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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: itkSymmetricEigenAnalysisTest.cxx
Language: C++
Date: $Date$
Version: $Revision$
Copyright (c) Insight Software Consortium. All rights reserved.
See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details.
This software is distributed WITHOUT ANY WARRANTY; without even
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
#if defined(_MSC_VER)
#pragma warning ( disable : 4786 )
#endif
#include <iostream>
#include "itkSymmetricEigenAnalysis.h"
#include "vnl/vnl_matrix.h"
#include "itkFixedArray.h"
#include "itkMatrix.h"
#include "itkSymmetricSecondRankTensor.h"
int itkSymmetricEigenAnalysisTest(int, char* [] )
{
// Test SymmetricEigenAnalysis class with symmetric matrices
{
// Test using vnl_matrix
std::cout << "Testing ComputeEigenValuesAndVectors() "
<< "with SymmetricEigenAnalysis< vnl_matrix, itk::FixedArray, itk::Matrix >"
<< std::endl;
typedef vnl_matrix< double > InputMatrixType;
typedef itk::FixedArray< double, 6 > EigenValuesArrayType;
typedef itk::Matrix< double, 6, 6 > EigenVectorMatrixType;
typedef itk::SymmetricEigenAnalysis< InputMatrixType,
EigenValuesArrayType, EigenVectorMatrixType > SymmetricEigenAnalysisType;
double Sdata[36] = {
30.0000, -3.4273, 13.9254, 13.7049, -2.4446, 20.2380,
-3.4273, 13.7049, -2.4446, 1.3659, 3.6702, -0.2282,
13.9254, -2.4446, 20.2380, 3.6702, -0.2282, 28.6779,
13.7049, 1.3659, 3.6702, 12.5273, -1.6045, 3.9419,
-2.4446, 3.6702, -0.2282, -1.6045, 3.9419, 2.5821,
20.2380, -0.2282, 28.6779, 3.9419, 2.5821, 44.0636,
};
InputMatrixType S(Sdata, 6,6);
EigenValuesArrayType eigenvalues;
EigenVectorMatrixType eigenvectors;
SymmetricEigenAnalysisType symmetricEigenSystem(6);
symmetricEigenSystem.ComputeEigenValuesAndVectors(S, eigenvalues, eigenvectors );
std::cout << "EigenValues: " << eigenvalues << std::endl;
std::cout << "EigenVectors (each row is an an eigen vector): " << std::endl;
std::cout << eigenvectors << std::endl;
double eigvec3[6] = { 0.5236407, -0.0013422, -0.4199706, -0.5942299, 0.4381326, 0.0659837 };
double eigvals[6]= {0.170864, 2.16934, 3.79272, 15.435, 24.6083, 78.2994};
double tolerance = 0.01;
for( unsigned int i=0; i<6; i++ )
{
if (vnl_math_abs( eigvals[i] - eigenvalues[i] ) > tolerance)
{
std::cout << "Eigen value computation failed" << std::endl;
return EXIT_FAILURE;
}
if (vnl_math_abs( eigvec3[i] - eigenvectors[2][i] ) > tolerance)
{
std::cout << "Eigen vector computation failed" << std::endl;
return EXIT_FAILURE;
}
}
}
{
// Test using itk Matrix
std::cout << "Testing ComputeEigenValuesAndVectors() "
<< "with SymmetricEigenAnalysis< itk::Matrix, itk::FixedArray, itk::Matrix >"
<< std::endl;
typedef itk::Matrix< double, 6, 6 > InputMatrixType;
typedef itk::FixedArray< double, 6 > EigenValuesArrayType;
typedef itk::Matrix< double, 6, 6 > EigenVectorMatrixType;
typedef itk::SymmetricEigenAnalysis< InputMatrixType,
EigenValuesArrayType, EigenVectorMatrixType > SymmetricEigenAnalysisType;
double Sdata[36] = {
30.0000, -3.4273, 13.9254, 13.7049, -2.4446, 20.2380,
-3.4273, 13.7049, -2.4446, 1.3659, 3.6702, -0.2282,
13.9254, -2.4446, 20.2380, 3.6702, -0.2282, 28.6779,
13.7049, 1.3659, 3.6702, 12.5273, -1.6045, 3.9419,
