasedKmeansValidationApp_txx
#include "KdTreeBasedKmeansValidationApp.h"
template< class TPixel, unsigned int VMeasurementVectorSize >
KdTreeBasedKmeansValidationApp< TPixel, VMeasurementVectorSize >
::KdTreeBasedKmeansValidationApp()
{
}
template< class TPixel, unsigned int VMeasurementVectorSize >
KdTreeBasedKmeansValidationApp< TPixel, VMeasurementVectorSize >
::~KdTreeBasedKmeansValidationApp()
{
}
template< class TPixel, unsigned int VMeasurementVectorSize >
void
KdTreeBasedKmeansValidationApp< TPixel, VMeasurementVectorSize >
::GenerateSample()
{
m_SampleGenerator.SetOutputSampleType(SampleGeneratorType::WEIGHTED_CENTROID_KD_TREE) ;
m_SampleGenerator.SetKdTreeBucketSize(m_KdTreeBucketSize) ;
m_SampleGenerator.SetImageFileNames(m_ImageFileNames) ;
if ( m_MaskImageSliceOffset == -1 )
{
// do not use SliceFiller
m_SampleGenerator.SetClassMaskImageFileName(m_MaskFileName.c_str(), 0) ;
}
else
{
m_SampleGenerator.SetClassMaskImageFileName(m_MaskFileName.c_str(), 0, m_MaskImageSliceOffset) ;
}
m_SampleGenerator.SetSelectedClasses(m_SelectedClasses) ;
m_SampleGenerator.SetOutputNormalized(m_NormalizedSample) ;
m_SampleGenerator.GenerateData() ;
}
template< class TPixel, unsigned int VMeasurementVectorSize >
void
KdTreeBasedKmeansValidationApp< TPixel, VMeasurementVectorSize >
::PrepareInputParameterFilter()
{
std::vector< std::string > filter ;
for ( unsigned int i = 0 ; i < VMeasurementVectorSize ; i++ )
{
itk::OStringStream field ;
field << "mean." << i + 1;
m_InputFilter.push_back(field.str()) ;
}
}
template< class TPixel, unsigned int VMeasurementVectorSize >
void
KdTreeBasedKmeansValidationApp< TPixel, VMeasurementVectorSize >
::PrepareOutputParameterTableHeader()
{
m_OutputHeader.push_back("mapped class") ;
for ( unsigned int i = 0 ; i < VMeasurementVectorSize ; i++ )
{
itk::OStringStream field ;
field << "mean." << i + 1 ;
m_OutputHeader.push_back(field.str()) ;
}
for ( unsigned int i = 0 ; i < m_NumberOfClasses ; i++)
{
itk::OStringStream field ;
field << m_ClassLabels[i] ;
m_OutputHeader.push_back(field.str()) ;
}
m_OutputHeader.push_back("iterations") ;
m_OutputHeader.push_back("time estimation") ;
m_OutputHeader.push_back("time total") ;
}
template< class TPixel, unsigned int VMeasurementVectorSize >
void
KdTreeBasedKmeansValidationApp< TPixel, VMeasurementVectorSize >
::StartClustering(ParametersType& params)
{
m_ClusteringMethod.SetMaximumIteration(m_MaximumIteration) ;
m_ClusteringMethod.SetKdTree(m_SampleGenerator.GetKdTree()) ;
m_ClusteringMethod.SetInitialParameters(params) ;
m_ClusteringMethod.Run() ;
m_EstimatedParameters = m_ClusteringMethod.GetEstimatedParameters() ;
m_ClusterLabels = m_ClusteringMethod.GetClusterLabels() ;
}
template< class TPixel, unsigned int VMeasurementVectorSize >
void
KdTreeBasedKmeansValidationApp< TPixel, VMeasurementVectorSize >
::MapClusterToClass()
{
VectorType x ;
double temp, minDistance ;
unsigned int paramIndex = 0 ;
unsigned int numberOfParametersPerClass = VMeasurementVectorSize ;
ParametersType tempParameters(m_EstimatedParameters) ;
m_ClusterMap.resize(m_ClassLabels.size()) ;
for ( unsigned int i = 0 ; i < m_NumberOfClasses ; i++ )
{
minDistance = itk::NumericTraits< double >::max() ;
for ( unsigned int k = 0 ; k < VMeasurementVectorSize ; k++ )
{
x[k] = m_EstimatedParameters[paramIndex] ;
++paramIndex ;
}
for ( unsigned int j = 0 ; j < m_NumberOfClasses ; j++ )
{
temp = m_DistanceMetric->Evaluate(m_SampleGenerator.GetClassMean(m_ClassLabels[j]),
x) ;
if ( temp < minDistance )
{
minDistance = temp ;
m_ClusterMap[i] = m_ClassLabels[j] ;
}
}
}
m_EstimatedParameters = tempParameters ;
}
template< class TPixel, unsigned int VMeasurementVectorSize >
void
KdTreeBasedKmeansValidationApp< TPixel, VMeasurementVectorSize >
::PutResult(unsigned int caseNo)
{
unsigned int numberOfParametersPerClass = VMeasurementVectorSize ;
ParameterTable::ParametersType tempOutputParams(m_OutputParameterTable.GetHeaderSize()) ;
unsigned int classIndex ;
unsigned int i ;
unsigned int classLabel ;
unsigned int outputParamIndex ;
unsigned int paramIndex = 0 ;
for ( classIndex = 0 ; classIndex < m_NumberOfClasses ; classIndex++)
{
classLabel = m_ClassLabels[classIndex] ;
outputParamIndex = 0 ;
tempOutputParams[outputParamIndex] = m_ClusterMap[classIndex] ;
++outputParamIndex ;
for ( i = 0 ; i < numberOfParametersPerClass ; i++ )
{
tempOutputParams[outputParamIndex] = m_EstimatedParameters[paramIndex] ;
++outputParamIndex ;
++paramIndex ;
}
for ( i = 0 ; i < m_NumberOfClasses ; i++)
{
tempOutputParams[outputParamIndex] =
(m_Evaluator.GetComposition(classLabel))[i] ;
++outputParamIndex ;
}
tempOutputParams[outputParamIndex] = m_ClusteringMethod.GetLastIteration() ;
++outputParamIndex ;
tempOutputParams[outputParamIndex] = m_ClusteringMethod.GetEstimationElapsedTime() ;
++outputParamIndex ;
tempOutputParams[outputParamIndex] = m_ClusteringMethod.GetTotalElapsedTime() ;
++outputParamIndex ;
m_OutputParameterTable.SetParameters(caseNo, classLabel, tempOutputParams) ;
}
}
#endif
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