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Exponential Random Numbers

NPM versionBuild StatusCoverage Status

Generate pseudorandom numbers drawn from an exponential distribution.

Installation

npm install @stdlib/random-exponential

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

varexponential=require('@stdlib/random-exponential');

exponential( shape, lambda[, options] )

Returns an ndarray containing pseudorandom numbers drawn from an exponential distribution.

vararr=exponential([3,3],2.0);// returns <ndarray>

The function has the following parameters:

  • shape: output shape.
  • lambda: rate parameter. May be either a scalar or an ndarray. When providing an ndarray, the ndarray must be broadcast compatible with the specified output shape.
  • options: function options.

When provided a scalar distribution parameter, every element in the output ndarray is drawn from the same distribution. To generate pseudorandom numbers drawn from different distributions, provide a distribution parameter argument as an ndarray. The following example demonstrates broadcasting an ndarray containing distribution parameters to generate sub-matrices drawn from different distributions.

vargetShape=require('@stdlib/ndarray-shape');vararray=require('@stdlib/ndarray-array');varlambda=array([[[2.0]],[[10.0]]]);// returns <ndarray>varshape=getShape(lambda);// returns [ 2, 1, 1 ]vararr=exponential([2,3,3],lambda);// returns <ndarray>

If provided an empty shape, the function returns a zero-dimensional ndarray.

vargetShape=require('@stdlib/ndarray-shape');vararr=exponential([],2.0);// returns <ndarray>varshape=getShape(arr);// returns []varv=arr.get();// returns <number>

The function accepts the following options:

  • dtype: output ndarray data type. Must be a real-valued floating-point or "generic" data type.
  • order: ndarray order (i.e., memory layout), which is either row-major (C-style) or column-major (Fortran-style). Default: 'row-major'.
  • mode: specifies how to handle indices which exceed ndarray dimensions. For a list of supported modes, see ndarray. Default: 'throw'.
  • submode: a mode array which specifies for each dimension how to handle subscripts which exceed ndarray dimensions. If provided fewer modes than dimensions, an ndarray instance recycles modes using modulo arithmetic. Default: [ options.mode ].
  • readonly: boolean indicating whether an ndarray should be read-only. Default: false.

By default, the function returns an ndarray having a data type determined by the function's output data type policy. To override the default behavior, set the dtype option.

vargetDType=require('@stdlib/ndarray-dtype');varopts={'dtype': 'generic'};vararr=exponential([3,3],2.0,opts);// returns <ndarray>vardt=String(getDType(arr));// returns 'generic'

exponential.assign( lambda, out )

Fills an ndarray with pseudorandom numbers drawn from an exponential distribution.

varzeros=require('@stdlib/ndarray-zeros');varout=zeros([3,3]);// returns <ndarray>varv=exponential.assign(2.0,out);// returns <ndarray>varbool=(v===out);// returns true

The method has the following parameters:

exponential.factory( [options] )

Returns a function for generating pseudorandom numbers drawn from an exponential distribution.

vargetShape=require('@stdlib/ndarray-shape');varrandom=exponential.factory();varout=random([3,3],2.0);// returns <ndarray>varsh=getShape(out);// returns [ 3, 3 ]

The method accepts the following options:

  • prng: pseudorandom number generator for generating uniformly distributed pseudorandom numbers on the interval [0,1). If provided, the function ignores both the state and seed options. In order to seed the underlying pseudorandom number generator, one must seed the provided prng (assuming the provided prng is seedable).
  • seed: pseudorandom number generator seed.
  • state: a Uint32Array 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 an underlying generator has exclusive control over its internal state. Default: true.

To use a custom PRNG as the underlying source of uniformly distributed pseudorandom numbers, set the prng option.

varminstd=require('@stdlib/random-base-minstd');varopts={'prng': minstd.normalized};varrandom=exponential.factory(opts);varout=random([3,3],2.0);// returns <ndarray>

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

varopts={'seed': 12345};varrandom=exponential.factory(opts);varout=random([3,3],2.0);// returns <ndarray>

The function returned by the factory method has the same interface and accepts the same options as the exponential function above.

exponential.PRNG

The underlying pseudorandom number generator.

varprng=exponential.PRNG;// returns <Function>

exponential.seed

The value used to seed the underlying pseudorandom number generator.

varseed=exponential.seed;// returns <Uint32Array>

If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.

varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varseed=random.seed;// returns null

exponential.seedLength

Length of underlying pseudorandom number generator seed.

varlen=exponential.seedLength;// returns <number>

If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.

varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varlen=random.seedLength;// returns null

exponential.state

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

varstate=exponential.state;// returns <Uint32Array>

If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.

varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varstate=random.state;// returns null

exponential.stateLength

Length of underlying pseudorandom number generator state.

varlen=exponential.stateLength;// returns <number>

If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.

varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varlen=random.stateLength;// returns null

exponential.byteLength

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

varsz=exponential.byteLength;// returns <number>

If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.

varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varsz=random.byteLength;// returns null

Notes

  • If PRNG state is "shared" (meaning a state array was provided during function creation and not copied) and one sets the underlying generator state to a state array having a different length, the function returned by the factory method does not update the existing shared state and, instead, points to the newly provided state array. In order to synchronize the output of the underlying generator according to the new shared state array, the state array for each relevant creation function and/or PRNG must be explicitly set.
  • If PRNG state is "shared" and one sets the underlying generator state to a state array of the same length, the PRNG state is updated (along with the state of all other creation functions and/or PRNGs sharing the PRNG's state array).
  • The output data type policy only applies to the main function and specifies that, by default, the function must return an ndarray having a real-valued floating-point or "generic" data type. For the assign method, the output ndarray is allowed to have any supported output data type.

Examples

varlogEach=require('@stdlib/console-log-each');varndarray2array=require('@stdlib/ndarray-to-array');varexponential=require('@stdlib/random-exponential');// Create a function for generating random arrays originating from the same state:varrandom=exponential.factory({'state': exponential.state,'copy': true});// Generate 3 one-dimensional arrays:varx1=random([5],2.0);varx2=random([5],2.0);varx3=random([5],2.0);// 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=exponential.factory({'state': exponential.state,'copy': true});// Generate a two-dimensional array which replicates the above pseudorandom number generation sequence:varx4=random([3,5],2.0);// Convert to a list of nested arrays:vararr=ndarray2array(x4);// Print the contents:console.log('');logEach('%f, %f, %f',arr[0],arr[1],arr[2]);

See Also

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

Chat

License

See LICENSE.

Copyright

Copyright © 2016-2026. The Stdlib Authors.

About

Generate pseudorandom numbers drawn from an exponential distribution.

github.com/stdlib-js/stdlib

Topics

continuousexpexponentialgeneratorjavascriptmathmathematicsnodenode-jsnodejsprngpseudorandomrandrandomrngseedseedablestatisticsstatsstdlib

Resources

Readme
Apache-2.0 license

Code of conduct

Code of conduct

Contributing

Contributing

Security policy

Security policy
Activity
Custom properties

Stars

1 star

Watchers

2 watching

Forks

0 forks
Report repository

Releases

Sponsor this project

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About stdlib...

