SphinxExamples/src/Numerics/Statistics/DistributeSamplingUsingGMM/Code.cxx

/*=========================================================================
 *
 *  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.
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 *=========================================================================*/
#include "itkVector.h"
#include "itkListSample.h"
#include "itkGaussianMixtureModelComponent.h"
#include "itkExpectationMaximizationMixtureModelEstimator.h"
#include "itkNormalVariateGenerator.h"
 
int
main()
{
  unsigned int numberOfClasses = 2;
  using MeasurementVectorType = itk::Vector<double, 1>;
  using SampleType = itk::Statistics::ListSample<MeasurementVectorType>;
  auto sample = SampleType::New();
 
  using NormalGeneratorType = itk::Statistics::NormalVariateGenerator;
  auto normalGenerator = NormalGeneratorType::New();
 
  normalGenerator->Initialize(101);
 
  MeasurementVectorType mv;
  double                mean = 100;
  double                standardDeviation = 30;
  for (unsigned int i = 0; i < 10; ++i)
  {
    mv[0] = (normalGenerator->GetVariate() * standardDeviation) + mean;
    std::cout << "m[" << i << "] = " << mv[0] << std::endl;
    sample->PushBack(mv);
  }
 
  normalGenerator->Initialize(3024);
  mean = 200;
  standardDeviation = 30;
  for (unsigned int i = 0; i < 10; ++i)
  {
    mv[0] = (normalGenerator->GetVariate() * standardDeviation) + mean;
    std::cout << "m[" << i << "] = " << mv[0] << std::endl;
    sample->PushBack(mv);
  }
 
  using ParametersType = itk::Array<double>;
  ParametersType params1(2);
 
  std::vector<ParametersType> initialParameters(numberOfClasses);
  params1[0] = 110.0;
  params1[1] = 50.0;
  initialParameters[0] = params1;
 
  ParametersType params2(2);
  params2[0] = 210.0;
  params2[1] = 50.0;
  initialParameters[1] = params2;
 
  using ComponentType = itk::Statistics::GaussianMixtureModelComponent<SampleType>;
 
  std::vector<ComponentType::Pointer> components;
  for (unsigned int i = 0; i < numberOfClasses; ++i)
  {
    components.push_back(ComponentType::New());
    components[i]->SetSample(sample);
    components[i]->SetParameters(initialParameters[i]);
  }
 
  using EstimatorType = itk::Statistics::ExpectationMaximizationMixtureModelEstimator<SampleType>;
  auto estimator = EstimatorType::New();
 
  estimator->SetSample(sample);
  estimator->SetMaximumIteration(500);
 
  itk::Array<double> initialProportions(numberOfClasses);
  initialProportions[0] = 0.5;
  initialProportions[1] = 0.5;
 
  estimator->SetInitialProportions(initialProportions);
 
  for (unsigned int i = 0; i < numberOfClasses; ++i)
  {
    estimator->AddComponent((ComponentType::Superclass *)components[i].GetPointer());
  }
 
  estimator->Update();
 
  for (unsigned int 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;
  }
 
  return EXIT_SUCCESS;
}