org.apache.commons.math.distribution
Class WeibullDistributionImpl

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

All Implemented Interfaces:

java.io.Serializable, ContinuousDistribution, Distribution, WeibullDistribution

public class WeibullDistributionImpl
extends AbstractContinuousDistribution
implements WeibullDistribution, java.io.Serializable

Default implementation of WeibullDistribution.

Since:

1.1

Version:

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

See Also:

Serialized Form

Field Summary

static double	DEFAULT_INVERSE_ABSOLUTE_ACCURACY Default inverse cumulative probability accuracy
private double	numericalMean Cached numerical mean
private boolean	numericalMeanIsCalculated Whether or not the numerical mean has been calculated
private double	numericalVariance Cached numerical variance
private boolean	numericalVarianceIsCalculated Whether or not the numerical variance has been calculated
private double	scale The scale parameter.
private static long	serialVersionUID Serializable version identifier
private double	shape The shape parameter.
private double	solverAbsoluteAccuracy Inverse cumulative probability accuracy

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

randomData

Constructor Summary

WeibullDistributionImpl(double alpha, double beta)
Creates weibull distribution with the given shape and scale and a location equal to zero.

WeibullDistributionImpl(double alpha, double beta, double inverseCumAccuracy)
Creates weibull distribution with the given shape, scale and inverse cumulative probability accuracy and a location equal to zero.

Method Summary

protected double	calculateNumericalMean() Calculates the mean.
private double	calculateNumericalVariance() Calculates the variance.
double	cumulativeProbability(double x) For this distribution, X, this method returns P(X < x).
double	density(double x) Returns the probability density for a particular point.
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	getNumericalMean() Returns the mean of the distribution.
double	getNumericalVariance() Returns the variance of the distribution.
double	getScale() Access the scale parameter.
double	getShape() Access the shape parameter.
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.
private void	invalidateParameterDependentMoments() Invalidates the cached mean and variance.
double	inverseCumulativeProbability(double p) For this distribution, X, this method returns the critical point x, such that P(X < x) = p.
void	setScale(double beta) Deprecated. as of 2.1 (class will become immutable in 3.0)
private void	setScaleInternal(double beta) Modify the scale parameter.
void	setShape(double alpha) Deprecated. as of 2.1 (class will become immutable in 3.0)
private void	setShapeInternal(double alpha) Modify the shape parameter.

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

reseedRandomGenerator, sample, 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

serialVersionUID

private static final long serialVersionUID

Serializable version identifier

See Also:

Constant Field Values

shape

private double shape

The shape parameter.

scale

private double scale

The scale parameter.

solverAbsoluteAccuracy

private final double solverAbsoluteAccuracy

Inverse cumulative probability accuracy

numericalMean

private double numericalMean

Cached numerical mean

numericalMeanIsCalculated

private boolean numericalMeanIsCalculated

Whether or not the numerical mean has been calculated

numericalVariance

private double numericalVariance

Cached numerical variance

numericalVarianceIsCalculated

private boolean numericalVarianceIsCalculated

Whether or not the numerical variance has been calculated

Constructor Detail

WeibullDistributionImpl

public WeibullDistributionImpl(double alpha,
                               double beta)

Creates weibull distribution with the given shape and scale and a location equal to zero.

Parameters:

alpha - the shape parameter.

beta - the scale parameter.

WeibullDistributionImpl

public WeibullDistributionImpl(double alpha,
                               double beta,
                               double inverseCumAccuracy)

Creates weibull distribution with the given shape, scale and inverse cumulative probability accuracy and a location equal to zero.

Parameters:

alpha - the shape parameter.

beta - the scale parameter.

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

Since:

2.1

Method Detail

cumulativeProbability

public double cumulativeProbability(double x)

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

Specified by:

cumulativeProbability in interface Distribution

Parameters:

x - the value at which the CDF is evaluated.

Returns:

CDF evaluated at x.

getShape

public double getShape()

Access the shape parameter.

Specified by:

getShape in interface WeibullDistribution

Returns:

the shape parameter.

getScale

public double getScale()

Access the scale parameter.

Specified by:

getScale in interface WeibullDistribution

Returns:

the scale parameter.

density

public double density(double x)

Returns 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

inverseCumulativeProbability

public double inverseCumulativeProbability(double p)

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

Returns Double.NEGATIVE_INFINITY 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:

java.lang.IllegalArgumentException - if p is not a valid probability.

setShape

@Deprecated
public void setShape(double alpha)

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

Modify the shape parameter.

Specified by:

setShape in interface WeibullDistribution

Parameters:

alpha - the new shape parameter value.

setShapeInternal

private void setShapeInternal(double alpha)

Modify the shape parameter.

Parameters:

alpha - the new shape parameter value.

setScale

@Deprecated
public void setScale(double beta)

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

Modify the scale parameter.

Specified by:

setScale in interface WeibullDistribution

Parameters:

beta - the new scale parameter value.

setScaleInternal

private void setScaleInternal(double beta)

Modify the scale parameter.

Parameters:

beta - the new scale parameter value.

getDomainLowerBound

protected double getDomainLowerBound(double p)

Access the domain value lower bound, based on p, used to bracket a CDF root. This method is used by inverseCumulativeProbability(double) to find critical values.

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. This method is used by inverseCumulativeProbability(double) to find critical values.

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. This method is used by inverseCumulativeProbability(double) to find critical values.

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 no matter the parameters.

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 no matter the parameters.

Returns:

upper bound of the support (always Double.POSITIVE_INFINITY)

Since:

2.2

calculateNumericalMean

protected double calculateNumericalMean()

Calculates the mean. The mean is scale * Gamma(1 + (1 / shape)) where Gamma(...) is the Gamma-function

Returns:

the mean

Since:

2.2

calculateNumericalVariance

private double calculateNumericalVariance()

Calculates the variance. The variance is scale^2 * Gamma(1 + (2 / shape)) - mean^2 where Gamma(...) is the Gamma-function

Returns:

the variance

Since:

2.2

getNumericalMean

public double getNumericalMean()

Returns the mean of the distribution.

Returns:

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

Since:

2.2

getNumericalVariance

public double getNumericalVariance()

Returns the variance of the distribution.

Returns:

the variance (possibly Double.POSITIVE_INFINITY as for certain cases in TDistributionImpl) or Double.NaN if it's not defined

Since:

2.2

invalidateParameterDependentMoments

private void invalidateParameterDependentMoments()

Invalidates the cached mean and variance.