ITK/Examples/Statistics/ExpectationMaximizationMixtureModelEstimator 2D: Difference between revisions
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Calculate a mixture model on a collection of measurements using EM. | |||
==ExpectationMaximizationMixtureModelEstimator_2D.cxx== | ==ExpectationMaximizationMixtureModelEstimator_2D.cxx== | ||
<source lang="cpp"> | <source lang="cpp"> |
Revision as of 17:51, 20 November 2010
Calculate a mixture model on a collection of measurements using EM.
ExpectationMaximizationMixtureModelEstimator_2D.cxx
<source lang="cpp">
- include "itkVector.h"
- include "itkListSample.h"
- include "itkGaussianMixtureModelComponent.h"
- include "itkExpectationMaximizationMixtureModelEstimator.h"
- 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>