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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: $RCSfile: itkSampleClassifierTest.cxx,v $
Language: C++
Date: $Date: 2005-07-26 15:55:14 $
Version: $Revision: 1.9 $
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 "itkVector.h"
#include "itkPoint.h"
#include "itkImage.h"
#include "itkImageRegionIteratorWithIndex.h"
#include "vnl/vnl_matrix.h"
#include "itkPointSetToListAdaptor.h"
#include "itkSubsample.h"
#include "itkEuclideanDistance.h"
#include "itkMinimumDecisionRule.h"
#include "itkSampleClassifier.h"
int itkSampleClassifierTest(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 ;
unsigned int numberOfClasses = 2 ;
/* 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);
/** preparing classifier and decision rule object */
typedef itk::Statistics::SampleClassifier< DataSampleType > ClassifierType ;
typedef itk::MinimumDecisionRule DecisionRuleType ;
typedef itk::Statistics::EuclideanDistance< DataSampleType::MeasurementVectorType >
MembershipFunctionType ;
typedef MembershipFunctionType::OriginType MeanType;
std::vector< MeanType > trueMeans ;
MeanType m1( 2 );
m1[0] = 99.261;
m1[1] = 100.078;
MeanType m2( 2 );
m2[0]=200.1;
m2[1]=201.3;
trueMeans.push_back( m1 );
trueMeans.push_back( m2 );
ClassifierType::Pointer classifier = ClassifierType::New() ;
DecisionRuleType::Pointer decisionRule = DecisionRuleType::New() ;
classifier->SetDecisionRule(decisionRule) ;
classifier->SetNumberOfClasses(numberOfClasses) ;
classifier->SetSample(sample.GetPointer()) ;
std::vector< MembershipFunctionType::Pointer > membershipFunctions ;
std::vector< unsigned int > classLabels ;
for ( i = 0 ; i < numberOfClasses ; i++ )
{
membershipFunctions.push_back(MembershipFunctionType::New()) ;
classLabels.push_back(i + 1) ;
for ( j = 0 ; j < DataSampleType::MeasurementVectorSize ; j++ )
{
membershipFunctions[i]->SetOrigin(trueMeans[i]) ;
}
classifier->AddMembershipFunction(membershipFunctions[i].GetPointer()) ;
}
classifier->SetMembershipFunctionClassLabels(classLabels) ;
/* start classification process */
classifier->Update() ;
/* evaluate the classification result */
ClassifierType::OutputType* membershipSample =
classifier->GetOutput() ;
ClassifierType::OutputType::ConstIterator m_iter =
membershipSample->Begin() ;
ClassifierType::OutputType::ConstIterator m_last =
membershipSample->End() ;
unsigned int index = 0 ;
unsigned int error1 = 0 ;
unsigned int error2 = 0 ;
while ( m_iter != m_last )
{
if ( index < 1000 )
{
if ( m_iter.GetClassLabel() != classLabels[0] )
{
++error1 ;
}
}
else
{
if ( m_iter.GetClassLabel() != classLabels[1] )
{
++error2 ;
}
}
++index ;
++m_iter ;
}
std::cout << "Among 2000 measurement vectors, " << error1 + error2
<< " vectors are misclassified." << std::endl ;
if( double(error1 / 10) > 2 || double(error2 / 10) > 2)
{
std::cout << "Test failed." << std::endl;
return EXIT_FAILURE;
}
std::cout << "Test passed." << std::endl;
// following three lines to increase test coverage of the
// DecisionRuleBase
std::cout << "Decision rule base class = "
<< decisionRule->DecisionRuleType::Superclass::GetNameOfClass()
<< std::endl ;
return EXIT_SUCCESS;
}
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