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# MINSTD [![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url] > A linear congruential pseudorandom number generator ([LCG][lcg]) based on Park and Miller.
## Installation ```bash npm install @stdlib/random-base-minstd ```
## Usage ```javascript var minstd = require( '@stdlib/random-base-minstd' ); ``` #### minstd() Returns a pseudorandom integer on the interval `[1, 2147483646]`. ```javascript var r = minstd(); // returns ``` #### minstd.normalized() Returns a pseudorandom number on the interval `[0,1)`. ```javascript var r = minstd.normalized(); // returns ``` #### minstd.factory( \[options] ) Returns a linear congruential pseudorandom number generator ([LCG][lcg]). ```javascript var rand = minstd.factory(); ``` The function accepts the following `options`: - **seed**: pseudorandom number generator seed. - **state**: an [`Int32Array`][@stdlib/array/int32] containing pseudorandom number generator state. If provided, the function ignores the `seed` option. - **copy**: `boolean` indicating whether to copy a provided pseudorandom number generator state. Setting this option to `false` allows sharing state between two or more pseudorandom number generators. Setting this option to `true` ensures that a returned generator has exclusive control over its internal state. Default: `true`. By default, a random integer is used to seed the returned generator. To seed the generator, provide either an `integer` on the interval `[1, 2147483646]` ```javascript var rand = minstd.factory({ 'seed': 1234 }); var r = rand(); // returns 20739838 ``` or, for arbitrary length seeds, an array-like `object` containing signed 32-bit integers ```javascript var Int32Array = require( '@stdlib/array-int32' ); var rand = minstd.factory({ 'seed': new Int32Array( [ 1234 ] ) }); var r = rand(); // returns 20739838 ``` To return a generator having a specific initial state, set the generator `state` option. ```javascript // Generate pseudorandom numbers, thus progressing the generator state: var r; var i; for ( i = 0; i < 1000; i++ ) { r = minstd(); } // Create a new PRNG initialized to the current state of `minstd`: var rand = minstd.factory({ 'state': minstd.state }); // Test that the generated pseudorandom numbers are the same: var bool = ( rand() === minstd() ); // returns true ``` #### minstd.NAME The generator name. ```javascript var str = minstd.NAME; // returns 'minstd' ``` #### minstd.MIN Minimum possible value. ```javascript var min = minstd.MIN; // returns 1 ``` #### minstd.MAX Maximum possible value. ```javascript var max = minstd.MAX; // returns 2147483646 ``` #### minstd.seed The value used to seed `minstd()`. ```javascript // Generate pseudorandom values... var r; var i; for ( i = 0; i < 100; i++ ) { r = minstd(); } // Generate the same pseudorandom values... var rand = minstd.factory({ 'seed': minstd.seed }); for ( i = 0; i < 100; i++ ) { r = rand(); } ``` #### minstd.seedLength Length of generator seed. ```javascript var len = minstd.seedLength; // returns ``` #### minstd.state Writable property for getting and setting the generator state. ```javascript var r = minstd(); // returns r = minstd(); // returns // ... // Get the current state: var state = minstd.state; // returns r = minstd(); // returns r = minstd(); // returns // Reset the state: minstd.state = state; // Replay the last two pseudorandom numbers: r = minstd(); // returns r = minstd(); // returns // ... ``` #### minstd.stateLength Length of generator state. ```javascript var len = minstd.stateLength; // returns ``` #### minstd.byteLength Size (in bytes) of generator state. ```javascript var sz = minstd.byteLength; // returns ``` #### minstd.toJSON() Serializes the pseudorandom number generator as a JSON object. ```javascript var o = minstd.toJSON(); // returns { 'type': 'PRNG', 'name': '...', 'state': {...}, 'params': [] } ```
## Notes - The generator has a period of approximately `2.1e9` (see [Numerical Recipes in C, 2nd Edition](#references), p. 279). - An [LCG][lcg] is fast and uses little memory. On the other hand, because the generator is a simple [linear congruential generator][lcg], the generator has recognized shortcomings. By today's PRNG standards, the generator's period is relatively short. More importantly, the "randomness quality" of the generator's output is lacking. These defects make the generator unsuitable, for example, in Monte Carlo simulations and in cryptographic applications. For more on the advantages and disadvantages of [LCGs][lcg], see [Wikipedia][pros-cons]. - If PRNG state is "shared" (meaning a state array was provided during PRNG creation and **not** copied) and one sets the generator state to a state array having a different length, the PRNG does **not** update the existing shared state and, instead, points to the newly provided state array. In order to synchronize PRNG output according to the new shared state array, the state array for **each** relevant PRNG must be **explicitly** set. - If PRNG state is "shared" and one sets the generator state to a state array of the same length, the PRNG state is updated (along with the state of all other PRNGs sharing the PRNG's state array).
