ITK/Examples/Statistics/ExpectationMaximizationMixtureModelEstimator 2D

Calculate a mixture model on a collection of measurements using EM.

ExpectationMaximizationMixtureModelEstimator_2D.cxx

<source lang="cpp">

1. include "itkVector.h"
2. include "itkListSample.h"
3. include "itkGaussianMixtureModelComponent.h"
4. include "itkExpectationMaximizationMixtureModelEstimator.h"
5. include "itkNormalVariateGenerator.h"

int main(int, char *[]) {

 unsigned int numberOfClasses = 2;
 typedef itk::Vector< double, 2 > MeasurementVectorType;
 typedef itk::Statistics::ListSample< MeasurementVectorType > SampleType;
 SampleType::Pointer sample = SampleType::New();
 

 typedef itk::Statistics::NormalVariateGenerator NormalGeneratorType;
 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 );
   }

 typedef itk::Array< double > ParametersType;
 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;

 typedef itk::Statistics::GaussianMixtureModelComponent< SampleType >
   ComponentType;

 // 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] );
   }

 typedef itk::Statistics::ExpectationMaximizationMixtureModelEstimator<
   SampleType > EstimatorType;
 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;

} </source>

CMakeLists.txt

<source lang="cmake"> cmake_minimum_required(VERSION 2.6)

PROJECT(ExpectationMaximizationMixtureModelEstimator)

FIND_PACKAGE(ITK REQUIRED) INCLUDE(${ITK_USE_FILE})

ADD_EXECUTABLE(ExpectationMaximizationMixtureModelEstimator ExpectationMaximizationMixtureModelEstimator.cxx) TARGET_LINK_LIBRARIES(ExpectationMaximizationMixtureModelEstimator ITKBasicFilters ITKCommon ITKIO ITKStatistics)

</source>