Package "r_random"

Title:	Purely relational predicates that implement Pseudo-Random Number Generation (PRNG) and Random Sampling (to-do).
Rating:	Not rated. Create the first rating!
Latest version:	0.2.2
SHA1 sum:	97967c4012837cd0d3832fd53b05974fd9d8ca66
Author:	Luiz Fernando https://github.com/LuizFBR
Maintainer:	Luiz Fernando https://github.com/LuizFBR
Packager:	Luiz Fernando https://github.com/LuizFBR
Home page:	https://github.com/LuizFBR/r_random
Download URL:	https://github.com/LuizFBR/r_random.git

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Details by download location

Version	SHA1	#Downloads	URL
0.2.2	97967c4012837cd0d3832fd53b05974fd9d8ca66	1	https://github.com/LuizFBR/r_random.git
0.2.1	0aef08908df6f3e6b3b97d25d1a3c7b562c3cda2	1	https://github.com/LuizFBR/r_random.git
0.1.1	a36533304bdb3f80d099fe9076774e366d83413f	1	https://github.com/LuizFBR/r_random.git
0.0.0	018b0406b70745709d216d2c04234bf062afae8b	1	https://github.com/LuizFBR/r_random.git

Relational-Randomness

Purely relational predicates that implement Pseudo-Random Number Generation (PRNG), Random Sampling (to-do) and Noise algorithms (to-do).

Side-note:

This project is still in its early versions and things like predicate names and argument ordering are all prone to change. This is not a (and probably will never be) "finished project".

##### To-do

• PrologDocs;
• Robust PRNG tests;
• Sampling algorithms;
• Noise generating algorithms;
• Implement other PRNGs (Mersenne Twister, LCG, etc...);

About

This package provides the following predicates:

Predicate	Description
rg/1	An abbreviation of random_generator/1
rg/3	An abbreviation of random_generator/3
random_generator/1	A pre-built random generator
random_generator/3	Random generator with a intial seed and sequence.
random/3	Random number from 0 to 2^32-1
random_bounded/4	Random number from 0 to Max-1
random_between/5	Random number from Min to Max-1 (Max - Min < 2^32)
random_list/4	List of N random numbers from 0 to 2^32-1
random_boundedlist/5	List of N random numbers from 0 to Max-1
random_betweenlist/6	List of N random numbers from Min to Max-1 (Max - Min < 2^32)
probability/4	Reifies the truth value of Probability = Num / Den chance of an event happening into T
random_permutation/4	Relates a list and one of its random permutation (shuffled)

And the following DCGs:

Predicate	Description
grandom_generator//1	Random generator with a intial seed and sequence.
grandom//1	Random number from 0 to 2^32-1
grandom_bounded//2	Random number from 0 to Max-1
grandom_between//3	Random number from Min to Max-1 (Max - Min < 2^32)
grandom_list//2	List of N random numbers from 0 to 2^32-1
grandom_boundedlist//3	List of N random numbers from 0 to Max-1
grandom_betweenlist//4	List of N random numbers from Min to Max-1 (Max - Min < 2^32)
gprobability/2	Reifies the truth value of Probability/Precision chance of an event happening into T
grandom_permutation/4	Relates a list and one of its random permutation (shuffled)

All predicates implemented so far use PCG number generation.

How to use

Predicates

Each predicate (except random_generator/1 ) provides the two initial arguments RandomGenerator and NextRandomGenerator, e.g.:

random(RandomInt, Pcg32randomt, NextPcg32randomt) :-
    pcg32_random_r(Pcg32randomt, NextPcg32randomt, RandomInt).

And to use them you need an instance of a random generator and plug it into the first argument then into the second argument, like so:

?- random_generator(X),random(N1,X,X1), random_bounded(5,N2,X1,X2), random_betweenlist(-5,10,5,L1,X2,X3).
X = pcg32randomt(9600629759793949339, 15726070495360670683),
X1 = pcg32randomt(14425053563923168794, 15726070495360670683),
N1 = 355248013,
X2 = pcg32randomt(13821270098544127085, 15726070495360670683),
N2 = 0,
X3 = pcg32randomt(2638482799351248520, 15726070495360670683),
L1 = [5, 5, -1, 3, -4].

DCGs

For using the DCGs, all you need to do is call phrase/3 (phrase/2 won't work, as the dcgs expect to pass state) and call the predicates with their arguments without worrying about passing the number generators. E.g.:

?- random_generator(X), phrase( ( grandom(A), grandom_bounded(5,B), grandom_betweenlist(-5,10,5,L) ) , [X], Y).
X = pcg32randomt(9600629759793949339, 15726070495360670683),
A = 355248013,
B = 0,
L = [5, 5, -1, 3, -4],
Y = [pcg32randomt(2638482799351248520, 15726070495360670683)].

Installation

To install the r_random library, type the following in the SWI-Prolog shell:

? - pack_install('r_random').
  true.

or, alternatively, you can install directly from this github repo:

?- pack_install('https://github.com/LuizFBR/r_random.git').
  true.

Importing

Import this module with

:- use_module(library(r_random)).

Dependencies

This library depends on Markus Triskas' clpfd library: https://www.swi-prolog.org/man/clpfd.html

And Ulrich Neumerkel's reif package: https://github.com/meditans/reif

Credits

Credits for the PCG algorithms goes to:

PCG: A Family of Simple Fast Space-Efficient Statistically Good Algorithms for Random Number Generation Author: "Melissa E. O'Neill"

PCG implemenation based on https://github.com/imneme/pcg-c-basic/blob/master/pcg_basic.c

Contents of pack "r_random"

Pack contains 4 files holding a total of 11.5K bytes.