List of usage examples for org.apache.commons.math.distribution BinomialDistributionImpl BinomialDistributionImpl
public BinomialDistributionImpl(int trials, double p)
From source file:es.cnio.bioinfo.bicycle.BinomialAnnotator.java
public static void main(String[] args) throws IOException, MathException { BufferedReader in = new BufferedReader(new InputStreamReader(System.in)); double p = Double.parseDouble(args[0]); String line = null;//w w w .ja va2 s. c om while ((line = in.readLine()) != null) { String[] tokens = line.split("\t"); int reads = Integer.parseInt(tokens[5]); int cCount = Integer.parseInt(tokens[4]); BinomialDistribution binomial = new BinomialDistributionImpl(reads, p); double pval = (reads == 0) ? 1.0d : (1.0d - binomial.cumulativeProbability(cCount - 1)); if (System.out.checkError()) { System.exit(1); } System.out.println(line + "\t" + pval); } }
From source file:de.unihannover.se.processSimulation.dataGenerator.StatisticsUtil.java
static int qBinom(double q, int trials, double p) { try {//from ww w . j a v a 2s. c om final BinomialDistribution d = new BinomialDistributionImpl(trials, p); final int result = d.inverseCumulativeProbability(q); if (result == -1) { return 0; } else if (result == Integer.MAX_VALUE) { return trials; } else { return result + 1; } } catch (final MathException e) { throw new RuntimeException(e); } }
From source file:cal.cumulativeBinomialProbability.score.CumulativeBinomialProbabilityBasedScoring.java
/** * To calculate CumulativeBinominalProbability with given n,N and p values. * by calling BinomialDistribution class on Apache * * @return/*w w w . jav a2 s. co m*/ * @throws Exception */ protected double calculateCumulativeBinominalProbability() throws Exception { BinomialDistribution b = new BinomialDistributionImpl(N, p); int tmp = n - 1; double probability = 1 - b.cumulativeProbability(tmp); return probability; }
From source file:desmoj.core.dist.DiscreteDistBinomial.java
/** * Creates a stream of pseudo random numbers following a binomial * distribution. The specific parameters p (probability) and n (amount) have * to be given here at creation time./* w w w. ja v a2s . c o m*/ * * @param owner * Model : The distribution's owner * @param name * java.lang.String : The distribution's name * @param probability * double : The probability of success in each separate Bernoulli * experiment. * @param amount * int : The amount of separate Bernoulli experiments that lead to * the result. * @param showInReport * boolean : Flag for producing reports * @param showInTrace * boolean : Flag for producing trace output */ public DiscreteDistBinomial(Model owner, String name, double probability, int amount, boolean showInReport, boolean showInTrace) { super(owner, name, showInReport, showInTrace); this.probability = probability; this.amount = amount; valueList = new ArrayList<Entry>(); Entry e; BinomialDistribution bdist = new BinomialDistributionImpl(this.amount, this.probability); for (int i = 0; i < this.amount; i++) { try { e = new Entry(i, bdist.cumulativeProbability(i)); valueList.add(e); } catch (MathException e1) { sendWarning( "Failed to compute cumulative Probability of value " + Long.toString(i) + ", entry ignored", "DiscreteDistBinomial : " + getName() + " at construction time", "Impossible to compute cumulative Probability", "Make sure the probabilty is set between 0 and 1 and the amount of trials is positive"); } } }
From source file:edu.utexas.cs.tactex.servercustomers.factoredcustomer.ProbabilityDistribution.java
ProbabilityDistribution(FactoredCustomerService service, Element xml) {
if (null == randomSeedRepo)
randomSeedRepo = (RandomSeedRepo) SpringApplicationContext.getBean("randomSeedRepo");
type = Enum.valueOf(DistType.class, xml.getAttribute("distribution"));
switch (type) {
case POINTMASS:
case DEGENERATE:
param1 = Double.parseDouble(xml.getAttribute("value"));
sampler = new DegenerateSampler(param1);
break;//from ww w .j a v a 2 s .co m
case UNIFORM:
param1 = Double.parseDouble(xml.getAttribute("low"));
