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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: itkSampleClassifierFilterTest5.cxx
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.
=========================================================================*/
#if defined(_MSC_VER)
#pragma warning ( disable : 4786 )
#endif
#include "itkVariableLengthVector.h"
#include "itkVariableLengthVector.h"
#include "itkListSample.h"
#include "itkSampleClassifierFilter.h"
#include "itkMinimumDecisionRule2.h"
#include "itkNormalVariateGenerator.h"
#include "itkKdTree.h"
#include "itkKdTreeBasedKmeansEstimator.h"
#include "itkWeightedCentroidKdTreeGenerator.h"
//run sample classifer using itk::VariableLengthVector type measurment vector
int itkSampleClassifierFilterTest5( int, char * [] )
{
const unsigned int numberOfComponents = 1;
typedef float MeasurementType;
const unsigned int numberOfClasses = 2;
typedef itk::VariableLengthVector< MeasurementType > MeasurementVectorType;
typedef itk::Statistics::ListSample< MeasurementVectorType > SampleType;
typedef itk::Statistics::SampleClassifierFilter< SampleType > FilterType;
typedef itk::Statistics::WeightedCentroidKdTreeGenerator< SampleType > GeneratorType;
typedef itk::Statistics::KdTreeBasedKmeansEstimator< GeneratorType::KdTreeType > EstimatorType;
//Generate a sample list
SampleType::Pointer sample = SampleType::New();
sample->SetMeasurementVectorSize( numberOfComponents );
typedef itk::Statistics::NormalVariateGenerator NormalGeneratorType;
NormalGeneratorType::Pointer normalGenerator = NormalGeneratorType::New();
normalGenerator->Initialize( 101 );
//Populate the list with samples from two normal distributions
EstimatorType::DistanceToCentroidMembershipFunctionType::CentroidType mean1;
itk::Statistics::MeasurementVectorTraits::SetLength( mean1, numberOfComponents );
mean1[0] = 10.5;
EstimatorType::DistanceToCentroidMembershipFunctionType::CentroidType mean2;
itk::Statistics::MeasurementVectorTraits::SetLength( mean2, numberOfComponents );
mean2[0] = 200.5;
MeasurementVectorType mv;
itk::Statistics::MeasurementVectorTraits::SetLength( mv, numberOfComponents );
double mean = mean1[0];
double standardDeviation = 0.1;
unsigned int numberOfSampleEachClass = 10;
//Add sample from the first gaussian
for ( unsigned int i = 0; i < numberOfSampleEachClass; ++i )
{
mv[0] = (normalGenerator->GetVariate() * standardDeviation ) + mean;
sample->PushBack( mv );
}
//Add samples from the second gaussian
mean = mean2[0];
standardDeviation = 0.1;
for ( unsigned int i = 0; i < numberOfSampleEachClass; ++i )
{
mv[0] = (normalGenerator->GetVariate() * standardDeviation ) + mean;
sample->PushBack( mv );
}
typedef FilterType::ClassLabelVectorObjectType ClassLabelVectorObjectType;
typedef FilterType::ClassLabelVectorType ClassLabelVectorType;
ClassLabelVectorObjectType::Pointer classLabelsObject = ClassLabelVectorObjectType::New();
/* Creating k-d tree */
GeneratorType::Pointer generator = GeneratorType::New();
generator->SetSample(sample.GetPointer());
unsigned int bucketSize = 1;
generator->SetBucketSize(bucketSize);
generator->GenerateData();
/* Searching kmeans */
EstimatorType::Pointer estimator = EstimatorType::New();
itk::Array< double > initialMeans(2);
initialMeans[0] = 5;
initialMeans[1] = 70;
estimator->SetParameters(initialMeans);
unsigned int maximumIteration = 100;
estimator->SetMaximumIteration(maximumIteration);
estimator->SetKdTree(generator->GetOutput());
estimator->SetCentroidPositionChangesThreshold(0.0);
estimator->StartOptimization();
EstimatorType::ParametersType estimatedMeans = estimator->GetParameters();
// Add class labels
ClassLabelVectorType & classLabelVector = classLabelsObject->Get();
typedef FilterType::ClassLabelType ClassLabelType;
ClassLabelType class1 = 0;
classLabelVector.push_back( class1 );
ClassLabelType class2 = 1;
classLabelVector.push_back( class2 );
//Set a decision rule type
typedef itk::Statistics::MinimumDecisionRule2 DecisionRuleType;
DecisionRuleType::Pointer decisionRule = DecisionRuleType::New();
const FilterType::MembershipFunctionVectorObjectType *
membershipFunctionsObject = estimator->GetOutput();
/* Print out estimated parameters of the membership function */
const FilterType::MembershipFunctionVectorType
membershipFunctions = membershipFunctionsObject->Get();
FilterType::MembershipFunctionVectorType::const_iterator
begin = membershipFunctions.begin();
FilterType::MembershipFunctionVectorType::const_iterator
end = membershipFunctions.end();
FilterType::MembershipFunctionVectorType::const_iterator functionIter;
functionIter=begin;
unsigned int counter=1;
while( functionIter != end )
{
FilterType::MembershipFunctionPointer membershipFunction = *functionIter;
const EstimatorType::DistanceToCentroidMembershipFunctionType *
distanceMemberShpFunction =
dynamic_cast<const EstimatorType::DistanceToCentroidMembershipFunctionType*>(membershipFunction.GetPointer());
std::cout << "Centroid of the " << counter << " membership function "
<< distanceMemberShpFunction->GetCentroid() << std::endl;
functionIter++;
counter++;
}
//Instantiate and pass all the required inputs to the filter
FilterType::Pointer filter = FilterType::New();
filter->SetInput( sample );
filter->SetNumberOfClasses( numberOfClasses );
filter->SetClassLabels( classLabelsObject );
filter->SetDecisionRule( decisionRule );
filter->SetMembershipFunctions( membershipFunctionsObject );
try
{
filter->Update();
}
catch( itk::ExceptionObject & excp )
{
std::cerr << excp << std::endl;
return EXIT_FAILURE;
}
//Check if the measurement vectors are correctly labelled.
const FilterType::MembershipSampleType* membershipSample = filter->GetOutput();
FilterType::MembershipSampleType::ConstIterator iter = membershipSample->Begin();
unsigned int sampleCounter = 0;
while ( iter != membershipSample->End() )
{
if( sampleCounter < numberOfSampleEachClass )
{
if( iter.GetClassLabel() != class1 )
{
std::cerr << "Classification error: " << sampleCounter
<< "\t" << iter.GetMeasurementVector() << iter.GetClassLabel()
<< "\tclass1=" << class1 << std::endl;
return EXIT_FAILURE;
}
}
else
{
if( iter.GetClassLabel() != class2 )
{
std::cerr << "Classification error: " << sampleCounter
<< "\t" << iter.GetMeasurementVector() << iter.GetClassLabel()
<< "\tclass2=" << class2 << std::endl;
return EXIT_FAILURE;
}
}
++iter;
++sampleCounter;
}
std::cout << "Test passed." << std::endl;
return EXIT_SUCCESS;
}
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