Example usage for org.apache.commons.math3.distribution NormalDistribution NormalDistribution

Introduction

In this page you can find the example usage for org.apache.commons.math3.distribution NormalDistribution NormalDistribution.

Prototype

public NormalDistribution(double mean, double sd) throws NotStrictlyPositiveException 

Source Link

Document

Create a normal distribution using the given mean and standard deviation.

Usage

From source file:eu.crisis_economics.abm.model.parameters.ParameterUtils.java

/**
  * Create a model parameter with a double value drawn from a
  * normal distribution.//  w  w w .  j av  a  2s  . com
  * 
  * @param parameterName
  *        The name of the model parameter.
  * @param mean
  *        The minimum value of the paremeter.
  * @param stdDeviation
  *        The maximum value of the parameter.
  */
public static ModelParameter<?> createParameterWithNormalDistribution(final String parameterName,
        final double mean, final double stdDeviation) {
    Preconditions.checkArgument(stdDeviation >= 0.);
    return new FromRealDistributionModelParameter(new NormalDistribution(mean, stdDeviation), parameterName);
}

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From source file:es.us.isa.sedl.module.statcharts.renderer.HighChartsRenderer.java

private String[][] generateNormalDistribution(HistogramResult histogramResult) {
    Integer total = 0;//from ww w . ja  v a2  s . c om
    for (String value : histogramResult.getCounts())
        total += Integer.valueOf(value);
    Double mean = Double.valueOf(histogramResult.getMean());
    Double sigma = Double.valueOf(histogramResult.getSigma());
    NormalDistribution normal = new NormalDistribution(mean, sigma);
    Double[] xPoints = { -3.2807020192309, -3.0425988742109, -2.8044957291909, -2.5663925841709,
            -2.3282894391509, -2.0901862941309, -1.8520831491109, -1.6139800040909, -1.3758768590709,
            -1.1377737140509, -0.89967056903087, -0.66156742401087, -0.42346427899086, -0.18536113397085,
            0.052742011049155, 0.29084515606916, 0.52894830108917, 0.76705144610918, 1.0051545911292,
            1.2432577361492, 1.4813608811692, 1.7194640261892, 1.9575671712092, 2.1956703162292,
            2.4337734612492, 2.6718766062692, 2.9099797512892, 3.1480828963092 };
    String[][] result = new String[xPoints.length][2];
    for (int i = 0; i < xPoints.length; i++) {
        result[i][0] = String.valueOf(mean + xPoints[i] * sigma);
        result[i][1] = String.valueOf(normal.density(mean + xPoints[i] * sigma) * total);
    }
    return result;
}

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From source file:ffx.algorithms.mc.CoordShakeMove.java

public void setSigma(double sigma) {
    this.sigma = sigma;
    dist = new NormalDistribution(0, sigma);
}

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From source file:de.bund.bfr.math.LodFunction.java

@Override
public double value(double[] point) {
    double sd = Double.NaN;

    for (int ip = 0; ip < nParams; ip++) {
        if (parameters.get(ip).equals(sdParam)) {
            sd = Math.abs(point[ip]);
        } else {// w ww.j  ava 2  s . co  m
            parser.setVarValue(parameters.get(ip), point[ip]);
        }
    }

    if (sd == 0.0) {
        return Double.NaN;
    }

    double logLikelihood = 0.0;

    for (int iv = 0; iv < nValues; iv++) {
        for (Map.Entry<String, List<Double>> entry : variableValues.entrySet()) {
            parser.setVarValue(entry.getKey(), entry.getValue().get(iv));
        }

        try {
            double value = parser.evaluate(function);

            if (!Double.isFinite(value)) {
                return Double.NaN;
            }

            NormalDistribution normDist = new NormalDistribution(value, sd);

            logLikelihood += targetValues.get(iv) > levelOfDetection
                    ? Math.log(normDist.density(targetValues.get(iv)))
                    : Math.log(normDist.cumulativeProbability(levelOfDetection));
        } catch (ParseException e) {
            e.printStackTrace();
            return Double.NaN;
        }
    }

    return logLikelihood;
}

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From source file:adams.data.distribution.Normal.java

