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https://github.com/stdlib-js/random-streams-minstd

Create a readable stream for a linear congruential pseudorandom number generator (LCG) based on Park and Miller.
https://github.com/stdlib-js/random-streams-minstd

discrete generator integer javascript math mathematics node node-js nodejs prng pseudorandom rand randint random randu rng statistics stats stdlib uniform

Last synced: 3 months ago
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Create a readable stream for a linear congruential pseudorandom number generator (LCG) based on Park and Miller.

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README 

          



  

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# MINSTD



[![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url] 



> Create a [readable stream][readable-stream] for a linear congruential pseudorandom number generator ([LCG][@stdlib/random/base/minstd]) based on Park and Miller.



## Installation



```bash

npm install @stdlib/random-streams-minstd

```



Alternatively,



-   To load the package in a website via a `script` tag without installation and bundlers, use the [ES Module][es-module] available on the [`esm`][esm-url] branch (see [README][esm-readme]).

-   If you are using Deno, visit the [`deno`][deno-url] branch (see [README][deno-readme] for usage intructions).

-   For use in Observable, or in browser/node environments, use the [Universal Module Definition (UMD)][umd] build available on the [`umd`][umd-url] branch (see [README][umd-readme]).

-   To use as a general utility for the command line, install the corresponding [CLI package][cli-section] globally.



The [branches.md][branches-url] 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



```javascript

var randomStream = require( '@stdlib/random-streams-minstd' );

```






#### randomStream( \[options] )



Returns a [readable stream][readable-stream] for a linear congruential pseudorandom number generator ([LCG][@stdlib/random/base/minstd]) based on Park and Miller.



```javascript

var inspectStream = require( '@stdlib/streams-node-inspect-sink' );



var iStream;

var stream;



function log( chunk, idx ) {

    console.log( chunk.toString() );

    if ( idx === 10 ) {

        stream.destroy();

    }

}



stream = randomStream();

iStream = inspectStream( log );



stream.pipe( iStream );

```



The function accepts the following `options`:



-   **objectMode**: specifies whether a [stream][stream] should operate in [objectMode][object-mode]. Default: `false`.

-   **encoding**: specifies how `Buffer` objects should be decoded to `strings`. Default: `null`.

-   **highWaterMark**: specifies the maximum number of bytes to store in an internal buffer before ceasing to generate additional pseudorandom numbers.

-   **sep**: separator used to join streamed data. This option is only applicable when a stream is **not** in [objectMode][object-mode]. Default: `'\n'`.

-   **iter**: number of iterations.

-   **normalized**: `boolean` indicating whether to return pseudorandom numbers on the interval `[0,1)`.

-   **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 and/or streams. Setting this option to `true` ensures that a stream generator has exclusive control over its internal state. Default: `true`.

-   **siter**: number of iterations after which to emit the pseudorandom number generator state. This option is useful when wanting to deterministically capture a stream's underlying PRNG state. Default: `1e308`.



To set [stream][stream] `options`,



```javascript

var opts = {

    'objectMode': true,

    'encoding': 'utf8',

    'highWaterMark': 64

};



var stream = randomStream( opts );

```



By default, the function returns a [stream][stream] which can generate an infinite number of values (i.e., the [stream][stream] will **never** end). To limit the number of generated pseudorandom numbers, set the `iter` option.



```javascript

var inspectStream = require( '@stdlib/streams-node-inspect-sink' );



function log( chunk ) {

    console.log( chunk.toString() );

}



var opts = {

    'iter': 10

};



var stream = randomStream( opts );

var iStream = inspectStream( log );



stream.pipe( iStream );

```



To return pseudorandom numbers on the interval `[0,1)`, set the `normalized` option.



```javascript

var inspectStream = require( '@stdlib/streams-node-inspect-sink' );



function log( chunk ) {

    console.log( chunk.toString() );

}



var opts = {

    'iter': 10,

    'normalized': true

};



var stream = randomStream( opts );

var iStream = inspectStream( log );



stream.pipe( iStream );

```



By default, when not operating in [objectMode][object-mode], a returned [stream][stream] delineates generated pseudorandom numbers using a newline character. To specify an alternative separator, set the `sep` option.



```javascript

var inspectStream = require( '@stdlib/streams-node-inspect-sink' );



function log( chunk ) {

    console.log( chunk.toString() );

}



var opts = {

    'iter': 10,

    'sep': ','

};



var stream = randomStream( opts );

var iStream = inspectStream( log );



stream.pipe( iStream );

```



To seed the underlying pseudorandom number generator, set the `seed` option.



```javascript

var inspectStream = require( '@stdlib/streams-node-inspect-sink' );



function log( v ) {

    console.log( v );

}



var opts = {

    'objectMode': true,

    'iter': 10,

    'seed': 1234

};



var stream = randomStream( opts );



opts = {

    'objectMode': true

};

var iStream = inspectStream( opts, log );



stream.pipe( iStream );

