ITK  4.13.0
Insight Segmentation and Registration Toolkit
WikiExamples/Statistics/ExpectationMaximizationMixtureModelEstimator_2D.cxx
#include "itkVector.h"
#include "itkListSample.h"
int main(int, char *[])
{
unsigned int numberOfClasses = 2;
typedef itk::Vector< double, 2 > MeasurementVectorType;
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;
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] );
}
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;
}