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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 <fstream>
#include "itkPointSetToListSampleAdaptor.h"
#include "itkGaussianMixtureModelComponent.h"
#include "itkExpectationMaximizationMixtureModelEstimator.h"
int
itkExpectationMaximizationMixtureModelEstimatorTest(int argc, char * argv[])
{
namespace stat = itk::Statistics;
using PointSetType = itk::PointSet<double, 2>;
using DataSampleType = stat::PointSetToListSampleAdaptor<PointSetType>;
using EstimatorType = stat::ExpectationMaximizationMixtureModelEstimator<DataSampleType>;
using ComponentType = stat::GaussianMixtureModelComponent<DataSampleType>;
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 maximumIteration = 200;
using ParametersType = itk::Array<double>;
double minStandardDeviation = 28.54746;
unsigned int numberOfClasses = 2;
std::vector<ParametersType> trueParameters(numberOfClasses);
ParametersType params(6);
params[0] = 99.261;
params[1] = 100.078;
params[2] = 814.95741;
params[3] = 38.40308;
params[4] = 38.40308;
params[5] = 817.64446;
trueParameters[0] = params;
params[0] = 200.1;
params[1] = 201.3;
params[2] = 859.785295;
params[3] = -3.617316;
params[4] = -3.617316;
params[5] = 848.991508;
trueParameters[1] = params;
// only the means are altered
std::vector<ParametersType> initialParameters(numberOfClasses);
params[0] = 80.0;
params[1] = 80.0;
params[2] = 814.95741;
params[3] = 38.40308;
params[4] = 38.40308;
params[5] = 817.64446;
initialParameters[0] = params;
params[0] = 180.0;
params[1] = 180.0;
params[2] = 859.785295;
params[3] = -3.617316;
params[4] = -3.617316;
params[5] = 848.991508;
initialParameters[1] = params;
itk::Array<double> trueProportions(numberOfClasses);
trueProportions[0] = 0.5;
trueProportions[1] = 0.5;
itk::Array<double> initialProportions(numberOfClasses);
initialProportions[0] = 0.5;
initialProportions[1] = 0.5;
/* Loading point data */
auto pointSet = PointSetType::New();
PointSetType::PointsContainerPointer pointsContainer = PointSetType::PointsContainer::New();
pointsContainer->Reserve(dataSize);
pointSet->SetPoints(pointsContainer);
PointSetType::PointsContainerIterator p_iter = pointsContainer->Begin();
PointSetType::PointType point;
double temp;
std::ifstream dataStream(dataFileName);
if (!dataStream)
{
std::cout << "ERROR: fail to open the data file." << std::endl;
return EXIT_FAILURE;
}
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 */
auto sample = DataSampleType::New();
sample->SetPointSet(pointSet);
/* Preparing the gaussian mixture components */
using ComponentPointer = ComponentType::Pointer;
std::vector<ComponentPointer> components;
for (i = 0; i < numberOfClasses; ++i)
{
components.push_back(ComponentType::New());
(components[i])->SetSample(sample);
(components[i])->SetParameters(initialParameters[i]);
}
/* Estimating */
auto estimator = EstimatorType::New();
estimator->SetSample(sample);
estimator->SetMaximumIteration(maximumIteration);
estimator->SetInitialProportions(initialProportions);
for (i = 0; i < numberOfClasses; ++i)
{
estimator->AddComponent((ComponentType::Superclass *)(components[i]).GetPointer());
}
estimator->Update();
std::cout << "DEBUG: current iteration = " << estimator->GetCurrentIteration() << std::endl;
bool passed = true;
double displacement;
const unsigned int measurementVectorSize = sample->GetMeasurementVectorSize();
for (i = 0; i < numberOfClasses; ++i)
{
std::cout << "Cluster[" << i << ']' << std::endl;
std::cout << " Parameters:" << std::endl;
std::cout << " " << (components[i])->GetFullParameters() << std::endl;
std::cout << " Proportion: ";
std::cout << " " << (estimator->GetProportions())[i] << std::endl;
displacement = 0.0;
for (j = 0; j < measurementVectorSize; ++j)
{
temp = (components[i])->GetFullParameters()[j] - trueParameters[i][j];
displacement += (temp * temp);
}
displacement = std::sqrt(displacement);
std::cout << " Mean displacement: " << std::endl;
std::cout << " " << displacement << std::endl << std::endl;
if (displacement > (minStandardDeviation / 100.0) * 3)
{
passed = false;
}
}
if (!passed)
{
std::cout << "Test failed." << std::endl;
return EXIT_FAILURE;
}
estimator->Print(std::cout);
// Test streaming enumeration for ExpectationMaximizationMixtureModelEstimatorEnums::TERMINATION_CODE elements
const std::set<itk::Statistics::ExpectationMaximizationMixtureModelEstimatorEnums::TERMINATION_CODE>
allTERMINATION_CODE{
itk::Statistics::ExpectationMaximizationMixtureModelEstimatorEnums::TERMINATION_CODE::CONVERGED,
itk::Statistics::ExpectationMaximizationMixtureModelEstimatorEnums::TERMINATION_CODE::NOT_CONVERGED
};
for (const auto & ee : allTERMINATION_CODE)
{
std::cout << "STREAMED ENUM VALUE ExpectationMaximizationMixtureModelEstimatorEnums::TERMINATION_CODE: " << ee
<< std::endl;
}
std::cout << "Test passed." << std::endl;
return EXIT_SUCCESS;
}
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