List of usage examples for org.apache.commons.math3.distribution NormalDistribution NormalDistribution
public NormalDistribution(double mean, double sd) throws NotStrictlyPositiveException
From source file:gedi.util.math.stat.kernel.GaussianKernel.java
public GaussianKernel(double sd, double maxMassOutside) { super(new NormalDistribution(0, sd), maxMassOutside); }
From source file:kr.ac.kaist.se.simulator.NormalDistributor.java
public NormalDistributor() { this.distGenerator = new NormalDistribution(0, 1); this.mean = 0; this.stDev = 1; }
From source file:gedi.util.math.stat.kernel.GaussianKernel.java
public void setSd(double sd) { dist = new NormalDistribution(0, sd); updateDistribution(); }
From source file:iac_soap.statsq.NormVerdService.java
@Override public NormVerdResponse calculateNormVerd(List<Double> data) throws MyFault { //Service Requirements if (data.isEmpty()) { throw new MyFault("No data is provided"); } else if (data.size() < 2) { throw new MyFault("A minimum of two data elements is required."); }//from w w w . j a v a 2 s . c o m //Declaring Apache Commons DescriptiveStatistics DescriptiveStatistics stats = new DescriptiveStatistics(); //Filling DescriptiveStatistics class with the provided dataset for (int i = 0; i < data.size(); i++) { stats.addValue(data.get(i)); } //Let the DescriptiveStatistics class calculate the mean and standard deviation double mean = stats.getMean(); double std = stats.getStandardDeviation(); //Implementing the KolmogorovSmirnov test & calculating the kurtosis and skewness NormalDistribution x = new NormalDistribution(mean, std); double p_value = TestUtils.kolmogorovSmirnovTest(x, stats.getValues(), false); double kurtosis = stats.getKurtosis(); double skewness = stats.getSkewness(); boolean result = false; //Check if the dataset is a normal distribution: //KolmogorovSmirnov p_value should be >= 0.05 //Both kurtosis and skewness should be between -2.0 and 2.0 if (kurtosis < 2.0 && kurtosis > -2.0 && skewness < 2.0 && skewness > -2.0 && p_value >= 0.05) { result = true; } //Response message: NormVerdResponse nvr = new NormVerdResponse(result, p_value, kurtosis, skewness); return nvr; }
From source file:cz.cuni.mff.d3s.spl.interpretation.MannWhitneyInterpretation.java
/** {@inheritDoc} */ @Override// w w w . j av a 2 s . c o m public ComparisonResult compare(DataSnapshot left, DataSnapshot right) { double[] leftSamples = mergeSamples(left); double[] rightSamples = mergeSamples(right); double uStatMax = utest.mannWhitneyU(leftSamples, rightSamples); long lengthsMultiplied = (long) leftSamples.length * rightSamples.length; double uStatMin = lengthsMultiplied - uStatMax; /* https://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test#Normal_approximation */ double meanU = lengthsMultiplied / 2.0; double varU = lengthsMultiplied * (leftSamples.length + rightSamples.length + 1) / 12.0; double z = (uStatMin - meanU) / Math.sqrt(varU); NormalDistribution distribution = new NormalDistribution(0.0, 1.0); return new DistributionBasedComparisonResult(z, distribution); }
From source file:com.ibm.og.util.Distributions.java
/** * Creates a normal distribution with a rangge of [average - 3*spread, average + 3*spread]. * /* ww w . jav a 2s. c om*/ * @param average the center of this distribution * @param spread distance of one standard deviation * @return a normal distribution instance * @throws IllegalArgumentException if average or spread are negative, or if average - 3*spread is * negative */ public static Distribution normal(final double average, final double spread) { checkArgument(average >= 0.0, "average must be >= 0.0 [%s]", average); checkArgument(spread >= 0.0, "spread must be >= 0.0 [%s]", spread); if (DoubleMath.fuzzyEquals(spread, 0.0, Distributions.ERR)) { return constant(average); } final double min = average - (3 * spread); checkArgument(min >= 0.0, "three standard deviations must be >= 0.0 [%s]", min); final String s = String.format("NormalDistribution [average=%s, spread=%s]", average, spread); return new RealDistributionAdapter(new NormalDistribution(average, spread), s); }
From source file:de.uniwuerzburg.info3.ofcprobe.vswitch.trafficgen.IATGen.java
/** * Constructor//ww w . ja v a 2 s .c om * * @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:it.cnr.isti.smartfed.test.DatacenterFacilities.java
public static List<FederationDatacenter> getNormalDistribution(int numOfDatacenters, int numHost) { Random r = new Random(13213); int core_variance = maxNumOfCores - minNumOfCores; int delta_cores = core_variance > 0 ? r.nextInt(core_variance) : 0; List<FederationDatacenter> list = new ArrayList<FederationDatacenter>(); NormalDistribution nd = new NormalDistribution(numOfDatacenters / 2d, numOfDatacenters / 4d); // System.out.println("Aa"+numHost); for (int i = 0; i < numOfDatacenters; i++) { // create the virtual processor (PE) List<Pe> peList = new ArrayList<Pe>(); int mips = 25000; for (int j = 0; j < minNumOfCores + delta_cores; j++) { peList.add(new Pe(j, new PeProvisionerSimple(mips))); }/*from w w w . j av a 2 s. c o m*/ // create the hosts List<Host> hostList = new ArrayList<Host>(); HostProfile prof = HostProfile.getDefault(); prof.set(HostParams.RAM_AMOUNT_MB, 16 * 1024 + ""); int num; if (numOfDatacenters == 1) { num = numHost; } else { Double value = new Double(nd.density(i)) * numHost; num = value.intValue(); } if (num < 1) num = 1; for (int k = 0; k < num; k++) { hostList.add(HostFactory.get(prof, peList)); } // create the storage List<Storage> storageList = new ArrayList<Storage>(); // if empty, no SAN attached // create the datacenters list.add(FederationDatacenterFactory.getDefault(hostList, storageList)); } return list; }
From source file:akori.Impact.java
static public void normalMatrix(double[][] matrix, int x, int y, int std) { double max = 0; NormalDistribution n = new NormalDistribution(0, 0.4); for (int i = x - std; i < x + std && matrix.length > i && i >= 0; ++i) { for (int j = y - std; j < y + std && matrix[0].length > j && j >= 0; ++j) { double r = Math.sqrt((i - x) * (i - x) + (j - y) * (j - y)); if (r > 0 && r <= std) { matrix[i][j] = matrix[i][j] + n.density(r / std); }// w w w . j av a2 s.c o m } } }
From source file:ffx.algorithms.mc.CoordShakeMove.java
public CoordShakeMove(Atom[] atoms) { int nAtoms = atoms.length; this.atoms = new Atom[nAtoms]; System.arraycopy(atoms, 0, this.atoms, 0, nAtoms); originalCoords = ResidueState.storeAtomicCoordinates(this.atoms); dist = new NormalDistribution(0, sigma); }