Package org.apache.commons.math3.optimization

Examples of org.apache.commons.math3.optimization.PointVectorValuePair

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        Arrays.fill(weights, 2.0);
        for (int i = 0; i < points.length; ++i) {
            circle.addPoint(points[i][0], points[i][1]);
        }
        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum
            = optimizer.optimize(100, circle, target, weights, new double[] { -12, -12 });
        Vector2D center = new Vector2D(optimum.getPointRef()[0], optimum.getPointRef()[1]);
        Assert.assertTrue(optimizer.getEvaluations() < 25);
        Assert.assertTrue(optimizer.getJacobianEvaluations() < 20);
        Assert.assertEquals( 0.043, optimizer.getRMS(), 1.0e-3);
        Assert.assertEquals( 0.292235,  circle.getRadius(center), 1.0e-6);
        Assert.assertEquals(-0.151738,  center.getX(),            1.0e-6);
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        Arrays.fill(weights, 2.0);
        for (int i = 0; i < points.length; ++i) {
            circle.addPoint(points[i][0], points[i][1]);
        }
        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum =
            optimizer.optimize(100, circle, target, weights, new double[] { 0, 0 });
        Assert.assertEquals(-0.1517383071957963, optimum.getPointRef()[0], 1.0e-6);
        Assert.assertEquals(0.2074999736353867,  optimum.getPointRef()[1], 1.0e-6);
        Assert.assertEquals(0.04268731682389561, optimizer.getRMS(),       1.0e-8);
    }
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        final double[][] data = dataset.getData();
        final double[] initial = dataset.getStartingPoint(0);
        final MultivariateDifferentiableVectorFunction problem;
        problem = dataset.getLeastSquaresProblem();
        final PointVectorValuePair optimum;
        optimum = optimizer.optimize(100, problem, data[1], w, initial);

        final double[] actual = optimum.getPoint();
        for (int i = 0; i < actual.length; i++) {
            double expected = dataset.getParameter(i);
            double delta = FastMath.abs(errParams * expected);
            Assert.assertEquals(dataset.getName() + ", param #" + i,
                                expected, actual[i], delta);
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    @Override
    public PointVectorValuePair doOptimize() {
        final double[] params = getStartPoint();
        final double[] res = computeResiduals(computeObjectiveValue(params));
        setCost(computeCost(res));
        return new PointVectorValuePair(params, null);
    }
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                {  2, 1,  3 },
                { -3, 0, -9 }
        }, new double[] { 1, 1, 1 });

        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum = optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 0, 0, 0 });
        Assert.assertTrue(FastMath.sqrt(problem.target.length) * optimizer.getRMS() > 0.6);

        optimizer.computeCovariances(optimum.getPoint(), 1.5e-14);
    }
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        }

        final LevenbergMarquardtOptimizer optimizer
            = new LevenbergMarquardtOptimizer();

        final PointVectorValuePair optimum
            = optimizer.optimize(100, problem, dataPoints[1], weights,
                               new double[] { 10, 900, 80, 27, 225 });

        final double[] solution = optimum.getPoint();
        final double[] expectedSolution = { 10.4, 958.3, 131.4, 33.9, 205.0 };

        final double[][] covarMatrix = optimizer.computeCovariances(solution, 1e-14);
        final double[][] expectedCovarMatrix = {
            { 3.38, -3.69, 27.98, -2.34, -49.24 },
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        // First guess for the center's coordinates and radius.
        final double[] init = { 90, 659, 115 };

        final LevenbergMarquardtOptimizer optimizer
            = new LevenbergMarquardtOptimizer();
        final PointVectorValuePair optimum = optimizer.optimize(100, circle,
                                                                circle.target(), circle.weight(),
                                                                init);

        final double[] paramFound = optimum.getPoint();

        // Retrieve errors estimation.
        final double[][] covMatrix = optimizer.computeCovariances(paramFound, 1e-14);
        final double[] asymptoticStandardErrorFound = optimizer.guessParametersErrors();
        final double[] sigmaFound = new double[covMatrix.length];
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    @Test
    public void testTrivial() {
        LinearProblem problem =
            new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum =
            optimizer.optimize(100, problem, problem.target, new double[] { 1 }, new double[] { 0 });
        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
        Assert.assertEquals(1.5, optimum.getPoint()[0], 1.0e-10);
        Assert.assertEquals(3.0, optimum.getValue()[0], 1.0e-10);
        try {
            optimizer.guessParametersErrors();
            Assert.fail("an exception should have been thrown");
        } catch (NumberIsTooSmallException ee) {
            // expected behavior
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        LinearProblem problem =
            new LinearProblem(new double[][] { { 1.0, -1.0 }, { 0.0, 2.0 }, { 1.0, -2.0 } },
                              new double[] { 4.0, 6.0, 1.0 });

        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum =
            optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 0, 0 });
        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
        Assert.assertEquals(7.0, optimum.getPoint()[0], 1.0e-10);
        Assert.assertEquals(3.0, optimum.getPoint()[1], 1.0e-10);
        Assert.assertEquals(4.0, optimum.getValue()[0], 1.0e-10);
        Assert.assertEquals(6.0, optimum.getValue()[1], 1.0e-10);
        Assert.assertEquals(1.0, optimum.getValue()[2], 1.0e-10);
    }
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                { 0, 0, 0, 2, 0, 0 },
                { 0, 0, 0, 0, 2, 0 },
                { 0, 0, 0, 0, 0, 2 }
        }, new double[] { 0.0, 1.1, 2.2, 3.3, 4.4, 5.5 });
        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum =
            optimizer.optimize(100, problem, problem.target, new double[] { 1, 1, 1, 1, 1, 1 },
                               new double[] { 0, 0, 0, 0, 0, 0 });
        Assert.assertEquals(0, optimizer.getRMS(), 1.0e-10);
        for (int i = 0; i < problem.target.length; ++i) {
            Assert.assertEquals(0.55 * i, optimum.getPoint()[i], 1.0e-10);
        }
    }
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Related Classes of org.apache.commons.math3.optimization.PointVectorValuePair

  • 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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