We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.
The library is fully decomposable, being architected in such a way that you can swap out and mix and match APIs and functionality to cater to your exact preferences and use cases.
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To join us in bringing numerical computing to the web, get started by checking us out on GitHub, and please consider financially supporting stdlib. We greatly appreciate your continued support!
Exponential Random Numbers
NPM versionBuild StatusCoverage Status
Generate pseudorandom numbers drawn from an exponential distribution.
Installation
npm install @stdlib/random-exponential
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
varexponential=require('@stdlib/random-exponential');
exponential( shape, lambda[, options] )
Returns an ndarray containing pseudorandom numbers drawn from an exponential distribution.
vararr=exponential([3,3],2.0);// returns <ndarray>
The function has the following parameters:
  • shape: output shape.
  • lambda: rate parameter. May be either a scalar or an ndarray. When providing an ndarray, the ndarray must be broadcast compatible with the specified output shape.
  • options: function options.
When provided a scalar distribution parameter, every element in the output ndarray is drawn from the same distribution. To generate pseudorandom numbers drawn from different distributions, provide a distribution parameter argument as an ndarray. The following example demonstrates broadcasting an ndarray containing distribution parameters to generate sub-matrices drawn from different distributions.
vargetShape=require('@stdlib/ndarray-shape');vararray=require('@stdlib/ndarray-array');varlambda=array([[[2.0]],[[10.0]]]);// returns <ndarray>varshape=getShape(lambda);// returns [ 2, 1, 1 ]vararr=exponential([2,3,3],lambda);// returns <ndarray>
If provided an empty shape, the function returns a zero-dimensional ndarray.
vargetShape=require('@stdlib/ndarray-shape');vararr=exponential([],2.0);// returns <ndarray>varshape=getShape(arr);// returns []varv=arr.get();// returns <number>
The function accepts the following options:
  • dtype: output ndarray data type. Must be a real-valued floating-point or "generic" data type.
  • order: ndarray order (i.e., memory layout), which is either row-major (C-style) or column-major (Fortran-style). Default: 'row-major'.
  • mode: specifies how to handle indices which exceed ndarray dimensions. For a list of supported modes, see ndarray. Default: 'throw'.
  • submode: a mode array which specifies for each dimension how to handle subscripts which exceed ndarray dimensions. If provided fewer modes than dimensions, an ndarray instance recycles modes using modulo arithmetic. Default: [ options.mode ].
  • readonly: boolean indicating whether an ndarray should be read-only. Default: false.
By default, the function returns an ndarray having a data type determined by the function's output data type policy. To override the default behavior, set the dtype option.
vargetDType=require('@stdlib/ndarray-dtype');varopts={'dtype': 'generic'};vararr=exponential([3,3],2.0,opts);// returns <ndarray>vardt=String(getDType(arr));// returns 'generic'
exponential.assign( lambda, out )
Fills an ndarray with pseudorandom numbers drawn from an exponential distribution.
varzeros=require('@stdlib/ndarray-zeros');varout=zeros([3,3]);// returns <ndarray>varv=exponential.assign(2.0,out);// returns <ndarray>varbool=(v===out);// returns true
The method has the following parameters:
exponential.factory( [options] )
Returns a function for generating pseudorandom numbers drawn from an exponential distribution.
vargetShape=require('@stdlib/ndarray-shape');varrandom=exponential.factory();varout=random([3,3],2.0);// returns <ndarray>varsh=getShape(out);// returns [ 3, 3 ]
The method accepts the following options:
  • prng: pseudorandom number generator for generating uniformly distributed pseudorandom numbers on the interval [0,1). If provided, the function ignores both the state and seed options. In order to seed the underlying pseudorandom number generator, one must seed the provided prng (assuming the provided prng is seedable).
  • seed: pseudorandom number generator seed.
  • state: a Uint32Array 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 an underlying generator has exclusive control over its internal state. Default: true.
To use a custom PRNG as the underlying source of uniformly distributed pseudorandom numbers, set the prng option.
varminstd=require('@stdlib/random-base-minstd');varopts={'prng': minstd.normalized};varrandom=exponential.factory(opts);varout=random([3,3],2.0);// returns <ndarray>
To seed the underlying pseudorandom number generator, set the seed option.
varopts={'seed': 12345};varrandom=exponential.factory(opts);varout=random([3,3],2.0);// returns <ndarray>
The function returned by the factory method has the same interface and accepts the same options as the exponential function above.
exponential.PRNG
The underlying pseudorandom number generator.
varprng=exponential.PRNG;// returns <Function>
exponential.seed
The value used to seed the underlying pseudorandom number generator.
varseed=exponential.seed;// returns <Uint32Array>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varseed=random.seed;// returns null
exponential.seedLength
Length of underlying pseudorandom number generator seed.
varlen=exponential.seedLength;// returns <number>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varlen=random.seedLength;// returns null
exponential.state
Writable property for getting and setting the underlying pseudorandom number generator state.
varstate=exponential.state;// returns <Uint32Array>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varstate=random.state;// returns null
exponential.stateLength
Length of underlying pseudorandom number generator state.
varlen=exponential.stateLength;// returns <number>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varlen=random.stateLength;// returns null
exponential.byteLength
Size (in bytes) of underlying pseudorandom number generator state.
varsz=exponential.byteLength;// returns <number>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varsz=random.byteLength;// returns null
Notes
  • If PRNG state is "shared" (meaning a state array was provided during function creation and not copied) and one sets the underlying generator state to a state array having a different length, the function returned by the factory method does not update the existing shared state and, instead, points to the newly provided state array. In order to synchronize the output of the underlying generator according to the new shared state array, the state array for each relevant creation function and/or PRNG must be explicitly set.
  • If PRNG state is "shared" and one sets the underlying generator state to a state array of the same length, the PRNG state is updated (along with the state of all other creation functions and/or PRNGs sharing the PRNG's state array).
  • The output data type policy only applies to the main function and specifies that, by default, the function must return an ndarray having a real-valued floating-point or "generic" data type. For the assign method, the output ndarray is allowed to have any supported output data type.
Examples
varlogEach=require('@stdlib/console-log-each');varndarray2array=require('@stdlib/ndarray-to-array');varexponential=require('@stdlib/random-exponential');// Create a function for generating random arrays originating from the same state:varrandom=exponential.factory({'state': exponential.state,'copy': true});// Generate 3 one-dimensional arrays:varx1=random([5],2.0);varx2=random([5],2.0);varx3=random([5],2.0);// 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=exponential.factory({'state': exponential.state,'copy': true});// Generate a two-dimensional array which replicates the above pseudorandom number generation sequence:varx4=random([3,5],2.0);// Convert to a list of nested arrays:vararr=ndarray2array(x4);// Print the contents:console.log('');logEach('%f, %f, %f',arr[0],arr[1],arr[2]);
See Also
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
Chat
License
See LICENSE.
Copyright
Copyright © 2016-2026. The Stdlib Authors.
About
Generate pseudorandom numbers drawn from an exponential distribution.
github.com/stdlib-js/stdlib
Topics
continuousexpexponentialgeneratorjavascriptmathmathematicsnodenode-jsnodejsprngpseudorandomrandrandomrngseedseedablestatisticsstatsstdlib
Resources
Readme
Apache-2.0 license
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
Stars
1 star
Watchers
2 watching
Forks
0 forks
Report repository
Releases
Sponsor this project
Packages
Used by
Contributors
Languages

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GitHub - stdlib-js/random-exponential: Generate pseudorandom numbers drawn from an exponential distribution. · GitHub
Skip to content
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About stdlib...