```



To return a [readable stream][readable-stream] with an underlying pseudorandom number generator having a specific initial state, set the `state` option.



```javascript

var inspectStream = require( '@stdlib/streams-node-inspect-sink' );



function log( v ) {

    console.log( v );

}



var opts1 = {

    'objectMode': true,

    'iter': 10

};



var stream = randomStream( opts1 );



var opts2 = {

    'objectMode': true

};

var iStream = inspectStream( opts2, log );



// Stream pseudorandom numbers, thus progressing the underlying generator state:

stream.pipe( iStream );



// Create a new PRNG stream initialized to the last state of the previous stream:

var opts3 = {

    'objectMode': true,

    'iter': 10,

    'state': stream.state

};



stream = randomStream( opts3 );

iStream = inspectStream( opts2, log );



// Stream pseudorandom numbers starting from the last state of the previous stream:

stream.pipe( iStream );

```



##### stream.seed



The value used to seed the underlying pseudorandom number generator.



```javascript

var stream = randomStream();



var seed = stream.seed;

// returns 

```



##### stream.seedLength



Length of underlying pseudorandom number generator seed.



```javascript

var stream = randomStream();



var len = stream.seedLength;

// returns 

```



##### stream.state



Writable property for getting and setting the underlying pseudorandom number generator state.



```javascript

var stream = randomStream();



var state = stream.state;

// returns 

```



##### stream.stateLength



Length of underlying pseudorandom number generator state.



```javascript

var stream = randomStream();



var len = stream.stateLength;

// returns 

```



##### stream.byteLength



Size (in bytes) of underlying pseudorandom number generator state.



```javascript

var stream = randomStream();



var sz = stream.byteLength;

// returns 

```



* * *



#### randomStream.factory( \[options] )



Returns a `function` for creating [readable streams][readable-stream] which generate pseudorandom numbers via a linear congruential pseudorandom number generator ([LCG][@stdlib/random/base/minstd]) based on Park and Miller.



```javascript

var opts = {

    'objectMode': true,

    'encoding': 'utf8',

    'highWaterMark': 64

};



var createStream = randomStream.factory( opts );

```



The method accepts the same `options` as [`randomStream()`](#random-stream).



* * *



#### randomStream.objectMode( \[options] )



This method is a convenience function to create [streams][stream] which **always** operate in [objectMode][object-mode].



```javascript

var inspectStream = require( '@stdlib/streams-node-inspect-sink' );



function log( v ) {

    console.log( v );

}



var opts = {

    'iter': 10

};

var stream = randomStream.objectMode( opts );



opts = {

    'objectMode': true

};

var iStream = inspectStream( opts, log );



stream.pipe( iStream );

```



This method accepts the same `options` as [`randomStream()`](#random-stream); however, the method will **always** override the [`objectMode`][object-mode] option in `options`.



* * *



### Events



In addition to the standard [readable stream][readable-stream] events, the following events are supported...



#### 'state'



Emitted after internally generating `siter` pseudorandom numbers.



```javascript

var opts = {

    'siter': 10 // emit the PRNG state every 10 pseudorandom numbers

};



var stream = randomStream( opts );



stream.on( 'state', onState );



function onState( state ) {

    // Do something with the emitted state, such as save to file...

}

```



* * *



## Notes



-   The underlying pseudorandom number generator has a period of approximately `2.1e9` (see [Numerical Recipes in C, 2nd Edition](#references), p. 279).

-   An [LCG][@stdlib/random/base/minstd] is fast and uses little memory. On the other hand, because the generator is a simple [linear congruential generator][@stdlib/random/base/minstd], 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.

-   If PRNG state is "shared" (meaning a state array was provided during stream creation and **not** copied) and one sets the generator state to a state array having a different length, the underlying 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).

-   In order to capture the PRNG state after a specific number of generated pseudorandom numbers, regardless of internal stream buffering, use the `siter` option in conjunction with a `state` event listener. Attempting to capture the underlying PRNG state after **reading** generated numbers is **not** likely to give expected results, as internal stream buffering will mean more values have been generated than have been read. Thus, the state returned by the `state` property will likely reflect a future PRNG state from the perspective of downstream consumers.



* * *



## Examples



```javascript

var inspectStream = require( '@stdlib/streams-node-inspect-sink' );

var randomStream = require( '@stdlib/random-streams-minstd' );



function log( v ) {

    console.log( v.toString() );

}



var opts = {

    'objectMode': true,

    'iter': 10

};



var stream = randomStream( opts );



opts = {

    'objectMode': true

};

var iStream = inspectStream( opts, log );



stream.pipe( iStream );

```



* * *



## CLI



## Installation



To use as a general utility, install the CLI package globally



```bash

npm install -g @stdlib/random-streams-minstd-cli

```



### Usage



```text

Usage: random-minstd [options]



Options:



  -h,  --help               Print this message.

  -V,  --version            Print the package version.

       --sep sep            Separator used to join streamed data. Default: '\n'.

  -n,  --iter iterations    Number of pseudorandom numbers.

       --normalized         Generate pseudorandom numbers on the interval [0,1).

       --seed seed          Pseudorandom number generator seed.

       --state filepath     Path to a file containing the pseudorandom number

                            generator state.

       --snapshot filepath  Output file path for saving the pseudorandom number

                            generator state upon exit.

