Package org.apache.commons.math.linear

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

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     * <p>This implementation computes and caches the QR decomposition of the X matrix.</p>
     */
    @Override
    public void newSampleData(double[] data, int nobs, int nvars) {
        super.newSampleData(data, nobs, nvars);
        qr = new QRDecompositionImpl(X);
    }
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     * once it is successfully loaded.</p>
     */
    @Override
    protected void newXSampleData(double[][] x) {
        super.newXSampleData(x);
        qr = new QRDecompositionImpl(X);
    }
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                // solve the linearized least squares problem
                RealMatrix mA = new BlockRealMatrix(a);
                DecompositionSolver solver = useLU ?
                        new LUDecompositionImpl(mA).getSolver() :
                        new QRDecompositionImpl(mA).getSolver();
                final double[] dX = solver.solve(b);

                // update the estimated parameters
                for (int i = 0; i < cols; ++i) {
                    point[i] += dX[i];
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    }

    /** test rank */
    public void testRank() {
        DecompositionSolver solver =
            new QRDecompositionImpl(MatrixUtils.createRealMatrix(testData3x3NonSingular)).getSolver();
        assertTrue(solver.isNonSingular());

        solver = new QRDecompositionImpl(MatrixUtils.createRealMatrix(testData3x3Singular)).getSolver();
        assertFalse(solver.isNonSingular());

        solver = new QRDecompositionImpl(MatrixUtils.createRealMatrix(testData3x4)).getSolver();
        assertTrue(solver.isNonSingular());

        solver = new QRDecompositionImpl(MatrixUtils.createRealMatrix(testData4x3)).getSolver();
        assertTrue(solver.isNonSingular());

    }
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    }

    /** test solve dimension errors */
    public void testSolveDimensionErrors() {
        DecompositionSolver solver =
            new QRDecompositionImpl(MatrixUtils.createRealMatrix(testData3x3NonSingular)).getSolver();
        RealMatrix b = MatrixUtils.createRealMatrix(new double[2][2]);
        try {
            solver.solve(b);
            fail("an exception should have been thrown");
        } catch (IllegalArgumentException iae) {
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    }

    /** test solve rank errors */
    public void testSolveRankErrors() {
        DecompositionSolver solver =
            new QRDecompositionImpl(MatrixUtils.createRealMatrix(testData3x3Singular)).getSolver();
        RealMatrix b = MatrixUtils.createRealMatrix(new double[3][2]);
        try {
            solver.solve(b);
            fail("an exception should have been thrown");
        } catch (InvalidMatrixException iae) {
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    }

    /** test solve */
    public void testSolve() {
        QRDecomposition decomposition =
            new QRDecompositionImpl(MatrixUtils.createRealMatrix(testData3x3NonSingular));
        DecompositionSolver solver = decomposition.getSolver();
        RealMatrix b = MatrixUtils.createRealMatrix(new double[][] {
                { -102, 12250 }, { 544, 24500 }, { 167, -36750 }
        });
        RealMatrix xRef = MatrixUtils.createRealMatrix(new double[][] {
                { 1, 2515 }, { 2, 422 }, { -3, 898 }
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                return value * (1.0 + noise * (2 * r.nextDouble() - 1));
            }
        });

        // despite perturbation, the least square solution should be pretty good
        RealMatrix x = new QRDecompositionImpl(a).getSolver().solve(b);
        assertEquals(0, x.subtract(xRef).getNorm(), 0.01 * noise * p * q);

    }
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        int          p    = (5 * BlockRealMatrix.BLOCK_SIZE) / 4;
        int          q    = (7 * BlockRealMatrix.BLOCK_SIZE) / 4;
        RealMatrix   a    = createTestMatrix(r, p, q);
        RealMatrix   xRef = createTestMatrix(r, q, BlockRealMatrix.BLOCK_SIZE + 3);
        RealMatrix   b    = a.multiply(xRef);
        RealMatrix   x = new QRDecompositionImpl(a).getSolver().solve(b);

        // too many equations, the system cannot be solved at all
        assertTrue(x.subtract(xRef).getNorm() / (p * q) > 0.01);

        // the last unknown should have been set to 0
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                // solve the linearized least squares problem
                RealMatrix mA = new BlockRealMatrix(a);
                DecompositionSolver solver = useLU ?
                        new LUDecompositionImpl(mA).getSolver() :
                        new QRDecompositionImpl(mA).getSolver();
                final double[] dX = solver.solve(b);

                // update the estimated parameters
                for (int i = 0; i < cols; ++i) {
                    point[i] += dX[i];
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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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