We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.
The library is fully decomposable, being architected in such a way that you can swap out and mix and match APIs and functionality to cater to your exact preferences and use cases.
When you use stdlib, you can be absolutely certain that you are using the most thorough, rigorous, well-written, studied, documented, tested, measured, and high-quality code out there.
To join us in bringing numerical computing to the web, get started by checking us out on GitHub, and please consider financially supporting stdlib. We greatly appreciate your continued support!
Exponential Random Numbers
NPM versionBuild StatusCoverage Status
Generate pseudorandom numbers drawn from an exponential distribution.
Installation
npm install @stdlib/random-exponential
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
varexponential=require('@stdlib/random-exponential');
exponential( shape, lambda[, options] )
Returns an ndarray containing pseudorandom numbers drawn from an exponential distribution.
vararr=exponential([3,3],2.0);// returns <ndarray>
The function has the following parameters:
  • shape: output shape.
  • lambda: rate parameter. May be either a scalar or an ndarray. When providing an ndarray, the ndarray must be broadcast compatible with the specified output shape.
  • options: function options.
When provided a scalar distribution parameter, every element in the output ndarray is drawn from the same distribution. To generate pseudorandom numbers drawn from different distributions, provide a distribution parameter argument as an ndarray. The following example demonstrates broadcasting an ndarray containing distribution parameters to generate sub-matrices drawn from different distributions.
vargetShape=require('@stdlib/ndarray-shape');vararray=require('@stdlib/ndarray-array');varlambda=array([[[2.0]],[[10.0]]]);// returns <ndarray>varshape=getShape(lambda);// returns [ 2, 1, 1 ]vararr=exponential([2,3,3],lambda);// returns <ndarray>
If provided an empty shape, the function returns a zero-dimensional ndarray.
vargetShape=require('@stdlib/ndarray-shape');vararr=exponential([],2.0);// returns <ndarray>varshape=getShape(arr);// returns []varv=arr.get();// returns <number>
The function accepts the following options:
  • dtype: output ndarray data type. Must be a real-valued floating-point or "generic" data type.
  • order: ndarray order (i.e., memory layout), which is either row-major (C-style) or column-major (Fortran-style). Default: 'row-major'.
  • mode: specifies how to handle indices which exceed ndarray dimensions. For a list of supported modes, see ndarray. Default: 'throw'.
  • submode: a mode array which specifies for each dimension how to handle subscripts which exceed ndarray dimensions. If provided fewer modes than dimensions, an ndarray instance recycles modes using modulo arithmetic. Default: [ options.mode ].
  • readonly: boolean indicating whether an ndarray should be read-only. Default: false.
By default, the function returns an ndarray having a data type determined by the function's output data type policy. To override the default behavior, set the dtype option.
vargetDType=require('@stdlib/ndarray-dtype');varopts={'dtype': 'generic'};vararr=exponential([3,3],2.0,opts);// returns <ndarray>vardt=String(getDType(arr));// returns 'generic'
exponential.assign( lambda, out )
Fills an ndarray with pseudorandom numbers drawn from an exponential distribution.
varzeros=require('@stdlib/ndarray-zeros');varout=zeros([3,3]);// returns <ndarray>varv=exponential.assign(2.0,out);// returns <ndarray>varbool=(v===out);// returns true
The method has the following parameters:
exponential.factory( [options] )
Returns a function for generating pseudorandom numbers drawn from an exponential distribution.
vargetShape=require('@stdlib/ndarray-shape');varrandom=exponential.factory();varout=random([3,3],2.0);// returns <ndarray>varsh=getShape(out);// returns [ 3, 3 ]
The method accepts the following options:
  • prng: pseudorandom number generator for generating uniformly distributed pseudorandom numbers on the interval [0,1). If provided, the function ignores both the state and seed options. In order to seed the underlying pseudorandom number generator, one must seed the provided prng (assuming the provided prng is seedable).
  • seed: pseudorandom number generator seed.
  • state: a Uint32Array 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 an underlying generator has exclusive control over its internal state. Default: true.
To use a custom PRNG as the underlying source of uniformly distributed pseudorandom numbers, set the prng option.
varminstd=require('@stdlib/random-base-minstd');varopts={'prng': minstd.normalized};varrandom=exponential.factory(opts);varout=random([3,3],2.0);// returns <ndarray>
To seed the underlying pseudorandom number generator, set the seed option.
varopts={'seed': 12345};varrandom=exponential.factory(opts);varout=random([3,3],2.0);// returns <ndarray>
The function returned by the factory method has the same interface and accepts the same options as the exponential function above.
exponential.PRNG
The underlying pseudorandom number generator.
varprng=exponential.PRNG;// returns <Function>
exponential.seed
The value used to seed the underlying pseudorandom number generator.
varseed=exponential.seed;// returns <Uint32Array>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varseed=random.seed;// returns null
exponential.seedLength
Length of underlying pseudorandom number generator seed.
varlen=exponential.seedLength;// returns <number>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varlen=random.seedLength;// returns null
exponential.state
Writable property for getting and setting the underlying pseudorandom number generator state.
varstate=exponential.state;// returns <Uint32Array>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varstate=random.state;// returns null
exponential.stateLength
Length of underlying pseudorandom number generator state.
varlen=exponential.stateLength;// returns <number>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varlen=random.stateLength;// returns null
exponential.byteLength
Size (in bytes) of underlying pseudorandom number generator state.
varsz=exponential.byteLength;// returns <number>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varsz=random.byteLength;// returns null
Notes
  • If PRNG state is "shared" (meaning a state array was provided during function creation and not copied) and one sets the underlying generator state to a state array having a different length, the function returned by the factory method does not update the existing shared state and, instead, points to the newly provided state array. In order to synchronize the output of the underlying generator according to the new shared state array, the state array for each relevant creation function and/or PRNG must be explicitly set.
  • If PRNG state is "shared" and one sets the underlying generator state to a state array of the same length, the PRNG state is updated (along with the state of all other creation functions and/or PRNGs sharing the PRNG's state array).
  • The output data type policy only applies to the main function and specifies that, by default, the function must return an ndarray having a real-valued floating-point or "generic" data type. For the assign method, the output ndarray is allowed to have any supported output data type.
Examples
varlogEach=require('@stdlib/console-log-each');varndarray2array=require('@stdlib/ndarray-to-array');varexponential=require('@stdlib/random-exponential');// Create a function for generating random arrays originating from the same state:varrandom=exponential.factory({'state': exponential.state,'copy': true});// Generate 3 one-dimensional arrays:varx1=random([5],2.0);varx2=random([5],2.0);varx3=random([5],2.0);// 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=exponential.factory({'state': exponential.state,'copy': true});// Generate a two-dimensional array which replicates the above pseudorandom number generation sequence:varx4=random([3,5],2.0);// Convert to a list of nested arrays:vararr=ndarray2array(x4);// Print the contents:console.log('');logEach('%f, %f, %f',arr[0],arr[1],arr[2]);
See Also
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
Chat
License
See LICENSE.
Copyright
Copyright © 2016-2026. The Stdlib Authors.
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Generate pseudorandom numbers drawn from an exponential distribution.
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GitHub - stdlib-js/random-exponential: Generate pseudorandom numbers drawn from an exponential distribution. · GitHub
