ITK  5.0.0 Insight Segmentation and Registration Toolkit
Examples/Statistics/GaussianMembershipFunction.cxx
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// Software Guide : BeginLatex
//
// \index{Statistics!Gaussian (normal) probability density function}
//
// \index{itk::Statistics::GaussianMembershipFunction}
//
// The Gaussian probability density function
// \subdoxygen{Statistics}{GaussianMembershipFunction} requires two
// distribution parameters---the mean vector and the covariance matrix.
//
// We include the header files for the class and the \doxygen{Vector}.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
#include "itkVector.h"
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// We define the type of the measurement vector that will be input to
// the Gaussian membership function.
//
// Software Guide : EndLatex
int main(int, char*[])
{
// Software Guide : BeginCodeSnippet
using MeasurementVectorType = itk::Vector< float, 2 >;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The instantiation of the function is done through the usual
// \code{New()} method and a smart pointer.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
using DensityFunctionType =
DensityFunctionType::Pointer densityFunction = DensityFunctionType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The length of the measurement vectors in the membership function, in this
// case a vector of length 2, is specified using the
// \code{SetMeasurementVectorSize()} method.
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
densityFunction->SetMeasurementVectorSize( 2 );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// We create the two distribution parameters and set them. The mean is
// [0, 0], and the covariance matrix is a 2 x 2 matrix:
// $// \begin{pmatrix} // 4 & 0 \cr // 0 & 4 // \end{pmatrix} //$
// We obtain the probability density for the measurement vector: [0, 0]
// using the \code{Evaluate(measurement vector)} method and print it out.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
DensityFunctionType::MeanVectorType mean( 2 );
mean.Fill( 0.0 );
DensityFunctionType::CovarianceMatrixType cov;
cov.SetSize( 2, 2 );
cov.SetIdentity();
cov *= 4;
densityFunction->SetMean( mean );
densityFunction->SetCovariance( cov );
MeasurementVectorType mv;
mv.Fill( 0 );
std::cout << densityFunction->Evaluate( mv ) << std::endl;
// Software Guide : EndCodeSnippet
return EXIT_SUCCESS;
}