/**
 * Returns the configured distribution.//  ww w . j av  a 2s  .  c om
 *
 * @return      the distribution
 */
@Override
public RealDistribution getRealDistribution() {
    return new NormalDistribution(m_Mean, m_StandardDeviation);
}

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From source file:me.datamining.cluster.STING.java

/**
 * //from  ww  w  .j a  v a  2s .  c o m
 * @param value
 * @param mean
 * @param std
 * @return
 */
public static double standardPDF(double value, double mean, double std) {
    if (std == 0) {
        return 0;
    }
    NormalDistribution sdf = new NormalDistribution(mean, std);
    try {
        return sdf.cumulativeProbability(value);
    } catch (NumberIsTooLargeException e) {
        return 0;
    }
}

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From source file:com.lyncode.performance.PerformanceRequester.java

public long normalDist() {
    NormalDistribution distribution = new NormalDistribution(average(), desvio());
    long sum = 0;
    double sumProb = 0;
    for (long value : measurements) {
        double probability = distribution.probability(value);
        sumProb += probability;// w w w. j  a  v a 2  s  .c om
        double result = (double) value * probability;
        sum += result;
    }
    double value = (double) sum / sumProb;
    return (long) value;
}

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From source file:de.huberlin.wbi.hiway.scheduler.WienerProcessModel.java

public double getEstimate(double timestamp, double alpha) {
    if (alpha == 0.5 && measurements.size() > 0) {
        return logarithmize ? Math.pow(Math.E, measurements.getLast().runtime)
                : Math.max(measurements.getLast().runtime, Double.MIN_NORMAL);
    }/* w  w w. j  a va2  s .co m*/

    if (differences.size() < 2) {
        return 0d;
    }

    Runtime lastMeasurement = measurements.getLast();

    double variance = 0d;
    double avgDifference = sumOfDifferences / differences.size();
    for (double difference : differences) {
        variance += Math.pow(difference - avgDifference, 2d);
    }
    variance /= differences.size() - 1;

    variance *= timestamp - lastMeasurement.timestamp;

    double estimate = lastMeasurement.runtime;
    if (variance > 0d) {
        NormalDistribution nd = new NormalDistribution(lastMeasurement.runtime, Math.sqrt(variance));
        estimate = nd.inverseCumulativeProbability(alpha);
    }

    estimate = logarithmize ? Math.pow(Math.E, estimate) : Math.max(estimate, 0d);

    return estimate;
}

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From source file:com.itemanalysis.psychometrics.distribution.NormalDistributionApproximation.java

private void initialize(double min, double max, double mean, double sd) {
    //create points
    double range = max - min;
    points = new double[numberOfPoints];
    double step = range / ((double) numberOfPoints - 1.0);
    points[0] = min;//from   w w w  . j  a  va  2 s  .  c o  m
    for (int i = 1; i < numberOfPoints; i++) {
        points[i] = points[i - 1] + step;
    }

    //compute density
    NormalDistribution normal = new NormalDistribution(mean, sd);
    density = new double[numberOfPoints];
    double densitySum = 0.0;
    for (int i = 0; i < numberOfPoints; i++) {
        density[i] = normal.density(points[i]);
        densitySum += density[i];
    }

    //make sure probabilities sum to unity
    for (int i = 0; i < numberOfPoints; i++) {
        density[i] = density[i] / densitySum;
    }

}

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From source file:gedi.util.math.stat.distributions.OccupancyNumberDistribution.java

private double[] computeApproximateNormal() {
    int maxA = getMaximalA();
    double[] re = new double[maxA + 1];
    if (normal == null)
        normal = new NormalDistribution(expectation(), sqrt(variance()));
    for (int a = 0; a <= maxA; a++) {
        re[a] = normal.density(a);/* w  w w  . ja v a  2 s . c  o m*/
    }
    ArrayUtils.normalize(re);
    return re;
}

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