Examples of NormalDistribution

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

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        return points;
    }

    public static List<Vector2D> makeMoons(int samples, boolean shuffle, double noise, RandomGenerator random) {
        NormalDistribution dist = new NormalDistribution(random, 0.0, noise, 1e-9);

        int nSamplesOut = samples / 2;
        int nSamplesIn = samples - nSamplesOut;
       
        List<Vector2D> points = new ArrayList<Vector2D>();
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Examples of org.apache.commons.math3.distribution.NormalDistribution

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    }

    public static List<Vector2D> makeBlobs(int samples, int centers, double clusterStd,
                                           double min, double max, boolean shuffle, RandomGenerator random) {

        NormalDistribution dist = new NormalDistribution(random, 0.0, clusterStd, 1e-9);

        double range = max - min;
        Vector2D[] centerPoints = new Vector2D[centers];
        for (int i = 0; i < centers; i++) {
            double x = random.nextDouble() * range + min;
            double y = random.nextDouble() * range + min;
            centerPoints[i] = new Vector2D(x, y);
        }
       
        int[] nSamplesPerCenter = new int[centers];
        int count = samples / centers;
        Arrays.fill(nSamplesPerCenter, count);
       
        for (int i = 0; i < samples % centers; i++) {
            nSamplesPerCenter[i]++;
        }
       
        List<Vector2D> points = new ArrayList<Vector2D>();
        for (int i = 0; i < centers; i++) {
            for (int j = 0; j < nSamplesPerCenter[i]; j++) {
                Vector2D point = new Vector2D(dist.sample(), dist.sample());
                points.add(point.add(centerPoints[i]));
            }
        }
       
        if (shuffle) {
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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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                                            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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    public ConfidenceInterval createInterval(int numberOfTrials, int numberOfSuccesses,
                                             double confidenceLevel) {
        IntervalUtils.checkParameters(numberOfTrials, numberOfSuccesses, confidenceLevel);
        final double mean = (double) numberOfSuccesses / (double) numberOfTrials;
        final double alpha = (1.0 - confidenceLevel) / 2;
        final NormalDistribution normalDistribution = new NormalDistribution();
        final double difference = normalDistribution.inverseCumulativeProbability(1 - alpha) *
                                  FastMath.sqrt(1.0 / numberOfTrials * mean * (1 - mean));
        return new ConfidenceInterval(mean - difference, mean + difference, confidenceLevel);
    }
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Examples of org.apache.commons.math3.distribution.NormalDistribution

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    /** {@inheritDoc} */
    public ConfidenceInterval createInterval(int numberOfTrials, int numberOfSuccesses, double confidenceLevel) {
        IntervalUtils.checkParameters(numberOfTrials, numberOfSuccesses, confidenceLevel);
        final double alpha = (1.0 - confidenceLevel) / 2;
        final NormalDistribution normalDistribution = new NormalDistribution();
        final double z = normalDistribution.inverseCumulativeProbability(1 - alpha);
        final double zSquared = FastMath.pow(z, 2);
        final double mean = (double) numberOfSuccesses / (double) numberOfTrials;

        final double factor = 1.0 / (1 + (1.0 / numberOfTrials) * zSquared);
        final double modifiedSuccessRatio = mean + (1.0 / (2 * numberOfTrials)) * zSquared;
 
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Examples of org.apache.commons.math3.distribution.NormalDistribution

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    /** {@inheritDoc} */
    public ConfidenceInterval createInterval(int numberOfTrials, int numberOfSuccesses, double confidenceLevel) {
        IntervalUtils.checkParameters(numberOfTrials, numberOfSuccesses, confidenceLevel);
        final double alpha = (1.0 - confidenceLevel) / 2;
        final NormalDistribution normalDistribution = new NormalDistribution();
        final double z = normalDistribution.inverseCumulativeProbability(1 - alpha);
        final double zSquared = FastMath.pow(z, 2);
        final double modifiedNumberOfTrials = numberOfTrials + zSquared;
        final double modifiedSuccessesRatio = (1.0 / modifiedNumberOfTrials) * (numberOfSuccesses + 0.5 * zSquared);
        final double difference = z *
                                  FastMath.sqrt(1.0 / modifiedNumberOfTrials * modifiedSuccessesRatio *
 
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Examples of org.jamesii.core.math.random.distributions.NormalDistribution

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              .intValue();
      addEdge(tree, actualNodeNum, oldLevel.get(randomParent));
      nodesOnLevel.add(actualNodeNum);
      actualNodeNum++;
      AbstractNormalDistribution normDist =
          new NormalDistribution(rand, 0., 1.);

      for (int i : oldLevel) {

        randomNumber =
            approxNumberOfChildren + 0.25 * approxNumberOfChildren
                * normDist.getRandomNumber();

        while (randomNumber > 0.5 && actualNodeNum < numOfNodes) {
          addEdge(tree, actualNodeNum, i);
          nodesOnLevel.add(actualNodeNum);
          actualNodeNum++;
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Examples of org.jquantlib.math.distributions.NormalDistribution

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        testSingle(I, "f(x) = 1",      new Constant(1.0),               0.0, 1.0, 1.0);
        testSingle(I, "f(x) = x",      new Identity(),                  0.0, 1.0, 0.5);
        testSingle(I, "f(x) = x^2",    new Square(),                    0.0, 1.0, 1.0/3.0);
        testSingle(I, "f(x) = sin(x)", new Sin(),                       0.0, Constants.M_PI, 2.0);
        testSingle(I, "f(x) = cos(x)", new Cos(),                       0.0, Constants.M_PI, 0.0);
        testSingle(I, "f(x) = Gaussian(x)", new NormalDistribution(), -10.0, 10.0, 1.0);

//TODO: http://bugs.jquantlib.org/view.php?id=452
//        testSingle(I, "f(x) = Abcd2(x)",
//                AbcdSquared(0.07, 0.07, 0.5, 0.1, 8.0, 10.0),
//                5.0, 6.0,
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Examples of org.jquantlib.math.distributions.NormalDistribution

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    public double gaussianRegret(final double target) {
        final double m = statistics.mean();
        final double std = statistics.standardDeviation();
        final double variance = std * std;
        final CumulativeNormalDistribution gIntegral = new CumulativeNormalDistribution(m, std);
        final NormalDistribution g = new NormalDistribution(m, std);
        final double firstTerm = variance + m * m - 2.0 * target * m + target * target;
        final double alfa = gIntegral.op(target);
        final double secondTerm = m - target;
        final double beta = variance * g.op(target);
        final double result = alfa * firstTerm - beta * secondTerm;
        return result / alfa;
    }
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