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We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.
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To join us in bringing numerical computing to the web, get started by checking us out on GitHub, and please consider financially supporting stdlib. We greatly appreciate your continued support!
Exponential Random Numbers
NPM versionBuild StatusCoverage Status
Generate pseudorandom numbers drawn from an exponential distribution.
Installation
npm install @stdlib/random-exponential
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
varexponential=require('@stdlib/random-exponential');
exponential( shape, lambda[, options] )
Returns an ndarray containing pseudorandom numbers drawn from an exponential distribution.
vararr=exponential([3,3],2.0);// returns <ndarray>
The function has the following parameters:
  • shape: output shape.
  • lambda: rate parameter. May be either a scalar or an ndarray. When providing an ndarray, the ndarray must be broadcast compatible with the specified output shape.
  • options: function options.
When provided a scalar distribution parameter, every element in the output ndarray is drawn from the same distribution. To generate pseudorandom numbers drawn from different distributions, provide a distribution parameter argument as an ndarray. The following example demonstrates broadcasting an ndarray containing distribution parameters to generate sub-matrices drawn from different distributions.
vargetShape=require('@stdlib/ndarray-shape');vararray=require('@stdlib/ndarray-array');varlambda=array([[[2.0]],[[10.0]]]);// returns <ndarray>varshape=getShape(lambda);// returns [ 2, 1, 1 ]vararr=exponential([2,3,3],lambda);// returns <ndarray>
If provided an empty shape, the function returns a zero-dimensional ndarray.
vargetShape=require('@stdlib/ndarray-shape');vararr=exponential([],2.0);// returns <ndarray>varshape=getShape(arr);// returns []varv=arr.get();// returns <number>
The function accepts the following options:
  • dtype: output ndarray data type. Must be a real-valued floating-point or "generic" data type.
  • order: ndarray order (i.e., memory layout), which is either row-major (C-style) or column-major (Fortran-style). Default: 'row-major'.
  • mode: specifies how to handle indices which exceed ndarray dimensions. For a list of supported modes, see ndarray. Default: 'throw'.
  • submode: a mode array which specifies for each dimension how to handle subscripts which exceed ndarray dimensions. If provided fewer modes than dimensions, an ndarray instance recycles modes using modulo arithmetic. Default: [ options.mode ].
  • readonly: boolean indicating whether an ndarray should be read-only. Default: false.
By default, the function returns an ndarray having a data type determined by the function's output data type policy. To override the default behavior, set the dtype option.
vargetDType=require('@stdlib/ndarray-dtype');varopts={'dtype': 'generic'};vararr=exponential([3,3],2.0,opts);// returns <ndarray>vardt=String(getDType(arr));// returns 'generic'
exponential.assign( lambda, out )
Fills an ndarray with pseudorandom numbers drawn from an exponential distribution.
varzeros=require('@stdlib/ndarray-zeros');varout=zeros([3,3]);// returns <ndarray>varv=exponential.assign(2.0,out);// returns <ndarray>varbool=(v===out);// returns true
The method has the following parameters:
exponential.factory( [options] )
Returns a function for generating pseudorandom numbers drawn from an exponential distribution.
vargetShape=require('@stdlib/ndarray-shape');varrandom=exponential.factory();varout=random([3,3],2.0);// returns <ndarray>varsh=getShape(out);// returns [ 3, 3 ]
The method accepts the following options:
  • prng: pseudorandom number generator for generating uniformly distributed pseudorandom numbers on the interval [0,1). If provided, the function ignores both the state and seed options. In order to seed the underlying pseudorandom number generator, one must seed the provided prng (assuming the provided prng is seedable).
  • seed: pseudorandom number generator seed.
  • state: a Uint32Array 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 an underlying generator has exclusive control over its internal state. Default: true.
To use a custom PRNG as the underlying source of uniformly distributed pseudorandom numbers, set the prng option.
varminstd=require('@stdlib/random-base-minstd');varopts={'prng': minstd.normalized};varrandom=exponential.factory(opts);varout=random([3,3],2.0);// returns <ndarray>
To seed the underlying pseudorandom number generator, set the seed option.
varopts={'seed': 12345};varrandom=exponential.factory(opts);varout=random([3,3],2.0);// returns <ndarray>
The function returned by the factory method has the same interface and accepts the same options as the exponential function above.
exponential.PRNG
The underlying pseudorandom number generator.
varprng=exponential.PRNG;// returns <Function>
exponential.seed
The value used to seed the underlying pseudorandom number generator.
varseed=exponential.seed;// returns <Uint32Array>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varseed=random.seed;// returns null
exponential.seedLength
Length of underlying pseudorandom number generator seed.
varlen=exponential.seedLength;// returns <number>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varlen=random.seedLength;// returns null
exponential.state
Writable property for getting and setting the underlying pseudorandom number generator state.
varstate=exponential.state;// returns <Uint32Array>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varstate=random.state;// returns null
exponential.stateLength
Length of underlying pseudorandom number generator state.
varlen=exponential.stateLength;// returns <number>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varlen=random.stateLength;// returns null
exponential.byteLength
Size (in bytes) of underlying pseudorandom number generator state.
varsz=exponential.byteLength;// returns <number>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varsz=random.byteLength;// returns null
Notes
  • If PRNG state is "shared" (meaning a state array was provided during function creation and not copied) and one sets the underlying generator state to a state array having a different length, the function returned by the factory method does not update the existing shared state and, instead, points to the newly provided state array. In order to synchronize the output of the underlying generator according to the new shared state array, the state array for each relevant creation function and/or PRNG must be explicitly set.
  • If PRNG state is "shared" and one sets the underlying generator state to a state array of the same length, the PRNG state is updated (along with the state of all other creation functions and/or PRNGs sharing the PRNG's state array).
  • The output data type policy only applies to the main function and specifies that, by default, the function must return an ndarray having a real-valued floating-point or "generic" data type. For the assign method, the output ndarray is allowed to have any supported output data type.
Examples
varlogEach=require('@stdlib/console-log-each');varndarray2array=require('@stdlib/ndarray-to-array');varexponential=require('@stdlib/random-exponential');// Create a function for generating random arrays originating from the same state:varrandom=exponential.factory({'state': exponential.state,'copy': true});// Generate 3 one-dimensional arrays:varx1=random([5],2.0);varx2=random([5],2.0);varx3=random([5],2.0);// 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=exponential.factory({'state': exponential.state,'copy': true});// Generate a two-dimensional array which replicates the above pseudorandom number generation sequence:varx4=random([3,5],2.0);// Convert to a list of nested arrays:vararr=ndarray2array(x4);// Print the contents:console.log('');logEach('%f, %f, %f',arr[0],arr[1],arr[2]);
See Also
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
Chat
License
See LICENSE.
Copyright
Copyright © 2016-2026. The Stdlib Authors.
About
Generate pseudorandom numbers drawn from an exponential distribution.
github.com/stdlib-js/stdlib
Topics
continuousexpexponentialgeneratorjavascriptmathmathematicsnodenode-jsnodejsprngpseudorandomrandrandomrngseedseedablestatisticsstatsstdlib
Resources
Readme
Apache-2.0 license
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
Stars
1 star
Watchers
2 watching
Forks
0 forks
Report repository
Releases
Sponsor this project
Packages
Used by
Contributors
Languages

