org.apache.commons.math3.distribution

## Class WeibullDistribution

- java.lang.Object
- - org.apache.commons.math3.distribution.AbstractRealDistribution
  - - org.apache.commons.math3.distribution.WeibullDistribution

All Implemented Interfaces:
  Serializable, RealDistribution

    public class WeibullDistribution
    extends AbstractRealDistribution

Implementation of the Weibull distribution. This implementation uses the two parameter form of the distribution defined by Weibull Distribution, equations (1) and (2).

Since:
  1.1 (changed to concrete class in 3.0)
Version:
  $Id: WeibullDistribution.java 1416643 2012-12-03 19:37:14Z tn $
See Also:
  Weibull distribution (Wikipedia), Weibull distribution (MathWorld), Serialized Form

### Field Summary

Fields
`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.math3.distribution.AbstractRealDistribution

`random, randomData, SOLVER_DEFAULT_ABSOLUTE_ACCURACY`

### Constructor Summary

Constructors
Constructor and Description
`WeibullDistribution(double alpha, double beta)` Create a Weibull distribution with the given shape and scale and a location equal to zero.
`WeibullDistribution(double alpha, double beta, double inverseCumAccuracy)` Create a Weibull distribution with the given shape, scale and inverse cumulative probability accuracy and a location equal to zero.
`WeibullDistribution(RandomGenerator rng, double alpha, double beta, double inverseCumAccuracy)` Creates a Weibull distribution.

### Method Summary

Methods
`protected double` `calculateNumericalMean()` used by `getNumericalMean()`
`protected double` `calculateNumericalVariance()` used by `getNumericalVariance()`
`double` `cumulativeProbability(double x)` For a random variable `X` whose values are distributed according to this distribution, this method returns `P(X <= x)`.
`double` `density(double x)` Returns the probability density function (PDF) of this distribution evaluated at the specified point `x`.
`double` `getNumericalMean()` Use this method to get the numerical value of the mean of this distribution.
`double` `getNumericalVariance()` Use this method to get the numerical value of the variance of this distribution.
`double` `getScale()` Access the scale parameter, `beta`.
`double` `getShape()` Access the shape parameter, `alpha`.
`protected double` `getSolverAbsoluteAccuracy()` Return the absolute accuracy setting of the solver used to estimate inverse cumulative probabilities.
`double` `getSupportLowerBound()` Access the lower bound of the support.
`double` `getSupportUpperBound()` Access the upper bound of the support.
`double` `inverseCumulativeProbability(double p)` Computes the quantile function of this distribution.
`boolean` `isSupportConnected()` Use this method to get information about whether the support is connected, i.e.
`boolean` `isSupportLowerBoundInclusive()` Whether or not the lower bound of support is in the domain of the density function.
`boolean` `isSupportUpperBoundInclusive()` Whether or not the upper bound of support is in the domain of the density function.

### Methods inherited from class org.apache.commons.math3.distribution.AbstractRealDistribution

`cumulativeProbability, probability, probability, reseedRandomGenerator, sample, sample`

### Methods inherited from class java.lang.Object

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

### 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 final double shape

The shape parameter.

#### scale

    private final 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

#### WeibullDistribution

    public WeibullDistribution(double alpha,
                       double beta)
                        throws NotStrictlyPositiveException

Create a Weibull distribution with the given shape and scale and a location equal to zero.

Parameters:
  `alpha` - Shape parameter.
  `beta` - Scale parameter.
Throws:
  `NotStrictlyPositiveException` - if `alpha <= 0` or `beta <= 0`.

#### WeibullDistribution

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

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

Parameters:
  `alpha` - Shape parameter.
  `beta` - Scale parameter.
  `inverseCumAccuracy` - Maximum absolute error in inverse cumulative probability estimates (defaults to `DEFAULT_INVERSE_ABSOLUTE_ACCURACY`).
Throws:
  `NotStrictlyPositiveException` - if `alpha <= 0` or `beta <= 0`.
Since:
  2.1

#### WeibullDistribution

    public WeibullDistribution(RandomGenerator rng,
                       double alpha,
                       double beta,
                       double inverseCumAccuracy)
                        throws NotStrictlyPositiveException

Creates a Weibull distribution.

Parameters:
  `rng` - Random number generator.
  `alpha` - Shape parameter.
  `beta` - Scale parameter.
  `inverseCumAccuracy` - Maximum absolute error in inverse cumulative probability estimates (defaults to `DEFAULT_INVERSE_ABSOLUTE_ACCURACY`).
Throws:
  `NotStrictlyPositiveException` - if `alpha <= 0` or `beta <= 0`.
Since:
  3.1

### Method Detail

#### getShape

    public double getShape()

Access the shape parameter, `alpha`.

Returns:
  the shape parameter, `alpha`.

