Examples of PointVectorValuePair

org.apache.commons.math3.optim.PointVectorValuePair
This class holds a point and the vectorial value of an objective function at that point. @see PointValuePair @see org.apache.commons.math3.analysis.MultivariateVectorFunction @since 3.0
org.apache.commons.math3.optimization.PointVectorValuePair
This class holds a point and the vectorial value of an objective function at that point. @see PointValuePair @see org.apache.commons.math3.analysis.MultivariateVectorFunction @deprecated As of 3.1 (to be removed in 4.0). @since 3.0

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

        final double[] y = dataset.getData()[1];
        final double[] w = new double[y.length];
        Arrays.fill(w, 1.0);


        final int dof = y.length - a.length;
        final PointVectorValuePair optimum = optimizer.optimize(1, dataset.getLeastSquaresProblem(), y, w, a);
        final double[] sig = optimizer.computeSigma(optimum.getPoint(), 1e-14);
        final double[] expected = dataset.getParametersStandardDeviations();
        for (int i = 0; i < sig.length; i++) {
            final double actual = FastMath.sqrt(optimizer.getChiSquare() / dof) * sig[i];
            Assert.assertEquals(dataset.getName() + ", parameter #" + i,
                                expected[i], actual, 1e-7 * expected[i]);

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

            // Direct solution (using simple regression).
            final double[] regress = problem.solve();


            // Estimation of the standard deviation (diagonal elements of the
            // covariance matrix).
            final PointVectorValuePair optimum = optim.optimize(Integer.MAX_VALUE,
                           problem, problem.target(), problem.weight(), init);
            final double[] sigma = optim.computeSigma(optimum.getPoint(), 1e-14);


            // Accumulate statistics.
            for (int i = 0; i < numParams; i++) {
                paramsFoundByDirectSolution[i].addValue(regress[i]);
                sigmaEstimate[i].addValue(sigma[i]);

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

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

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

        }


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

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

          = new LevenbergMarquardtOptimizer(FastMath.sqrt(2.22044604926e-16),
                                            FastMath.sqrt(2.22044604926e-16),
                                            2.22044604926e-16);
//      Assert.assertTrue(function.checkTheoreticalStartCost(optimizer.getRMS()));
      try {
          PointVectorValuePair optimum =
              optimizer.optimize(400 * (function.getN() + 1), function,
                                 function.getTarget(), function.getWeight(),
                                 function.getStartPoint());
          Assert.assertFalse(exceptionExpected);
          function.checkTheoreticalMinCost(optimizer.getRMS());

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

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

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

                { 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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