Package org.apache.commons.math3.distribution

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

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        BivariateGridInterpolator interpolator = new BicubicSplineInterpolator();
        BivariateFunction p = interpolator.interpolate(xval, yval, zval);
        double x, y;

        final RandomGenerator rng = new Well19937c(1234567L); // "tol" depends on the seed.
        final UniformRealDistribution distX
            = new UniformRealDistribution(rng, xval[0], xval[xval.length - 1]);
        final UniformRealDistribution distY
            = new UniformRealDistribution(rng, yval[0], yval[yval.length - 1]);

        final int numSamples = 50;
        final double tol = 251;
        for (int i = 0; i < numSamples; i++) {
            x = distX.sample();
            for (int j = 0; j < numSamples; j++) {
                y = distY.sample();
//                 System.out.println(x + " " + y + " " + f.value(x, y) + " " + p.value(x, y));
                Assert.assertEquals(f.value(x, y),  p.value(x, y), tol);
            }
//             System.out.println();
        }
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    /** Uniform distribution, uniform data */
    // @Test - takes about 6 seconds, uncomment for
    public void testOneSampleUniformUniform() {
        final KolmogorovSmirnovTest test = new KolmogorovSmirnovTest();
        final UniformRealDistribution unif = new UniformRealDistribution(-0.5, 0.5);
        Assert.assertEquals(8.881784197001252E-16, test.kolmogorovSmirnovTest(unif, uniform, false), TOLERANCE);
        Assert.assertTrue(test.kolmogorovSmirnovTest(unif, uniform, 0.05));
        Assert.assertEquals(0.5400666982352942, test.kolmogorovSmirnovStatistic(unif, uniform), TOLERANCE);
    }
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    /** Uniform distribution, uniform data, small dataset */
    @Test
    public void testOneSampleUniformUniformSmallSample() {
        final KolmogorovSmirnovTest test = new KolmogorovSmirnovTest();
        final UniformRealDistribution unif = new UniformRealDistribution(-0.5, 0.5);
        final double[] shortUniform = new double[20];
        System.arraycopy(uniform, 0, shortUniform, 0, 20);
        Assert.assertEquals(4.117594598618268E-9, test.kolmogorovSmirnovTest(unif, shortUniform, false), TOLERANCE);
        Assert.assertTrue(test.kolmogorovSmirnovTest(unif, shortUniform, 0.05));
        Assert.assertEquals(0.6610459, test.kolmogorovSmirnovStatistic(unif, shortUniform), TOLERANCE);
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    /** Uniform distribution, unit normal dataset */
    @Test
    public void testOneSampleUniformGaussian() {
        final KolmogorovSmirnovTest test = new KolmogorovSmirnovTest();
        final UniformRealDistribution unif = new UniformRealDistribution(-0.5, 0.5);
        // Value was obtained via exact test, validated against R. Running exact test takes a long
        // time.
        Assert.assertEquals(4.9405812774239166E-11, test.kolmogorovSmirnovTest(unif, gaussian, false), TOLERANCE);
        Assert.assertTrue(test.kolmogorovSmirnovTest(unif, gaussian, 0.05));
        Assert.assertEquals(0.3401058049019608, test.kolmogorovSmirnovStatistic(unif, gaussian), TOLERANCE);
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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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* Test for class {@link PolynomialCurveFitter}.
*/
public class PolynomialCurveFitterTest {
    @Test
    public void testFit() {
        final RealDistribution rng = new UniformRealDistribution(-100, 100);
        rng.reseedRandomGenerator(64925784252L);

        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.
        final WeightedObservedPoints obs = new WeightedObservedPoints();
        for (int i = 0; i < 100; i++) {
            final double x = rng.sample();
            obs.add(x, f.value(x));
        }

        // Start fit from initial guesses that are far from the optimal values.
        final PolynomialCurveFitter fitter
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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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        this.radius = radius;
        cX = new NormalDistribution(rng, x, xSigma,
                                    NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
        cY = new NormalDistribution(rng, y, ySigma,
                                    NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
        tP = new UniformRealDistribution(rng, 0, MathUtils.TWO_PI,
                                         UniformRealDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
    }
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        final RandomGenerator rng = new Well44497b(seed);
        slope = a;
        intercept = b;
        error = new NormalDistribution(rng, 0, sigma,
                                       NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
        x = new UniformRealDistribution(rng, lo, hi,
                                        UniformRealDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
    }
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Related Classes of org.apache.commons.math3.distribution.UniformRealDistribution

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  • 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.interpolation.BicubicSplineInterpolatingFunctionTest
  • org.apache.commons.math3.analysis.interpolation.BicubicSplineInterpolatorTest
  • org.apache.commons.math3.analysis.interpolation.PiecewiseBicubicSplineInterpolatorTest
  • org.apache.commons.math3.analysis.MultivariateFunction
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