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

Introduction

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

Prototype

public ChiSquaredDistribution(double degreesOfFreedom) 

Source Link

Document

Create a Chi-Squared distribution with the given degrees of freedom.

Usage

From source file:info.rmarcus.birkhoffvonneumann.ChiSquaredTest.java

public static void testMethod(SamplingAlgorithm samp, int n, int samples) throws BVNException {
    double[][] bistoc = MatrixUtils.uniformBistoc(n);

    long[] classes = new long[factorial(bistoc.length)];

    BVNDecomposer bvn = new BVNDecomposer();
    bvn.setSamplingAlgorithm(samp);//  ww w .  j  a v a2 s .  c  o m
    Random r = new Random(42);

    for (int i = 0; i < samples; i++) {
        int[] p = CoeffAndMatrix.asFlatPerm(bvn.sample(r, bistoc));
        classes[inv(p)]++;
    }

    double[] expected = new double[classes.length];
    for (int i = 0; i < expected.length; i++)
        expected[i] = (double) classes.length / (double) samples;

    ChiSquareTest chiT = new ChiSquareTest();
    double testStat = chiT.chiSquare(expected, classes);

    ChiSquaredDistribution chi = new ChiSquaredDistribution(classes.length - 1);
    System.out.println(Arrays.toString(classes));
    System.out.println(chi.cumulativeProbability(testStat) + "\t" + testStat);

}

---

From source file:edu.brandeis.wisedb.scheduler.experiments.SkewDistributionExperiment.java

public static void calculateBurn(int samples) throws Exception {
    TightenableSLA sla = PercentSLA.nintyTenSLA();
    //TightenableSLA sla = new SimpleLatencyModelSLA(9 * 60 * 1000, 1);
    //TightenableSLA sla = PerQuerySLA.getLatencyTimesN(2.0);
    //TightenableSLA sla = new AverageLatencyModelSLA(7 * 60 * 1000, 1);
    QueryTimePredictor qtp = new QueryTimePredictor();

    File f = new File("distSkew.csv");
    if (f.exists())
        f.delete();//  www  . j  av  a2s  . c  om

    try (Trainer t = new Trainer("distSkew.csv", sla)) {
        t.train(2000, 12);
    }

    DTSearcher dt = new DTSearcher("distSkew.csv", qtp, sla);
    AStarGraphSearch astar = new AStarGraphSearch(new UnassignedQueryTimeHeuristic(qtp), sla, qtp);
    //FirstFitDecreasingGraphSearch astar = new FirstFitDecreasingGraphSearch(sla, qtp);

    ChiSquareTest cst = new ChiSquareTest();
    ChiSquaredDistribution cqd = new ChiSquaredDistribution(qtp.QUERY_TYPES.length - 1);
    double[] expceted = Arrays.stream(qtp.QUERY_TYPES).mapToDouble(i -> 20.0 / (qtp.QUERY_TYPES.length))
            .toArray();

    System.out.println("Chi\tDT\tOpt");

    for (int i = 0; i < samples; i++) {
        Set<ModelQuery> smp = ModelWorkloadGenerator.randomQueries(20);

        // reject samples that don't have at least one of each query type
        long repr = smp.stream().mapToInt(q -> q.getType()).distinct().count();
        if (repr != qtp.QUERY_TYPES.length) {
            i--;
            continue;
        }

        Map<Integer, List<ModelQuery>> groups = smp.stream().collect(Collectors.groupingBy(q -> q.getType()));

        long obs[] = Arrays.stream(qtp.QUERY_TYPES).mapToLong(v -> groups.get(v).size()).toArray();

        double chi = cst.chiSquare(expceted, obs);
        chi = cqd.cumulativeProbability(chi);

        Cost dtCost = dt.getCostForQueries(smp, sla);
        Cost optCost = astar.getCostForQueries(smp, sla);

        System.out.println(chi + "\t" + dtCost.getTotalCost() + "\t" + optCost.getTotalCost());
    }

