java.lang.Object
- org.apache.commons.rng.sampling.distribution.RejectionInversionZipfSampler.RejectionInversionZipfSamplerImpl

All Implemented Interfaces:

DiscreteSampler, SharedStateDiscreteSampler, SharedStateSampler<SharedStateDiscreteSampler>

Enclosing class:

RejectionInversionZipfSampler
```
private static final class RejectionInversionZipfSampler.RejectionInversionZipfSamplerImpl
extends java.lang.Object
implements SharedStateDiscreteSampler
```
Implements the rejection-inversion method for the Zipf distribution.

Field Summary

Fields
Modifier and Type	Field	Description
`private double`	`exponent`	Exponent parameter of the distribution.
`private static double`	`F_1_2`	1/2.
`private static double`	`F_1_3`	1/3.
`private static double`	`F_1_4`	1/4.
`private double`	`hIntegralNumberOfElements`	`hIntegral(numberOfElements + 0.5)`.
`private double`	`hIntegralX1`	`hIntegral(1.5) - 1`.
`private int`	`numberOfElements`	Number of elements.
`private double`	`r`	`hIntegralX1 - hIntegralNumberOfElements`.
`private UniformRandomProvider`	`rng`	Underlying source of randomness.
`private double`	`s`	`2 - hIntegralInverse(hIntegral(2.5) - h(2)`.
`private static double`	`TAYLOR_THRESHOLD`	Threshold below which Taylor series will be used.

Constructor Summary

Constructors
Modifier	Constructor	Description
`(package private)`	`RejectionInversionZipfSamplerImpl(UniformRandomProvider rng, int numberOfElements, double exponent)`
`private`	`RejectionInversionZipfSamplerImpl(UniformRandomProvider rng, RejectionInversionZipfSampler.RejectionInversionZipfSamplerImpl source)`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`private double`	`h(double x)`	`h(x) = 1 / x^exponent`.
`private static double`	`helper1(double x)`	Helper function that calculates `log(1 + x) / x`.
`private static double`	`helper2(double x)`	Helper function to calculate `(exp(x) - 1) / x`.
`private double`	`hIntegral(double x)`	`H(x)` is defined as `(x^(1 - exponent) - 1) / (1 - exponent)`, if `exponent != 1` `log(x)`, if `exponent == 1` H(x) is an integral function of h(x), the derivative of H(x) is h(x).
`private double`	`hIntegralInverse(double x)`	The inverse function of `H(x)`.
`int`	`sample()`	Creates an `int` sample.
`java.lang.String`	`toString()`
`SharedStateDiscreteSampler`	`withUniformRandomProvider(UniformRandomProvider rng)`	Create a new instance of the sampler with the same underlying state using the given uniform random provider as the source of randomness.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface org.apache.commons.rng.sampling.distribution.DiscreteSampler
samples, samples

- Field Detail
  - TAYLOR_THRESHOLD
```
private static final double TAYLOR_THRESHOLD
```
    Threshold below which Taylor series will be used.
    
    See Also:
    
    Constant Field Values
  - F_1_2
```
private static final double F_1_2
```
    1/2.
    
    See Also:
    
    Constant Field Values
  - F_1_3
```
private static final double F_1_3
```
    1/3.
    
    See Also:
    
    Constant Field Values
  - F_1_4
```
private static final double F_1_4
```
    1/4.
    
    See Also:
    
    Constant Field Values
  - numberOfElements
```
private final int numberOfElements
```
    Number of elements.
  - exponent
```
private final double exponent
```
    Exponent parameter of the distribution.
  - hIntegralX1
```
private final double hIntegralX1
```
    hIntegral(1.5) - 1.
  - hIntegralNumberOfElements
```
private final double hIntegralNumberOfElements
```
    hIntegral(numberOfElements + 0.5).
  - r
```
private final double r
```
    hIntegralX1 - hIntegralNumberOfElements.
  - s
```
private final double s
```
    2 - hIntegralInverse(hIntegral(2.5) - h(2).
  - rng
```
private final UniformRandomProvider rng
```
    Underlying source of randomness.
- Constructor Detail
  - RejectionInversionZipfSamplerImpl
```
RejectionInversionZipfSamplerImpl(UniformRandomProvider rng,
                                  int numberOfElements,
                                  double exponent)
```
    Parameters:
    
    rng - Generator of uniformly distributed random numbers.
    
    numberOfElements - Number of elements (must be > 0).
    
    exponent - Exponent (must be > 0).
  - RejectionInversionZipfSamplerImpl
```
private RejectionInversionZipfSamplerImpl(UniformRandomProvider rng,
                                          RejectionInversionZipfSampler.RejectionInversionZipfSamplerImpl source)
```
    Parameters:
    
    rng - Generator of uniformly distributed random numbers.
    
    source - Source to copy.
- Method Detail
  - sample
```
public int sample()
```
    Description copied from interface: DiscreteSampler
    
    Creates an int sample.
    
    Specified by:
    
    sample in interface DiscreteSampler
    
    Returns:
    
    a sample.
  - toString
```
public java.lang.String toString()
```
    Overrides:
    
    toString in class java.lang.Object
  - withUniformRandomProvider
```
public SharedStateDiscreteSampler withUniformRandomProvider(UniformRandomProvider rng)
```
    Description copied from interface: SharedStateSampler
    
    Create a new instance of the sampler with the same underlying state using the given uniform random provider as the source of randomness.
    
    Specified by:
    
    withUniformRandomProvider in interface SharedStateSampler<SharedStateDiscreteSampler>
    
    Parameters:
    
    rng - Generator of uniformly distributed random numbers.
    
    Returns:
    
    the sampler
  - hIntegral
```
private double hIntegral(double x)
```
    H(x) is defined as
    
    (x^(1 - exponent) - 1) / (1 - exponent), if exponent != 1
    
    log(x), if exponent == 1
    
    H(x) is an integral function of h(x), the derivative of H(x) is h(x).
    Parameters:
    
    x - Free parameter.
    
    Returns:
    
    H(x).
  - h
```
private double h(double x)
```
    h(x) = 1 / x^exponent.
    
    Parameters:
    
    x - Free parameter.
    
    Returns:
    
    h(x).
  - hIntegralInverse
```
private double hIntegralInverse(double x)
```
    The inverse function of H(x).
    
    Parameters:
    
    x - Free parameter
    
    Returns:
    
    y for which H(y) = x.
  - helper1
```
private static double helper1(double x)
```
    Helper function that calculates log(1 + x) / x.
    A Taylor series expansion is used, if x is close to 0.
    
    Parameters:
    
    x - A value larger than or equal to -1.
    
    Returns:
    
    log(1 + x) / x.
  - helper2
```
private static double helper2(double x)
```
    Helper function to calculate (exp(x) - 1) / x.
    A Taylor series expansion is used, if x is close to 0.
    
    Parameters:
    
    x - Free parameter.
    
    Returns:
    
    (exp(x) - 1) / x if x is non-zero, or 1 if x = 0.

Class RejectionInversionZipfSampler.RejectionInversionZipfSamplerImpl

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Methods inherited from interface org.apache.commons.rng.sampling.distribution.DiscreteSampler

Field Detail

TAYLOR_THRESHOLD

F_1_2

F_1_3

F_1_4

numberOfElements

exponent

hIntegralX1

hIntegralNumberOfElements

r

s

rng

Constructor Detail

RejectionInversionZipfSamplerImpl

RejectionInversionZipfSamplerImpl

Method Detail

sample

toString

withUniformRandomProvider

hIntegral

h

hIntegralInverse

helper1

helper2