Examples of org.apache.commons.math3.optim.nonlinear.vector.jacobian.GaussNewtonOptimizer

Package org.apache.commons.math3.optim.nonlinear.vector.jacobian

Examples of org.apache.commons.math3.optim.nonlinear.vector.jacobian.GaussNewtonOptimizer

org.apache.commons.math3.util.Incrementor
Gauss-Newton least-squares solver.
Constraints are not supported: the call to {@link #optimize(OptimizationData[]) optimize} will throw{@link MathUnsupportedOperationException} if bounds are passed to it.
This class solve a least-square problem by solving the normal equations of the linearized problem at each iteration. Either LU decomposition or QR decomposition can be used to solve the normal equations. LU decomposition is faster but QR decomposition is more robust for difficult problems.
@since 2.0 @deprecated All classes and interfaces in this package are deprecated.The optimizers that were provided here were moved to the {@link org.apache.commons.math3.fitting.leastsquares} package(cf. MATH-1008).

        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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     * {@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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     * @param upper Upper bound (inclusive) of this distribution.
     * @throws NumberIsTooLargeException if {@code lower >= upper}.
     */
    public UniformIntegerDistribution(int lower, int upper)
        throws NumberIsTooLargeException {
        this(new Well19937c(), lower, upper);
    }

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     * @throws NumberIsTooLargeException if {@code a >= b} or if {@code c > b}.
     * @throws NumberIsTooSmallException if {@code c < a}.
     */
    public TriangularDistribution(double a, double c, double b)
        throws NumberIsTooLargeException, NumberIsTooSmallException {
        this(new Well19937c(), a, c, b);
    }

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        for (int i = 0; i < k; ++i) {
            sumImpl[i]     = new Sum();
            sumSqImpl[i]   = new SumOfSquares();
            minImpl[i]     = new Min();
            maxImpl[i]     = new Max();
            sumLogImpl[i]  = new SumOfLogs();
            geoMeanImpl[i] = new GeometricMean();
            meanImpl[i]    = new Mean();
        }


        covarianceImpl =

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        geoMeanImpl = new StorelessUnivariateStatistic[k];
        meanImpl    = new StorelessUnivariateStatistic[k];


        for (int i = 0; i < k; ++i) {
            sumImpl[i]     = new Sum();
            sumSqImpl[i]   = new SumOfSquares();
            minImpl[i]     = new Min();
            maxImpl[i]     = new Max();
            sumLogImpl[i]  = new SumOfLogs();
            geoMeanImpl[i] = new GeometricMean();
            meanImpl[i]    = new Mean();

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     * @param checker Convergence checker.
     */
    protected BaseOptimizer(ConvergenceChecker<PAIR> checker) {
        this.checker = checker;


        evaluations = new Incrementor(0, new MaxEvalCallback());
        iterations = new Incrementor(0, new MaxIterCallback());
    }

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