#### getScale

    public double getScale()

Access the scale parameter, `beta`.

Returns:
  the scale parameter, `beta`.

#### density

    public double density(double x)

Returns the probability density function (PDF) of this distribution evaluated at the specified point `x`. In general, the PDF is the derivative of the `CDF`. If the derivative does not exist at `x`, then an appropriate replacement should be returned, e.g. `Double.POSITIVE_INFINITY`, `Double.NaN`, or the limit inferior or limit superior of the difference quotient.

Parameters:
  `x` - the point at which the PDF is evaluated
Returns:
  the value of the probability density function at point `x`

#### cumulativeProbability

    public double cumulativeProbability(double x)

For a random variable `X` whose values are distributed according to this distribution, this method returns `P(X <= x)`. In other words, this method represents the (cumulative) distribution function (CDF) for this distribution.

Parameters:
  `x` - the point at which the CDF is evaluated
Returns:
  the probability that a random variable with this distribution takes a value less than or equal to `x`

#### inverseCumulativeProbability

    public double inverseCumulativeProbability(double p)

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

- `inf{x in R | P(X<=x) >= p}` for `0 < p <= 1`,
- `inf{x in R | P(X<=x) > 0}` for `p = 0`.

The default implementation returns

- `RealDistribution.getSupportLowerBound()` for `p = 0`,
- `RealDistribution.getSupportUpperBound()` for `p = 1`.

Returns `0` when `p == 0` and `Double.POSITIVE_INFINITY` when `p == 1`.

Specified by:
  `inverseCumulativeProbability` in interface `RealDistribution`
Overrides:
  `inverseCumulativeProbability` in class `AbstractRealDistribution`
Parameters:
  `p` - the cumulative probability
Returns:
  the smallest `p`-quantile of this distribution (largest 0-quantile for `p = 0`)

#### getSolverAbsoluteAccuracy

    protected double getSolverAbsoluteAccuracy()

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

Overrides:
  `getSolverAbsoluteAccuracy` in class `AbstractRealDistribution`
Returns:
  the solver absolute accuracy.
Since:
  2.1

#### getNumericalMean

    public double getNumericalMean()

Use this method to get the numerical value of the mean of this distribution. The mean is `scale * Gamma(1 + (1 / shape))`, where `Gamma()` is the Gamma-function.

Returns:
  the mean or `Double.NaN` if it is not defined

#### calculateNumericalMean

    protected double calculateNumericalMean()

used by `getNumericalMean()`

Returns:
  the mean of this distribution

#### getNumericalVariance

    public double getNumericalVariance()

Use this method to get the numerical value of the variance of this distribution. The variance is `scale^2 * Gamma(1 + (2 / shape)) - mean^2` where `Gamma()` is the Gamma-function.

Returns:
  the variance (possibly `Double.POSITIVE_INFINITY` as for certain cases in `TDistribution`) or `Double.NaN` if it is not defined

#### calculateNumericalVariance

    protected double calculateNumericalVariance()

used by `getNumericalVariance()`

Returns:
  the variance of this distribution

#### getSupportLowerBound

    public double getSupportLowerBound()

Access the lower bound of the support. This method must return the same value as `inverseCumulativeProbability(0)`. In other words, this method must return

`inf {x in R | P(X <= x) > 0}`.

The lower bound of the support is always 0 no matter the parameters.

Returns:
  lower bound of the support (always 0)

#### getSupportUpperBound

    public double getSupportUpperBound()

Access the upper bound of the support. This method must return the same value as `inverseCumulativeProbability(1)`. In other words, this method must return

`inf {x in R | P(X <= x) = 1}`.

The upper bound of the support is always positive infinity no matter the parameters.

Returns:
  upper bound of the support (always `Double.POSITIVE_INFINITY`)

#### isSupportLowerBoundInclusive

    public boolean isSupportLowerBoundInclusive()

Whether or not the lower bound of support is in the domain of the density function. Returns true iff `getSupporLowerBound()` is finite and `density(getSupportLowerBound())` returns a non-NaN, non-infinite value.

Returns:
  true if the lower bound of support is finite and the density function returns a non-NaN, non-infinite value there

#### isSupportUpperBoundInclusive

    public boolean isSupportUpperBoundInclusive()

Whether or not the upper bound of support is in the domain of the density function. Returns true iff `getSupportUpperBound()` is finite and `density(getSupportUpperBound())` returns a non-NaN, non-infinite value.

Returns:
  true if the upper bound of support is finite and the density function returns a non-NaN, non-infinite value there

#### isSupportConnected

    public boolean isSupportConnected()

Use this method to get information about whether the support is connected, i.e. whether all values between the lower and upper bound of the support are included in the support. The support of this distribution is connected.

Returns:
  `true`