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GitHub - stdlib-js/random-exponential: Generate pseudorandom numbers drawn from an exponential distribution. · GitHub
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About stdlib...

We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.
The library is fully decomposable, being architected in such a way that you can swap out and mix and match APIs and functionality to cater to your exact preferences and use cases.
When you use stdlib, you can be absolutely certain that you are using the most thorough, rigorous, well-written, studied, documented, tested, measured, and high-quality code out there.
To join us in bringing numerical computing to the web, get started by checking us out on GitHub, and please consider financially supporting stdlib. We greatly appreciate your continued support!
Exponential Random Numbers
NPM versionBuild StatusCoverage Status
Generate pseudorandom numbers drawn from an exponential distribution.
Installation
npm install @stdlib/random-exponential
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
varexponential=require('@stdlib/random-exponential');
exponential( shape, lambda[, options] )
Returns an ndarray containing pseudorandom numbers drawn from an exponential distribution.
vararr=exponential([3,3],2.0);// returns <ndarray>
The function has the following parameters:
  • shape: output shape.
  • lambda: rate parameter. May be either a scalar or an ndarray. When providing an ndarray, the ndarray must be broadcast compatible with the specified output shape.
  • options: function options.
When provided a scalar distribution parameter, every element in the output ndarray is drawn from the same distribution. To generate pseudorandom numbers drawn from different distributions, provide a distribution parameter argument as an ndarray. The following example demonstrates broadcasting an ndarray containing distribution parameters to generate sub-matrices drawn from different distributions.
vargetShape=require('@stdlib/ndarray-shape');vararray=require('@stdlib/ndarray-array');varlambda=array([[[2.0]],[[10.0]]]);// returns <ndarray>varshape=getShape(lambda);// returns [ 2, 1, 1 ]vararr=exponential([2,3,3],lambda);// returns <ndarray>
If provided an empty shape, the function returns a zero-dimensional ndarray.
vargetShape=require('@stdlib/ndarray-shape');vararr=exponential([],2.0);// returns <ndarray>varshape=getShape(arr);// returns []varv=arr.get();// returns <number>
The function accepts the following options:
  • dtype: output ndarray data type. Must be a real-valued floating-point or "generic" data type.
  • order: ndarray order (i.e., memory layout), which is either row-major (C-style) or column-major (Fortran-style). Default: 'row-major'.
  • mode: specifies how to handle indices which exceed ndarray dimensions. For a list of supported modes, see ndarray. Default: 'throw'.
  • submode: a mode array which specifies for each dimension how to handle subscripts which exceed ndarray dimensions. If provided fewer modes than dimensions, an ndarray instance recycles modes using modulo arithmetic. Default: [ options.mode ].
  • readonly: boolean indicating whether an ndarray should be read-only. Default: false.
By default, the function returns an ndarray having a data type determined by the function's output data type policy. To override the default behavior, set the dtype option.
vargetDType=require('@stdlib/ndarray-dtype');varopts={'dtype': 'generic'};vararr=exponential([3,3],2.0,opts);// returns <ndarray>vardt=String(getDType(arr));// returns 'generic'
exponential.assign( lambda, out )
Fills an ndarray with pseudorandom numbers drawn from an exponential distribution.
varzeros=require('@stdlib/ndarray-zeros');varout=zeros([3,3]);// returns <ndarray>varv=exponential.assign(2.0,out);// returns <ndarray>varbool=(v===out);// returns true
The method has the following parameters:
exponential.factory( [options] )
Returns a function for generating pseudorandom numbers drawn from an exponential distribution.
vargetShape=require('@stdlib/ndarray-shape');varrandom=exponential.factory();varout=random([3,3],2.0);// returns <ndarray>varsh=getShape(out);// returns [ 3, 3 ]
The method accepts the following options:
  • prng: pseudorandom number generator for generating uniformly distributed pseudorandom numbers on the interval [0,1). If provided, the function ignores both the state and seed options. In order to seed the underlying pseudorandom number generator, one must seed the provided prng (assuming the provided prng is seedable).
  • seed: pseudorandom number generator seed.
  • state: a Uint32Array 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 an underlying generator has exclusive control over its internal state. Default: true.
To use a custom PRNG as the underlying source of uniformly distributed pseudorandom numbers, set the prng option.
varminstd=require('@stdlib/random-base-minstd');varopts={'prng': minstd.normalized};varrandom=exponential.factory(opts);varout=random([3,3],2.0);// returns <ndarray>
To seed the underlying pseudorandom number generator, set the seed option.
varopts={'seed': 12345};varrandom=exponential.factory(opts);varout=random([3,3],2.0);// returns <ndarray>
The function returned by the factory method has the same interface and accepts the same options as the exponential function above.
exponential.PRNG
The underlying pseudorandom number generator.
varprng=exponential.PRNG;// returns <Function>
exponential.seed
The value used to seed the underlying pseudorandom number generator.
varseed=exponential.seed;// returns <Uint32Array>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varseed=random.seed;// returns null
exponential.seedLength
Length of underlying pseudorandom number generator seed.
varlen=exponential.seedLength;// returns <number>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varlen=random.seedLength;// returns null
exponential.state
Writable property for getting and setting the underlying pseudorandom number generator state.
varstate=exponential.state;// returns <Uint32Array>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varstate=random.state;// returns null
exponential.stateLength
Length of underlying pseudorandom number generator state.
varlen=exponential.stateLength;// returns <number>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varlen=random.stateLength;// returns null
exponential.byteLength
Size (in bytes) of underlying pseudorandom number generator state.
varsz=exponential.byteLength;// returns <number>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varsz=random.byteLength;// returns null
Notes
  • If PRNG state is "shared" (meaning a state array was provided during function creation and not copied) and one sets the underlying generator state to a state array having a different length, the function returned by the factory method does not update the existing shared state and, instead, points to the newly provided state array. In order to synchronize the output of the underlying generator according to the new shared state array, the state array for each relevant creation function and/or PRNG must be explicitly set.
  • If PRNG state is "shared" and one sets the underlying generator state to a state array of the same length, the PRNG state is updated (along with the state of all other creation functions and/or PRNGs sharing the PRNG's state array).
  • The output data type policy only applies to the main function and specifies that, by default, the function must return an ndarray having a real-valued floating-point or "generic" data type. For the assign method, the output ndarray is allowed to have any supported output data type.
Examples
varlogEach=require('@stdlib/console-log-each');varndarray2array=require('@stdlib/ndarray-to-array');varexponential=require('@stdlib/random-exponential');// Create a function for generating random arrays originating from the same state:varrandom=exponential.factory({'state': exponential.state,'copy': true});// Generate 3 one-dimensional arrays:varx1=random([5],2.0);varx2=random([5],2.0);varx3=random([5],2.0);// 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=exponential.factory({'state': exponential.state,'copy': true});// Generate a two-dimensional array which replicates the above pseudorandom number generation sequence:varx4=random([3,5],2.0);// Convert to a list of nested arrays:vararr=ndarray2array(x4);// Print the contents:console.log('');logEach('%f, %f, %f',arr[0],arr[1],arr[2]);
See Also
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
Chat
License
See LICENSE.
Copyright
Copyright © 2016-2026. The Stdlib Authors.
About
Generate pseudorandom numbers drawn from an exponential distribution.