}

---

From source file:com.actelion.research.spiritcore.util.StatUtils.java

/**
 * Calculate Kruskal-Walis (http://en.wikipedia.org/wiki/Kruskal%E2%80%93Wallis_one-way_analysis_of_variance)
 * @param values/*  w w w.j  av a2s  . c  o m*/
 * @return
 */
public static double getKruskalWallis(List<double[]> values) {

    //Assign ranks
    assert values.size() > 1;

    List<Double> allDoubles = new ArrayList<>();
    for (double[] a : values) {
        assert a.length > 0;
        for (double d : a) {
            allDoubles.add(d);
        }
    }
    int N = allDoubles.size();
    Collections.sort(allDoubles);
    double[] allDoublesArray = new double[allDoubles.size()];
    for (int i = 0; i < allDoubles.size(); i++)
        allDoublesArray[i] = allDoubles.get(i);
    List<double[]> ranks = new ArrayList<double[]>();
    for (double[] a : values) {
        double[] rankArray = new double[a.length];
        ranks.add(rankArray);
        for (int i = 0; i < a.length; i++) {
            int r = Arrays.binarySearch(allDoublesArray, a[i]);
            assert r >= 0;
            int r1 = r, r2 = r;
            while (r1 > 0 && allDoublesArray[r1 - 1] == a[i])
                r1--;
            while (r2 < allDoublesArray.length - 1 && allDoublesArray[r2 + 1] == a[i])
                r2++;
            rankArray[i] = (r1 + r2) / 2.0 + 1;
        }
    }

    //Calculate rank average per group
    List<Double> rankSum = new ArrayList<Double>();
    for (double[] a : ranks) {
        double sum = 0;
        for (double d : a)
            sum += d;
        rankSum.add(sum);
    }

    double sum = 0;
    for (int i = 0; i < ranks.size(); i++) {
        sum += rankSum.get(i) * rankSum.get(i) / ranks.get(i).length;
    }
    double H = 12.0 / (N * (N + 1)) * sum - 3 * (N + 1);

    ChiSquaredDistribution chi = new ChiSquaredDistribution(values.size() - 1);
    double K = 1 - chi.cumulativeProbability(H);

    return K;
}

---

From source file:de.uniwuerzburg.info3.ofcprobe.vswitch.trafficgen.IATGen.java

/**
 * Constructor/* ww w.  jav  a 2 s . co  m*/
 *
 * @param distribution Distribution as String
 * @param para1 Parameter 1
 * @param para2 Parameter 2 (only needed when applicable)
 */
public IATGen(String distribution, double para1, double para2) {

    logger.trace("Distribution selected: {} with Parameters {} & {}", distribution, para1, para2);

    switch (distribution) {
    case "ChiSquared":
        this.distri = new ChiSquaredDistribution(para1);
        break;
    case "Exponential":
        this.distri = new ExponentialDistribution(para1);
        break;
    case "Gamma":
        this.distri = new GammaDistribution(para1, para2);
        break;
    case "Poisson":
        this.intDistri = new PoissonDistribution(para1, para2);
        break;
    default:
        this.distri = new NormalDistribution(para1, para2);
        break;
    }
}

---

From source file:jasima.core.random.continuous.DblChiSquared.java

public DblChiSquared(int degreesOfFreedom) {
    super();
    setDistribution(new ChiSquaredDistribution(degreesOfFreedom));
}

---

From source file:eu.betaas.taas.securitymanager.taastrustmanager.taastrustcalculator.StatisticsCalculator.java

public boolean calculateNumericVariance(double[] values) {
    double alpha = 0.05;
    double mean = StatUtils.mean(values);
    double variance = StatUtils.variance(values);
    double expected = Math.pow(mean * 0.05, 2);
    //double expected = 0.01;
    double degFreedom = values.length - 1.0;

    double T = (degFreedom * variance) / expected;
    logger.debug("Mean = " + mean);
    logger.debug("Standard Deviation = " + Math.sqrt(variance));
    logger.debug("Test Statistic calculated T = " + T);

    ChiSquaredDistribution myDist = new ChiSquaredDistribution(degFreedom);
    double myTLeft = myDist.inverseCumulativeProbability(alpha / 2.0);
    double myTRight = myDist.inverseCumulativeProbability(1.0 - alpha / 2.0);

    logger.debug("Boundaries: " + myTLeft + " to " + myTRight);

