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| Someone please confirm that this outputs the mean and the variance (i.e. I used a standard deviation of 30 to create the samples and the second estimated parameter is near 1000 (~30^2) . Is this correct?)
| | {{warning|1=The media wiki content on this page is no longer maintained.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_1D.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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| {
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| unsigned int numberOfClasses = 2;
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| typedef itk::Vector< double, 1 > MeasurementVectorType;
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| typedef itk::Statistics::ListSample< MeasurementVectorType > SampleType;
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| SampleType::Pointer sample = SampleType::New();
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| typedef itk::Statistics::NormalVariateGenerator NormalGeneratorType;
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| NormalGeneratorType::Pointer normalGenerator = NormalGeneratorType::New();
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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 < 10; ++i )
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| {
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| mv[0] = ( normalGenerator->GetVariate() * standardDeviation ) + mean;
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| std::cout << "m[" << i << "] = " << mv[0] << std::endl;
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| sample->PushBack( mv );
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| }
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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 < 10; ++i )
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| {
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| mv[0] = ( normalGenerator->GetVariate() * standardDeviation ) + mean;
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| std::cout << "m[" << i << "] = " << mv[0] << std::endl;
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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 params1( 2 );
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| std::vector< ParametersType > initialParameters( numberOfClasses );
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| params1[0] = 110.0;
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| params1[1] = 50.0;
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| initialParameters[0] = params1;
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| ParametersType params2( 2 );
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| params2[0] = 210.0;
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| params2[1] = 50.0;
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| initialParameters[1] = params2;
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| | |
| typedef itk::Statistics::GaussianMixtureModelComponent< SampleType > ComponentType;
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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(500);
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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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| 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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| 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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| return EXIT_SUCCESS;
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| }
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| </source>
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| {{ITKCMakeLists|ExpectationMaximizationMixtureModelEstimator_1D}}
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