AbstractRealDistribution (Apache Commons Math 4.0-SNAPSHOT API)

java.lang.Object
- org.apache.commons.math4.distribution.AbstractRealDistribution

All Implemented Interfaces:

Serializable, RealDistribution

Direct Known Subclasses:

BetaDistribution, CauchyDistribution, ChiSquaredDistribution, ConstantRealDistribution, EmpiricalDistribution, EnumeratedRealDistribution, ExponentialDistribution, FDistribution, GammaDistribution, GumbelDistribution, LaplaceDistribution, LevyDistribution, LogisticDistribution, LogNormalDistribution, NakagamiDistribution, NormalDistribution, ParetoDistribution, TDistribution, TriangularDistribution, UniformRealDistribution, WeibullDistribution
```
public abstract class AbstractRealDistribution
extends Object
implements RealDistribution, Serializable
```
Base class for probability distributions on the reals. Default implementations are provided for some of the methods that do not vary from distribution to distribution.

Since:

3.0

See Also:
Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`protected RandomGenerator`	`random` RNG instance used to generate samples from the distribution.
`static double`	`SOLVER_DEFAULT_ABSOLUTE_ACCURACY` Default absolute accuracy for inverse cumulative computation.

Constructor Summary

Constructors
Modifier Constructor and Description

protected AbstractRealDistribution(RandomGenerator rng)

Constructors
Modifier	Constructor and Description
`protected`	`AbstractRealDistribution(RandomGenerator rng)`

Method Summary

Methods
Modifier and Type	Method and Description
`protected double`	`getSolverAbsoluteAccuracy()` Returns the solver absolute accuracy for inverse cumulative computation.
`double`	`inverseCumulativeProbability(double p)` Computes the quantile function of this distribution.
`double`	`logDensity(double x)` Returns the natural logarithm of the probability density function (PDF) of this distribution evaluated at the specified point `x`.
`double`	`probability(double x)` For a random variable `X` whose values are distributed according to this distribution, this method returns `P(X = x)`.
`double`	`probability(double x0, double x1)` For a random variable `X` whose values are distributed according to this distribution, this method returns `P(x0 < X <= x1)`.
`void`	`reseedRandomGenerator(long seed)` Reseed the random generator used to generate samples.
`double`	`sample()` Generate a random value sampled from this distribution.
`double[]`	`sample(int sampleSize)` Generate a random sample from the distribution.

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.math4.distribution.RealDistribution
cumulativeProbability, density, getNumericalMean, getNumericalVariance, getSupportLowerBound, getSupportUpperBound, isSupportConnected

- Field Detail
  - SOLVER_DEFAULT_ABSOLUTE_ACCURACY
```
public static final double SOLVER_DEFAULT_ABSOLUTE_ACCURACY
```
    Default absolute accuracy for inverse cumulative computation.
    
    See Also:
    Constant Field Values
  - random
```
protected final RandomGenerator random
```
    RNG instance used to generate samples from the distribution.
    
    Since:
    
    3.1
- Constructor Detail
  - AbstractRealDistribution
```
protected AbstractRealDistribution(RandomGenerator rng)
```
    Parameters:
    rng - Random number generator.
    Since:
    
    3.1
- Method Detail
  - probability
```
public double probability(double x0,
                 double x1)
```
    For a random variable X whose values are distributed according to this distribution, this method returns P(x0 < X <= x1).
    
    Specified by:
    
    probability in interface RealDistribution
    
    Parameters:
    x0 - Lower bound (excluded).
    x1 - Upper bound (included).
    
    Returns:
    the probability that a random variable with this distribution takes a value between x0 and x1, excluding the lower and including the upper endpoint.
    
    Throws:
    
    NumberIsTooLargeException - if x0 > x1. The default implementation uses the identity P(x0 < X <= x1) = P(X <= x1) - P(X <= x0)
    Since:
    
    3.1
  - inverseCumulativeProbability
```
public double 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 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.
    Specified by:
    
    inverseCumulativeProbability in interface RealDistribution
    
    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
  - getSolverAbsoluteAccuracy
```
protected double getSolverAbsoluteAccuracy()
```
    Returns the solver absolute accuracy for inverse cumulative computation. You can override this method in order to use a Brent solver with an absolute accuracy different from the default.
    
    Returns:
    the maximum absolute error in inverse cumulative probability estimates
  - reseedRandomGenerator
```
public void reseedRandomGenerator(long seed)
```
    Reseed the random generator used to generate samples.
    
    Specified by:
    
    reseedRandomGenerator in interface RealDistribution
    
    Parameters:
    seed - the new seed
  - sample
```
public double sample()
```
    Generate a random value sampled from this distribution. The default implementation uses the inversion method.
    
    Specified by:
    
    sample in interface RealDistribution
    
    Returns:
    a random value.
  - sample
```
public double[] 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 RealDistribution
    
    Parameters:
    sampleSize - the number of random values to generate
    
    Returns:
    an array representing the random sample
  - probability
```
public double probability(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 probability mass function (PMF) for the distribution.
    
    Specified by:
    
    probability in interface RealDistribution
    
    Parameters:
    x - the point at which the PMF is evaluated
    
    Returns:
    zero.
    Since:
    
    3.1
  - logDensity
```
public double logDensity(double x)
```
    Returns the natural logarithm of 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. 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 RealDistribution.density(double).
    The default implementation simply computes the logarithm of density(x).
    
    Specified by:
    
    logDensity in interface RealDistribution
    
    Parameters:
    x - the point at which the PDF is evaluated
    
    Returns:
    the logarithm of the value of the probability density function at point x

Class AbstractRealDistribution

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Methods inherited from interface org.apache.commons.math4.distribution.RealDistribution

Field Detail

SOLVER_DEFAULT_ABSOLUTE_ACCURACY

random

Constructor Detail

AbstractRealDistribution

Method Detail

probability

inverseCumulativeProbability

getSolverAbsoluteAccuracy

reseedRandomGenerator

sample

sample

probability

logDensity