List of usage examples for org.apache.commons.math.distribution CauchyDistributionImpl CauchyDistributionImpl
public CauchyDistributionImpl(double median, double s)
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 www . j a va2 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:es.udc.gii.common.eaf.plugin.parameter.jade.JADEFAdaptiveParameter.java
@Override public double get(EvolutionaryAlgorithm algorithm) { //Lehmer mean: double meanl_f; double sum_f_i, sum_f_i_2; List<Individual> individuals; int f_individuals; double f_ind, f_i; //Hay que "chequear" que los individuos sean del tipo JADE: sum_f_i = 0.0;/*from ww w .ja v a 2 s.c om*/ sum_f_i_2 = 0.0; if (algorithm.getGenerations() > this.alg_generation) { //Calculamos mu; individuals = algorithm.getPopulation().getIndividuals(); meanl_f = 0.0; f_individuals = 0; for (Individual i : individuals) { if (i instanceof JADEIndividual) { f_ind = ((JADEIndividual) i).getF(); if (f_ind != -Double.MAX_VALUE) { sum_f_i += f_ind; sum_f_i_2 += f_ind * f_ind; f_individuals++; } } else { throw new ConfigurationException( "JADECRAdaptiveParameter requires individuals of type JADEIndividual"); } } if (f_individuals > 0) { meanl_f = sum_f_i_2 / sum_f_i; this.mu_f = (1.0 - this.c) * this.mu_f + this.c * meanl_f; } this.mu_f = (this.mu_f > 1.0 ? 1.0 : (this.mu_f < 0.0 ? 0.0 : this.mu_f)); this.alg_generation++; } CauchyDistribution d = new CauchyDistributionImpl(this.mu_f, this.std_f); f_i = 0.0; try { do { double r = EAFRandom.nextDouble(); f_i = d.inverseCumulativeProbability(r); } while (f_i < 1.0e-8); f_i = (f_i > 1.0 ? 1.0 : (f_i < 0.0 ? 0.0 : f_i)); } catch (MathException ex) { Logger.getLogger(JADEFAdaptiveParameter.class.getName()).log(Level.SEVERE, null, ex); } return f_i; }
From source file:geogebra.kernel.statistics.AlgoDistribution.java
CauchyDistribution getCauchyDistribution(double param, double param2) { if (cauchy == null) cauchy = new CauchyDistributionImpl(param, param2); else {//from ww w.java 2 s.c om cauchy.setMedian(param); cauchy.setScale(param2); } return cauchy; }
From source file:geogebra.common.kernel.statistics.AlgoDistribution.java
/** * @param param//w w w .ja va 2 s . co m * median * @param param2 * scale * @return cauchy distribution */ protected CauchyDistribution getCauchyDistribution(double param, double param2) { if (cauchy == null || cauchy.getMedian() != param || cauchy.getScale() != param2) cauchy = new CauchyDistributionImpl(param, param2); return cauchy; }
From source file:org.renjin.Distributions.java
public static double dcauchy(final double x, final double location, final double scale, boolean log) { return d(new CauchyDistributionImpl(location, scale), x, log); }
From source file:org.renjin.Distributions.java
public static double pcauchy(final double q, final double location, final double scale, boolean lowerTail, boolean logP) { return p(new CauchyDistributionImpl(location, scale), q, lowerTail, logP); }
From source file:org.renjin.Distributions.java
public static double qcauchy(final double p, final double location, final double scale, boolean lowerTail, boolean logP) { return q(new CauchyDistributionImpl(location, scale), p, lowerTail, logP); }
From source file:org.renjin.primitives.random.Distributions.java
public static double dcauchy(@Recycle double x, @Recycle double location, @Recycle double scale, boolean log) { return d(new CauchyDistributionImpl(location, scale), x, log); }
From source file:org.renjin.primitives.random.Distributions.java
public static double pcauchy(@Recycle double q, @Recycle double location, @Recycle double scale, boolean lowerTail, boolean logP) { return p(new CauchyDistributionImpl(location, scale), q, lowerTail, logP); }
From source file:org.renjin.primitives.random.Distributions.java
public static double qcauchy(@Recycle double p, @Recycle double location, @Recycle double scale, boolean lowerTail, boolean logP) { return q(new CauchyDistributionImpl(location, scale), p, lowerTail, logP); }