param2 = Double.parseDouble(xml.getAttribute("high"));
sampler = new UniformSampler(param1, param2);
break;
case INTERVAL:
param1 = Double.parseDouble(xml.getAttribute("mean"));
param2 = Double.parseDouble(xml.getAttribute("stdDev"));
param3 = Double.parseDouble(xml.getAttribute("low"));
param4 = Double.parseDouble(xml.getAttribute("high"));
sampler = new IntervalSampler(param1, param2, param3, param4);
break;
case NORMAL:
case GAUSSIAN:
param1 = Double.parseDouble(xml.getAttribute("mean"));
param2 = Double.parseDouble(xml.getAttribute("stdDev"));
sampler = new ContinuousSampler(new NormalDistributionImpl(param1, param2));
break;
case STDNORMAL:
param1 = 0;
param2 = 1;
sampler = new ContinuousSampler(new NormalDistributionImpl(param1, param2));
break;
case LOGNORMAL:
param1 = Double.parseDouble(xml.getAttribute("expMean"));
param2 = Double.parseDouble(xml.getAttribute("expStdDev"));
sampler = new LogNormalSampler(param1, param2);
break;
case CAUCHY:
param1 = Double.parseDouble(xml.getAttribute("median"));
param2 = Double.parseDouble(xml.getAttribute("scale"));
sampler = new ContinuousSampler(new CauchyDistributionImpl(param1, param2));
break;
case BETA:
param1 = Double.parseDouble(xml.getAttribute("alpha"));
param2 = Double.parseDouble(xml.getAttribute("beta"));
sampler = new ContinuousSampler(new BetaDistributionImpl(param1, param2));
break;
case BINOMIAL:
param1 = Double.parseDouble(xml.getAttribute("trials"));
param2 = Double.parseDouble(xml.getAttribute("success"));
sampler = new DiscreteSampler(new BinomialDistributionImpl((int) param1, param2));
break;
case POISSON:
param1 = Double.parseDouble(xml.getAttribute("lambda"));
sampler = new DiscreteSampler(new PoissonDistributionImpl(param1));
break;
case CHISQUARED:
param1 = Double.parseDouble(xml.getAttribute("dof"));
sampler = new ContinuousSampler(new ChiSquaredDistributionImpl(param1));
break;
case EXPONENTIAL:
param1 = Double.parseDouble(xml.getAttribute("mean"));
sampler = new ContinuousSampler(new ExponentialDistributionImpl(param1));
break;
case GAMMA:
param1 = Double.parseDouble(xml.getAttribute("alpha"));
param2 = Double.parseDouble(xml.getAttribute("beta"));
sampler = new ContinuousSampler(new GammaDistributionImpl(param1, param2));
break;
case WEIBULL:
param1 = Double.parseDouble(xml.getAttribute("alpha"));
param2 = Double.parseDouble(xml.getAttribute("beta"));
sampler = new ContinuousSampler(new WeibullDistributionImpl(param1, param2));
break;
case STUDENT:
param1 = Double.parseDouble(xml.getAttribute("dof"));
sampler = new ContinuousSampler(new TDistributionImpl(param1));
break;
case SNEDECOR:
param1 = Double.parseDouble(xml.getAttribute("d1"));
param2 = Double.parseDouble(xml.getAttribute("d2"));
sampler = new ContinuousSampler(new FDistributionImpl(param1, param2));
break;
default:
throw new Error("Invalid probability distribution type!");
}
sampler.reseedRandomGenerator(service.getRandomSeedRepo()
.getRandomSeed("factoredcustomer.ProbabilityDistribution", SeedIdGenerator.getId(), "Sampler")
.getValue());
}
From source file:geogebra.kernel.statistics.AlgoDistribution.java
BinomialDistribution getBinomialDistribution(int param, double param2) { if (binomial == null) binomial = new BinomialDistributionImpl(param, param2); else {//ww w. ja v a 2s . c o m binomial.setNumberOfTrials(param); binomial.setProbabilityOfSuccess(param2); } return binomial; }
From source file:geogebra.common.kernel.statistics.AlgoDistribution.java
/** * @param param//from w w w . j ava 2 s .com * number of trials * @param param2 * prob. of success * @return binomial distribution */ protected BinomialDistribution getBinomialDistribution(int param, double param2) { if (binomial == null || binomial.getNumberOfTrials() != param || binomial.getProbabilityOfSuccess() != param2) binomial = new BinomialDistributionImpl(param, param2); return binomial; }
From source file:geogebra.common.kernel.algos.AlgoBarChart.java
