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:

$Revision: 1054524 $ $Date: 2011-01-03 05:59:18 +0100 (lun. 03 janv. 2011) $

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
private int	numberOfSuccesses The number of successes in the population.
private int	populationSize The population size.
private int	sampleSize The sample size.
private static long	serialVersionUID Serializable version identifier

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

randomData

Constructor Summary

Constructors

Constructor and Description
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

Methods

Modifier and Type	Method and Description
double	cumulativeProbability(int x) For this distribution, X, this method returns P(X ≤ x).
private int[]	getDomain(int n, int m, int k) Return the domain for the given hypergeometric distribution parameters.
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.
private int	getLowerDomain(int n, int m, int k) Return the lowest domain value for the given hypergeometric distribution parameters.
int	getNumberOfSuccesses() Access the number of successes.
protected double	getNumericalMean() Returns the mean.
double	getNumericalVariance() Returns the variance.
int	getPopulationSize() Access the population size.
int	getSampleSize() Access the sample size.
int	getSupportLowerBound() Returns the lower bound for the support for the distribution.
int	getSupportUpperBound() Returns the upper bound for the support of the distribution.
private int	getUpperDomain(int m, int k) Return the highest domain value for the given hypergeometric distribution parameters.
private double	innerCumulativeProbability(int x0, int x1, int dx, int n, int m, int k) For this distribution, X, this method returns P(x0 ≤ X ≤ x1).
double	probability(int x) For this distribution, X, this method returns P(X = x).
private double	probability(int n, int m, int k, int x) For the distribution, X, defined by the given hypergeometric distribution parameters, this method returns P(X = x).
void	setNumberOfSuccesses(int num) Deprecated. as of 2.1 (class will become immutable in 3.0)
private void	setNumberOfSuccessesInternal(int num) Modify the number of successes.
void	setPopulationSize(int size) Deprecated. as of 2.1 (class will become immutable in 3.0)
private void	setPopulationSizeInternal(int size) Modify the population size.
void	setSampleSize(int size) Deprecated. as of 2.1 (class will become immutable in 3.0)
private void	setSampleSizeInternal(int size) Modify the sample size.
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 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

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

probability

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

cumulativeProbability, cumulativeProbability

Field Detail

serialVersionUID

private static final long serialVersionUID

Serializable version identifier

See Also:

Constant Field Values

numberOfSuccesses

private int numberOfSuccesses

The number of successes in the population.

populationSize

private int populationSize

The population size.

sampleSize

private int sampleSize

The sample size.

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 - the population size.

numberOfSuccesses - number of successes in the population.

sampleSize - the sample size.

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 - the value at which the PDF is evaluated.

Returns:

PDF for this distribution.

getDomain

private int[] getDomain(int n,
              int m,
              int k)

Return the domain for the given hypergeometric distribution parameters.

Parameters:

n - the population size.

m - number of successes in the population.

k - the sample size.

Returns:

a two element array containing the lower and upper bounds of the hypergeometric 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 - the desired probability for the critical value

Returns:

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 - the desired probability for the critical value

Returns:

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

getLowerDomain

private int getLowerDomain(int n,
                 int m,
                 int k)

Return the lowest domain value for the given hypergeometric distribution parameters.

Parameters:

n - the population size.

m - number of successes in the population.

k - the sample size.

Returns:

the lowest domain value of the hypergeometric distribution.

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.

getUpperDomain

private int getUpperDomain(int m,
                 int k)

Return the highest domain value for the given hypergeometric distribution parameters.

Parameters:

m - number of successes in the population.

k - the sample size.

Returns:

the highest domain value of the hypergeometric distribution.

probability

public double probability(int x)

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

Specified by:

probability in interface IntegerDistribution

Parameters:

x - the value at which the PMF is evaluated.

Returns:

PMF for this distribution.

probability

private double probability(int n,
                 int m,
                 int k,
                 int x)

For the distribution, X, defined by the given hypergeometric distribution parameters, this method returns P(X = x).

Parameters:

n - the population size.

m - number of successes in the population.

k - the sample size.

x - the value at which the PMF is evaluated.

Returns:

PMF for the distribution.

setNumberOfSuccesses

@Deprecated
public void setNumberOfSuccesses(int num)

Deprecated. as of 2.1 (class will become immutable in 3.0)

Modify the number of successes.

Specified by:

setNumberOfSuccesses in interface HypergeometricDistribution

Parameters:

num - the new number of successes.

Throws:

java.lang.IllegalArgumentException - if num is negative.

setNumberOfSuccessesInternal

private void setNumberOfSuccessesInternal(int num)

Modify the number of successes.

Parameters:

num - the new number of successes.

Throws:

java.lang.IllegalArgumentException - if num is negative.

setPopulationSize

@Deprecated
public void setPopulationSize(int size)

Deprecated. as of 2.1 (class will become immutable in 3.0)

Modify the population size.

Specified by:

setPopulationSize in interface HypergeometricDistribution

Parameters:

size - the new population size.

Throws:

java.lang.IllegalArgumentException - if size is not positive.

setPopulationSizeInternal

private void setPopulationSizeInternal(int size)

Modify the population size.

Parameters:

size - the new population size.

Throws:

java.lang.IllegalArgumentException - if size is not positive.

setSampleSize

@Deprecated
public void setSampleSize(int size)

Deprecated. as of 2.1 (class will become immutable in 3.0)

Modify the sample size.

Specified by:

setSampleSize in interface HypergeometricDistribution

Parameters:

size - the new sample size.

Throws:

java.lang.IllegalArgumentException - if size is negative.

setSampleSizeInternal

private void setSampleSizeInternal(int size)

Modify the sample size.

Parameters:

size - the new sample size.

Throws:

java.lang.IllegalArgumentException - if size is negative.

upperCumulativeProbability

public double upperCumulativeProbability(int x)

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

Parameters:

x - the value at which the CDF is evaluated.

Returns:

upper tail CDF for this distribution.

Since:

1.1

innerCumulativeProbability

private double innerCumulativeProbability(int x0,
                                int x1,
                                int dx,
                                int n,
                                int m,
                                int k)

For this distribution, X, this method returns P(x0 ≤ X ≤ x1). This probability is computed by summing the point probabilities for the values x0, x0 + 1, x0 + 2, ..., x1, in the order directed by dx.

Parameters:

x0 - the inclusive, lower bound

x1 - the inclusive, upper bound

dx - the direction of summation. 1 indicates summing from x0 to x1. 0 indicates summing from x1 to x0.

n - the population size.

m - number of successes in the population.

k - the sample size.

Returns:

P(x0 ≤ X ≤ x1).

getSupportLowerBound

public int getSupportLowerBound()

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

Returns:

lower bound of the support

Since:

2.2

getSupportUpperBound

public int getSupportUpperBound()

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

Returns:

upper bound of the support

Since:

2.2

getNumericalMean

protected double getNumericalMean()

Returns the mean. For population size N, number of successes m, and sample size n, the mean is n * m / N

Returns:

the mean

Since:

2.2

getNumericalVariance

public double getNumericalVariance()

Returns 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) ]

Returns:

the variance

Since:

2.2