Package org.apache.commons.math3.optimization.general

Examples of org.apache.commons.math3.optimization.general.GaussNewtonOptimizer

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        // version 3.1 of the library. It should be removed when GaussNewtonOptimizer
        // will officialy be declared as implementing MultivariateDifferentiableVectorOptimizer
        MultivariateDifferentiableVectorOptimizer underlyingOptimizer =
                new MultivariateDifferentiableVectorOptimizer() {
            private GaussNewtonOptimizer gn =
                    new GaussNewtonOptimizer(true,
                                             new SimpleVectorValueChecker(1.0e-6, 1.0e-6));

            public PointVectorValuePair optimize(int maxEval,
                                                 MultivariateDifferentiableVectorFunction f,
                                                 double[] target,
                                                 double[] weight,
                                                 double[] startPoint) {
                return gn.optimize(maxEval, f, target, weight, startPoint);
            }

            public int getMaxEvaluations() {
                return gn.getMaxEvaluations();
            }

            public int getEvaluations() {
                return gn.getEvaluations();
            }

            public ConvergenceChecker<PointVectorValuePair> getConvergenceChecker() {
                return gn.getConvergenceChecker();
            }
        };
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(16069223052l);
        RandomVectorGenerator generator =
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        // version 3.1 of the library. It should be removed when GaussNewtonOptimizer
        // will officialy be declared as implementing MultivariateDifferentiableVectorOptimizer
        MultivariateDifferentiableVectorOptimizer underlyingOptimizer =
                new MultivariateDifferentiableVectorOptimizer() {
            private GaussNewtonOptimizer gn =
                    new GaussNewtonOptimizer(true,
                                             new SimpleVectorValueChecker(1.0e-6, 1.0e-6));

            public PointVectorValuePair optimize(int maxEval,
                                                 MultivariateDifferentiableVectorFunction f,
                                                 double[] target,
                                                 double[] weight,
                                                 double[] startPoint) {
                return gn.optimize(maxEval, f, target, weight, startPoint);
            }

            public int getMaxEvaluations() {
                return gn.getMaxEvaluations();
            }

            public int getEvaluations() {
                return gn.getEvaluations();
            }

            public ConvergenceChecker<PointVectorValuePair> getConvergenceChecker() {
                return gn.getConvergenceChecker();
            }
        };
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(12373523445l);
        RandomVectorGenerator generator =
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        final SimpleVectorValueChecker checker = new SimpleVectorValueChecker(tol, tol);
        final double[] init = new double[] { 0, 0 };
        final int maxEval = 3;

        final double[] lm = doMath798(new LevenbergMarquardtOptimizer(checker), maxEval, init);
        final double[] gn = doMath798(new GaussNewtonOptimizer(checker), maxEval, init);

        for (int i = 0; i <= 1; i++) {
            Assert.assertEquals(lm[i], gn[i], tol);
        }
    }
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        final double tol = 1e-100;
        final SimpleVectorValueChecker checker = new SimpleVectorValueChecker(tol, tol);
        final double[] init = new double[] { 0, 0 };
        final int maxEval = 10000; // Trying hard to fit.

        final double[] gn = doMath798(new GaussNewtonOptimizer(checker), maxEval, init);
    }
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        final double[] init = new double[] { 0, 0 };
        final int maxEval = 10000; // Trying hard to fit.
        final SimpleVectorValueChecker checker = new SimpleVectorValueChecker(tol, tol, maxEval);

        final double[] lm = doMath798(new LevenbergMarquardtOptimizer(checker), maxEval, init);
        final double[] gn = doMath798(new GaussNewtonOptimizer(checker), maxEval, init);

        for (int i = 0; i <= 1; i++) {
            Assert.assertEquals(lm[i], gn[i], 1e-15);
        }
    }
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    }

    @Test
    public void testRedundantUnsolvable() {
        // Gauss-Newton should not be able to solve redundant information
        checkUnsolvableProblem(new GaussNewtonOptimizer(true, new SimpleVectorValueChecker(1e-15, 1e-15)), false);
    }
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    @Test
    public void testTrivial() {
        LinearProblem problem =
            new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
        DifferentiableMultivariateVectorOptimizer underlyingOptimizer =
            new GaussNewtonOptimizer(true,
                                     new SimpleVectorValueChecker(1.0e-6, 1.0e-6));
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(16069223052l);
        RandomVectorGenerator generator =
            new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
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    }

    @Test(expected=TestException.class)
    public void testNoOptimum() {
        DifferentiableMultivariateVectorOptimizer underlyingOptimizer =
            new GaussNewtonOptimizer(true,
                                     new SimpleVectorValueChecker(1.0e-6, 1.0e-6));
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(12373523445l);
        RandomVectorGenerator generator =
            new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
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    @Test
    public void testRedundantUnsolvable() {
        // Gauss-Newton should not be able to solve redundant information
        DifferentiableMultivariateVectorOptimizer optimizer =
            new GaussNewtonOptimizer(true);
        checkUnsolvableProblem(optimizer, false);
    }
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     * {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY}).
     * @throws NotStrictlyPositiveException if {@code mean <= 0}.
     * @since 2.1
     */
    public ExponentialDistribution(double mean, double inverseCumAccuracy) {
        this(new Well19937c(), mean, inverseCumAccuracy);
    }
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Related Classes of org.apache.commons.math3.optimization.general.GaussNewtonOptimizer

  • 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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