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!
Calculate the sum of strided array elements, ignoring
NaNvalues and using an improved Kahan–Babuška algorithm.
npm install @stdlib/blas-ext-base-gnansumkbnAlternatively,
- To load the package in a website via a
scripttag without installation and bundlers, use the ES Module available on theesmbranch (see README). - If you are using Deno, visit the
denobranch (see README for usage intructions). - For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the
umdbranch (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.
var gnansumkbn = require( '@stdlib/blas-ext-base-gnansumkbn' );Computes the sum of strided array elements, ignoring NaN values and using an improved Kahan–Babuška algorithm.
var x = [ 1.0, -2.0, NaN, 2.0 ];
var N = x.length;
var v = gnansumkbn( N, x, 1 );
// returns 1.0The function has the following parameters:
- N: number of indexed elements.
- x: input
Arrayortyped array. - stride: index increment for
x.
The N and stride parameters determine which elements in x are accessed at runtime. For example, to compute the sum of every other element in x,
var floor = require( '@stdlib/math-base-special-floor' );
var x = [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0, NaN, NaN ];
var N = floor( x.length / 2 );
var v = gnansumkbn( N, x, 2 );
// returns 5.0Note that indexing is relative to the first index. To introduce an offset, use typed array views.
var Float64Array = require( '@stdlib/array-float64' );
var floor = require( '@stdlib/math-base-special-floor' );
var x0 = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var N = floor( x0.length / 2 );
var v = gnansumkbn( N, x1, 2 );
// returns 5.0Computes the sum of strided array elements, ignoring NaN values and using an improved Kahan–Babuška algorithm and alternative indexing semantics.
var x = [ 1.0, -2.0, NaN, 2.0 ];
var N = x.length;
var v = gnansumkbn.ndarray( N, x, 1, 0 );
// returns 1.0The function has the following additional parameters:
- offset: starting index for
x.
While typed array views mandate a view offset based on the underlying buffer, the offset parameter supports indexing semantics based on a starting index. For example, to calculate the sum of every other value in x starting from the second value
var floor = require( '@stdlib/math-base-special-floor' );
var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN, NaN ];
var N = floor( x.length / 2 );
var v = gnansumkbn.ndarray( N, x, 2, 1 );
// returns 5.0- If
N <= 0, both functions return0.0. - Depending on the environment, the typed versions (
dnansumkbn,snansumkbn, etc.) are likely to be significantly more performant.
var randu = require( '@stdlib/random-base-randu' );
var round = require( '@stdlib/math-base-special-round' );
var Float64Array = require( '@stdlib/array-float64' );
var gnansumkbn = require( '@stdlib/blas-ext-base-gnansumkbn' );
var x;
var i;
x = new Float64Array( 10 );
for ( i = 0; i < x.length; i++ ) {
if ( randu() < 0.2 ) {
x[ i ] = NaN;
} else {
x[ i ] = round( randu()*100.0 );
}
}
console.log( x );
var v = gnansumkbn( x.length, x, 1 );
console.log( v );- Neumaier, Arnold. 1974. "Rounding Error Analysis of Some Methods for Summing Finite Sums." Zeitschrift Für Angewandte Mathematik Und Mechanik 54 (1): 39–51. doi:10.1002/zamm.19740540106.
@stdlib/blas-ext/base/dnansumkbn: calculate the sum of double-precision floating-point strided array elements, ignoring NaN values and using an improved Kahan–Babuška algorithm.@stdlib/blas-ext/base/gnansum: calculate the sum of strided array elements, ignoring NaN values.@stdlib/blas-ext/base/gnansumkbn2: calculate the sum of strided array elements, ignoring NaN values and using a second-order iterative Kahan–Babuška algorithm.@stdlib/blas-ext/base/gnansumors: calculate the sum of strided array elements, ignoring NaN values and using ordinary recursive summation.@stdlib/blas-ext/base/gnansumpw: calculate the sum of strided array elements, ignoring NaN values and using pairwise summation.@stdlib/blas-ext/base/gsumkbn: calculate the sum of strided array elements using an improved Kahan–Babuška algorithm.@stdlib/blas-ext/base/snansumkbn: calculate the sum of single-precision floating-point strided array elements, ignoring NaN values and using an improved Kahan–Babuška algorithm.
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.
See LICENSE.
Copyright © 2016-2024. The Stdlib Authors.
