Examples of org.apache.commons.math.linear.EigenDecompositionImpl

org.apache.commons.math.linear.EigenDecompositionImpl
Calculates the eigen decomposition of a real symmetric matrix.
The eigen decomposition of matrix A is a set of two matrices: V and D such that A = V D V^T. A, V and D are all m × m matrices.

As of 2.0, this class supports only symmetric matrices, and hence computes only real realEigenvalues. This implies the D matrix returned by {@link #getD()} is always diagonal and the imaginary values returned{@link #getImagEigenvalue(int)} and {@link #getImagEigenvalues()} are alwaysnull.

When called with a {@link RealMatrix} argument, this implementation only usesthe upper part of the matrix, the part below the diagonal is not accessed at all.

This implementation is based on the paper by A. Drubrulle, R.S. Martin and J.H. Wilkinson 'The Implicit QL Algorithm' in Wilksinson and Reinsch (1971) Handbook for automatic computation, vol. 2, Linear algebra, Springer-Verlag, New-York
@version $Revision: 1002040 $ $Date: 2010-09-28 09:18:31 +0200 (mar. 28 sept. 2010) $ @since 2.0

    }


    public void testDimension1() {
        RealMatrix matrix =
            MatrixUtils.createRealMatrix(new double[][] { { 1.5 } });
        EigenDecomposition ed = new EigenDecompositionImpl(matrix, MathUtils.SAFE_MIN);
        assertEquals(1.5, ed.getRealEigenvalue(0), 1.0e-15);
    }

View Full Code Here

        RealMatrix matrix =
            MatrixUtils.createRealMatrix(new double[][] {
                    { 59.0, 12.0 },
                    { 12.0, 66.0 }
            });
        EigenDecomposition ed = new EigenDecompositionImpl(matrix, MathUtils.SAFE_MIN);
        assertEquals(75.0, ed.getRealEigenvalue(0), 1.0e-15);
        assertEquals(50.0, ed.getRealEigenvalue(1), 1.0e-15);
    }

View Full Code Here

            MatrixUtils.createRealMatrix(new double[][] {
                                   {  39632.0, -4824.0, -16560.0 },
                                   {  -4824.0,  8693.0,   7920.0 },
                                   { -16560.0,  7920.0,  17300.0 }
                               });
        EigenDecomposition ed = new EigenDecompositionImpl(matrix, MathUtils.SAFE_MIN);
        assertEquals(50000.0, ed.getRealEigenvalue(0), 3.0e-11);
        assertEquals(12500.0, ed.getRealEigenvalue(1), 3.0e-11);
        assertEquals( 3125.0, ed.getRealEigenvalue(2), 3.0e-11);
    }

View Full Code Here

                                   {  0.784, -0.288,  0.000,  0.000 },
                                   { -0.288,  0.616,  0.000,  0.000 },
                                   {  0.000,  0.000,  0.164, -0.048 },
                                   {  0.000,  0.000, -0.048,  0.136 }
                               });
        EigenDecomposition ed = new EigenDecompositionImpl(matrix, MathUtils.SAFE_MIN);
        assertEquals(1.0, ed.getRealEigenvalue(0), 1.0e-15);
        assertEquals(0.4, ed.getRealEigenvalue(1), 1.0e-15);
        assertEquals(0.2, ed.getRealEigenvalue(2), 1.0e-15);
        assertEquals(0.1, ed.getRealEigenvalue(3), 1.0e-15);
    }

View Full Code Here

                                   {  0.5608, -0.2016,  0.1152, -0.2976 },
                                   { -0.2016,  0.4432, -0.2304,  0.1152 },
                                   {  0.1152, -0.2304,  0.3088, -0.1344 },
                                   { -0.2976,  0.1152, -0.1344,  0.3872 }
                               });
        EigenDecomposition ed = new EigenDecompositionImpl(matrix, MathUtils.SAFE_MIN);
        assertEquals(1.0, ed.getRealEigenvalue(0), 1.0e-15);
        assertEquals(0.4, ed.getRealEigenvalue(1), 1.0e-15);
        assertEquals(0.2, ed.getRealEigenvalue(2), 1.0e-15);
        assertEquals(0.1, ed.getRealEigenvalue(3), 1.0e-15);
    }

View Full Code Here

        }
        Arrays.sort(ref);
        TriDiagonalTransformer t =
            new TriDiagonalTransformer(createTestMatrix(r, ref));
        EigenDecomposition ed =
            new EigenDecompositionImpl(t.getMainDiagonalRef(),
                                       t.getSecondaryDiagonalRef(),
                                       MathUtils.SAFE_MIN);
        double[] eigenValues = ed.getRealEigenvalues();
        assertEquals(ref.length, eigenValues.length);
        for (int i = 0; i < ref.length; ++i) {
            assertEquals(ref[ref.length - i - 1], eigenValues[i], 2.0e-14);
        }

View Full Code Here

    }


    /** test dimensions */
    public void testDimensions() {
        final int m = matrix.getRowDimension();
        EigenDecomposition ed = new EigenDecompositionImpl(matrix, MathUtils.SAFE_MIN);
        assertEquals(m, ed.getV().getRowDimension());
        assertEquals(m, ed.getV().getColumnDimension());
        assertEquals(m, ed.getD().getColumnDimension());
        assertEquals(m, ed.getD().getColumnDimension());
        assertEquals(m, ed.getVT().getRowDimension());
        assertEquals(m, ed.getVT().getColumnDimension());
    }

View Full Code Here

        assertEquals(m, ed.getVT().getColumnDimension());
    }


    /** test eigenvalues */
    public void testEigenvalues() {
        EigenDecomposition ed = new EigenDecompositionImpl(matrix, MathUtils.SAFE_MIN);
        double[] eigenValues = ed.getRealEigenvalues();
        assertEquals(refValues.length, eigenValues.length);
        for (int i = 0; i < refValues.length; ++i) {
            assertEquals(refValues[i], eigenValues[i], 3.0e-15);
        }
    }

View Full Code Here

        for (int i = 0; i < bigValues.length; ++i) {
            bigValues[i] = 2 * r.nextDouble() - 1;
        }
        Arrays.sort(bigValues);
        EigenDecomposition ed =
            new EigenDecompositionImpl(createTestMatrix(r, bigValues), MathUtils.SAFE_MIN);
        double[] eigenValues = ed.getRealEigenvalues();
        assertEquals(bigValues.length, eigenValues.length);
        for (int i = 0; i < bigValues.length; ++i) {
            assertEquals(bigValues[bigValues.length - i - 1], eigenValues[i], 2.0e-14);
        }
    }

View Full Code Here

        }
    }


    /** test eigenvectors */
    public void testEigenvectors() {
        EigenDecomposition ed = new EigenDecompositionImpl(matrix, MathUtils.SAFE_MIN);
        for (int i = 0; i < matrix.getRowDimension(); ++i) {
            double lambda = ed.getRealEigenvalue(i);
            RealVector v  = ed.getEigenvector(i);
            RealVector mV = matrix.operate(v);
            assertEquals(0, mV.subtract(v.mapMultiplyToSelf(lambda)).getNorm(), 1.0e-13);
        }
    }

View Full Code Here

0 1 2 3

TOP

Related Classes of org.apache.commons.math.linear.EigenDecompositionImpl

com.opengamma.analytics.math.matrix.CommonsMatrixAlgebra

org.apache.commons.math.linear.EigenDecompositionImplTest

org.apache.commons.math.linear.EigenSolverTest

org.apache.mahout.math.ssvd.EigenSolverWrapper

org.apache.commons.math.MaxIterationsExceededException

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.