Examples of org.ejml.factory.EigenDecomposition

org.ejml.factory.EigenDecomposition

This is a generic interface for computing the eigenvalues and eigenvectors of a matrix. Eigenvalues and eigenvectors have the following property:

A*v=λ*v

where A is a square matrix and v is an eigenvector associated with the eigenvalue λ.

In general, both eigenvalues and eigenvectors can be complex numbers. For symmetric matrices the eigenvalues and eigenvectors are always real numbers. EJML does not support complex matrices but it does have minimal support for complex numbers. As a result complex eigenvalues are found, but only the real eigenvectors are computed.

To create a new instance of {@link EigenDecomposition} use {@link DecompositionFactory}. If the matrix is known to be symmetric be sure to use the symmetric decomposition, which is much faster and more accurate than the general purpose one.
@author Peter Abeles

     * eigen decompositions tests.
     */
    public void checkRandom() {
        int sizes[] = new int[]{1,2,5,10,20,50,100,200};


        EigenDecomposition alg = createDecomposition();


        for( int s = 2; s < sizes.length; s++ ) {
            int N = sizes[s];
//            System.out.println("N = "+N);

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     * are real.  Octave was used to test the known values.
     */
    public void checkKnownReal() {
        DenseMatrix64F A = new DenseMatrix64F(3,3, true, 0.907265, 0.832472, 0.255310, 0.667810, 0.871323, 0.612657, 0.025059, 0.126475, 0.427002);


        EigenDecomposition alg = createDecomposition();


        assertTrue(safeDecomposition(alg,A));
        performStandardTests(alg,A,-1);


        testForEigenpair(alg,1.686542,0,-0.739990,-0.667630,-0.081761);

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     * Sees if it correctly computed the eigenvalues.  Does not check eigenvectors.
     */
    public void checkKnownReal_JustValue() {
        DenseMatrix64F A = new DenseMatrix64F(3,3, true, 0.907265, 0.832472, 0.255310, 0.667810, 0.871323, 0.612657, 0.025059, 0.126475, 0.427002);


        EigenDecomposition alg = createDecomposition();


        assertTrue(safeDecomposition(alg,A));


        testForEigenvalue(alg,A,1.686542,0,1);
        testForEigenvalue(alg,A,0.079014,0,1);

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    public void checkKnownSymmetric_JustValue() {
        DenseMatrix64F A = new DenseMatrix64F(3,3, true,
                0.98139,   0.78650,   0.78564,
                0.78650,   1.03207,   0.29794,
                0.78564,   0.29794,   0.91926);
        EigenDecomposition alg = createDecomposition();


        assertTrue(safeDecomposition(alg,A));


        testForEigenvalue(alg,A,0.00426,0,1);
        testForEigenvalue(alg,A,0.67856,0,1);

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     * are real and some are complex.
     */
    public void checkKnownComplex() {
        DenseMatrix64F A = new DenseMatrix64F(3,3, true, -0.418284, 0.279875, 0.452912, -0.093748, -0.045179, 0.310949, 0.250513, -0.304077, -0.031414);


        EigenDecomposition alg = createDecomposition();


        assertTrue(safeDecomposition(alg,A));
        performStandardTests(alg,A,-1);


        testForEigenpair(alg,-0.39996,0,0.87010,0.43425,-0.23314);

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    public void checkRandomSymmetric() {
        for( int N = 1; N <= 15; N++ ) {
            for( int i = 0; i < 20; i++ ) {
                DenseMatrix64F A = RandomMatrices.createSymmetric(N,-1,1,rand);


                EigenDecomposition alg = createDecomposition();


                assertTrue(safeDecomposition(alg,A));


                performStandardTests(alg,A,N);
            }

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     * check for.  If it fails that check it will either loop forever or exit before converging.
     */
    public void checkExceptional() {
        DenseMatrix64F A = new DenseMatrix64F(5,5, true, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0);


        EigenDecomposition alg = createDecomposition();


        assertTrue(safeDecomposition(alg,A));


        performStandardTests(alg,A,1);
    }

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    }


    public void checkIdentity() {
        DenseMatrix64F I = CommonOps.identity(4);


        EigenDecomposition alg = createDecomposition();


        assertTrue(safeDecomposition(alg,I));


        performStandardTests(alg,I,4);

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    }


    public void checkAllZeros() {
        DenseMatrix64F A = new DenseMatrix64F(5,5);


        EigenDecomposition alg = createDecomposition();


        assertTrue(safeDecomposition(alg,A));


        performStandardTests(alg,A,5);
        testEigenvalues(alg,0);

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        performStandardTests(alg,A,5);
        testEigenvalues(alg,0);
    }


    public void checkWithSomeRepeatedValuesSymm() {
        EigenDecomposition alg = createDecomposition();


        checkSymmetricMatrix(alg,2,-3,-3,-3);
        checkSymmetricMatrix(alg,2,-3,2,2);
        checkSymmetricMatrix(alg,1,1,1,2);
    }

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Related Classes of org.ejml.factory.EigenDecomposition

org.ejml.alg.dense.decomposition.eig.GeneralEigenDecompositionCheck

org.ejml.alg.dense.decomposition.eig.symm.SymmetricEigenStressTest

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