Package org.apache.commons.math3.distribution

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

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                                      double xSigma,
                                      double ySigma,
                                      long seed) {
        final RandomGenerator rng = new Well44497b(seed);
        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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        Assert.assertEquals(FastMath.sqrt(5714.932), TestUtils.rootLogLikelihoodRatio(1000, 1000, 1000, 99000), 0.001);
    }
   
    @Test
    public void testKSOneSample() throws Exception {
       final NormalDistribution unitNormal = new NormalDistribution(0d, 1d);
       final double[] sample = KolmogorovSmirnovTestTest.gaussian;
       final double tol = KolmogorovSmirnovTestTest.TOLERANCE;
       Assert.assertEquals(0.3172069207622391, TestUtils.kolmogorovSmirnovTest(unitNormal, sample), tol);
       Assert.assertEquals(0.0932947561266756, TestUtils.kolmogorovSmirnovStatistic(unitNormal, sample), tol);
    }
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    /**
     * Test Various Dist
     */
    @Test
    public void testDistribution() {
        doDistributionTest(new NormalDistribution(randomGenerator, 4000, 50));
        doDistributionTest(new LogNormalDistribution(randomGenerator,4000, 50));
        // doDistributionTest((new ExponentialDistribution(4000));
        // doDistributionTest(new GammaDistribution(5d,1d),0.1);
    }
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                                      double xSigma,
                                      double ySigma,
                                      long seed) {
        final RandomGenerator rng = new Well44497b(seed);
        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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     * The first bin includes its lower bound, 0, so has different mean and
     * standard deviation.
     */
    private RealDistribution findKernel(double lower, double upper) {
        if (lower < 1) {
            return new NormalDistribution(5d, 3.3166247903554);
        } else {
            return new NormalDistribution((upper + lower + 1) / 2d, 3.0276503540974917);
        }
    }
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    @Test
    public void testStoredVsDirect() {
        final RandomGenerator rand= new JDKRandomGenerator();
        rand.setSeed(Long.MAX_VALUE);
        for (final int sampleSize:sampleSizes) {
            final double[] data = new NormalDistribution(rand,4000, 50)
                                .sample(sampleSize);
            for (final double p:new double[] {50d,95d}) {
                for (final Percentile.EstimationType e : Percentile.EstimationType.values()) {
                    reset(p, e);
                    final Percentile pStoredData = getUnivariateStatistic();
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                                            double hi,
                                            long seed) {
        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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    /** Unit normal distribution, unit normal data */
    @Test
    public void testOneSampleGaussianGaussian() {
        final KolmogorovSmirnovTest test = new KolmogorovSmirnovTest();
        final NormalDistribution unitNormal = new NormalDistribution(0d, 1d);
        // Uncomment to run exact test - takes about a minute. Same value is used in R tests and for
        // approx.
        // Assert.assertEquals(0.3172069207622391, test.kolmogorovSmirnovTest(unitNormal, gaussian,
        // true), TOLERANCE);
        Assert.assertEquals(0.3172069207622391, test.kolmogorovSmirnovTest(unitNormal, gaussian, false), TOLERANCE);
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    /** Unit normal distribution, unit normal data, small dataset */
    @Test
    public void testOneSampleGaussianGaussianSmallSample() {
        final KolmogorovSmirnovTest test = new KolmogorovSmirnovTest();
        final NormalDistribution unitNormal = new NormalDistribution(0d, 1d);
        final double[] shortGaussian = new double[50];
        System.arraycopy(gaussian, 0, shortGaussian, 0, 50);
        Assert.assertEquals(0.683736463728347, test.kolmogorovSmirnovTest(unitNormal, shortGaussian, false), TOLERANCE);
        Assert.assertFalse(test.kolmogorovSmirnovTest(unitNormal, gaussian, 0.05));
        Assert.assertEquals(0.09820779969463278, test.kolmogorovSmirnovStatistic(unitNormal, shortGaussian), TOLERANCE);
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    /** Unit normal distribution, uniform data */
    @Test
    public void testOneSampleGaussianUniform() {
        final KolmogorovSmirnovTest test = new KolmogorovSmirnovTest();
        final NormalDistribution unitNormal = new NormalDistribution(0d, 1d);
        // Uncomment to run exact test - takes a long time. Same value is used in R tests and for
        // approx.
        // Assert.assertEquals(0.3172069207622391, test.kolmogorovSmirnovTest(unitNormal, uniform,
        // true), TOLERANCE);
        Assert.assertEquals(8.881784197001252E-16, test.kolmogorovSmirnovTest(unitNormal, uniform, false), TOLERANCE);
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