/** * Prepares list1 and list2 for use with probability distribution bar charts *//* w w w. j a va2 s .c o m*/ private boolean prepareDistributionLists() { IntegerDistribution dist = null; int first = 0, last = 0; try { // get the distribution and the first, last list values for given // distribution type switch (type) { case TYPE_BARCHART_BINOMIAL: if (!(p1geo.isDefined() && p2geo.isDefined())) return false; int n = (int) Math.round(p1.getDouble()); double p = p2.getDouble(); dist = new BinomialDistributionImpl(n, p); first = 0; last = n; break; case TYPE_BARCHART_PASCAL: if (!(p1geo.isDefined() && p2geo.isDefined())) return false; n = (int) Math.round(p1.getDouble()); p = p2.getDouble(); dist = new PascalDistributionImpl(n, p); first = 0; last = (int) Math.max(1, (kernel).getXmax() + 1); break; case TYPE_BARCHART_ZIPF: if (!(p1geo.isDefined() && p2geo.isDefined())) return false; n = (int) Math.round(p1.getDouble()); p = p2.getDouble(); dist = new ZipfDistributionImpl(n, p); first = 0; last = n; break; case TYPE_BARCHART_POISSON: if (!p1geo.isDefined()) return false; double lambda = p1.getDouble(); dist = new PoissonDistributionImpl(lambda); first = 0; last = (int) Math.max(1, kernel.getXmax() + 1); break; case TYPE_BARCHART_HYPERGEOMETRIC: if (!(p1geo.isDefined() && p2geo.isDefined() && p3geo.isDefined())) return false; int pop = (int) p1.getDouble(); int successes = (int) p2.getDouble(); int sample = (int) p3.getDouble(); dist = new HypergeometricDistributionImpl(pop, successes, sample); first = Math.max(0, successes + sample - pop); last = Math.min(successes, sample); break; } // load class list and probability list loadDistributionLists(first, last, dist); } catch (Exception e) { App.debug(e.getMessage()); return false; } return true; }
From source file:es.cnio.bioinfo.bicycle.gatk.MethylationFilePair.java
private double computePval(Strand strand, Context context, ContigBisulfiteError error, int mCCount, int depth) { BinomialDistribution binomial = new BinomialDistributionImpl(depth, error.getError(strand, context).getError()); double pval = 1.0; try {//from w w w .ja v a2 s.c o m pval = (mCCount == 0) ? 1.0d : (1.0d - binomial.cumulativeProbability(mCCount - 1)); } catch (MathException e) { e.printStackTrace(); throw new RuntimeException(e); } return pval; }
From source file:geogebra.kernel.AlgoFunctionAreaSums.java
/** * Prepares list1 and list2 for use with probability distribution bar charts *//* w ww. j ava 2 s. c o m*/ private boolean prepareDistributionLists() { IntegerDistribution dist = null; int first = 0, last = 0; try { // get the distribution and the first, last list values for given distribution type switch (type) { case TYPE_BARCHART_BINOMIAL: if (!(p1geo.isDefined() && p2geo.isDefined())) return false; int n = (int) Math.round(p1.getDouble()); double p = p2.getDouble(); dist = new BinomialDistributionImpl(n, p); first = 0; last = n; break; case TYPE_BARCHART_PASCAL: if (!(p1geo.isDefined() && p2geo.isDefined())) return false; n = (int) Math.round(p1.getDouble()); p = p2.getDouble(); dist = new PascalDistributionImpl(n, p); first = 0; last = (int) Math.max(1, kernel.getXmax() + 1); break; case TYPE_BARCHART_ZIPF: if (!(p1geo.isDefined() && p2geo.isDefined())) return false; n = (int) Math.round(p1.getDouble()); p = p2.getDouble(); dist = new ZipfDistributionImpl(n, p); first = 0; last = n; break; case TYPE_BARCHART_POISSON: if (!p1geo.isDefined()) return false; double lambda = p1.getDouble(); dist = new PoissonDistributionImpl(lambda); first = 0; last = (int) Math.max(1, kernel.getXmax() + 1); break; case TYPE_BARCHART_HYPERGEOMETRIC: if (!(p1geo.isDefined() && p2geo.isDefined() && p3geo.isDefined())) return false; int pop = (int) p1.getDouble(); int successes = (int) p2.getDouble(); int sample = (int) p3.getDouble(); dist = new HypergeometricDistributionImpl(pop, successes, sample); first = Math.max(0, successes + sample - pop); last = Math.min(successes, sample); break; } // load class list and probability list loadDistributionLists(first, last, dist); } catch (Exception e) { Application.debug(e.getMessage()); return false; } return true; }