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java.lang.Objectorg.apache.commons.math.distribution.DistributionFactory
This factory provids the means to create common statistical distributions. The following distributions are supported:
DistributionFactory factory = DistributionFactory.newInstance(); // create a Chi-Square distribution with 5 degrees of freedom. ChiSquaredDistribution chi = factory.createChiSquareDistribution(5.0);
| Constructor Summary | |
protected |
DistributionFactory()
Default constructor. |
| Method Summary | |
abstract BinomialDistribution |
createBinomialDistribution(int numberOfTrials,
double probabilityOfSuccess)
Create a binomial distribution with the given number of trials and probability of success. |
CauchyDistribution |
createCauchyDistribution(double median,
double scale)
Create a new cauchy distribution with the given median and scale. |
abstract ChiSquaredDistribution |
createChiSquareDistribution(double degreesOfFreedom)
Create a new chi-square distribution with the given degrees of freedom. |
abstract ExponentialDistribution |
createExponentialDistribution(double mean)
Create a new exponential distribution with the given degrees of freedom. |
abstract FDistribution |
createFDistribution(double numeratorDegreesOfFreedom,
double denominatorDegreesOfFreedom)
Create a new F-distribution with the given degrees of freedom. |
abstract GammaDistribution |
createGammaDistribution(double alpha,
double beta)
Create a new gamma distribution with the given shape and scale parameters. |
abstract HypergeometricDistribution |
createHypergeometricDistribution(int populationSize,
int numberOfSuccesses,
int sampleSize)
Create a new hypergeometric distribution with the given the population size, the number of successes in the population, and the sample size. |
abstract NormalDistribution |
createNormalDistribution()
Create a new normal distribution with mean zero and standard deviation one. |
abstract NormalDistribution |
createNormalDistribution(double mean,
double sd)
Create a new normal distribution with the given mean and standard deviation. |
abstract PoissonDistribution |
createPoissonDistribution(double lambda)
Create a new Poisson distribution with poisson parameter lambda. |
abstract TDistribution |
createTDistribution(double degreesOfFreedom)
Create a new t distribution with the given degrees of freedom. |
WeibullDistribution |
createWeibullDistribution(double alpha,
double beta)
Create a new Weibull distribution with the given shape and scale parameters. |
static DistributionFactory |
newInstance()
Create an instance of a DistributionFactory |
| Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
protected DistributionFactory()
| Method Detail |
public static DistributionFactory newInstance()
DistributionFactory
public abstract BinomialDistribution createBinomialDistribution(int numberOfTrials,
double probabilityOfSuccess)
numberOfTrials - the number of trials.probabilityOfSuccess - the probability of success
public CauchyDistribution createCauchyDistribution(double median,
double scale)
median - the median of the distributionscale - the scale
public abstract ChiSquaredDistribution createChiSquareDistribution(double degreesOfFreedom)
degreesOfFreedom - degrees of freedom
public abstract ExponentialDistribution createExponentialDistribution(double mean)
mean - mean
public abstract FDistribution createFDistribution(double numeratorDegreesOfFreedom,
double denominatorDegreesOfFreedom)
numeratorDegreesOfFreedom - numerator degrees of freedomdenominatorDegreesOfFreedom - denominator degrees of freedom
public abstract GammaDistribution createGammaDistribution(double alpha,
double beta)
alpha - the shape parameterbeta - the scale parameter
public abstract TDistribution createTDistribution(double degreesOfFreedom)
degreesOfFreedom - degrees of freedom
public abstract HypergeometricDistribution createHypergeometricDistribution(int populationSize,
int numberOfSuccesses,
int sampleSize)
populationSize - the population sizenumberOfSuccesses - number of successes in the populationsampleSize - the sample size
public abstract NormalDistribution createNormalDistribution(double mean,
double sd)
mean - the mean of the distributionsd - standard deviation
public abstract NormalDistribution createNormalDistribution()
public abstract PoissonDistribution createPoissonDistribution(double lambda)
lambda - poisson parameter
public WeibullDistribution createWeibullDistribution(double alpha,
double beta)
alpha - the shape parameter.beta - the scale parameter.
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