gnansumors

Calculate the sum of strided array elements, ignoring NaN values and using ordinary recursive summation.

Installation

npm install @stdlib/blas-ext-base-gnansumors

Usage

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

gnansumors( N, x, strideX )

Computes the sum of strided array elements, ignoring NaN values and using ordinary recursive summation.

var x = [ 1.0, -2.0, NaN, 2.0 ];

var v = gnansumors( x.length, x, 1 );
// returns 1.0

The function has the following parameters:

• N: number of indexed elements.
• x: input Array or typed array.
• strideX: stride length for x.

The N and stride parameters determine which elements in the strided array are accessed at runtime. For example, to compute the sum of every other element:

var x = [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0, NaN, NaN ];

var v = gnansumors( 5, x, 2 );
// returns 5.0

Note that indexing is relative to the first index. To introduce an offset, use typed array views.

var Float64Array = require( '@stdlib/array-float64' );

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 v = gnansumors( 4, x1, 2 );
// returns 5.0

gnansumors.ndarray( N, x, strideX, offsetX )

Computes the sum of strided array elements, ignoring NaN values and using ordinary recursive summation and alternative indexing semantics.

var x = [ 1.0, -2.0, NaN, 2.0 ];

var v = gnansumors.ndarray( x.length, x, 1, 0 );
// returns 1.0

The function has the following additional parameters:

• offsetX: 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 element starting from the second element:

var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN, NaN ];

var v = gnansumors.ndarray( 5, x, 2, 1 );
// returns 5.0

Notes

• If N <= 0, both functions return 0.0.
• Both functions support array-like objects having getter and setter accessors for array element access (e.g., @stdlib/array-base/accessor).
• Ordinary recursive summation (i.e., a "simple" sum) is performant, but can incur significant numerical error. If performance is paramount and error tolerated, using ordinary recursive summation is acceptable; in all other cases, exercise due caution.
• Depending on the environment, the typed versions (dnansumors, snansumors, etc.) are likely to be significantly more performant.

Examples

var discreteUniform = require( '@stdlib/random-base-discrete-uniform' );
var bernoulli = require( '@stdlib/random-base-bernoulli' );
var filledarrayBy = require( '@stdlib/array-filled-by' );
var gnansumors = require( '@stdlib/blas-ext-base-gnansumors' );

function rand() {
    if ( bernoulli( 0.7 ) > 0 ) {
        return discreteUniform( 0, 100 );
    }
    return NaN;
}

var x = filledarrayBy( 10, 'float64', rand );
console.log( x );

var v = gnansumors( x.length, x, 1 );
console.log( v );

See Also

• @stdlib/blas-ext/base/dnansumors: calculate the sum of double-precision floating-point strided array elements, ignoring NaN values and using ordinary recursive summation.
• @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/gnansumpw: calculate the sum of strided array elements, ignoring NaN values and using pairwise summation.
• @stdlib/blas-ext/base/gsumors: calculate the sum of strided array elements using ordinary recursive summation.
• @stdlib/blas-ext/base/snansumors: calculate the sum of single-precision floating-point strided array elements, ignoring NaN values and using ordinary recursive summation.

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 © 2016-2026. The Stdlib Authors.