Package org.apache.commons.math3.optim

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


    public void testInconsistentSizes1() {
        LinearProblem problem
            = new LinearProblem(new double[][] { { 1, 0 }, { 0, 1 } },
                                new double[] { -1, 1 });
        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum =
            optimizer.optimize(new MaxEval(100),
                               problem.getModelFunction(),
                               problem.getModelFunctionJacobian(),
                               problem.getTarget(),
                               new Weight(new double[] { 1, 1 }),
                               new InitialGuess(new double[] { 0, 0 }));
        Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
        Assert.assertEquals(-1, optimum.getPoint()[0], 1e-10);
        Assert.assertEquals(1, optimum.getPoint()[1], 1e-10);

        optimizer.optimize(new MaxEval(100),
                           problem.getModelFunction(),
                           problem.getModelFunctionJacobian(),
                           problem.getTarget(),
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    public void testInconsistentSizes2() {
        LinearProblem problem
            = new LinearProblem(new double[][] { { 1, 0 }, { 0, 1 } },
                                new double[] { -1, 1 });
        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum
            = optimizer.optimize(new MaxEval(100),
                                 problem.getModelFunction(),
                                 problem.getModelFunctionJacobian(),
                                 problem.getTarget(),
                                 new Weight(new double[] { 1, 1 }),
                                 new InitialGuess(new double[] { 0, 0 }));
        Assert.assertEquals(0, optimizer.getRMS(), 1e-10);
        Assert.assertEquals(-1, optimum.getPoint()[0], 1e-10);
        Assert.assertEquals(1, optimum.getPoint()[1], 1e-10);

        optimizer.optimize(new MaxEval(100),
                           problem.getModelFunction(),
                           problem.getModelFunctionJacobian(),
                           new Target(new double[] { 1 }),
View Full Code Here

        circle.addPoint( 50,  -6);
        circle.addPoint(110, -20);
        circle.addPoint( 3515);
        circle.addPoint( 4597);
        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum
            = optimizer.optimize(new MaxEval(100),
                                 circle.getModelFunction(),
                                 circle.getModelFunctionJacobian(),
                                 new Target(new double[] { 0, 0, 0, 0, 0 }),
                                 new Weight(new double[] { 1, 1, 1, 1, 1 }),
                                 new InitialGuess(new double[] { 98.680, 47.345 }));
        Assert.assertTrue(optimizer.getEvaluations() < 10);
        double rms = optimizer.getRMS();
        Assert.assertEquals(1.768262623567235,  FastMath.sqrt(circle.getN()) * rms,  1e-10);
        Vector2D center = new Vector2D(optimum.getPointRef()[0], optimum.getPointRef()[1]);
        Assert.assertEquals(69.96016176931406, circle.getRadius(center), 1e-6);
        Assert.assertEquals(96.07590211815305, center.getX(),            1e-6);
        Assert.assertEquals(48.13516790438953, center.getY(),            1e-6);
        double[][] cov = optimizer.computeCovariances(optimum.getPoint(), 1e-14);
        Assert.assertEquals(1.839, cov[0][0], 0.001);
        Assert.assertEquals(0.731, cov[0][1], 0.001);
        Assert.assertEquals(cov[0][1], cov[1][0], 1e-14);
        Assert.assertEquals(0.786, cov[1][1], 0.001);

