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/*=========================================================================
*
* Copyright NumFOCUS
*
* 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
*
* https://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 *[])
{
constexpr unsigned int numberOfComponents = 1;
using MeasurementType = float;
constexpr unsigned int numberOfClasses = 2;
using MeasurementVectorType = itk::Array<MeasurementType>;
using SampleType = itk::Statistics::ListSample<MeasurementVectorType>;
using FilterType = itk::Statistics::SampleClassifierFilter<SampleType>;
auto filter = FilterType::New();
auto 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;
}
using ClassLabelVectorObjectType = FilterType::ClassLabelVectorObjectType;
using ClassLabelVectorType = FilterType::ClassLabelVectorType;
using MembershipFunctionVectorObjectType = FilterType::MembershipFunctionVectorObjectType;
using MembershipFunctionVectorType = FilterType::MembershipFunctionVectorType;
using MembershipFunctionType = itk::Statistics::GaussianMembershipFunction<MeasurementVectorType>;
using MeanVectorType = MembershipFunctionType::MeanVectorType;
using CovarianceMatrixType = MembershipFunctionType::CovarianceMatrixType;
using MembershipFunctionPointer = MembershipFunctionType::Pointer;
auto classLabelsObject = ClassLabelVectorObjectType::New();
filter->SetClassLabels(classLabelsObject);
auto 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);
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);
// Add class labels
ClassLabelVectorType & classLabelVector = classLabelsObject->Get();
using ClassLabelType = FilterType::ClassLabelType;
ClassLabelType class1 = 0;
classLabelVector.push_back(class1);
ClassLabelType class2 = 1;
classLabelVector.push_back(class2);
// Set a decision rule type
using DecisionRuleType = itk::Statistics::MaximumDecisionRule;
auto 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
using NormalGeneratorType = itk::Statistics::NormalVariateGenerator;
auto 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 (const 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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