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() 

Source Link

Document

Create a normal distribution with mean equal to zero and standard deviation equal to one.

Usage

From source file:com.itemanalysis.psychometrics.statistics.NormalScores.java

public NormalScores() {
    norm = new NormalDistribution();
}

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From source file:blackscholes.EuropeanCall.java

public double ValuationMethod() {
    NormalDistribution N = new NormalDistribution();
    double _b = r - b;
    double d1 = (Math.log(S / X) + (b + 0.5 * Math.pow(sigma, 2)) * T) / (sigma * Math.sqrt(T));
    double d2 = d1 - sigma * Math.sqrt(T);

    double Nd1 = N.cumulativeProbability(d1);
    double Nd2 = N.cumulativeProbability(d2);
    return S * Nd1 - X * Nd2 * Math.exp((b - r) * T);
}

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From source file:com.itemanalysis.psychometrics.statistics.NormalDensity.java

public NormalDensity() {
    normal = new NormalDistribution();
}

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From source file:com.itemanalysis.psychometrics.statistics.RobustZ.java

private void compute(double x, double median, double iqr) {
    NormalDistribution normal = new NormalDistribution();
    if (iqr > 0.0) {
        z = (x - median) / (0.74 * iqr);
        pvalue = normal.cumulativeProbability(z);
        if (z > 0.0) {
            pvalue = 1.0 - pvalue;/*from   ww  w  . j av  a  2  s  . com*/
        }
    }
}

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From source file:com.itemanalysis.psychometrics.polycor.PolyserialPlugin.java

public PolyserialPlugin() {
    r = new PearsonCorrelation();
    sdX = new StandardDeviation();
    sdY = new StandardDeviation();
    freqY = new Frequency();
    norm = new NormalDistribution();
}

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From source file:com.itemanalysis.psychometrics.scaling.NormalizedScore.java

/**
 * Creates a TreeMap<Integer, Double> lookup table of normalized scores.
 * The key is a raw score level and the rho is a normalized score. This
 * method is useful when finding normalized scores that correspond to an examinee's
 * raw score. After calling this method, individual elements in the
 * TreeMap can be accessed with getNormalizedScoreAt(int rho) or
 * valueIterator()./*from   w w  w.j  a  va 2 s .  c o m*/
 *
 */
public void createLookupTable(PercentileRank prank, LinearTransformation linear) {
    this.prank = prank;
    NormalDistribution normal = new NormalDistribution();
    normScoreTable = new TreeMap<Integer, Double>();
    prank.createLookupTable();
    Iterator<Integer> iter = prank.valueIterator();
    double p = 0.0;
    double q = 0.0;
    Integer i = null;
    while (iter.hasNext()) {
        i = iter.next();
        p = prank.getPercentileRankAt(i) / 100.0;
        q = normal.inverseCumulativeProbability(p);
        normScoreTable.put(i, linear.transform(q));
    }
}

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From source file:com.itemanalysis.psychometrics.polycor.PolyserialLogLikelihoodTwoStep.java

public PolyserialLogLikelihoodTwoStep(double[] dataX, int[] dataY) {
    this.dataX = dataX;
    this.dataY = dataY;
    this.normal = new NormalDistribution();
    alpha = new double[nrow];
}

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From source file:com.example.PJS.java

/** Nuevas funciones usando la lib de Apache Commons
* http://commons.apache.org/proper/commons-math/apidocs/index.html
    * @param z/*from  ww w  .  j  ava2 s  .  co  m*/
    * @return 
*/
public static double CNDF(double z) {

    NormalDistribution nD = new NormalDistribution();
    double cndf;

    cndf = nD.cumulativeProbability(z);
    return cndf;

}

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From source file:com.github.thorbenlindhauer.cluster.ep.TruncatedGaussianPotentialResolver.java

public TruncatedGaussianPotentialResolver(ContinuousVariable predictionVariable, double lowerBound,
        double upperBound) {
    this.lowerBound = lowerBound;
    this.upperBound = upperBound;
    this.predictionVariable = predictionVariable;
    this.standardNormal = new NormalDistribution();
}

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From source file:cn.edu.pku.cbi.mosaichunter.math.WilcoxonRankSumTest.java

public static double twoSided(double[] x, double[] y) {
    int nx = x.length;
    int ny = y.length;
    double[] v = new double[nx + ny];
    Arrays.sort(x);/*  w ww.j  a va 2s  .  c  o m*/
    System.arraycopy(x, 0, v, 0, nx);
    System.arraycopy(y, 0, v, nx, ny);
    Arrays.sort(v);

    double[] u = new double[v.length];
    double[] rank = new double[v.length];
    int[] cnt = new int[v.length];
    int n = 0;
    for (int i = 0; i < v.length; ++i) {
        if (i == 0 || v[i] - v[i - 1] > EPS) {
            u[n] = v[i];
            n++;
        }
        cnt[n - 1]++;
        rank[n - 1] += i + 1;
    }
    for (int i = 0; i < n; ++i) {
        rank[i] /= cnt[i];
    }

    double stats = 0;
    int j = 0;
    for (int i = 0; i < nx; ++i) {
        while (x[i] > u[j] + EPS) {
            j++;
        }
        stats += rank[j];
    }
    stats -= nx * (nx + 1) / 2;
    double z = stats - x.length * y.length / 2;

    double tmp = 0;
    for (int i = 0; i < n; ++i) {
        tmp += (double) cnt[i] * cnt[i] * cnt[i] - cnt[i];
    }
    double sigma = Math.sqrt(((double) nx * ny / 12) * (nx + ny + 1 - tmp / (nx + ny) / (nx + ny - 1)));
    if (z > EPS) {
        z -= 0.5;
    } else if (z < -EPS) {
        z += 0.5;
    }
    z /= sigma;

    double p = new NormalDistribution().cumulativeProbability(z);
    double pValue = 2 * Math.min(p, 1 - p);
    if (Double.isNaN(pValue)) {
        return 1;
    }
    return pValue;
}

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