ITK  5.4.0 Insight Toolkit
Examples/Statistics/GaussianMembershipFunction.cxx
/*=========================================================================
*
*
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
*
* Unless required by applicable law or agreed to in writing, software
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
*
*=========================================================================*/
// 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 =
auto 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;
}
itkGaussianMembershipFunction.h
itk::Vector
A templated class holding a n-Dimensional vector.
Definition: itkVector.h:62
itk::Statistics::GaussianMembershipFunction
GaussianMembershipFunction models class membership through a multivariate Gaussian function.
Definition: itkGaussianMembershipFunction.h:56
itkVector.h
New
static Pointer New()