Package org.apache.commons.math.linear

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

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     * Computes and caches QR decomposition of the X matrix
     */
    @Override
    public void newSampleData(double[] data, int nobs, int nvars) {
        super.newSampleData(data, nobs, nvars);
        qr = new QRDecompositionImpl(X);
    }
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     * @param x the [n,k] array representing the x sample
     */
    @Override
    protected void newXSampleData(double[][] x) {
        this.X = new Array2DRowRealMatrix(x);
        qr = new QRDecompositionImpl(X);
    }
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    }

    private void checkDimension(RealMatrix m) {
        int rows = m.getRowDimension();
        int columns = m.getColumnDimension();
        QRDecomposition qr = new QRDecompositionImpl(m);
        assertEquals(rows,    qr.getQ().getRowDimension());
        assertEquals(rows,    qr.getQ().getColumnDimension());
        assertEquals(rows,    qr.getR().getRowDimension());
        assertEquals(columns, qr.getR().getColumnDimension());       
    }
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        checkAEqualQR(createTestMatrix(r, q, p));

    }

    private void checkAEqualQR(RealMatrix m) {
        QRDecomposition qr = new QRDecompositionImpl(m);
        double norm = qr.getQ().multiply(qr.getR()).subtract(m).getNorm();
        assertEquals(0, norm, normTolerance);
    }
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        checkQOrthogonal(createTestMatrix(r, q, p));

    }

    private void checkQOrthogonal(RealMatrix m) {
        QRDecomposition qr = new QRDecompositionImpl(m);
        RealMatrix eye = MatrixUtils.createRealIdentityMatrix(m.getRowDimension());
        double norm = qr.getQT().multiply(qr.getQ()).subtract(eye).getNorm();
        assertEquals(0, norm, normTolerance);
    }
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    }

    /** test that R is upper triangular */
    public void testRUpperTriangular() {
        RealMatrix matrix = MatrixUtils.createRealMatrix(testData3x3NonSingular);
        checkUpperTriangular(new QRDecompositionImpl(matrix).getR());

        matrix = MatrixUtils.createRealMatrix(testData3x3Singular);
        checkUpperTriangular(new QRDecompositionImpl(matrix).getR());

        matrix = MatrixUtils.createRealMatrix(testData3x4);
        checkUpperTriangular(new QRDecompositionImpl(matrix).getR());

        matrix = MatrixUtils.createRealMatrix(testData4x3);
        checkUpperTriangular(new QRDecompositionImpl(matrix).getR());

        Random r = new Random(643895747384642l);
        int    p = (5 * BlockRealMatrix.BLOCK_SIZE) / 4;
        int    q = (7 * BlockRealMatrix.BLOCK_SIZE) / 4;
        matrix = createTestMatrix(r, p, q);
        checkUpperTriangular(new QRDecompositionImpl(matrix).getR());

        matrix = createTestMatrix(r, p, q);
        checkUpperTriangular(new QRDecompositionImpl(matrix).getR());

    }
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    }

    /** test that H is trapezoidal */
    public void testHTrapezoidal() {
        RealMatrix matrix = MatrixUtils.createRealMatrix(testData3x3NonSingular);
        checkTrapezoidal(new QRDecompositionImpl(matrix).getH());

        matrix = MatrixUtils.createRealMatrix(testData3x3Singular);
        checkTrapezoidal(new QRDecompositionImpl(matrix).getH());

        matrix = MatrixUtils.createRealMatrix(testData3x4);
        checkTrapezoidal(new QRDecompositionImpl(matrix).getH());

        matrix = MatrixUtils.createRealMatrix(testData4x3);
        checkTrapezoidal(new QRDecompositionImpl(matrix).getH());

        Random r = new Random(643895747384642l);
        int    p = (5 * BlockRealMatrix.BLOCK_SIZE) / 4;
        int    q = (7 * BlockRealMatrix.BLOCK_SIZE) / 4;
        matrix = createTestMatrix(r, p, q);
        checkTrapezoidal(new QRDecompositionImpl(matrix).getH());

        matrix = createTestMatrix(r, p, q);
        checkTrapezoidal(new QRDecompositionImpl(matrix).getH());

    }
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        });
    }
    /** test matrices values */
    public void testMatricesValues() {
        QRDecomposition qr =
            new QRDecompositionImpl(MatrixUtils.createRealMatrix(testData3x3NonSingular));
        RealMatrix qRef = MatrixUtils.createRealMatrix(new double[][] {
                { -12.0 / 14.0,   69.0 / 175.0,  -58.0 / 175.0 },
                {  -6.0 / 14.0, -158.0 / 175.0,    6.0 / 175.0 },
                {   4.0 / 14.0,  -30.0 / 175.0, -165.0 / 175.0 }
        });
        RealMatrix rRef = MatrixUtils.createRealMatrix(new double[][] {
                { -14.0,  -21.0, 14.0 },
                {   0.0, -175.0, 70.0 },
                {   0.0,    0.0, 35.0 }
        });
        RealMatrix hRef = MatrixUtils.createRealMatrix(new double[][] {
                { 26.0 / 14.0, 0.0, 0.0 },
                {  6.0 / 14.0, 648.0 / 325.0, 0.0 },
                { -4.0 / 14.0,  36.0 / 325.0, 2.0 }
        });

        // check values against known references
        RealMatrix q = qr.getQ();
        assertEquals(0, q.subtract(qRef).getNorm(), 1.0e-13);
        RealMatrix qT = qr.getQT();
        assertEquals(0, qT.subtract(qRef.transpose()).getNorm(), 1.0e-13);
        RealMatrix r = qr.getR();
        assertEquals(0, r.subtract(rRef).getNorm(), 1.0e-13);
        RealMatrix h = qr.getH();
        assertEquals(0, h.subtract(hRef).getNorm(), 1.0e-13);

        // check the same cached instance is returned the second time
        assertTrue(q == qr.getQ());
        assertTrue(r == qr.getR());
        assertTrue(h == qr.getH());
       
    }
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     * Computes and caches QR decomposition of the X matrix
     */
    @Override
    public void newSampleData(double[] data, int nobs, int nvars) {
        super.newSampleData(data, nobs, nvars);
        qr = new QRDecompositionImpl(X);
    }
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     * @param x the [n,k] array representing the x sample
     */
    @Override
    protected void newXSampleData(double[][] x) {
        this.X = new Array2DRowRealMatrix(x);
        qr = new QRDecompositionImpl(X);
    }
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Related Classes of org.apache.commons.math.linear.QRDecompositionImpl

  • com.opengamma.analytics.math.linearalgebra.QRDecompositionCommons
  • org.apache.commons.math.linear.QRDecompositionImplTest
  • org.apache.commons.math.linear.QRSolverTest
  • org.apache.commons.math.optimization.general.GaussNewtonOptimizer
  • org.apache.commons.math.stat.regression.OLSMultipleLinearRegression
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