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https://issues.apache.org/jira/browse/RNG-32?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Abhishek Singh Dhadwal updated RNG-32:
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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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