```



### Notes



-   In accordance with POSIX convention, a trailing newline is **always** appended to generated output prior to exit.

-   Specifying a "snapshot" file path is useful when wanting to resume pseudorandom number generation due to, e.g., a downstream failure in an analysis pipeline. Before exiting, the process will store the pseudorandom number generator state in a file specified according to a provided file path. Upon loading a snapshot (state), the process will generate pseudorandom numbers starting from the loaded state, thus avoiding having to seed and replay an entire analysis.



### Examples



```bash

$ random-minstd -n 10 --seed 1234

```



* * *



## 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-base/minstd`][@stdlib/random/base/minstd]: A linear congruential pseudorandom number generator (LCG) based on Park and Miller.

-   [`@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-shuffle`][@stdlib/random/streams/minstd-shuffle]: create a readable stream for a linear congruential pseudorandom number generator (LCG) whose output is shuffled.

-   [`@stdlib/random-streams/mt19937`][@stdlib/random/streams/mt19937]: create a readable stream for a 32-bit Mersenne Twister pseudorandom number generator.

-   [`@stdlib/random-streams/randi`][@stdlib/random/streams/randi]: create a readable stream for generating pseudorandom numbers having integer values.

-   [`@stdlib/random-streams/randu`][@stdlib/random/streams/randu]: create a readable stream for generating uniformly distributed pseudorandom numbers between 0 and 1.



* * *



## 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 © 2016-2025. The Stdlib [Authors][stdlib-authors].



[npm-image]: http://img.shields.io/npm/v/@stdlib/random-streams-minstd.svg

[npm-url]: https://npmjs.org/package/@stdlib/random-streams-minstd



[test-image]: https://github.com/stdlib-js/random-streams-minstd/actions/workflows/test.yml/badge.svg?branch=main

[test-url]: https://github.com/stdlib-js/random-streams-minstd/actions/workflows/test.yml?query=branch:main



[coverage-image]: https://img.shields.io/codecov/c/github/stdlib-js/random-streams-minstd/main.svg

[coverage-url]: https://codecov.io/github/stdlib-js/random-streams-minstd?branch=main



[chat-image]: https://img.shields.io/gitter/room/stdlib-js/stdlib.svg

[chat-url]: https://app.gitter.im/#/room/#stdlib-js_stdlib:gitter.im



[stdlib]: https://github.com/stdlib-js/stdlib



[stdlib-authors]: https://github.com/stdlib-js/stdlib/graphs/contributors



[cli-section]: https://github.com/stdlib-js/random-streams-minstd#cli

[cli-url]: https://github.com/stdlib-js/random-streams-minstd/tree/cli

[@stdlib/random-streams-minstd]: https://github.com/stdlib-js/random-streams-minstd/tree/main



[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-streams-minstd/tree/deno

[deno-readme]: https://github.com/stdlib-js/random-streams-minstd/blob/deno/README.md

[umd-url]: https://github.com/stdlib-js/random-streams-minstd/tree/umd

[umd-readme]: https://github.com/stdlib-js/random-streams-minstd/blob/umd/README.md

[esm-url]: https://github.com/stdlib-js/random-streams-minstd/tree/esm

[esm-readme]: https://github.com/stdlib-js/random-streams-minstd/blob/esm/README.md

[branches-url]: https://github.com/stdlib-js/random-streams-minstd/blob/main/branches.md



[stdlib-license]: https://raw.githubusercontent.com/stdlib-js/random-streams-minstd/main/LICENSE



[stream]: https://nodejs.org/api/stream.html



[object-mode]: https://nodejs.org/api/stream.html#stream_object_mode



[readable-stream]: https://nodejs.org/api/stream.html



[@stdlib/array/int32]: https://github.com/stdlib-js/array-int32



[@park:1988]: http://dx.doi.org/10.1145/63039.63042



[@stdlib/random/base/minstd]: https://github.com/stdlib-js/random-base-minstd



[@stdlib/random/iter/minstd]: https://github.com/stdlib-js/random-iter-minstd



[@stdlib/random/streams/minstd-shuffle]: https://github.com/stdlib-js/random-streams-minstd-shuffle



[@stdlib/random/streams/mt19937]: https://github.com/stdlib-js/random-streams-mt19937



[@stdlib/random/streams/randi]: https://github.com/stdlib-js/random-streams-randi



[@stdlib/random/streams/randu]: https://github.com/stdlib-js/random-streams-randu