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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: $RCSfile: itkKdTreeBasedKmeansEstimatorTest.cxx,v $
Language: C++
Date: $Date: 2005-02-08 03:18:41 $
Version: $Revision: 1.10 $
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.
=========================================================================*/
#if defined(_MSC_VER)
#pragma warning ( disable : 4786 )
#endif
#include "itkWin32Header.h"
#include <fstream>
#include "itkFixedArray.h"
#include "itkPoint.h"
#include "itkImage.h"
#include "itkImageRegionIteratorWithIndex.h"
#include "vnl/vnl_matrix.h"
#include "itkPointSetToListAdaptor.h"
#include "itkSubsample.h"
#include "itkKdTree.h"
#include "itkEuclideanDistance.h"
#include "itkWeightedCentroidKdTreeGenerator.h"
#include "itkKdTreeBasedKmeansEstimator.h"
int itkKdTreeBasedKmeansEstimatorTest(int argc, char* argv[] )
{
namespace stat = itk::Statistics ;
if (argc < 2)
{
std::cout << "ERROR: data file name argument missing."
<< std::endl ;
return EXIT_FAILURE;
}
unsigned int i, j ;
char* dataFileName = argv[1] ;
int dataSize = 2000 ;
int bucketSize = 10 ;
typedef itk::FixedArray< double, 2 > MeanType ;
double minStandardDeviation =28.54746 ;
itk::Array< double > trueMeans(4) ;
trueMeans[0] = 99.261 ;
trueMeans[1] = 100.078 ;
trueMeans[2] = 200.1 ;
trueMeans[3] = 201.3 ;
itk::Array< double > initialMeans(4) ;
initialMeans[0] = 80.0 ;
initialMeans[1] = 80.0 ;
initialMeans[2] = 180.0 ;
initialMeans[3] = 180.0 ;
int maximumIteration = 200 ;
/* Loading point data */
typedef itk::PointSet< double, 2 > PointSetType ;
PointSetType::Pointer pointSet = PointSetType::New() ;
PointSetType::PointsContainerPointer pointsContainer =
PointSetType::PointsContainer::New() ;
pointsContainer->Reserve(dataSize) ;
pointSet->SetPoints(pointsContainer.GetPointer()) ;
PointSetType::PointsContainerIterator p_iter = pointsContainer->Begin() ;
PointSetType::PointType point ;
double temp ;
std::ifstream dataStream(dataFileName) ;
while (p_iter != pointsContainer->End())
{
for ( i = 0 ; i < PointSetType::PointDimension ; i++)
{
dataStream >> temp ;
point[i] = temp ;
}
p_iter.Value() = point ;
++p_iter ;
}
dataStream.close() ;
/* Importing the point set to the sample */
typedef stat::PointSetToListAdaptor< PointSetType >
DataSampleType;
DataSampleType::Pointer sample =
DataSampleType::New() ;
sample->SetPointSet(pointSet);
/* Creating k-d tree */
typedef stat::WeightedCentroidKdTreeGenerator< DataSampleType > Generator ;
Generator::Pointer generator = Generator::New() ;
generator->SetSample(sample.GetPointer()) ;
generator->SetBucketSize(bucketSize) ;
generator->GenerateData() ;
/* Searching kmeans */
typedef stat::KdTreeBasedKmeansEstimator< Generator::KdTreeType > Estimator ;
Estimator::Pointer estimator = Estimator::New() ;
estimator->SetParameters(initialMeans) ;
estimator->SetMaximumIteration(maximumIteration) ;
estimator->SetKdTree(generator->GetOutput()) ;
estimator->SetCentroidPositionChangesThreshold(0.0) ;
estimator->StartOptimization() ;
Estimator::ParametersType estimatedMeans = estimator->GetParameters() ;
bool passed = true ;
int index ;
unsigned int numberOfMeasurements = DataSampleType::MeasurementVectorSize ;
unsigned int numberOfClasses = trueMeans.size() / numberOfMeasurements ;
for (i = 0 ; i < numberOfClasses ; i++)
{
std::cout << "cluster[" << i << "] " << std::endl ;
double displacement = 0.0 ;
std::cout << " true mean :" << std::endl ;
std::cout << " " ;
index = numberOfMeasurements * i ;
for (j = 0 ; j < numberOfMeasurements ; j++)
{
std::cout << trueMeans[index] << " " ;
++index ;
}
std::cout << std::endl ;
std::cout << " estimated mean :" << std::endl ;
std::cout << " " ;
index = numberOfMeasurements * i ;
for (j = 0 ; j < numberOfMeasurements ; j++)
{
std::cout << estimatedMeans[index] << " " ;
temp = estimatedMeans[index] - trueMeans[index] ;
++index ;
displacement += (temp * temp) ;
}
std::cout << std::endl ;
displacement = sqrt(displacement) ;
std::cout << " Mean displacement: " << std::endl ;
std::cout << " " << displacement
<< std::endl << std::endl ;
// if the displacement of the estimates are within 3 % of
// standardDeviation then we assume it is successful
if ( displacement > (minStandardDeviation / 100.0) * 3 )
{
passed = false ;
}
}
if( !passed )
{
std::cout << "Test failed." << std::endl;
return EXIT_FAILURE;
}
std::cout << "Test passed." << std::endl;
return EXIT_SUCCESS;
}
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