SphinxExamples/src/Numerics/Statistics/2DGaussianMixtureModelExpectMax/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
 *
 *         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.
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#include "itkVector.h"
#include "itkListSample.h"
#include "itkGaussianMixtureModelComponent.h"
#include "itkExpectationMaximizationMixtureModelEstimator.h"
#include "itkNormalVariateGenerator.h"
 
int
main(int, char *[])
{
  unsigned int numberOfClasses = 2;
  using MeasurementVectorType = itk::Vector<double, 2>;
  using SampleType = itk::Statistics::ListSample<MeasurementVectorType>;
  SampleType::Pointer sample = SampleType::New();
 
 
  using NormalGeneratorType = itk::Statistics::NormalVariateGenerator;
  NormalGeneratorType::Pointer normalGenerator = NormalGeneratorType::New();
 
  // Create the first set of 2D Gaussian samples
  normalGenerator->Initialize(101);
 
  MeasurementVectorType mv;
  double                mean = 100;
  double                standardDeviation = 30;
  for (unsigned int i = 0; i < 100; ++i)
  {
    mv[0] = (normalGenerator->GetVariate() * standardDeviation) + mean;
    mv[1] = (normalGenerator->GetVariate() * standardDeviation) + mean;
    sample->PushBack(mv);
  }
 
  // Create the second set of 2D Gaussian samples
  normalGenerator->Initialize(3024);
  mean = 200;
  standardDeviation = 30;
  for (unsigned int i = 0; i < 100; ++i)
  {
    mv[0] = (normalGenerator->GetVariate() * standardDeviation) + mean;
    mv[1] = (normalGenerator->GetVariate() * standardDeviation) + mean;
    sample->PushBack(mv);
  }
 
  using ParametersType = itk::Array<double>;
  ParametersType params(6);
 
  // Create the first set of initial parameters
  std::vector<ParametersType> initialParameters(numberOfClasses);
  params[0] = 110.0; // mean of dimension 1
  params[1] = 115.0; // mean of dimension 2
  params[2] = 800.0; // covariance(0,0)
  params[3] = 0;     // covariance(0,1)
  params[4] = 0;     // covariance(1,0)
  params[5] = 805.0; // covariance(1,1)
  initialParameters[0] = params;
 
  // Create the second set of initial parameters
  params[0] = 210.0; // mean of dimension 1
  params[1] = 215.0; // mean of dimension 2
  params[2] = 850.0; // covariance(0,0)
  params[3] = 0;     // covariance(0,1)
  params[4] = 0;     // covariance(1,0)
  params[5] = 855.0; // covariance(1,1)
  initialParameters[1] = params;
 
  using ComponentType = itk::Statistics::GaussianMixtureModelComponent<SampleType>;
 
  // Create the components
  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>;
  EstimatorType::Pointer estimator = EstimatorType::New();
 
  estimator->SetSample(sample);
  estimator->SetMaximumIteration(200);
 
  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();
 
  // Output the results
  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;
}