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Pseudorandom Number Generators

Base (i.e., lower-level) pseudorandom number generators (PRNGs).

Installation

npm install @stdlib/random-base

Alternatively,

• To load the package in a website via a script tag without installation and bundlers, use the ES Module available on the esm branch (see README).
• If you are using Deno, visit the deno branch (see README for usage intructions).
• For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the umd branch (see README).

The branches.md file summarizes the available branches and displays a diagram illustrating their relationships.

To view installation and usage instructions specific to each branch build, be sure to explicitly navigate to the respective README files on each branch, as linked to above.

Usage

var random = require( '@stdlib/random-base' );

random

Namespace containing "base" (i.e., lower-level) pseudorandom number generators (PRNGs).

var ns = random;
// returns {...}

The namespace contains the following PRNGs:

• arcsine( a, b ): arcsine distributed pseudorandom numbers.
• bernoulli( p ): Bernoulli distributed pseudorandom numbers.
• beta( alpha, beta ): beta distributed pseudorandom numbers.
• betaprime( alpha, beta ): beta prime distributed pseudorandom numbers.
• binomial( n, p ): binomial distributed pseudorandom numbers.
• boxMuller(): standard normally distributed pseudorandom numbers using the Box-Muller transform.
• cauchy( x0, gamma ): Cauchy distributed pseudorandom numbers.
• chi( k ): Chi distributed pseudorandom numbers.
• chisquare( k ): Chi-square distributed pseudorandom numbers.
• cosine( mu, s ): raised cosine distributed pseudorandom numbers.
• discreteUniform( a, b ): discrete uniform distributed pseudorandom numbers.
• erlang( k, lambda ): Erlang distributed pseudorandom numbers.
• exponential( lambda ): exponentially distributed pseudorandom numbers.
• f( d1, d2 ): F distributed pseudorandom numbers.
• frechet( alpha, s, m ): Fréchet distributed pseudorandom numbers.
• gamma( alpha, beta ): gamma distributed pseudorandom numbers.
• geometric( p ): geometric distributed pseudorandom numbers.
• gumbel( mu, beta ): Gumbel distributed pseudorandom numbers.
• hypergeometric( N, K, n ): hypergeometric distributed pseudorandom numbers.
• improvedZiggurat(): standard normally distributed pseudorandom numbers using the Improved Ziggurat method.
• invgamma( alpha, beta ): inverse gamma distributed pseudorandom numbers.
• kumaraswamy( a, b ): Kumaraswamy's double bounded distributed pseudorandom numbers.
• laplace( mu, b ): Laplace (double exponential) distributed pseudorandom numbers.
• levy( mu, c ): Lévy distributed pseudorandom numbers.
• logistic( mu, s ): logistic distributed pseudorandom numbers.
• lognormal( mu, sigma ): lognormal distributed pseudorandom numbers.
• minstdShuffle(): A linear congruential pseudorandom number generator (LCG) whose output is shuffled.
• minstd(): A linear congruential pseudorandom number generator (LCG) based on Park and Miller.
• mt19937(): A 32-bit Mersenne Twister pseudorandom number generator.
• negativeBinomial( r, p ): negative binomially distributed pseudorandom numbers.
• normal( mu, sigma ): normally distributed pseudorandom numbers.
• pareto1( alpha, beta ): Pareto (Type I) distributed pseudorandom numbers.
• poisson( lambda ): Poisson distributed pseudorandom numbers.
• randi(): pseudorandom numbers having integer values.
• randn(): standard normally distributed pseudorandom numbers.
• randu(): uniformly distributed pseudorandom numbers between 0 and 1.
• rayleigh( sigma ): Rayleigh distributed pseudorandom numbers.
• reviveBasePRNG( key, value ): revive a JSON-serialized pseudorandom number generator (PRNG).
• t( v ): Student's t-distributed pseudorandom numbers.
• triangular( a, b, c ): triangular distributed pseudorandom numbers.
• uniform( a, b ): uniformly distributed pseudorandom numbers.
• weibull( k, lambda ): Weibull distributed pseudorandom numbers.

Attached to each PRNG are the following properties:

• NAME: the generator name.
• seed: the value used to seed the PRNG.
• seedLength: the length of the PRNG seed.
• state: the PRNG state.
• stateLength: the length of the PRNG state.
• byteLength: the size of the PRNG state.
• PRNG: the underlying pseudorandom number generator.

Additionally, attached to each PRNG is a .factory() method which supports creating a seeded PRNG and thus generating a reproducible sequence of pseudorandom numbers.

var rand;
var v;
var i;

// Generate pseudorandom values...
for ( i = 0; i < 100; i++ ) {
    v = random.randu();
}

// Generate the same pseudorandom values...
rand = random.randu.factory({
    'seed': random.randu.seed
});
for ( i = 0; i < 100; i++ ) {
    v = rand();
}

For parameterized PRNGs, the .factory() method supports specifying parameters upon either PRNG creation or invocation. For example,

// Create a PRNG which requires providing parameters at each invocation:
var rand = random.normal.factory({
    'seed': 12345
});

var r = rand( 1.0, 2.0 );
// returns <number>

// Create a PRNG with fixed parameters:
rand = random.normal.factory( 1.0, 2.0, {
    'seed': 12345
});

r = rand();
// returns <number>

Examples

var objectKeys = require( '@stdlib/utils-keys' );
var random = require( '@stdlib/random-base' );

console.log( objectKeys( random ) );

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Notice

This package is part of stdlib, a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.

For more information on the project, filing bug reports and feature requests, and guidance on how to develop stdlib, see the main project repository.

Community

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License

See LICENSE.

Copyright

Copyright © 2016-2024. The Stdlib Authors.