Package org.apache.commons.math3.distribution

Examples of org.apache.commons.math3.distribution.UniformRealDistribution

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     * if {@code min >= max}.
     */
    public static FeatureInitializer uniform(final RandomGenerator rng,
                                             final double min,
                                             final double max) {
        return randomize(new UniformRealDistribution(rng, min, max),
                         function(new Constant(0), 0, 0));
    }
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     * @throws org.apache.commons.math3.exception.NumberIsTooLargeException
     * if {@code min >= max}.
     */
    public static FeatureInitializer uniform(final double min,
                                             final double max) {
        return randomize(new UniformRealDistribution(min, max),
                         function(new Constant(0), 0, 0));
    }
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        }

        @Override
        public Distribution get()
        {
            return new DistributionBoundApache(new UniformRealDistribution(min, max + 1), min, max);
        }
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* polynomial.
*/
public class PolynomialFitterTest {
    @Test
    public void testFit() {
        final RealDistribution rng = new UniformRealDistribution(-100, 100);
        rng.reseedRandomGenerator(64925784252L);

        final LevenbergMarquardtOptimizer optim = new LevenbergMarquardtOptimizer();
        final PolynomialFitter fitter = new PolynomialFitter(optim);
        final double[] coeff = { 12.9, -3.4, 2.1 }; // 12.9 - 3.4 x + 2.1 x^2
        final PolynomialFunction f = new PolynomialFunction(coeff);

        // Collect data from a known polynomial.
        for (int i = 0; i < 100; i++) {
            final double x = rng.sample();
            fitter.addObservedPoint(x, f.value(x));
        }

        // Start fit from initial guesses that are far from the optimal values.
        final double[] best = fitter.fit(new double[] { -1e-20, 3e15, -5e25 });
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    private class UniformKernelEmpiricalDistribution extends EmpiricalDistribution {
        public UniformKernelEmpiricalDistribution(int i) {
            super(i);
        }
        protected RealDistribution getKernel(SummaryStatistics bStats) {
            return new UniformRealDistribution(randomData.getRandomGenerator(), bStats.getMin(), bStats.getMax(),
                    UniformRealDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
        }
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        double[] permuted = new double[10];
        RandomDataImpl random = new RandomDataImpl();

        // Generate 10 distinct random values
        for (int i = 0; i < 10; i++) {
            final RealDistribution u = new UniformRealDistribution(i + 0.5, i + 0.75);
            original[i] = u.sample();
        }

        // Generate a random permutation, making sure it is not the identity
        boolean isIdentity = true;
        do {
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        return new DataGenHexFromDistribution(new DistributionBoundApache(new NormalDistribution(mean, stdev), minKey, maxKey));
    }

    public static DataGenHex buildUniform(long minKey, long maxKey)
    {
        return new DataGenHexFromDistribution(new DistributionBoundApache(new UniformRealDistribution(minKey, maxKey), minKey, maxKey));
    }
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        }

        @Override
        public Distribution get()
        {
            return new DistributionBoundApache(new UniformRealDistribution(min, max), min, max);
        }
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        }

        @Override
        public Distribution get()
        {
            return new DistributionBoundApache(new UniformRealDistribution(new JDKRandomGenerator(), min, max + 1), min, max);
        }
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* polynomial.
*/
public class PolynomialFitterTest {
    @Test
    public void testFit() {
        final RealDistribution rng = new UniformRealDistribution(-100, 100);
        rng.reseedRandomGenerator(64925784252L);

        final LevenbergMarquardtOptimizer optim = new LevenbergMarquardtOptimizer();
        final PolynomialFitter fitter = new PolynomialFitter(optim);
        final double[] coeff = { 12.9, -3.4, 2.1 }; // 12.9 - 3.4 x + 2.1 x^2
        final PolynomialFunction f = new PolynomialFunction(coeff);

        // Collect data from a known polynomial.
        for (int i = 0; i < 100; i++) {
            final double x = rng.sample();
            fitter.addObservedPoint(x, f.value(x));
        }

        // Start fit from initial guesses that are far from the optimal values.
        final double[] best = fitter.fit(new double[] { -1e-20, 3e15, -5e25 });
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