List of usage examples for org.apache.commons.math.distribution WeibullDistributionImpl WeibullDistributionImpl
public WeibullDistributionImpl(double alpha, double beta)
From source file:de.tud.kom.p2psim.impl.churn.KadChurnModel.java
public KadChurnModel() { sessionTime = new WeibullDistributionImpl(0.61511, 169.5385); interSessionTime = new WeibullDistributionImpl(0.47648, 413.6765); }
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;//ww w . j a v a 2 s . c o 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:eu.optimis.infrastructureproviderriskassessmenttool.core.InfrastructureProviderRiskAssessmentServer.java
public double calculatePhyHostPoF(String hostName, Long timePeriod) { if (timePeriod == null) { timePeriod = new Long(24); }/*from w w w .ja v a2 s. c om*/ double pof = 0.0; long alreadyOnTime = 1000 * 3600 * 24; String last_reboot_ts = null; double a = 0.3455; double m = 265000; getClient MonClient = null; try { PropertiesConfiguration configOptimis = ConfigManager .getPropertiesConfiguration(ConfigManager.OPTIMIS_CONFIG_FILE); PropertiesConfiguration configIPRA = ConfigManager .getPropertiesConfiguration(ConfigManager.IPRA_CONFIG_FILE); a = Double.parseDouble(configIPRA.getString("config.weibullpara1")); m = Double.parseDouble(configIPRA.getString("config.weibullpara2")); MonClient = new getClient(configOptimis.getString("optimis-ipvm"), Integer.parseInt(configOptimis.getString("monitoringport")), configOptimis.getString("monitoringpath")); } catch (Throwable ex) { ex.printStackTrace(); } WeibullDistribution wbd = new WeibullDistributionImpl(a, m); log.info("IPRA: hostName is: " + hostName); MonitoringResourceDatasets mrd = MonClient.getLatestReportForPhysical(hostName); List<MonitoringResourceDataset> resources = mrd.getMonitoring_resource(); for (MonitoringResourceDataset resource : resources) { if (resource.getMetric_name().equals("last_reboot")) { last_reboot_ts = resource.getMetric_value().toString(); log.debug("IPRA: last reboot time stamp is: " + last_reboot_ts); alreadyOnTime = Long.parseLong( last_reboot_ts.substring(last_reboot_ts.indexOf('(') + 1, last_reboot_ts.indexOf(')'))); log.debug("IPRA: elapse_time_secs: " + alreadyOnTime); break; } } try { pof = (wbd.cumulativeProbability(alreadyOnTime + timePeriod * 3600) - wbd.cumulativeProbability(alreadyOnTime)) / (1 - wbd.cumulativeProbability(alreadyOnTime)); log.debug("IPRA: Prob(X < " + timePeriod + " hours) is " + pof); } catch (Exception e) { } return pof; }
From source file:geogebra.kernel.statistics.AlgoDistribution.java
WeibullDistribution getWeibullDistribution(double param, double param2) { if (weibull == null) weibull = new WeibullDistributionImpl(param, param2); else {//from w w w . j av a 2s .c om weibull.setShape(param); weibull.setScale(param2); } return weibull; }
From source file:geogebra.common.kernel.statistics.AlgoDistribution.java
/** * @param param// w ww. j ava 2s. c o m * shape * @param param2 * scale * @return Weibull distribution */ WeibullDistribution getWeibullDistribution(double param, double param2) { if (weibull == null || weibull.getShape() != param || weibull.getScale() != param2) weibull = new WeibullDistributionImpl(param, param2); return weibull; }
From source file:org.peerfact.impl.churn.model.ZeroAccessChurnModel.java
public ZeroAccessChurnModel() { sessionTime = new WeibullDistributionImpl(1.098592, 50.7444); interSessionTime = new WeibullDistributionImpl(5.8533, 181.004); }
From source file:org.renjin.Distributions.java
public static double dweibull(final double x, final double shape, final double scale, boolean log) { return d(new WeibullDistributionImpl(shape, scale), x, log); }
From source file:org.renjin.Distributions.java
public static double pweibull(final double q, final double shape, final double scale, boolean lowerTail, boolean logP) { return p(new WeibullDistributionImpl(shape, scale), q, lowerTail, logP); }
From source file:org.renjin.Distributions.java
public static double qweibull(final double p, final double shape, final double scale, boolean lowerTail, boolean logP) { return q(new WeibullDistributionImpl(shape, scale), p, lowerTail, logP); }
From source file:org.renjin.primitives.random.Distributions.java
public static double dweibull(@Recycle double x, @Recycle double shape, @Recycle double scale, boolean log) { return d(new WeibullDistributionImpl(shape, scale), x, log); }