Example usage for org.apache.commons.math.distribution NormalDistribution inverseCumulativeProbability

Introduction

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

Prototype

double inverseCumulativeProbability(double p) throws MathException;

Source Link

Document

For this distribution, X, this method returns x such that P(X < x) = p.

Usage

From source file:geogebra.kernel.statistics.AlgoInverseNormal.java

protected final void compute() {

    if (input[0].isDefined() && input[1].isDefined() && input[2].isDefined()) {
        double param = a.getDouble();
        double param2 = b.getDouble();
        double val = c.getDouble();
        try {/*from   w ww.ja va  2 s .  c om*/
            NormalDistribution dist = getNormalDistribution(param, param2);
            num.setValue(dist.inverseCumulativeProbability(val));

        } catch (Exception e) {
            num.setUndefined();
        }
    } else
        num.setUndefined();
}

---

From source file:geogebra.common.kernel.statistics.AlgoInverseNormal.java

@Override
public final void compute() {

    if (input[0].isDefined() && input[1].isDefined() && input[2].isDefined()) {
        double param = a.getDouble();
        double param2 = b.getDouble();
        double val = c.getDouble();
        try {//from  w  ww  .j a  v  a2 s  .  c  o  m
            NormalDistribution dist = getNormalDistribution(param, param2);
            num.setValue(dist.inverseCumulativeProbability(val));

        } catch (Exception e) {
            num.setUndefined();
        }
    } else
        num.setUndefined();
}

---

From source file:datafu.pig.stats.WilsonBinConf.java

/**
 * @param x The number of positive (success) outcomes
 * @param n The number of observations// w w  w  . j a  va2s.  c om
 * @return The (lower,upper) confidence interval
 */
public Tuple binconf(Long x, Long n) throws IOException {
    NormalDistribution normalDist = new NormalDistributionImpl();

    if (x == null || n == null)
        return null;
    if (x < 0 || n < 0)
        throw new IllegalArgumentException("non-negative values expected");
    if (x > n)
        throw new IllegalArgumentException("invariant violation: number of successes > number of obs");
    if (n == 0)
        return tupleFactory.newTuple(ImmutableList.of(Double.valueOf(0), Double.valueOf(0)));

    try {
        double zcrit = -1.0 * normalDist.inverseCumulativeProbability(alpha / 2);
        double z2 = zcrit * zcrit;
        double p = x / (double) n;

        double a = p + z2 / 2 / n;
        double b = zcrit * Math.sqrt((p * (1 - p) + z2 / 4 / n) / n);
        double c = (1 + z2 / n);

        double lower = (a - b) / c;
        double upper = (a + b) / c;

        // Add corrections for when x is very close to n.  This improves the estimates.
        // For more info on wilson binomial confidence interval, see paper:
        // L.D. Brown, T.T. Cai and A. DasGupta, Interval estimation for a binomial proportion (with discussion), 
        //   _Statistical Science,_*16*:101-133, 2001. 
        // http://www-stat.wharton.upenn.edu/~tcai/paper/Binomial-StatSci.pdf

        if (x == 1)
            lower = -Math.log(1 - alpha) / n;
        if (x == (n - 1))
            upper = 1 + Math.log(1 - alpha) / n;

        return tupleFactory.newTuple(ImmutableList.of(lower, upper));
    } catch (MathException e) {
        throw new IOException("math error", e);
    }
}

---

From source file:org.apache.mahout.knn.generate.NormalTest.java

@Test
public void testSample() throws MathException {
    double[] data = new double[10001];
    Sampler<Double> sampler = new Normal();
    for (int i = 0; i < 10001; i++) {
        data[i] = sampler.sample();/* w  w  w .  ja  v  a 2s . c o  m*/
    }
    Arrays.sort(data);

    NormalDistribution reference = new NormalDistributionImpl();

    assertEquals("Median", reference.inverseCumulativeProbability(0.5), data[5000], 0.04);
}

---

From source file:org.bdval.MaqciiHelper.java

private double stouffer(final double[] pval) {
    double rval = 1.0;
    final NormalDistribution normd = new NormalDistributionImpl();
    normd.setMean(0);//w w  w .ja v  a 2s.com
    normd.setStandardDeviation(1);
    final int size = pval.length;
    final double[] z = new double[size];
    double zCombine = 0;

    for (int i = 0; i < size; i++) {
        try {
            z[i] = normd.inverseCumulativeProbability(pval[i]);
        } catch (MathException e) {
            LOG.warn("Error calculating probability", e);
        }
        zCombine += z[i];
    }
    zCombine /= Math.sqrt(size);

    try {
        rval = normd.cumulativeProbability(zCombine);
    } catch (MathException e) {
    }

    return rval;
}

---