import cern.jet.random.ChiSquare; //导入依赖的package包/类
private void assertWellBalanced(int numNodes, Multiset<Integer> ids) {
    // compute the chi-sq statistic
    double expected = ids.size() / (double) numNodes;
    double chiSq = 0.0;
    int df = numNodes - 1;
    NumberFormat prct = NumberFormat.getPercentInstance();
    prct.setMaximumFractionDigits(4);
    NumberFormat num = NumberFormat.getInstance();
    num.setMaximumFractionDigits(4);
    num.setMinimumFractionDigits(4);
    System.out.println("node\treqs\tX^2\tskew");
    for(Integer id: ids.elementSet()) {
        System.out.println(id + "\t" + ids.count(id) + "\t"
                           + num.format(chiSq(ids.count(id), expected)) + "\t"
                           + prct.format((ids.count(id) - expected) / expected));
        chiSq += chiSq(ids.count(id), expected);
    }
    System.out.println("X^2 = " + chiSq);
    ChiSquare dist = new ChiSquare(df, new MersenneTwister());
    // p-value is ~= prob of seeing this distribution from fair router
    double pValue = 1.0 - dist.cdf(chiSq);
    System.out.println("p-value = " + pValue);
    assertTrue("Non-uniform load distribution detected.", pValue >= 0.05);
}
 

import cern.jet.random.ChiSquare; //导入依赖的package包/类
public EnrichmentSignificance(EventsConfig con, ExperimentManager exptman, BindingManager bman, double minFoldChange, double genomeLength){
	this.config = con;
	this.manager = exptman;
	this.bindingManager = bman;
	this.minFoldChange = minFoldChange;
	this.genomeLength = genomeLength;
	binomial = new Binomial(100, .5, new DRand());
	poisson = new Poisson(1, new DRand());
	chisquare = new ChiSquare(1, new DRand());
}
 

import cern.jet.random.ChiSquare; //导入依赖的package包/类
/**
 * @param degrees The degrees of freedom of the distribution, not less than one
 * @param engine A uniform random number generator, not null
 */
public ChiSquareDistribution(final double degrees, final RandomEngine engine) {
  Validate.isTrue(degrees >= 1, "Degrees of freedom must be greater than or equal to one");
  Validate.notNull(engine);
  _chiSquare = new ChiSquare(degrees, engine);
  _degrees = degrees;
}
 

import cern.jet.random.ChiSquare; //导入依赖的package包/类
public EnrichmentSignificance(Config con, ExperimentSet exptSet, List<BindingEvent> features, double minFoldChange, double genomeLength){
	this.config = con;
	this.exptSet = exptSet;
	this.features = features;
	this.minFoldChange = minFoldChange;
	this.genomeLength = genomeLength;
	binomial = new Binomial(100, .5, new DRand());
	poisson = new Poisson(1, new DRand());
	chisquare = new ChiSquare(1, new DRand());
}
 

import cern.jet.random.ChiSquare; //导入依赖的package包/类
ExperienceSanityCheck() {
	freedom = 6; // osi x
	k = 15;

	chi = new ChiSquare(freedom,
			cern.jet.random.ChiSquare.makeDefaultGenerator());
	// sigmoidCurve = new SigmoidCurve();

	checkChi();
	checkSigmoid();

	EpsilonCutValue = checkEpsilonFromChi();
}
 

import cern.jet.random.ChiSquare; //导入依赖的package包/类
public double sampleVar() { 
	double diff = (double)(getN() - getK());
	ChiSquare chiSquare = new cern.jet.random.ChiSquare(diff, engine);
	double x = chiSquare.nextDouble();
	return (diff * s2) / x;
}
 

import cern.jet.random.ChiSquare; //导入依赖的package包/类
LearningCurve() {
	say("Object LearningCurve created, with ref: " + this);
	chi = new ChiSquare(freedom,
			cern.jet.random.ChiSquare.makeDefaultGenerator());
}