org.apache.commons.math3.distribution

Class AbstractIntegerDistribution

java.lang.Object

org.apache.commons.math3.distribution.AbstractIntegerDistribution

All Implemented Interfaces:

Serializable, IntegerDistribution

Direct Known Subclasses:

BinomialDistribution, EnumeratedIntegerDistribution, GeometricDistribution, HypergeometricDistribution, PascalDistribution, PoissonDistribution, UniformIntegerDistribution, ZipfDistribution

public abstract class AbstractIntegerDistribution
extends Object
implements IntegerDistribution, Serializable

Base class for integer-valued discrete distributions. Default implementations are provided for some of the methods that do not vary from distribution to distribution.

See Also:

Serialized Form

Method Summary

Modifier and Type	Method and Description
double	cumulativeProbability(int x0, int x1) For a random variable X whose values are distributed according to this distribution, this method returns P(x0 < X <= x1).
int	inverseCumulativeProbability(double p) Computes the quantile function of this distribution.
double	logProbability(int x) For a random variable X whose values are distributed according to this distribution, this method returns log(P(X = x)), where log is the natural logarithm.
void	reseedRandomGenerator(long seed) Reseed the random generator used to generate samples.
int	sample() Generate a random value sampled from this distribution.
int[]	sample(int sampleSize) Generate a random sample from the distribution.

Methods inherited from class java.lang.Object

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

Methods inherited from interface org.apache.commons.math3.distribution.IntegerDistribution

cumulativeProbability, getNumericalMean, getNumericalVariance, getSupportLowerBound, getSupportUpperBound, isSupportConnected, probability

Method Detail

cumulativeProbability

public double cumulativeProbability(int x0,
                                    int x1)
                             throws NumberIsTooLargeException

For a random variable X whose values are distributed according to this distribution, this method returns P(x0 < X <= x1). The default implementation uses the identity

P(x0 < X <= x1) = P(X <= x1) - P(X <= x0)

Specified by:

cumulativeProbability in interface IntegerDistribution

Parameters:

x0 - the exclusive lower bound

x1 - the inclusive upper bound

Returns:

the probability that a random variable with this distribution will take a value between x0 and x1, excluding the lower and including the upper endpoint

Throws:

NumberIsTooLargeException - if x0 > x1

inverseCumulativeProbability

public int inverseCumulativeProbability(double p)
                                 throws OutOfRangeException

Computes the quantile function of this distribution. For a random variable X distributed according to this distribution, the returned value is

• inf{x in Z | P(X<=x) >= p} for 0 < p <= 1,
• inf{x in Z | P(X<=x) > 0} for p = 0.

If the result exceeds the range of the data type int, then Integer.MIN_VALUE or Integer.MAX_VALUE is returned. The default implementation returns

• IntegerDistribution.getSupportLowerBound() for p = 0,
• IntegerDistribution.getSupportUpperBound() for p = 1, and
• solveInverseCumulativeProbability(double, int, int) for 0 < p < 1.

Specified by:

inverseCumulativeProbability in interface IntegerDistribution

Parameters:

p - the cumulative probability

Returns:

the smallest p-quantile of this distribution (largest 0-quantile for p = 0)

Throws:

OutOfRangeException - if p < 0 or p > 1

reseedRandomGenerator

public void reseedRandomGenerator(long seed)

Reseed the random generator used to generate samples.

Specified by:

reseedRandomGenerator in interface IntegerDistribution

Parameters:

seed - the new seed

sample

public int sample()

Generate a random value sampled from this distribution. The default implementation uses the inversion method.

Specified by:

sample in interface IntegerDistribution

Returns:

a random value

sample

public int[] sample(int sampleSize)

Generate a random sample from the distribution. The default implementation generates the sample by calling sample() in a loop.

Specified by:

sample in interface IntegerDistribution

Parameters:

sampleSize - the number of random values to generate

Returns:

an array representing the random sample

logProbability

public double logProbability(int x)

For a random variable X whose values are distributed according to this distribution, this method returns log(P(X = x)), where log is the natural logarithm. In other words, this method represents the logarithm of the probability mass function (PMF) for the distribution. Note that due to the floating point precision and under/overflow issues, this method will for some distributions be more precise and faster than computing the logarithm of IntegerDistribution.probability(int).

The default implementation simply computes the logarithm of probability(x).

Parameters:

x - the point at which the PMF is evaluated

Returns:

the logarithm of the value of the probability mass function at x