@stdlib/blas-ext-base-gnannsumkbn
v0.2.2
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Calculate the sum of strided array elements, ignoring NaN values and using an improved Kahan–Babuška algorithm.
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gnannsumkbn
Calculate the sum of strided array elements, ignoring
NaN
values and using an improved Kahan–Babuška algorithm.
Installation
npm install @stdlib/blas-ext-base-gnannsumkbn
Usage
var gnannsumkbn = require( '@stdlib/blas-ext-base-gnannsumkbn' );
gnannsumkbn( N, x, strideX, out, strideOut )
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 out = [ 0.0, 0 ];
var v = gnannsumkbn( x.length, x, 1, out, 1 );
// returns [ 1.0, 3 ]
The function has the following parameters:
- N: number of indexed elements.
- x: input
Array
ortyped array
. - strideX: index increment for
x
. - out: output
Array
ortyped array
whose first element is the sum and whose second element is the number of non-NaN elements. - strideOut: index increment for
out
.
The N
and stride
parameters determine which elements 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, NaN, -7.0, NaN, 3.0, 4.0, 2.0 ];
var out = [ 0.0, 0 ];
var N = floor( x.length / 2 );
var v = gnannsumkbn( N, x, 2, out, 1 );
// returns [ 5.0, 2 ]
Note 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, NaN, -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 out0 = new Float64Array( 4 );
var out1 = new Float64Array( out0.buffer, out0.BYTES_PER_ELEMENT*2 ); // start at 3rd element
var N = floor( x0.length / 2 );
var v = gnannsumkbn( N, x1, 2, out1, 1 );
// returns <Float64Array>[ 5.0, 4 ]
gnannsumkbn.ndarray( N, x, strideX, offsetX, out, strideOut, offsetOut )
Computes 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 out = [ 0.0, 0 ];
var v = gnannsumkbn.ndarray( x.length, x, 1, 0, out, 1, 0 );
// returns [ 1.0, 3 ]
The function has the following additional parameters:
- offsetX: starting index for
x
. - offsetOut: starting index for
out
.
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, NaN, -2.0, -2.0, 2.0, 3.0, 4.0 ];
var out = [ 0.0, 0.0, 0.0, 0 ];
var N = floor( x.length / 2 );
var v = gnannsumkbn.ndarray( N, x, 2, 1, out, 2, 1 );
// returns <Float64Array>[ 0.0, 5.0, 0.0, 4 ]
Notes
- If
N <= 0
, both functions return a sum equal to0.0
.
Examples
var randu = require( '@stdlib/random-base-randu' );
var round = require( '@stdlib/math-base-special-round' );
var Float64Array = require( '@stdlib/array-float64' );
var gnannsumkbn = require( '@stdlib/blas-ext-base-gnannsumkbn' );
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 out = new Float64Array( 2 );
gnannsumkbn( x.length, x, 1, out, 1 );
console.log( out );
References
- 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.
See Also
@stdlib/blas-ext/base/dnannsumkbn
: calculate the sum of double-precision floating-point strided array elements, ignoring NaN values and using an improved Kahan–Babuška algorithm.
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
License
See LICENSE.
Copyright
Copyright © 2016-2024. The Stdlib Authors.