[jira] [Issue Comment Deleted] (RNG-32) Implement more generators

["Abhishek Singh Dhadwal (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/RNG-32?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Abhishek Singh Dhadwal updated RNG-32:
--------------------------------------
    Comment: was deleted

(was: Hello,

I shall be working on the task at hand. Upon discussion with Gilles and Alex Herbert, following are the details about the plan for implementation of the RNG.

There shall be a base abstract class (AbstractLCG) which shall take inputs of a,c,m and the seed and return integer values as required.

There shall be a child class (KnuthLewisLCG) which shall extend the aforementioned class with the values of a, c and m referred from [Numerical Recipes |https://en.wikipedia.org/wiki/Linear_congruential_generator#Parameters_in_common_use]

The current questions/queries at hand are :
 * Will it pass the test suite?
 * Can using modulo 2^32 increase performance (to be tested using JMH)
 * Comparison between KnuthLewisDirect and the aforementioned child class)

> Implement more generators
> -------------------------
>
>                 Key: RNG-32
>                 URL: https://issues.apache.org/jira/browse/RNG-32
>             Project: Commons RNG
>          Issue Type: Wish
>            Reporter: Gilles
>            Priority: Minor
>              Labels: contributors, gsoc2019, scope
>         Attachments: lsf.java
>
>
> Commons RNG is focused on pure-Java implementations of standard deterministic generators.
> Quite a few algorithms could be added, but priority is on fast generators that generate sequences of _pseudo-random_ numbers; i.e. the requirement is strong _uniformity_, but *not* strong _unpredictability_ (a.k.a. _true_ random numbers).
> In particular, in Commons RNG, there is no provision for using an external entropy pool.
> Beware that some well-known (and much used) algorithms have been proven to fail spectacularly on the uniformity requirement.
> Would-be contributors should look at the {{commons-rng-core}} module for how to implement a generator, and at the {{commons-rng-examples}} module for how to test the uniformity requirement.



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