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| Calculate a mixture model on a collection of measurements using EM.
| | {{warning|1=The media wiki content on this page is no longer maintained. The examples presented on the https://itk.org/Wiki/* pages likely require ITK version 4.13 or earlier releases. In many cases, the examples on this page no longer conform to the best practices for modern ITK versions.}} |
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| ==ExpectationMaximizationMixtureModelEstimator_2D.cxx==
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| <source lang="cpp">
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| #include "itkVector.h"
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| #include "itkListSample.h"
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| #include "itkGaussianMixtureModelComponent.h"
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| #include "itkExpectationMaximizationMixtureModelEstimator.h"
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| #include "itkNormalVariateGenerator.h"
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| int main(int, char *[])
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| { | |
| unsigned int numberOfClasses = 2;
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| typedef itk::Vector< double, 2 > MeasurementVectorType;
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| typedef itk::Statistics::ListSample< MeasurementVectorType > SampleType;
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| SampleType::Pointer sample = SampleType::New();
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|
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| typedef itk::Statistics::NormalVariateGenerator NormalGeneratorType;
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| NormalGeneratorType::Pointer normalGenerator = NormalGeneratorType::New();
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| // Create the first set of 2D Gaussian samples
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| normalGenerator->Initialize( 101 );
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| MeasurementVectorType mv;
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| double mean = 100;
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| double standardDeviation = 30;
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| for ( unsigned int i = 0 ; i < 100 ; ++i )
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| {
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| mv[0] = ( normalGenerator->GetVariate() * standardDeviation ) + mean;
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| mv[1] = ( normalGenerator->GetVariate() * standardDeviation ) + mean;
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| sample->PushBack( mv );
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| }
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| // Create the second set of 2D Gaussian samples
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| normalGenerator->Initialize( 3024 );
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| mean = 200;
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| standardDeviation = 30;
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| for ( unsigned int i = 0 ; i < 100 ; ++i )
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| {
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| mv[0] = ( normalGenerator->GetVariate() * standardDeviation ) + mean;
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| mv[1] = ( normalGenerator->GetVariate() * standardDeviation ) + mean;
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| sample->PushBack( mv );
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| }
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| typedef itk::Array< double > ParametersType;
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| ParametersType params( 6 );
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| // Create the first set of initial parameters
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| std::vector< ParametersType > initialParameters( numberOfClasses );
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| params[0] = 110.0; // mean of dimension 1
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| params[1] = 115.0; // mean of dimension 2
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| params[2] = 800.0; // covariance(0,0)
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| params[3] = 0; // covariance(0,1) | |
| params[4] = 0; // covariance(1,0)
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| params[5] = 805.0; // covariance(1,1)
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| initialParameters[0] = params;
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| // Create the second set of initial parameters
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| params[0] = 210.0; // mean of dimension 1
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| params[1] = 215.0; // mean of dimension 2
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| params[2] = 850.0; // covariance(0,0)
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| params[3] = 0; // covariance(0,1)
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| params[4] = 0; // covariance(1,0)
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| params[5] = 855.0; // covariance(1,1)
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| initialParameters[1] = params;
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| typedef itk::Statistics::GaussianMixtureModelComponent< SampleType >
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| ComponentType;
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| // Create the components
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| std::vector< ComponentType::Pointer > components;
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| for ( unsigned int i = 0 ; i < numberOfClasses ; i++ )
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| {
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| components.push_back( ComponentType::New() );
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| (components[i])->SetSample( sample );
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| (components[i])->SetParameters( initialParameters[i] );
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| }
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| typedef itk::Statistics::ExpectationMaximizationMixtureModelEstimator<
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| SampleType > EstimatorType;
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| EstimatorType::Pointer estimator = EstimatorType::New();
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| estimator->SetSample( sample );
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| estimator->SetMaximumIteration( 200 );
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| itk::Array< double > initialProportions(numberOfClasses);
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| initialProportions[0] = 0.5;
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| initialProportions[1] = 0.5;
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| estimator->SetInitialProportions( initialProportions );
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| for ( unsigned int i = 0 ; i < numberOfClasses ; i++)
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| {
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| estimator->AddComponent( (ComponentType::Superclass*)
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| (components[i]).GetPointer() );
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| }
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| estimator->Update();
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| // Output the results
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| for ( unsigned int i = 0 ; i < numberOfClasses ; i++ )
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| {
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| std::cout << "Cluster[" << i << "]" << std::endl;
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| std::cout << " Parameters:" << std::endl;
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| std::cout << " " << (components[i])->GetFullParameters()
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| << std::endl;
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| std::cout << " Proportion: ";
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| std::cout << " " << estimator->GetProportions()[i] << std::endl;
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| }
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|
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| return EXIT_SUCCESS;
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| }
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| </source>
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| {{ITKCMakeLists|{{SUBPAGENAME}}}}
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