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

     */
    protected RealDistribution getKernel(SummaryStatistics bStats) {
        if (bStats.getN() == 1) {
            return new ConstantRealDistribution(bStats.getMean());
        } else {
            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

        // - 0.5 is a continuity correction
        final double z = (Wmin - ES - 0.5) / FastMath.sqrt(VarS);


        // No try-catch or advertised exception because args are valid
        // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
        final NormalDistribution standardNormal = new NormalDistribution(null, 0, 1);


        return 2*standardNormal.cumulativeProbability(z);
    }

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


        final double z = (Umin - EU) / FastMath.sqrt(VarU);


        // No try-catch or advertised exception because args are valid
        // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
        final NormalDistribution standardNormal = new NormalDistribution(null, 0, 1);


        return 2 * standardNormal.cumulativeProbability(z);
    }

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

                                            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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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

        }


        // Fill values array with random data from N(mu, sigma)
        // and fill valuesList with values from values array with
        // values[i] repeated weights[i] times, each i
        final RealDistribution valueDist = new NormalDistribution(mu, sigma);
        List<Double> valuesList = new ArrayList<Double>();
        for (int i = 0; i < len; i++) {
            double value = valueDist.sample();
            values[i] = value;
            for (int j = 0; j < intWeights[i]; j++) {
                valuesList.add(new Double(value));
            }
        }

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

        final ProcessModel pm = new DefaultProcessModel(A, B, Q, initialState, initialErrorCovariance);
        final MeasurementModel mm = new DefaultMeasurementModel(H, R);
        final KalmanFilter filter = new KalmanFilter(pm, mm);


        final RandomGenerator rng = new Well19937c(1000);
        final NormalDistribution dist = new NormalDistribution(rng, 0, measurementNoise);


        for (int i = 0; i < iterations; i++) {
            // get the "real" cannonball position
            double x = cannonball.getX();
            double y = cannonball.getY();
            
            // apply measurement noise to current cannonball position
            double nx = x + dist.sample();
            double ny = y + dist.sample();


            cannonball.step();
            
            filter.predict(controlVector);
            // correct the filter with our measurements

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

                                      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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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

    /**
     * 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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