github.com/stdlib-js/stdlib
Topics
continuousexpexponentialgeneratorjavascriptmathmathematicsnodenode-jsnodejsprngpseudorandomrandrandomrngseedseedablestatisticsstatsstdlib
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Exponential Random Numbers
NPM versionBuild StatusCoverage Status
Generate pseudorandom numbers drawn from an exponential distribution.
Installation
npm install @stdlib/random-exponential
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
varexponential=require('@stdlib/random-exponential');
exponential( shape, lambda[, options] )
Returns an ndarray containing pseudorandom numbers drawn from an exponential distribution.
vararr=exponential([3,3],2.0);// returns <ndarray>
The function has the following parameters:
  • shape: output shape.
  • lambda: rate parameter. May be either a scalar or an ndarray. When providing an ndarray, the ndarray must be broadcast compatible with the specified output shape.
  • options: function options.
When provided a scalar distribution parameter, every element in the output ndarray is drawn from the same distribution. To generate pseudorandom numbers drawn from different distributions, provide a distribution parameter argument as an ndarray. The following example demonstrates broadcasting an ndarray containing distribution parameters to generate sub-matrices drawn from different distributions.
vargetShape=require('@stdlib/ndarray-shape');vararray=require('@stdlib/ndarray-array');varlambda=array([[[2.0]],[[10.0]]]);// returns <ndarray>varshape=getShape(lambda);// returns [ 2, 1, 1 ]vararr=exponential([2,3,3],lambda);// returns <ndarray>
If provided an empty shape, the function returns a zero-dimensional ndarray.
vargetShape=require('@stdlib/ndarray-shape');vararr=exponential([],2.0);// returns <ndarray>varshape=getShape(arr);// returns []varv=arr.get();// returns <number>
The function accepts the following options:
  • dtype: output ndarray data type. Must be a real-valued floating-point or "generic" data type.
  • order: ndarray order (i.e., memory layout), which is either row-major (C-style) or column-major (Fortran-style). Default: 'row-major'.
  • mode: specifies how to handle indices which exceed ndarray dimensions. For a list of supported modes, see ndarray. Default: 'throw'.
  • submode: a mode array which specifies for each dimension how to handle subscripts which exceed ndarray dimensions. If provided fewer modes than dimensions, an ndarray instance recycles modes using modulo arithmetic. Default: [ options.mode ].
  • readonly: boolean indicating whether an ndarray should be read-only. Default: false.
By default, the function returns an ndarray having a data type determined by the function's output data type policy. To override the default behavior, set the dtype option.
vargetDType=require('@stdlib/ndarray-dtype');varopts={'dtype': 'generic'};vararr=exponential([3,3],2.0,opts);// returns <ndarray>vardt=String(getDType(arr));// returns 'generic'
exponential.assign( lambda, out )
Fills an ndarray with pseudorandom numbers drawn from an exponential distribution.
varzeros=require('@stdlib/ndarray-zeros');varout=zeros([3,3]);// returns <ndarray>varv=exponential.assign(2.0,out);// returns <ndarray>varbool=(v===out);// returns true
The method has the following parameters:
exponential.factory( [options] )
Returns a function for generating pseudorandom numbers drawn from an exponential distribution.
vargetShape=require('@stdlib/ndarray-shape');varrandom=exponential.factory();varout=random([3,3],2.0);// returns <ndarray>varsh=getShape(out);// returns [ 3, 3 ]
The method accepts the following options:
  • prng: pseudorandom number generator for generating uniformly distributed pseudorandom numbers on the interval [0,1). If provided, the function ignores both the state and seed options. In order to seed the underlying pseudorandom number generator, one must seed the provided prng (assuming the provided prng is seedable).
  • seed: pseudorandom number generator seed.
  • state: a Uint32Array 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 an underlying generator has exclusive control over its internal state. Default: true.
To use a custom PRNG as the underlying source of uniformly distributed pseudorandom numbers, set the prng option.
varminstd=require('@stdlib/random-base-minstd');varopts={'prng': minstd.normalized};varrandom=exponential.factory(opts);varout=random([3,3],2.0);// returns <ndarray>
To seed the underlying pseudorandom number generator, set the seed option.
varopts={'seed': 12345};varrandom=exponential.factory(opts);varout=random([3,3],2.0);// returns <ndarray>
The function returned by the factory method has the same interface and accepts the same options as the exponential function above.
exponential.PRNG
The underlying pseudorandom number generator.
varprng=exponential.PRNG;// returns <Function>
exponential.seed
The value used to seed the underlying pseudorandom number generator.
varseed=exponential.seed;// returns <Uint32Array>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varseed=random.seed;// returns null
exponential.seedLength
Length of underlying pseudorandom number generator seed.
varlen=exponential.seedLength;// returns <number>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varlen=random.seedLength;// returns null
exponential.state
Writable property for getting and setting the underlying pseudorandom number generator state.
varstate=exponential.state;// returns <Uint32Array>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varstate=random.state;// returns null
exponential.stateLength
Length of underlying pseudorandom number generator state.
varlen=exponential.stateLength;// returns <number>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varlen=random.stateLength;// returns null
exponential.byteLength
Size (in bytes) of underlying pseudorandom number generator state.
varsz=exponential.byteLength;// returns <number>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varsz=random.byteLength;// returns null
Notes
  • If PRNG state is "shared" (meaning a state array was provided during function creation and not copied) and one sets the underlying generator state to a state array having a different length, the function returned by the factory method does not update the existing shared state and, instead, points to the newly provided state array. In order to synchronize the output of the underlying generator according to the new shared state array, the state array for each relevant creation function and/or PRNG must be explicitly set.
  • If PRNG state is "shared" and one sets the underlying generator state to a state array of the same length, the PRNG state is updated (along with the state of all other creation functions and/or PRNGs sharing the PRNG's state array).
  • The output data type policy only applies to the main function and specifies that, by default, the function must return an ndarray having a real-valued floating-point or "generic" data type. For the assign method, the output ndarray is allowed to have any supported output data type.
Examples
varlogEach=require('@stdlib/console-log-each');varndarray2array=require('@stdlib/ndarray-to-array');varexponential=require('@stdlib/random-exponential');// Create a function for generating random arrays originating from the same state:varrandom=exponential.factory({'state': exponential.state,'copy': true});// Generate 3 one-dimensional arrays:varx1=random([5],2.0);varx2=random([5],2.0);varx3=random([5],2.0);// 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=exponential.factory({'state': exponential.state,'copy': true});// Generate a two-dimensional array which replicates the above pseudorandom number generation sequence:varx4=random([3,5],2.0);// Convert to a list of nested arrays:vararr=ndarray2array(x4);// Print the contents:console.log('');logEach('%f, %f, %f',arr[0],arr[1],arr[2]);
See Also
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
Chat
License
See LICENSE.
Copyright
Copyright © 2016-2026. The Stdlib Authors.
About
Generate pseudorandom numbers drawn from an exponential distribution.
github.com/stdlib-js/stdlib
Topics
continuousexpexponentialgeneratorjavascriptmathmathematicsnodenode-jsnodejsprngpseudorandomrandrandomrngseedseedablestatisticsstatsstdlib
Resources
Readme
Apache-2.0 license
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
Stars
1 star
Watchers
2 watching
Forks
0 forks
Report repository
Releases
Sponsor this project
Packages
Used by
Contributors
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GitHub - stdlib-js/random-exponential: Generate pseudorandom numbers drawn from an exponential distribution. · GitHub
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About stdlib...

