Skip to content

stdlib-js/blas-ext-base-ndarray-dcusumkbn2

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!

dcusumkbn2

NPM version Build Status Coverage Status

Compute the cumulative sum of a one-dimensional double-precision floating-point ndarray using a second-order iterative Kahan–Babuška algorithm.

Installation

npm install @stdlib/blas-ext-base-ndarray-dcusumkbn2

Alternatively,

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

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

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

Usage

var dcusumkbn2 = require( '@stdlib/blas-ext-base-ndarray-dcusumkbn2' );

dcusumkbn2( arrays )

Computes the cumulative sum of a one-dimensional double-precision floating-point ndarray using a second-order iterative Kahan–Babuška algorithm.

var Float64Array = require( '@stdlib/array-float64' );
var scalar2ndarray = require( '@stdlib/ndarray-base-from-scalar' );
var ndarray = require( '@stdlib/ndarray-base-ctor' );

var xbuf = new Float64Array( [ 1.0, 3.0, 4.0, 2.0 ] );
var x = new ndarray( 'float64', xbuf, [ 4 ], [ 1 ], 0, 'row-major' );

var ybuf = new Float64Array( [ 0.0, 0.0, 0.0, 0.0 ] );
var y = new ndarray( 'float64', ybuf, [ 4 ], [ 1 ], 0, 'row-major' );

var initial = scalar2ndarray( 0.0, 'float64', 'row-major' );

var v = dcusumkbn2( [ x, y, initial ] );
// returns <ndarray>[ 1.0, 4.0, 8.0, 10.0 ]

var bool = ( v === y );
// returns true

The function has the following parameters:

  • arrays: array-like object containing a one-dimensional input ndarray, a one-dimensional output ndarray, and a zero-dimensional ndarray containing the initial sum.

Notes

  • If provided an empty one-dimensional input ndarray, the function returns the output ndarray unchanged.

Examples

var discreteUniform = require( '@stdlib/random-array-discrete-uniform' );
var ndarray = require( '@stdlib/ndarray-base-ctor' );
var zerosLike = require( '@stdlib/ndarray-zeros-like' );
var scalar2ndarray = require( '@stdlib/ndarray-from-scalar' );
var ndarray2array = require( '@stdlib/ndarray-to-array' );
var dcusumkbn2 = require( '@stdlib/blas-ext-base-ndarray-dcusumkbn2' );

var xbuf = discreteUniform( 10, -50, 50, {
    'dtype': 'float64'
});
var x = new ndarray( 'float64', xbuf, [ xbuf.length ], [ 1 ], 0, 'row-major' );
console.log( ndarray2array( x ) );

var y = zerosLike( x );
console.log( ndarray2array( y ) );

var initial = scalar2ndarray( 100.0, {
    'dtype': 'float64'
});

var v = dcusumkbn2( [ x, y, initial ] );
console.log( ndarray2array( v ) );

References

  • Klein, Andreas. 2005. "A Generalized Kahan-Babuška-Summation-Algorithm." Computing 76 (3): 279–93. doi:10.1007/s00607-005-0139-x.

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

Compute the cumulative sum of a one-dimensional double-precision floating-point ndarray using a second-order iterative Kahan–Babuška algorithm.

Topics

Resources

License

Code of conduct

Contributing

Security policy

Stars

Watchers

Forks

Packages

 
 
 

Contributors