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/*=========================================================================
*
* Copyright Insight Software Consortium
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0.txt
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
*=========================================================================*/
// Software Guide : BeginLatex
//
// \index{itk::Statistics::MeanCalculator}
// \index{itk::Statistics::CovarianceSampleFilter}
// \index{Statistics!Mean}
// \index{Statistics!Covariance}
//
// We include the header file for the \doxygen{Vector} class that will
// be our measurement vector template in this example.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
#include "itkVector.h"
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// We will use the \subdoxygen{Statistics}{ListSample} as our sample
// template. We include the header for the class too.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
#include "itkListSample.h"
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The following headers are for sample statistics algorithms.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
#include "itkMeanSampleFilter.h"
#include "itkCovarianceSampleFilter.h"
// Software Guide : EndCodeSnippet
int main()
{
// Software Guide : BeginLatex
//
// The following code snippet will create a ListSample object
// with three-component float measurement vectors and put five
// measurement vectors in the ListSample object.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
const unsigned int MeasurementVectorLength = 3;
typedef itk::Vector< float, MeasurementVectorLength > MeasurementVectorType;
typedef itk::Statistics::ListSample< MeasurementVectorType > SampleType;
SampleType::Pointer sample = SampleType::New();
sample->SetMeasurementVectorSize( MeasurementVectorLength );
MeasurementVectorType mv;
mv[0] = 1.0;
mv[1] = 2.0;
mv[2] = 4.0;
sample->PushBack( mv );
mv[0] = 2.0;
mv[1] = 4.0;
mv[2] = 5.0;
sample->PushBack( mv );
mv[0] = 3.0;
mv[1] = 8.0;
mv[2] = 6.0;
sample->PushBack( mv );
mv[0] = 2.0;
mv[1] = 7.0;
mv[2] = 4.0;
sample->PushBack( mv );
mv[0] = 3.0;
mv[1] = 2.0;
mv[2] = 7.0;
sample->PushBack( mv );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// To calculate the mean (vector) of a sample, we instantiate the
// \subdoxygen{Statistics}{MeanSampleFilter} class that implements the mean
// algorithm and plug in the sample using the
// \code{SetInputSample(sample*)} method. By calling the \code{Update()}
// method, we run the algorithm. We get the mean vector using the
// \code{GetMean()} method. The output from the \code{GetOutput()} method
// is the pointer to the mean vector.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef itk::Statistics::MeanSampleFilter< SampleType > MeanAlgorithmType;
MeanAlgorithmType::Pointer meanAlgorithm = MeanAlgorithmType::New();
meanAlgorithm->SetInput( sample );
meanAlgorithm->Update();
std::cout << "Sample mean = " << meanAlgorithm->GetMean() << std::endl;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The covariance calculation algorithm will also calculate the mean while
// performing the covariance matrix calculation. The mean can be accessed
// using the \code{GetMean()} method while the covariance can be accessed
// using the \code{GetCovarianceMatrix()} method.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef itk::Statistics::CovarianceSampleFilter< SampleType >
CovarianceAlgorithmType;
CovarianceAlgorithmType::Pointer covarianceAlgorithm =
CovarianceAlgorithmType::New();
covarianceAlgorithm->SetInput( sample );
covarianceAlgorithm->Update();
std::cout << "Mean = " << std::endl;
std::cout << covarianceAlgorithm->GetMean() << std::endl;
std::cout << "Covariance = " << std::endl;
std::cout << covarianceAlgorithm->GetCovarianceMatrix() << std::endl;
// Software Guide : EndCodeSnippet
return EXIT_SUCCESS;
}
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