Examples of NormalDistribution

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

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

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

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

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

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

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

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        final double EU = (double) n1n2prod / 2.0;
        final double VarU = (double) (n1n2prod * (n1 + n2 + 1)) / 12.0;

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

        final NormalDistribution standardNormal = new NormalDistribution(0, 1);

        return 2 * standardNormal.cumulativeProbability(z);
    }
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Examples of org.apache.commons.math3.distribution.NormalDistribution

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        final double VarS = ES * ((double) (2 * N + 1) / 6.0);

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

        final NormalDistribution standardNormal = new NormalDistribution(0, 1);

        return 2*standardNormal.cumulativeProbability(z);
    }
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Examples of org.apache.commons.math3.distribution.NormalDistribution

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        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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