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/*=========================================================================
*
* Copyright Insight Software Consortium
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0.txt
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
*=========================================================================*/
#include "itkListSample.h"
#include "itkSampleClassifierFilter.h"
#include "itkMaximumDecisionRule.h"
#include "itkGaussianMembershipFunction.h"
#include "itkNormalVariateGenerator.h"
//Test if the SampleClassifier filter labels observations correctly
int itkSampleClassifierFilterTest2( int, char * [] )
{
const unsigned int numberOfComponents = 1;
typedef float MeasurementType;
const unsigned int numberOfClasses = 2;
typedef itk::Array< MeasurementType > MeasurementVectorType;
typedef itk::Statistics::ListSample< MeasurementVectorType > SampleType;
typedef itk::Statistics::SampleClassifierFilter< SampleType > FilterType;
FilterType::Pointer filter = FilterType::New();
SampleType::Pointer sample = SampleType::New();
sample->SetMeasurementVectorSize( numberOfComponents );
filter->SetNumberOfClasses( numberOfClasses );
if( filter->GetNumberOfClasses() != numberOfClasses )
{
std::cerr << "GetNumberOfClasses() didn't matched SetNumberOfClasses()" << std::endl;
return EXIT_FAILURE;
}
typedef FilterType::ClassLabelVectorObjectType ClassLabelVectorObjectType;
typedef FilterType::ClassLabelVectorType ClassLabelVectorType;
typedef FilterType::MembershipFunctionVectorObjectType MembershipFunctionVectorObjectType;
typedef FilterType::MembershipFunctionVectorType MembershipFunctionVectorType;
typedef itk::Statistics::GaussianMembershipFunction< MeasurementVectorType >
MembershipFunctionType;
typedef MembershipFunctionType::MeanVectorType MeanVectorType;
typedef MembershipFunctionType::CovarianceMatrixType CovarianceMatrixType;
typedef MembershipFunctionType::Pointer MembershipFunctionPointer;
ClassLabelVectorObjectType::Pointer classLabelsObject = ClassLabelVectorObjectType::New();
filter->SetClassLabels( classLabelsObject );
MembershipFunctionVectorObjectType::Pointer membershipFunctionsObject =
MembershipFunctionVectorObjectType::New();
filter->SetMembershipFunctions( membershipFunctionsObject );
// Add three membership functions and rerun the filter
MembershipFunctionVectorType & membershipFunctionsVector = membershipFunctionsObject->Get();
MembershipFunctionPointer membershipFunction1 = MembershipFunctionType::New();
membershipFunction1->SetMeasurementVectorSize( numberOfComponents );
MeanVectorType mean1;
itk::NumericTraits<MeanVectorType>::SetLength( mean1, numberOfComponents );
mean1[0] = 10.5;
membershipFunction1->SetMean( mean1 );
CovarianceMatrixType covariance1;
covariance1.SetSize( numberOfComponents, numberOfComponents );
covariance1.SetIdentity();
covariance1[0][0] = 0.5;
membershipFunction1->SetCovariance( covariance1 );
membershipFunctionsVector.push_back( membershipFunction1.GetPointer() );
MembershipFunctionPointer membershipFunction2 = MembershipFunctionType::New();
membershipFunction1->SetMeasurementVectorSize( numberOfComponents );
MeanVectorType mean2;
itk::NumericTraits<MeanVectorType>::SetLength( mean2, numberOfComponents );
mean2[0] = 200.5;
membershipFunction2->SetMean( mean2 );
CovarianceMatrixType covariance2;
covariance2.SetSize( numberOfComponents, numberOfComponents );
covariance2.SetIdentity();
covariance2[0][0] = 0.5;
membershipFunction2->SetCovariance( covariance2 );
membershipFunctionsVector.push_back( membershipFunction2.GetPointer() );
// 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::MaximumDecisionRule DecisionRuleType;
DecisionRuleType::Pointer decisionRule = DecisionRuleType::New();
filter->SetDecisionRule( decisionRule );
//Generate samples from a Gaussian distribution with mean and
//covariance parameter as the first membership function.
//All the samples should be labeled by the classifier as
//the first class
typedef itk::Statistics::NormalVariateGenerator NormalGeneratorType;
NormalGeneratorType::Pointer normalGenerator = NormalGeneratorType::New();
normalGenerator->Initialize( 101 );
MeasurementVectorType mv;
itk::NumericTraits<MeasurementVectorType>::SetLength( mv, numberOfComponents );
double mean = mean1[0];
double standardDeviation = std::sqrt(covariance1[0][0]);
unsigned int numberOfSampleEachClass = 10;
for ( unsigned int i = 0; i < numberOfSampleEachClass; ++i )
{
mv[0] = (normalGenerator->GetVariate() * standardDeviation ) + mean;
sample->PushBack( mv );
}
//Add samples for the second gaussian
mean = mean2[0];
standardDeviation = std::sqrt(covariance1[0][0]);
for ( unsigned int i = 0; i < numberOfSampleEachClass; ++i )
{
mv[0] = (normalGenerator->GetVariate() * standardDeviation ) + mean;
sample->PushBack( mv );
}
filter->SetInput( sample );
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.GetClassLabel()
<< "\tclass1=" << class1 << std::endl;
return EXIT_FAILURE;
}
}
else
{
if( iter.GetClassLabel() != class2 )
{
std::cerr << "Classification error: " << sampleCounter
<< "\t" << iter.GetClassLabel()
<< "\tclass2=" << class2 << std::endl;
return EXIT_FAILURE;
}
}
++iter;
++sampleCounter;
}
std::cout << "Test passed." << std::endl;
return EXIT_SUCCESS;
}
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