org.apache.commons.math.distribution
Class ExponentialDistributionImpl

java.lang.Object
  org.apache.commons.math.distribution.AbstractDistribution
      org.apache.commons.math.distribution.AbstractContinuousDistribution
          org.apache.commons.math.distribution.ExponentialDistributionImpl

All Implemented Interfaces:

Serializable, ContinuousDistribution, Distribution, ExponentialDistribution, HasDensity<Double>

public class ExponentialDistributionImpl
extends AbstractContinuousDistribution
implements ExponentialDistribution, Serializable

The default implementation of ExponentialDistribution.

Version:

$Revision: 1055914 $ $Date: 2011-01-06 16:34:34 +0100 (jeu. 06 janv. 2011) $

See Also:

Serialized Form

Field Summary

static double

DEFAULT_INVERSE_ABSOLUTE_ACCURACY Default inverse cumulative probability accuracy

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

randomData

Constructor Summary

ExponentialDistributionImpl(double mean)
Create a exponential distribution with the given mean.

ExponentialDistributionImpl(double mean, double inverseCumAccuracy)
Create a exponential distribution with the given mean.

Method Summary

double	cumulativeProbability(double x) For this distribution, X, this method returns P(X < x).
double	density(double x) Return the probability density for a particular point.
double	density(Double x) Deprecated. - use density(double)
protected double	getDomainLowerBound(double p) Access the domain value lower bound, based on p, used to bracket a CDF root.
protected double	getDomainUpperBound(double p) Access the domain value upper bound, based on p, used to bracket a CDF root.
protected double	getInitialDomain(double p) Access the initial domain value, based on p, used to bracket a CDF root.
double	getMean() Access the mean.
double	getNumericalMean() Returns the mean of the distribution.
double	getNumericalVariance() Returns the variance of the distribution.
protected double	getSolverAbsoluteAccuracy() Return the absolute accuracy setting of the solver used to estimate inverse cumulative probabilities.
double	getSupportLowerBound() Returns the lower bound of the support for the distribution.
double	getSupportUpperBound() Returns the upper bound of the support for the distribution.
double	inverseCumulativeProbability(double p) For this distribution, X, this method returns the critical point x, such that P(X < x) = p.
double	sample() Generates a random value sampled from this distribution.
void	setMean(double mean) Deprecated. as of 2.1 (class will become immutable in 3.0)

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

reseedRandomGenerator, sample

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

cumulativeProbability

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.Distribution

cumulativeProbability

Field Detail

DEFAULT_INVERSE_ABSOLUTE_ACCURACY

public static final double DEFAULT_INVERSE_ABSOLUTE_ACCURACY

Default inverse cumulative probability accuracy

Since:

2.1

See Also:

Constant Field Values

Constructor Detail

ExponentialDistributionImpl

public ExponentialDistributionImpl(double mean)

Create a exponential distribution with the given mean.

Parameters:

mean - mean of this distribution.

ExponentialDistributionImpl

public ExponentialDistributionImpl(double mean,
                                   double inverseCumAccuracy)

Create a exponential distribution with the given mean.

Parameters:

mean - mean of this distribution.

inverseCumAccuracy - the maximum absolute error in inverse cumulative probability estimates (defaults to DEFAULT_INVERSE_ABSOLUTE_ACCURACY)

Since:

2.1

Method Detail

setMean

@Deprecated
public void setMean(double mean)

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

Modify the mean.

Specified by:

setMean in interface ExponentialDistribution

Parameters:

mean - the new mean.

Throws:

IllegalArgumentException - if mean is not positive.

getMean

public double getMean()

Access the mean.

Specified by:

getMean in interface ExponentialDistribution

Returns:

the mean.

density

@Deprecated
public double density(Double x)

Deprecated. - use density(double)

Return the probability density for a particular point.

Specified by:

density in interface ExponentialDistribution

Specified by:

density in interface HasDensity<Double>

Parameters:

x - The point at which the density should be computed.

Returns:

The pdf at point x.

density

public double density(double x)

Return the probability density for a particular point.

Overrides:

density in class AbstractContinuousDistribution

Parameters:

x - The point at which the density should be computed.

Returns:

The pdf at point x.

Since:

2.1

cumulativeProbability

public double cumulativeProbability(double x)
                             throws MathException

For this distribution, X, this method returns P(X < x). The implementation of this method is based on:

• Exponential Distribution, equation (1).

Specified by:

cumulativeProbability in interface Distribution

Parameters:

x - the value at which the CDF is evaluated.

Returns:

CDF for this distribution.

Throws:

MathException - if the cumulative probability can not be computed due to convergence or other numerical errors.

inverseCumulativeProbability

public double inverseCumulativeProbability(double p)
                                    throws MathException

For this distribution, X, this method returns the critical point x, such that P(X < x) = p.

Returns 0 for p=0 and Double.POSITIVE_INFINITY for p=1.

Specified by:

inverseCumulativeProbability in interface ContinuousDistribution

Overrides:

inverseCumulativeProbability in class AbstractContinuousDistribution

Parameters:

p - the desired probability

Returns:

x, such that P(X < x) = p

Throws:

MathException - if the inverse cumulative probability can not be computed due to convergence or other numerical errors.

IllegalArgumentException - if p < 0 or p > 1.

sample

public double sample()
              throws MathException

Generates a random value sampled from this distribution.

Algorithm Description: Uses the Inversion Method to generate exponentially distributed random values from uniform deviates.

Overrides:

sample in class AbstractContinuousDistribution

Returns:

random value

Throws:

MathException - if an error occurs generating the random value

Since:

2.2

getDomainLowerBound

protected double getDomainLowerBound(double p)

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

Specified by:

getDomainLowerBound in class AbstractContinuousDistribution

Parameters:

p - the desired probability for the critical value

Returns:

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

getDomainUpperBound

protected double getDomainUpperBound(double p)

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

Specified by:

getDomainUpperBound in class AbstractContinuousDistribution

Parameters:

p - the desired probability for the critical value

Returns:

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

getInitialDomain

protected double getInitialDomain(double p)

Access the initial domain value, based on p, used to bracket a CDF root.

Specified by:

getInitialDomain in class AbstractContinuousDistribution

Parameters:

p - the desired probability for the critical value

Returns:

initial domain value

getSolverAbsoluteAccuracy

protected double getSolverAbsoluteAccuracy()

Return the absolute accuracy setting of the solver used to estimate inverse cumulative probabilities.

Overrides:

getSolverAbsoluteAccuracy in class AbstractContinuousDistribution

Returns:

the solver absolute accuracy

Since:

2.1

getSupportLowerBound

public double getSupportLowerBound()

Returns the lower bound of the support for the distribution. The lower bound of the support is always 0, regardless of the mean.

Returns:

lower bound of the support (always 0)

Since:

2.2

getSupportUpperBound

public double getSupportUpperBound()

Returns the upper bound of the support for the distribution. The upper bound of the support is always positive infinity, regardless of the mean.

Returns:

upper bound of the support (always Double.POSITIVE_INFINITY)

Since:

2.2

getNumericalMean

public double getNumericalMean()

Returns the mean of the distribution. For mean parameter k, the mean is k

Returns:

the mean

Since:

2.2

getNumericalVariance

public double getNumericalVariance()

Returns the variance of the distribution. For mean parameter k, the variance is k^2

Returns:

the variance

Since:

2.2