    // Determine if z score is in the region of acceptance
    if ((myTLeft <= T) && (T <= myTRight)) {
        // H0 -> Variance of the data is equal to the expected one
        return true;
    }/* ww  w  .j av a2 s.  c o  m*/

    // H1 -> Variance of the data is different to the expected one
    return false;
}

---

From source file:net.atos.ari.vital.taastrustcalculator.StatisticsCalculator.java

public boolean calculateNumericVariance(double[] values) {
    double alpha = 0.05;
    double mean = StatUtils.mean(values);
    double variance = StatUtils.variance(values);
    double expected = Math.pow(mean * 0.05, 2);
    //double expected = 0.01;
    double degFreedom = values.length - 1.0;

    double T = (degFreedom * variance) / expected;
    logger.debug("Mean = " + mean);
    logger.debug("Standard Deviation = " + Math.sqrt(variance));
    logger.debug("Test Statistic calculated T = " + T);

    if (degFreedom <= 0)
        return false;
    ChiSquaredDistribution myDist = new ChiSquaredDistribution(degFreedom);
    double myTLeft = myDist.inverseCumulativeProbability(alpha / 2.0);
    double myTRight = myDist.inverseCumulativeProbability(1.0 - alpha / 2.0);

    logger.debug("Boundaries: " + myTLeft + " to " + myTRight);

    // Determine if z score is in the region of acceptance
    if ((myTLeft <= T) && (T <= myTRight)) {
        // H0 -> Variance of the data is equal to the expected one
        return true;
    }/*w ww .j  a  v  a2s . co m*/

    // H1 -> Variance of the data is different to the expected one
    return false;
}

---

From source file:gedi.atac.DirichletTest.java

@Override
public void accept(ReferenceSequence reference, GenomicRegion region, PixelLocationMapping pixelMapping,
        PixelBlockToValuesMap data) {//from  w  ww.j  a v a  2 s .c om

    int bases = 0;
    int bins = data.size();

    double[] alpha = new double[bins];

    for (int b = 0; b < data.size(); b++) {
        PixelLocationMappingBlock bl = data.getBlock(b);
        bases += bl.getStopBp() + 1 - bl.getStartBp();

        alpha[b] = NumericArrayFunction.Sum.applyAsDouble(data.getValues(b)) + 1;
    }

    double[] p = alpha.clone();
    ArrayUtils.normalize(p);

    double lh1 = logProbability(alpha, p);
    Arrays.fill(p, 1.0 / bins);
    double lh0 = logProbability(alpha, p);

    double lr = 2 * lh1 - 2 * lh0;

    ChiSquaredDistribution d = new ChiSquaredDistribution(bins - 2);

    System.out.printf("Bases=%d\nBins=%d\nLR=%.4g\np=%.5g", bases, bins, lr, 1 - d.cumulativeProbability(lr));

}

---

From source file:jasima.core.random.continuous.DblChiSquared.java

/**
 * Sets the degrees of freedom for this distribution.
 * /*from   w w w .jav  a2  s  .  co m*/
 * @param degreesOfFreedom
 *            The degrees of freedom to use.
 * @throws NotStrictlyPositiveException
 *             If the parameter value was {@code <=0.0}.
 */
public void setDegreesOfFreedom(int degreesOfFreedom) throws NotStrictlyPositiveException {
    setDistribution(new ChiSquaredDistribution(degreesOfFreedom));
}

---

From source file:iDynoOptimizer.MOEAFramework26.src.org.moeaframework.util.statistics.KruskalWallisTest.java

@Override
public boolean test(double alpha) {
    update();/*from   w ww.  j ava2 s.c o  m*/

    ChiSquaredDistribution dist = new ChiSquaredDistribution(numberOfGroups - 1);
    double H = H();
    double C = C();

    if (C == 0.0) {
        // all observations the same
        return false;
    }

    return 1.0 - dist.cumulativeProbability(H / C) < alpha;
}

---

• «
• 1
• 2
• 3
• 4
• 5
• 6
• »