|
|
| (3 intermediate revisions by 2 users not shown) |
| Line 1: |
Line 1: |
| ==ExpectationMaximizationMixtureModelEstimator_2D.cxx==
| | {{warning|1=The media wiki content on this page is no longer maintained.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.}} |
| <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>
| |