        // add perfect measurements and check errors are reduced
        double  r = circle.getRadius(center);
        for (double d= 0; d < 2 * FastMath.PI; d += 0.01) {
            circle.addPoint(center.getX() + r * FastMath.cos(d), center.getY() + r * FastMath.sin(d));
        }
        double[] target = new double[circle.getN()];
        Arrays.fill(target, 0);
        double[] weights = new double[circle.getN()];
        Arrays.fill(weights, 2);
        optimum = optimizer.optimize(new MaxEval(100),
                                     circle.getModelFunction(),
                                     circle.getModelFunctionJacobian(),
                                     new Target(target),
                                     new Weight(weights),
                                     new InitialGuess(new double[] { 98.680, 47.345 }));
        cov = optimizer.computeCovariances(optimum.getPoint(), 1e-14);
        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);
        Assert.assertEquals(0.0016, cov[1][1], 0.001);
    }
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        Arrays.fill(weights, 2);
        for (int i = 0; i < points.length; ++i) {
            circle.addPoint(points[i][0], points[i][1]);
        }
        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum
            = optimizer.optimize(new MaxEval(100),
                                 circle.getModelFunction(),
                                 circle.getModelFunctionJacobian(),
                                 new Target(target),
                                 new Weight(weights),
                                 new InitialGuess(new double[] { -12, -12 }));
        Vector2D center = new Vector2D(optimum.getPointRef()[0], optimum.getPointRef()[1]);
        Assert.assertTrue(optimizer.getEvaluations() < 25);
        Assert.assertEquals( 0.043, optimizer.getRMS(), 1e-3);
        Assert.assertEquals( 0.292235,  circle.getRadius(center), 1e-6);
        Assert.assertEquals(-0.151738,  center.getX(),            1e-6);
        Assert.assertEquals( 0.2075001, center.getY(),            1e-6);
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        Arrays.fill(weights, 2);
        for (int i = 0; i < points.length; ++i) {
            circle.addPoint(points[i][0], points[i][1]);
        }
        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum =
            optimizer.optimize(new MaxEval(100),
                               circle.getModelFunction(),
                               circle.getModelFunctionJacobian(),
                               new Target(target),
                               new Weight(weights),
                               new InitialGuess(new double[] { 0, 0 }));
        Assert.assertEquals(-0.1517383071957963, optimum.getPointRef()[0], 1e-6);
        Assert.assertEquals(0.2074999736353867,  optimum.getPointRef()[1], 1e-6);
        Assert.assertEquals(0.04268731682389561, optimizer.getRMS(),       1e-8);
    }
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        Arrays.fill(w, 1);

        final double[][] data = dataset.getData();
        final double[] initial = dataset.getStartingPoint(0);
        final StatisticalReferenceDataset.LeastSquaresProblem problem = dataset.getLeastSquaresProblem();
        final PointVectorValuePair optimum
            = optimizer.optimize(new MaxEval(100),
                                 problem.getModelFunction(),
                                 problem.getModelFunctionJacobian(),
                                 new Target(data[1]),
                                 new Weight(w),
                                 new InitialGuess(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);
    }
View Full Code Here

                2, 13 },
                { -3, 0, -9 }
        }, new double[] { 1, 1, 1 });

        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum
            = optimizer.optimize(new MaxEval(100),
                                 problem.getModelFunction(),
                                 problem.getModelFunctionJacobian(),
                                 problem.getTarget(),
                                 new Weight(new double[] { 1, 1, 1 }),
                                 new InitialGuess(new double[] { 0, 0, 0 }));
        Assert.assertTrue(FastMath.sqrt(optimizer.getTargetSize()) * optimizer.getRMS() > 0.6);

        optimizer.computeCovariances(optimum.getPoint(), 1.5e-14);
    }
View Full Code Here

        }

        final LevenbergMarquardtOptimizer optimizer
            = new LevenbergMarquardtOptimizer();

        final PointVectorValuePair optimum
            = optimizer.optimize(new MaxEval(100),
                                 problem.getModelFunction(),
                                 problem.getModelFunctionJacobian(),
                                 new Target(dataPoints[1]),
                                 new Weight(weights),
                                 new InitialGuess(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(new MaxEval(100),
                                                                circle.getModelFunction(),
                                                                circle.getModelFunctionJacobian(),
                                                                new Target(circle.target()),
                                                                new Weight(circle.weight()),
                                                                new InitialGuess(init));

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

        // Retrieve errors estimation.
        final double[] asymptoticStandardErrorFound = optimizer.computeSigma(paramFound, 1e-14);

        // Check that the parameters are found within the assumed error bars.
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