We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.
The library is fully decomposable, being architected in such a way that you can swap out and mix and match APIs and functionality to cater to your exact preferences and use cases.
When you use stdlib, you can be absolutely certain that you are using the most thorough, rigorous, well-written, studied, documented, tested, measured, and high-quality code out there.
To join us in bringing numerical computing to the web, get started by checking us out on GitHub, and please consider financially supporting stdlib. We greatly appreciate your continued support!
Exponential Random Numbers
NPM versionBuild StatusCoverage Status
Generate pseudorandom numbers drawn from an exponential distribution.
Installation
npm install @stdlib/random-exponential
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
varexponential=require('@stdlib/random-exponential');
exponential( shape, lambda[, options] )
Returns an ndarray containing pseudorandom numbers drawn from an exponential distribution.
vararr=exponential([3,3],2.0);// returns <ndarray>
The function has the following parameters:
  • shape: output shape.
  • lambda: rate parameter. May be either a scalar or an ndarray. When providing an ndarray, the ndarray must be broadcast compatible with the specified output shape.
  • options: function options.
When provided a scalar distribution parameter, every element in the output ndarray is drawn from the same distribution. To generate pseudorandom numbers drawn from different distributions, provide a distribution parameter argument as an ndarray. The following example demonstrates broadcasting an ndarray containing distribution parameters to generate sub-matrices drawn from different distributions.
vargetShape=require('@stdlib/ndarray-shape');vararray=require('@stdlib/ndarray-array');varlambda=array([[[2.0]],[[10.0]]]);// returns <ndarray>varshape=getShape(lambda);// returns [ 2, 1, 1 ]vararr=exponential([2,3,3],lambda);// returns <ndarray>
If provided an empty shape, the function returns a zero-dimensional ndarray.
vargetShape=require('@stdlib/ndarray-shape');vararr=exponential([],2.0);// returns <ndarray>varshape=getShape(arr);// returns []varv=arr.get();// returns <number>
The function accepts the following options:
  • dtype: output ndarray data type. Must be a real-valued floating-point or "generic" data type.
  • order: ndarray order (i.e., memory layout), which is either row-major (C-style) or column-major (Fortran-style). Default: 'row-major'.
  • mode: specifies how to handle indices which exceed ndarray dimensions. For a list of supported modes, see ndarray. Default: 'throw'.
  • submode: a mode array which specifies for each dimension how to handle subscripts which exceed ndarray dimensions. If provided fewer modes than dimensions, an ndarray instance recycles modes using modulo arithmetic. Default: [ options.mode ].
  • readonly: boolean indicating whether an ndarray should be read-only. Default: false.
By default, the function returns an ndarray having a data type determined by the function's output data type policy. To override the default behavior, set the dtype option.
vargetDType=require('@stdlib/ndarray-dtype');varopts={'dtype': 'generic'};vararr=exponential([3,3],2.0,opts);// returns <ndarray>vardt=String(getDType(arr));// returns 'generic'
exponential.assign( lambda, out )
Fills an ndarray with pseudorandom numbers drawn from an exponential distribution.
varzeros=require('@stdlib/ndarray-zeros');varout=zeros([3,3]);// returns <ndarray>varv=exponential.assign(2.0,out);// returns <ndarray>varbool=(v===out);// returns true
The method has the following parameters:
exponential.factory( [options] )
Returns a function for generating pseudorandom numbers drawn from an exponential distribution.
vargetShape=require('@stdlib/ndarray-shape');varrandom=exponential.factory();varout=random([3,3],2.0);// returns <ndarray>varsh=getShape(out);// returns [ 3, 3 ]
The method accepts the following options:
  • prng: pseudorandom number generator for generating uniformly distributed pseudorandom numbers on the interval [0,1). If provided, the function ignores both the state and seed options. In order to seed the underlying pseudorandom number generator, one must seed the provided prng (assuming the provided prng is seedable).
  • seed: pseudorandom number generator seed.
  • state: a Uint32Array 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 an underlying generator has exclusive control over its internal state. Default: true.
To use a custom PRNG as the underlying source of uniformly distributed pseudorandom numbers, set the prng option.
varminstd=require('@stdlib/random-base-minstd');varopts={'prng': minstd.normalized};varrandom=exponential.factory(opts);varout=random([3,3],2.0);// returns <ndarray>
To seed the underlying pseudorandom number generator, set the seed option.
varopts={'seed': 12345};varrandom=exponential.factory(opts);varout=random([3,3],2.0);// returns <ndarray>
The function returned by the factory method has the same interface and accepts the same options as the exponential function above.
exponential.PRNG
The underlying pseudorandom number generator.
varprng=exponential.PRNG;// returns <Function>
exponential.seed
The value used to seed the underlying pseudorandom number generator.
varseed=exponential.seed;// returns <Uint32Array>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varseed=random.seed;// returns null
exponential.seedLength
Length of underlying pseudorandom number generator seed.
varlen=exponential.seedLength;// returns <number>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varlen=random.seedLength;// returns null
exponential.state
Writable property for getting and setting the underlying pseudorandom number generator state.
varstate=exponential.state;// returns <Uint32Array>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varstate=random.state;// returns null
exponential.stateLength
Length of underlying pseudorandom number generator state.
varlen=exponential.stateLength;// returns <number>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varlen=random.stateLength;// returns null
exponential.byteLength
Size (in bytes) of underlying pseudorandom number generator state.
varsz=exponential.byteLength;// returns <number>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varsz=random.byteLength;// returns null
Notes
  • If PRNG state is "shared" (meaning a state array was provided during function creation and not copied) and one sets the underlying generator state to a state array having a different length, the function returned by the factory method does not update the existing shared state and, instead, points to the newly provided state array. In order to synchronize the output of the underlying generator according to the new shared state array, the state array for each relevant creation function and/or PRNG must be explicitly set.
  • If PRNG state is "shared" and one sets the underlying generator state to a state array of the same length, the PRNG state is updated (along with the state of all other creation functions and/or PRNGs sharing the PRNG's state array).
  • The output data type policy only applies to the main function and specifies that, by default, the function must return an ndarray having a real-valued floating-point or "generic" data type. For the assign method, the output ndarray is allowed to have any supported output data type.
Examples
varlogEach=require('@stdlib/console-log-each');varndarray2array=require('@stdlib/ndarray-to-array');varexponential=require('@stdlib/random-exponential');// Create a function for generating random arrays originating from the same state:varrandom=exponential.factory({'state': exponential.state,'copy': true});// Generate 3 one-dimensional arrays:varx1=random([5],2.0);varx2=random([5],2.0);varx3=random([5],2.0);// 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=exponential.factory({'state': exponential.state,'copy': true});// Generate a two-dimensional array which replicates the above pseudorandom number generation sequence:varx4=random([3,5],2.0);// Convert to a list of nested arrays:vararr=ndarray2array(x4);// Print the contents:console.log('');logEach('%f, %f, %f',arr[0],arr[1],arr[2]);
See Also
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
Chat
License
See LICENSE.
Copyright
Copyright © 2016-2026. The Stdlib Authors.
About
Generate pseudorandom numbers drawn from an exponential distribution.
github.com/stdlib-js/stdlib
Topics
continuousexpexponentialgeneratorjavascriptmathmathematicsnodenode-jsnodejsprngpseudorandomrandrandomrngseedseedablestatisticsstatsstdlib
Resources
