Package org.apache.commons.math3.linear

Examples of org.apache.commons.math3.linear.DiagonalMatrix

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     * @return the square-root of the weight matrix.
     */
    private RealMatrix squareRoot(RealMatrix m) {
        if (m instanceof DiagonalMatrix) {
            final int dim = m.getRowDimension();
            final RealMatrix sqrtM = new DiagonalMatrix(dim);
            for (int i = 0; i < dim; i++) {
               sqrtM.setEntry(i, i, FastMath.sqrt(m.getEntry(i, i)));
            }
            return sqrtM;
        } else {
            final EigenDecomposition dec = new EigenDecomposition(m);
            return dec.getSquareRoot();
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        LeastSquaresProblem problem = new LeastSquaresBuilder()
                .maxEvaluations(400 * (function.getN() + 1))
                .maxIterations(2000)
                .model(function.getModelFunction(), function.getModelFunctionJacobian())
                .target(function.getTarget())
                .weight(new DiagonalMatrix(function.getWeight()))
                .start(function.getStartPoint())
                .build();

        try {
            final Optimum optimum = optimizer.optimize(problem);
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    LeastSquaresBuilder builder(StraightLineProblem problem){
        return new LeastSquaresBuilder()
                .model(problem.getModelFunction(), problem.getModelFunctionJacobian())
                .target(problem.target())
                .weight(new DiagonalMatrix(problem.weight()))
                //unused start point to avoid NPE
                .start(new double[2]);
    }
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        final double[] weights = new double[c.getN()];
        Arrays.fill(weights, 1.0);
        return base()
                .model(c.getModelFunction(), c.getModelFunctionJacobian())
                .target(new double[c.getN()])
                .weight(new DiagonalMatrix(weights));
    }
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        final double[] weights = new double[dataset.getNumObservations()];
        Arrays.fill(weights, 1.0);
        return base()
                .model(problem.getModelFunction(), problem.getModelFunctionJacobian())
                .target(dataset.getData()[1])
                .weight(new DiagonalMatrix(weights))
                .start(dataset.getStartingPoint(0));
    }
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    @Test
    public void testGetIterations() {
        LeastSquaresProblem lsp = base()
                .target(new double[]{1})
                .weight(new DiagonalMatrix(new double[]{1}))
                .start(new double[]{3})
                .model(new MultivariateJacobianFunction() {
                    public Pair<RealVector, RealMatrix> value(final RealVector point) {
                        return new Pair<RealVector, RealMatrix>(
                                new ArrayRealVector(
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            Assert.assertEquals(0, optimum.getRMS(), TOl);
            assertEquals(TOl, optimum.getPoint(), -1, 1);

            //TODO move to builder test
            optimizer.optimize(
                    problem.getBuilder().weight(new DiagonalMatrix(new double[]{1})).build());

            fail(optimizer);
        } catch (DimensionMismatchException e) {
            //expected
        }
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            //TODO move to builder test
            optimizer.optimize(
                    problem.getBuilder()
                            .target(new double[]{1})
                            .weight(new DiagonalMatrix(new double[]{1}))
                            .build()
            );

            fail(optimizer);
        } catch (DimensionMismatchException e) {
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        double[] weights = new double[circle.getN()];
        Arrays.fill(weights, 2);

        optimum = optimizer.optimize(
                builder(circle).weight(new DiagonalMatrix(weights)).start(start).build());

        cov = optimum.getCovariances(1e-14).getData();
        Assert.assertEquals(0.0016, cov[0][0], 0.001);
        Assert.assertEquals(3.2e-7, cov[0][1], 1e-9);
        Assert.assertEquals(cov[0][1], cov[1][0], 1e-14);
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        Arrays.fill(weights, 2);
        for (int i = 0; i < points.length; ++i) {
            circle.addPoint(points[i][0], points[i][1]);
        }

        Optimum optimum = optimizer.optimize(builder(circle).weight(new DiagonalMatrix(weights)).start(start).build());

        Vector2D center = new Vector2D(optimum.getPoint().getEntry(0), optimum.getPoint().getEntry(1));
        Assert.assertTrue(optimum.getEvaluations() < 25);
        Assert.assertEquals(0.043, optimum.getRMS(), 1e-3);
        Assert.assertEquals(0.292235, circle.getRadius(center), 1e-6);
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Related Classes of org.apache.commons.math3.linear.DiagonalMatrix

  • org.apache.commons.math3.analysis.differentiation.DerivativeStructure
  • org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction
  • org.apache.commons.math3.analysis.function.HarmonicOscillator
  • org.apache.commons.math3.analysis.function.Sin
  • org.apache.commons.math3.analysis.function.Sinc
  • org.apache.commons.math3.analysis.MultivariateFunction
  • org.apache.commons.math3.analysis.QuinticFunction
  • org.apache.commons.math3.analysis.solvers.BrentSolver
  • org.apache.commons.math3.analysis.UnivariateFunction
  • org.apache.commons.math3.complex.Complex
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