## Examples ```javascript var minstd = require( '@stdlib/random-base-minstd' ); // Generate pseudorandom numbers... var i; for ( i = 0; i < 100; i++ ) { console.log( minstd() ); } // Create a new pseudorandom number generator... var seed = 1234; var rand = minstd.factory({ 'seed': seed }); for ( i = 0; i < 100; i++ ) { console.log( rand() ); } // Create another pseudorandom number generator using a previous seed... rand = minstd.factory({ 'seed': minstd.seed }); for ( i = 0; i < 100; i++ ) { console.log( rand() ); } ```
* * *
## References - Park, S. K., and K. W. Miller. 1988. "Random Number Generators: Good Ones Are Hard to Find." _Communications of the ACM_ 31 (10). New York, NY, USA: ACM: 1192–1201. doi:[10.1145/63039.63042][@park:1988]. - Press, William H., Brian P. Flannery, Saul A. Teukolsky, and William T. Vetterling. 1992. _Numerical Recipes in C: The Art of Scientific Computing, Second Edition_. Cambridge University Press.
* * * ## See Also - [`@stdlib/random-array/minstd`][@stdlib/random/array/minstd]: create an array containing pseudorandom numbers generated using a linear congruential pseudorandom number generator (LCG). - [`@stdlib/random-iter/minstd`][@stdlib/random/iter/minstd]: create an iterator for a linear congruential pseudorandom number generator (LCG) based on Park and Miller. - [`@stdlib/random-streams/minstd`][@stdlib/random/streams/minstd]: create a readable stream for a linear congruential pseudorandom number generator (LCG) based on Park and Miller. - [`@stdlib/random-base/minstd-shuffle`][@stdlib/random/base/minstd-shuffle]: A linear congruential pseudorandom number generator (LCG) whose output is shuffled. - [`@stdlib/random-base/mt19937`][@stdlib/random/base/mt19937]: A 32-bit Mersenne Twister pseudorandom number generator. - [`@stdlib/random-base/randi`][@stdlib/random/base/randi]: pseudorandom numbers having integer values.
* * * ## Notice This package is part of [stdlib][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][stdlib], see the main project [repository][stdlib]. #### Community [![Chat][chat-image]][chat-url] --- ## License See [LICENSE][stdlib-license]. ## Copyright Copyright © 2016-2026. The Stdlib [Authors][stdlib-authors].
[npm-image]: http://img.shields.io/npm/v/@stdlib/random-base-minstd.svg [npm-url]: https://npmjs.org/package/@stdlib/random-base-minstd [test-image]: https://github.com/stdlib-js/random-base-minstd/actions/workflows/test.yml/badge.svg?branch=v0.2.3 [test-url]: https://github.com/stdlib-js/random-base-minstd/actions/workflows/test.yml?query=branch:v0.2.3 [coverage-image]: https://img.shields.io/codecov/c/github/stdlib-js/random-base-minstd/main.svg [coverage-url]: https://codecov.io/github/stdlib-js/random-base-minstd?branch=main [chat-image]: https://img.shields.io/badge/zulip-join_chat-brightgreen.svg [chat-url]: https://stdlib.zulipchat.com [stdlib]: https://github.com/stdlib-js/stdlib [stdlib-authors]: https://github.com/stdlib-js/stdlib/graphs/contributors [umd]: https://github.com/umdjs/umd [es-module]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules [deno-url]: https://github.com/stdlib-js/random-base-minstd/tree/deno [deno-readme]: https://github.com/stdlib-js/random-base-minstd/blob/deno/README.md [umd-url]: https://github.com/stdlib-js/random-base-minstd/tree/umd [umd-readme]: https://github.com/stdlib-js/random-base-minstd/blob/umd/README.md [esm-url]: https://github.com/stdlib-js/random-base-minstd/tree/esm [esm-readme]: https://github.com/stdlib-js/random-base-minstd/blob/esm/README.md [branches-url]: https://github.com/stdlib-js/random-base-minstd/blob/main/branches.md [stdlib-license]: https://raw.githubusercontent.com/stdlib-js/random-base-minstd/main/LICENSE [lcg]: https://en.wikipedia.org/wiki/Linear_congruential_generator [pros-cons]: https://en.wikipedia.org/wiki/Linear_congruential_generator#Advantages_and_disadvantages_of_LCGs [@park:1988]: http://dx.doi.org/10.1145/63039.63042 [@stdlib/array/int32]: https://www.npmjs.com/package/@stdlib/array-int32 [@stdlib/random/array/minstd]: https://www.npmjs.com/package/@stdlib/random-array-minstd [@stdlib/random/iter/minstd]: https://www.npmjs.com/package/@stdlib/random-iter-minstd [@stdlib/random/streams/minstd]: https://www.npmjs.com/package/@stdlib/random-streams-minstd [@stdlib/random/base/minstd-shuffle]: https://www.npmjs.com/package/@stdlib/random-base-minstd-shuffle [@stdlib/random/base/mt19937]: https://www.npmjs.com/package/@stdlib/random-base-mt19937 [@stdlib/random/base/randi]: https://www.npmjs.com/package/@stdlib/random-base-randi