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

<syntaxhighlight lang="cmake"> cmake_minimum_required(VERSION 3.9.5)

project(ExpectationMaximizationMixtureModelEstimator 2D)

find_package(ITK REQUIRED) include(${ITK_USE_FILE}) if (ITKVtkGlue_LOADED)

 find_package(VTK REQUIRED)
 include(${VTK_USE_FILE})

endif()

add_executable(ExpectationMaximizationMixtureModelEstimator 2D MACOSX_BUNDLE ExpectationMaximizationMixtureModelEstimator 2D.cxx)

if( "${ITK_VERSION_MAJOR}" LESS 4 )

 target_link_libraries(ExpectationMaximizationMixtureModelEstimator 2D ITKReview ${ITK_LIBRARIES})

else( "${ITK_VERSION_MAJOR}" LESS 4 )

 target_link_libraries(ExpectationMaximizationMixtureModelEstimator 2D ${ITK_LIBRARIES})

endif( "${ITK_VERSION_MAJOR}" LESS 4 )

</syntaxhighlight>

Download and Build ExpectationMaximizationMixtureModelEstimator 2D

Click 2D.tar here to download ExpectationMaximizationMixtureModelEstimator 2D and its CMakeLists.txt file. Once the tarball ExpectationMaximizationMixtureModelEstimator 2D.tar has been downloaded and extracted,

cd ExpectationMaximizationMixtureModelEstimator 2D/build

• If ITK is installed:

cmake ..

• If ITK is not installed but compiled on your system, you will need to specify the path to your ITK build:

cmake -DITK_DIR:PATH=/home/me/itk_build ..

Build the project:

make

and run it:

./ExpectationMaximizationMixtureModelEstimator 2D

WINDOWS USERS PLEASE NOTE: Be sure to add the ITK bin directory to your path. This will resolve the ITK dll's at run time.

Building All of the Examples

Many of the examples in the ITK Wiki Examples Collection require VTK. You can build all of the the examples by following these instructions. If you are a new VTK user, you may want to try the Superbuild which will build a proper ITK and VTK.

ItkVtkGlue

ITK >= 4

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ITK < 4

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