random — Logtalk 3.100.1

# `random`

This library provides portable random number generators and an abstraction over the native backend Prolog compiler random number generator if available. It includes predicates for generating random floats and random integers in a given interval; predicates for generating random sequences and sets; predicates for randomly selecting, enumerating, and swapping elements from a list; predicates that succeed, fail, or call another predicate with a given probability; and predicates for sampling common probability distributions.

## API documentation

Open the ../../apis/library_index.html#random link in a web browser.

## Loading

To load all entities in this library, load the `loader.lgt` file:

    | ?- logtalk_load(random(loader)).

## Testing

To test this library predicates, load the `tester.lgt` file:

    | ?- logtalk_load(random(tester)).

## Algorithms

The `random(Algorithm)` and `fast_random(Algorithm)` parametric objects support the following pseudo-random number generator algorithms:

- `as183` - Algorithm AS 183 from Applied Statistics. 3x32-bit PRNG with period 2^60.

- `splitmix64` - SplitMix64 1x64-bit PRNG primarily used for seeding other generators. Algorithm by Guy L. Steele Jr. et al.

- `xoshiro128pp` - Xoshiro128++ 4x32-bit state-of-the-art PRNG with period 2^128-1. Algorithm by David Blackman and Sebastiano Vigna.

- `xoshiro128ss` - Xoshiro128** 4x32-bit state-of-the-art PRNG with period 2^128-1. Algorithm by David Blackman and Sebastiano Vigna.

- `xoshiro256pp` - Xoshiro256++ 4x64-bit state-of-the-art PRNG with period 2^256-1. Algorithm by David Blackman and Sebastiano Vigna.

- `xoshiro256ss` - Xoshiro256** 4x64-bit state-of-the-art PRNG with period 2^256-1. Algorithm by David Blackman and Sebastiano Vigna.

- `well512a` - WELL512a 16x32-bit state-of-the-art PRNG with period 2^512-1. Algorithm by by François Panneton, Pierre L’Ecuyer, and Makoto Matsumoto.

Note that none of the above algorithms are cryptographically secure.

The SplitMix64, Xoshiro256++, and Xoshiro256** algorithms require a backend supporting unbound integer arithmetic.

The `random` and `fast_random` objects use the `as183` algorithm and are provided for backward compatibility.

## Usage

The `random(Algorithm)` object implements a portable random number generator and supports multiple random number generators, using different seeds, by defining derived objects. For example:

    :- object(my_random_generator_1,
        extends(random(xoshiro128pp))).

        :- initialization(::reset_seed).

    :- end_object.

The `fast_random(Algorithm)` object also implements a portable random number generator but does not support deriving multiple random number generators, which makes it a bit faster than the `random(Algorithm)` object.

The `random(Algorithm)`, `random`, `fast_random(Algorithm)`, and `fast_random` objects manage the random number generator seed using internal dynamic state. The predicates that update the seed are declared as synchronized (when running on Prolog backends that support threads). Still, care must be taken when using these objects from multi-threaded applications, as there is no portable solution to protect seed updates from signals and prevent inconsistent state when threads are canceled.

The `random(Algorithm)`, `random`, `fast_random(Algorithm)`, and `fast_random` objects always initialize the random generator seed to the same value, thus providing a pseudo random number generator. The `randomize/1` predicate can be used to initialize the seed with a random value. The argument should be a large positive integer. In alternative, when using a small integer argument, discard the first dozen random values.

The `backend_random` object abstracts the native backend Prolog compiler random number generator for the basic `random/1`, `get_seed/1`, and `set_seed/1` predicates providing a portable implementation for the remaining predicates. This makes the object stateless, which allows reliable use from multiple threads. Consult the backend Prolog compiler documentation for details on its random number generator properties. Note that several of the supported backend Prolog systems, notably B-Prolog, CxProlog, ECLiPSe, JIProlog, and Quintus Prolog, do not provide implementations for both the `get_seed/1` and `set_seed/1` predicates and calling these predicates simply succeed without performing any action.

All random objects implement the `sampling_protocol` protocol. To maximize performance, the shared implementations of the sampling predicates is defined in the `sampling.lgt` file that’s included in the random objects. This allows these predicates to call the basic `random/1` and `random/3` predicates as locally defined predicates.