Readme
Apache-2.0 license
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
Stars
1 star
Watchers
2 watching
Forks
0 forks
Report repository
Releases
Sponsor this project
Packages
Used by
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GitHub - stdlib-js/random-exponential: Generate pseudorandom numbers drawn from an exponential distribution. · GitHub
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Exponential Random Numbers
NPM versionBuild StatusCoverage Status
Generate pseudorandom numbers drawn from an exponential distribution.
Installation
npm install @stdlib/random-exponential
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
varexponential=require('@stdlib/random-exponential');
exponential( shape, lambda[, options] )
Returns an ndarray containing pseudorandom numbers drawn from an exponential distribution.
vararr=exponential([3,3],2.0);// returns <ndarray>
The function has the following parameters:
  • shape: output shape.
  • lambda: rate parameter. May be either a scalar or an ndarray. When providing an ndarray, the ndarray must be broadcast compatible with the specified output shape.
  • options: function options.
When provided a scalar distribution parameter, every element in the output ndarray is drawn from the same distribution. To generate pseudorandom numbers drawn from different distributions, provide a distribution parameter argument as an ndarray. The following example demonstrates broadcasting an ndarray containing distribution parameters to generate sub-matrices drawn from different distributions.
vargetShape=require('@stdlib/ndarray-shape');vararray=require('@stdlib/ndarray-array');varlambda=array([[[2.0]],[[10.0]]]);// returns <ndarray>varshape=getShape(lambda);// returns [ 2, 1, 1 ]vararr=exponential([2,3,3],lambda);// returns <ndarray>
If provided an empty shape, the function returns a zero-dimensional ndarray.
vargetShape=require('@stdlib/ndarray-shape');vararr=exponential([],2.0);// returns <ndarray>varshape=getShape(arr);// returns []varv=arr.get();// returns <number>
The function accepts the following options:
  • dtype: output ndarray data type. Must be a real-valued floating-point or "generic" data type.
  • order: ndarray order (i.e., memory layout), which is either row-major (C-style) or column-major (Fortran-style). Default: 'row-major'.
  • mode: specifies how to handle indices which exceed ndarray dimensions. For a list of supported modes, see ndarray. Default: 'throw'.
  • submode: a mode array which specifies for each dimension how to handle subscripts which exceed ndarray dimensions. If provided fewer modes than dimensions, an ndarray instance recycles modes using modulo arithmetic. Default: [ options.mode ].
  • readonly: boolean indicating whether an ndarray should be read-only. Default: false.
By default, the function returns an ndarray having a data type determined by the function's output data type policy. To override the default behavior, set the dtype option.
vargetDType=require('@stdlib/ndarray-dtype');varopts={'dtype': 'generic'};vararr=exponential([3,3],2.0,opts);// returns <ndarray>vardt=String(getDType(arr));// returns 'generic'
exponential.assign( lambda, out )
Fills an ndarray with pseudorandom numbers drawn from an exponential distribution.
varzeros=require('@stdlib/ndarray-zeros');varout=zeros([3,3]);// returns <ndarray>varv=exponential.assign(2.0,out);// returns <ndarray>varbool=(v===out);// returns true
The method has the following parameters:
exponential.factory( [options] )
Returns a function for generating pseudorandom numbers drawn from an exponential distribution.
vargetShape=require('@stdlib/ndarray-shape');varrandom=exponential.factory();varout=random([3,3],2.0);// returns <ndarray>varsh=getShape(out);// returns [ 3, 3 ]
The method accepts the following options:
  • prng: pseudorandom number generator for generating uniformly distributed pseudorandom numbers on the interval [0,1). If provided, the function ignores both the state and seed options. In order to seed the underlying pseudorandom number generator, one must seed the provided prng (assuming the provided prng is seedable).
  • seed: pseudorandom number generator seed.
  • state: a Uint32Array 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 an underlying generator has exclusive control over its internal state. Default: true.
To use a custom PRNG as the underlying source of uniformly distributed pseudorandom numbers, set the prng option.
varminstd=require('@stdlib/random-base-minstd');varopts={'prng': minstd.normalized};varrandom=exponential.factory(opts);varout=random([3,3],2.0);// returns <ndarray>
To seed the underlying pseudorandom number generator, set the seed option.
varopts={'seed': 12345};varrandom=exponential.factory(opts);varout=random([3,3],2.0);// returns <ndarray>
The function returned by the factory method has the same interface and accepts the same options as the exponential function above.
exponential.PRNG
The underlying pseudorandom number generator.
varprng=exponential.PRNG;// returns <Function>
exponential.seed
The value used to seed the underlying pseudorandom number generator.
varseed=exponential.seed;// returns <Uint32Array>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varseed=random.seed;// returns null
exponential.seedLength
Length of underlying pseudorandom number generator seed.
varlen=exponential.seedLength;// returns <number>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varlen=random.seedLength;// returns null
exponential.state
Writable property for getting and setting the underlying pseudorandom number generator state.
varstate=exponential.state;// returns <Uint32Array>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varstate=random.state;// returns null
exponential.stateLength
Length of underlying pseudorandom number generator state.
varlen=exponential.stateLength;// returns <number>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varlen=random.stateLength;// returns null
exponential.byteLength
Size (in bytes) of underlying pseudorandom number generator state.
varsz=exponential.byteLength;// returns <number>
If the factory method is provided a PRNG for uniformly distributed numbers, the associated property value on the returned function is null.
varminstd=require('@stdlib/random-base-minstd-shuffle').normalized;varrandom=exponential.factory({'prng': minstd});varsz=random.byteLength;// returns null
Notes
  • If PRNG state is "shared" (meaning a state array was provided during function creation and not copied) and one sets the underlying generator state to a state array having a different length, the function returned by the factory method does not update the existing shared state and, instead, points to the newly provided state array. In order to synchronize the output of the underlying generator according to the new shared state array, the state array for each relevant creation function and/or PRNG must be explicitly set.
  • If PRNG state is "shared" and one sets the underlying generator state to a state array of the same length, the PRNG state is updated (along with the state of all other creation functions and/or PRNGs sharing the PRNG's state array).
  • The output data type policy only applies to the main function and specifies that, by default, the function must return an ndarray having a real-valued floating-point or "generic" data type. For the assign method, the output ndarray is allowed to have any supported output data type.
Examples
varlogEach=require('@stdlib/console-log-each');varndarray2array=require('@stdlib/ndarray-to-array');varexponential=require('@stdlib/random-exponential');// Create a function for generating random arrays originating from the same state:varrandom=exponential.factory({'state': exponential.state,'copy': true});// Generate 3 one-dimensional arrays:varx1=random([5],2.0);varx2=random([5],2.0);varx3=random([5],2.0);// 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=exponential.factory({'state': exponential.state,'copy': true});// Generate a two-dimensional array which replicates the above pseudorandom number generation sequence:varx4=random([3,5],2.0);// Convert to a list of nested arrays:vararr=ndarray2array(x4);// Print the contents:console.log('');logEach('%f, %f, %f',arr[0],arr[1],arr[2]);
See Also
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.
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See LICENSE.
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Copyright © 2016-2026. The Stdlib Authors.
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Generate pseudorandom numbers drawn from an exponential distribution.
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