Random

Random numbers.

Installation

npm install @stdlib/random

Usage

var ns = require( '@stdlib/random' );

ns

Namespace containing random number functionality.

var arr = ns.normal( [ 3, 3 ], 2.0, 5.0 );
// returns <ndarray>

The namespace exports the following functions to sample and shuffle elements from an array:

• sample( x[, options] ): sample elements from an array-like object.
• shuffle( arr[, options] ): shuffle elements of an array-like object.

The namespace contains the following functions for generating pseudorandom values drawn from probability distributions:

• arcsine( shape, a, b[, options] ): generate pseudorandom numbers drawn from an arcsine distribution.
• bernoulli( shape, p[, options] ): generate pseudorandom numbers drawn from a Bernoulli distribution.
• beta( shape, a, b[, options] ): generate pseudorandom numbers drawn from a beta distribution.
• betaprime( shape, alpha, beta[, options] ): generate pseudorandom numbers drawn from a betaprime distribution.
• binomial( shape, n, p[, options] ): generate pseudorandom numbers drawn from a binomial distribution.
• cauchy( shape, x0, gamma[, options] ): generate pseudorandom numbers drawn from a Cauchy distribution.
• chi( shape, k[, options] ): generate pseudorandom numbers drawn from a chi distribution.
• chisquare( shape, k[, options] ): generate pseudorandom numbers drawn from a chi-square distribution.
• cosine( shape, mu, s[, options] ): generate pseudorandom numbers drawn from a raised cosine distribution.
• discreteUniform( shape, a, b[, options] ): generate pseudorandom numbers drawn from a discrete uniform distribution.
• erlang( shape, k, lambda[, options] ): generate pseudorandom numbers drawn from an Erlang distribution.
• exponential( shape, lambda[, options] ): generate pseudorandom numbers drawn from an exponential distribution.
• f( shape, d1, d2[, options] ): generate pseudorandom numbers drawn from an F distribution.
• frechet( shape, alpha, s, m[, options] ): generate pseudorandom numbers drawn from a Fréchet distribution.
• gamma( shape, alpha, beta[, options] ): generate pseudorandom numbers drawn from a gamma distribution.
• geometric( shape, p[, options] ): generate pseudorandom numbers drawn from a geometric distribution.
• gumbel( shape, mu, beta[, options] ): generate pseudorandom numbers drawn from a Gumbel distribution.
• hypergeometric( shape, N, K, n[, options] ): generate pseudorandom numbers drawn from a hypergeometric distribution.
• invgamma( shape, alpha, beta[, options] ): generate pseudorandom numbers drawn from an inverse gamma distribution.
• kumaraswamy( shape, a, b[, options] ): generate pseudorandom numbers drawn from a Kumaraswamy distribution.
• laplace( shape, mu, b[, options] ): generate pseudorandom numbers drawn from a Laplace (double exponential) distribution.
• levy( shape, mu, c[, options] ): generate pseudorandom numbers drawn from a Lévy distribution.
• logistic( shape, mu, s[, options] ): generate pseudorandom numbers drawn from a logistic distribution.
• lognormal( shape, mu, sigma[, options] ): generate pseudorandom numbers drawn from a lognormal distribution.
• negativeBinomial( shape, r, p[, options] ): generate pseudorandom numbers drawn from a negative binomial distribution.
• normal( shape, mu, sigma[, options] ): generate pseudorandom numbers drawn from a normal distribution.
• pareto1( shape, alpha, beta[, options] ): generate pseudorandom numbers drawn from a Pareto (Type I) distribution.
• poisson( shape, lambda[, options] ): generate pseudorandom numbers drawn from a Poisson distribution.
• rayleigh( shape, sigma[, options] ): generate pseudorandom numbers drawn from a Rayleigh distribution.
• t( shape, v[, options] ): generate pseudorandom numbers drawn from a Student's t-distribution.
• triangular( shape, a, b, c[, options] ): generate pseudorandom numbers drawn from a triangular distribution.
• uniform( shape, a, b[, options] ): generate pseudorandom numbers drawn from a continuous uniform distribution.
• weibull( shape, k, lambda[, options] ): generate pseudorandom numbers drawn from a Weibull distribution.

The namespace contains the following sub-namespaces:

• array: pseudorandom number generator (PRNG) array creation functions.
• base: base (i.e., lower-level) pseudorandom number generators (PRNGs).
• iterators: pseudorandom number generator (PRNG) iterators.
• streams: pseudorandom number generator (PRNG) streams.
• strided: pseudorandom number generator (PRNG) strided array functions.

Examples

var logEach = require( '@stdlib/console/log-each' );
var ndarray2array = require( '@stdlib/ndarray/to-array' );
var ns = require( '@stdlib/random' );

// Create a function for generating arrays originating from the same state:
var random = ns.normal.factory({
    'state': ns.normal.state,
    'copy': true
});

// Generate 3 one-dimensional arrays:
var x1 = random( [ 5 ], 2.0, 5.0 );
var x2 = random( [ 5 ], 2.0, 5.0 );
var x3 = random( [ 5 ], 2.0, 5.0 );

// Print the contents:
logEach( '%f, %f, %f', ndarray2array( x1 ), ndarray2array( x2 ), ndarray2array( x3 ) );

// Create another function for generating random arrays with the original state:
random = ns.normal.factory({
    'state': ns.normal.state,
    'copy': true
});

// Generate a two-dimensional array which replicates the above pseudorandom number generation sequence:
var x4 = random( [ 3, 5 ], 2.0, 5.0 );

// Convert to a list of nested arrays:
var arr = ndarray2array( x4 );

// Print the contents:
console.log( '' );
logEach( '%f, %f, %f', arr[ 0 ], arr[ 1 ], arr[ 2 ] );

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

License

See LICENSE.

Copyright

Copyright © 2016-2026. The Stdlib Authors.