Examples of NormalDistribution

com.enterprisemath.math.probability.NormalDistribution
Normal 1 dimensional probability distribution. @author radek.hecl
com.mockturtlesolutions.snifflib.stats.NormalDistribution
com.opengamma.analytics.math.statistics.distribution.NormalDistribution
bl.gov/software/colt/api/cern/jet/random/Normal.html">Colt implementation of the normal distribution.
com.tinkerpop.furnace.generators.NormalDistribution
Generates values according to a normal distribution with the configured standard deviation. @author Matthias Broecheler (me@matthiasb.com)
de.lmu.ifi.dbs.elki.data.synthetic.bymodel.distribution.NormalDistribution
Simple generator for a Gaussian = Normal Distribution @author Erich Schubert
de.lmu.ifi.dbs.elki.math.statistics.distribution.NormalDistribution
Gaussian distribution aka normal distribution @author Erich Schubert
fork.lib.math.algebra.elementary.function.v1.distr.NormalDistribution
@author muxin gu
org.apache.commons.math3.distribution.NormalDistribution
pedia.org/wiki/Normal_distribution">Normal distribution (Wikipedia) @see Normal distribution (MathWorld)
org.jamesii.core.math.random.distributions.NormalDistribution
NormalDistribution - This class helps to generate normal distributed random numbers. @author Roland Ewald
org.jquantlib.math.distributions.NormalDistribution
pedia.org/wiki/Probability_density_function">Normal Distribution @author Richard Gomes

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

     * @param bStats summary statistics for the bin
     * @return within-bin kernel parameterized by bStats
     */
    protected RealDistribution getKernel(SummaryStatistics bStats) {
        // Default to Gaussian
        return new NormalDistribution(randomData.getRandomGenerator(),
                bStats.getMean(), bStats.getStandardDeviation(),
                NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
    }

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


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


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


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

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

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

    @Test
    @Ignore
    public void testNormalDistributionUnsymmetricMatrix() {
        for (int run = 0; run < 100; run++) {
            Random r = new Random(System.currentTimeMillis());
            NormalDistribution dist = new NormalDistribution(0.0, r.nextDouble() * 5);


            // matrix size
            int size = r.nextInt(20) + 4;


            double[][] data = new double[size][size];
            for (int i = 0; i < size; i++) {
                for (int j = 0; j < size; j++) {
                    data[i][j] = dist.sample();
                }
            }


            RealMatrix m = MatrixUtils.createRealMatrix(data);
            checkUnsymmetricMatrix(m);

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

            randomData.nextGaussian(0, 0);
            Assert.fail("zero sigma -- MathIllegalArgumentException expected");
        } catch (MathIllegalArgumentException ex) {
            // ignored
        }
        double[] quartiles = TestUtils.getDistributionQuartiles(new NormalDistribution(0,1));
        long[] counts = new long[4];
        randomData.reSeed(1000);
        for (int i = 0; i < 1000; i++) {
            double value = randomData.nextGaussian(0, 1);
            TestUtils.updateCounts(value, counts, quartiles);

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


            JComponent comp = null;


            comp = createComponent("Normal", -5, 5,
                                   new String[] { "μ=0,σ\u00B2=0.2", "μ=0,σ\u00B2=1", "μ=0,σ\u00B2=5", "μ=-2,σ\u00B2=0.5" },
                                   new NormalDistribution(0, FastMath.sqrt(0.2)),
                                   new NormalDistribution(),
                                   new NormalDistribution(0, FastMath.sqrt(5)),
                                   new NormalDistribution(-2, FastMath.sqrt(0.5)));
            container.add(comp, c);


            c.gridx++;
            comp = createComponent("Beta", 0, 1,
                                   new String[] { "α=β=0.5", "α=5,β=1", "α=1,β=3", "α=2,β=2", "α=2,β=5" },

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

    public static List<Vector2D> makeCircles(int samples, boolean shuffle, double noise, double factor, final RandomGenerator random) {
        if (factor < 0 || factor > 1) {
            throw new IllegalArgumentException();
        }
        
        NormalDistribution dist = new NormalDistribution(random, 0.0, noise, 1e-9);


        List<Vector2D> points = new ArrayList<Vector2D>();
        double range = 2.0 * FastMath.PI;
        double step = range / (samples / 2.0 + 1);
        for (double angle = 0; angle < range; angle += step) {

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