-2.4446, 3.6702, -0.2282, -1.6045, 3.9419, 2.5821,
20.2380, -0.2282, 28.6779, 3.9419, 2.5821, 44.0636,
};
InputMatrixType S;
for(unsigned int row=0; row<6; row++)
{
for(unsigned int col=0; col<6; col++)
{
S[row][col] = Sdata[ row * 6 + col ];
}
}
EigenValuesArrayType eigenvalues;
EigenVectorMatrixType eigenvectors;
SymmetricEigenAnalysisType symmetricEigenSystem(6);
symmetricEigenSystem.ComputeEigenValuesAndVectors(S, eigenvalues, eigenvectors );
std::cout << "EigenValues: " << eigenvalues << std::endl;
std::cout << "EigenVectors (each row is an an eigen vector): " << std::endl;
std::cout << eigenvectors << std::endl;
double eigvec3[6] = { 0.5236407, -0.0013422, -0.4199706, -0.5942299, 0.4381326, 0.0659837 };
double eigvals[6]= {0.170864, 2.16934, 3.79272, 15.435, 24.6083, 78.2994};
double tolerance = 0.01;
for( unsigned int i=0; i<6; i++ )
{
if (vnl_math_abs( eigvals[i] - eigenvalues[i] ) > tolerance)
{
std::cout << "Eigen value computation failed" << std::endl;
return EXIT_FAILURE;
}
if (vnl_math_abs( eigvec3[i] - eigenvectors[2][i] ) > tolerance)
{
std::cout << "Eigen vector computation failed" << std::endl;
return EXIT_FAILURE;
}
}
}
{
// Test using itk SymmetricSecondRankTensor
std::cout << "Testing ComputeEigenValuesAndVectors() "
<< "with SymmetricEigenAnalysis< itk::SymmetricSecondRankTensor, itk::FixedArray, itk::Matrix >"
<< std::endl;
typedef itk::SymmetricSecondRankTensor< double, 6 > InputMatrixType;
typedef itk::FixedArray< double, 6 > EigenValuesArrayType;
typedef itk::Matrix< double, 6, 6 > EigenVectorMatrixType;
typedef itk::SymmetricEigenAnalysis< InputMatrixType,
EigenValuesArrayType, EigenVectorMatrixType > SymmetricEigenAnalysisType;
double Sdata[36] = {
30.0000, -3.4273, 13.9254, 13.7049, -2.4446, 20.2380,
-3.4273, 13.7049, -2.4446, 1.3659, 3.6702, -0.2282,
13.9254, -2.4446, 20.2380, 3.6702, -0.2282, 28.6779,
13.7049, 1.3659, 3.6702, 12.5273, -1.6045, 3.9419,
-2.4446, 3.6702, -0.2282, -1.6045, 3.9419, 2.5821,
20.2380, -0.2282, 28.6779, 3.9419, 2.5821, 44.0636,
};
InputMatrixType S;
for(unsigned int row=0; row<6; row++)
{
for(unsigned int col=0; col<6; col++)
{
S(row,col) = Sdata[ row * 6 + col ];
}
}
EigenValuesArrayType eigenvalues;
EigenVectorMatrixType eigenvectors;
SymmetricEigenAnalysisType symmetricEigenSystem(6);
symmetricEigenSystem.ComputeEigenValuesAndVectors(S, eigenvalues, eigenvectors );
std::cout << "EigenValues: " << eigenvalues << std::endl;
std::cout << "EigenVectors (each row is an an eigen vector): " << std::endl;
std::cout << eigenvectors << std::endl;
double eigvec3[6] = { 0.5236407, -0.0013422, -0.4199706, -0.5942299, 0.4381326, 0.0659837 };
double eigvals[6]= {0.170864, 2.16934, 3.79272, 15.435, 24.6083, 78.2994};
double tolerance = 0.01;
for( unsigned int i=0; i<6; i++ )
{
if (vnl_math_abs( eigvals[i] - eigenvalues[i] ) > tolerance)
{
std::cout << "Eigen value computation failed" << std::endl;
return EXIT_FAILURE;
}
if (vnl_math_abs( eigvec3[i] - eigenvectors[2][i] ) > tolerance)
{
std::cout << "Eigen vector computation failed" << std::endl;
return EXIT_FAILURE;
}
}
}
std::cout << "[TEST PASSED]" << std::endl;
return EXIT_SUCCESS;
}
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