org.apache.commons.math.distribution
Class HypergeometricDistributionImpl

java.lang.Object
  org.apache.commons.math.distribution.AbstractDistribution
      org.apache.commons.math.distribution.AbstractIntegerDistribution
          org.apache.commons.math.distribution.HypergeometricDistributionImpl

All Implemented Interfaces:

java.io.Serializable, DiscreteDistribution, Distribution, HypergeometricDistribution, IntegerDistribution

public class HypergeometricDistributionImpl
extends AbstractIntegerDistribution
implements HypergeometricDistribution, java.io.Serializable

The default implementation of HypergeometricDistribution.

Version:

$Id: HypergeometricDistributionImpl.java 1182787 2011-10-13 11:20:48Z celestin $

See Also:

Serialized Form

Field Summary

Fields inherited from class org.apache.commons.math.distribution.AbstractIntegerDistribution

randomData

Constructor Summary

HypergeometricDistributionImpl(int populationSize, int numberOfSuccesses, int sampleSize)
Construct a new hypergeometric distribution with the given the population size, the number of successes in the population, and the sample size.

Method Summary

protected double	calculateNumericalMean() Use this method to actually calculate the mean for the specific distribution.
protected double	calculateNumericalVariance() Use this method to actually calculate the variance for the specific distribution.
double	cumulativeProbability(int x) For this distribution, X, this method returns P(X <= x).
protected int	getDomainLowerBound(double p) Access the domain value lower bound, based on p, used to bracket a PDF root.
protected int	getDomainUpperBound(double p) Access the domain value upper bound, based on p, used to bracket a PDF root.
int	getNumberOfSuccesses() Access the number of successes.
int	getPopulationSize() Access the population size.
int	getSampleSize() Access the sample size.
int	getSupportLowerBound() Access the lower bound of the support.
int	getSupportUpperBound() Access the upper bound of the support.
double	probability(int x) For this distribution, X, this method returns P(X = x).
double	upperCumulativeProbability(int x) For this distribution, X, this method returns P(X >= x).

Methods inherited from class org.apache.commons.math.distribution.AbstractIntegerDistribution

cumulativeProbability, cumulativeProbability, cumulativeProbability, inverseCumulativeProbability, isSupportLowerBoundInclusive, isSupportUpperBoundInclusive, probability, reseedRandomGenerator, sample, sample

Methods inherited from class org.apache.commons.math.distribution.AbstractDistribution

getNumericalMean, getNumericalVariance, isSupportConnected

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.commons.math.distribution.IntegerDistribution

cumulativeProbability, inverseCumulativeProbability, reseedRandomGenerator, sample, sample

Methods inherited from interface org.apache.commons.math.distribution.DiscreteDistribution

probability

Methods inherited from interface org.apache.commons.math.distribution.Distribution

cumulativeProbability, cumulativeProbability, getNumericalMean, getNumericalVariance, isSupportConnected, isSupportLowerBoundInclusive, isSupportUpperBoundInclusive

Constructor Detail

HypergeometricDistributionImpl

public HypergeometricDistributionImpl(int populationSize,
                                      int numberOfSuccesses,
                                      int sampleSize)

Construct a new hypergeometric distribution with the given the population size, the number of successes in the population, and the sample size.

Parameters:

populationSize - Population size.

numberOfSuccesses - Number of successes in the population.

sampleSize - Sample size.

Throws:

NotPositiveException - if numberOfSuccesses < 0.

NotStrictlyPositiveException - if populationSize <= 0.

NotPositiveException - if populationSize < 0.

NumberIsTooLargeException - if numberOfSuccesses > populationSize.

NumberIsTooLargeException - if sampleSize > populationSize.

Method Detail

cumulativeProbability

public double cumulativeProbability(int x)

For this distribution, X, this method returns P(X <= x).

Specified by:

cumulativeProbability in interface IntegerDistribution

Specified by:

cumulativeProbability in class AbstractIntegerDistribution

Parameters:

x - Value at which the PDF is evaluated.

Returns:

PDF for this distribution.

getDomainLowerBound

protected int getDomainLowerBound(double p)

Access the domain value lower bound, based on p, used to bracket a PDF root.

Specified by:

getDomainLowerBound in class AbstractIntegerDistribution

Parameters:

p - Desired probability for the critical value.

Returns:

the domain value lower bound, i.e. P(X < 'lower bound') < p.

getDomainUpperBound

protected int getDomainUpperBound(double p)

Access the domain value upper bound, based on p, used to bracket a PDF root.

Specified by:

getDomainUpperBound in class AbstractIntegerDistribution

Parameters:

p - Desired probability for the critical value

Returns:

the domain value upper bound, i.e. P(X < 'upper bound') > p.

getNumberOfSuccesses

public int getNumberOfSuccesses()

Access the number of successes.

Specified by:

getNumberOfSuccesses in interface HypergeometricDistribution

Returns:

the number of successes.

getPopulationSize

public int getPopulationSize()

Access the population size.

Specified by:

getPopulationSize in interface HypergeometricDistribution

Returns:

the population size.

getSampleSize

public int getSampleSize()

Access the sample size.

Specified by:

getSampleSize in interface HypergeometricDistribution

Returns:

the sample size.

probability

public double probability(int x)

For this distribution, X, this method returns P(X = x).

Specified by:

probability in interface IntegerDistribution

Parameters:

x - Value at which the PMF is evaluated.

Returns:

PMF for this distribution.

upperCumulativeProbability

public double upperCumulativeProbability(int x)

For this distribution, X, this method returns P(X >= x).

Parameters:

x - Value at which the CDF is evaluated.

Returns:

the upper tail CDF for this distribution.

Since:

1.1

getSupportLowerBound

public int getSupportLowerBound()

Access the lower bound of the support. For population size N, number of successes m, and sample size n, the lower bound of the support is max(0, n + m - N)

Specified by:

getSupportLowerBound in class AbstractIntegerDistribution

Returns:

lower bound of the support

getSupportUpperBound

public int getSupportUpperBound()

Access the upper bound of the support. For number of successes m and sample size n, the upper bound of the support is min(m, n)

Specified by:

getSupportUpperBound in class AbstractIntegerDistribution

Returns:

upper bound of the support

calculateNumericalMean

protected double calculateNumericalMean()

Use this method to actually calculate the mean for the specific distribution. Use AbstractDistribution.getNumericalMean() (which implements caching) to actually get the mean. For population size N, number of successes m, and sample size n, the mean is n * m / N

Specified by:

calculateNumericalMean in class AbstractDistribution

Returns:

the mean or Double.NaN if it's not defined

calculateNumericalVariance

protected double calculateNumericalVariance()

Use this method to actually calculate the variance for the specific distribution. Use AbstractDistribution.getNumericalVariance() (which implements caching) to actually get the variance. For population size N, number of successes m, and sample size n, the variance is [ n * m * (N - n) * (N - m) ] / [ N^2 * (N - 1) ]

Specified by:

calculateNumericalVariance in class AbstractDistribution

Returns:

the variance or Double.NaN if it's not defined