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About stdlib...

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dnannsumkbn

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Calculate the sum of double-precision floating-point strided array elements, ignoring NaN values and using an improved Kahan–Babuška algorithm.

Usage

To use in Observable,

dnannsumkbn = require( 'https://cdn.jsdelivr.net/gh/stdlib-js/blas-ext-base-dnannsumkbn@umd/browser.js' )

To vendor stdlib functionality and avoid installing dependency trees for Node.js, you can use the UMD server build:

var dnannsumkbn = require( 'path/to/vendor/umd/blas-ext-base-dnannsumkbn/index.js' )

To include the bundle in a webpage,

<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/blas-ext-base-dnannsumkbn@umd/browser.js"></script>

If no recognized module system is present, access bundle contents via the global scope:

<script type="text/javascript">
(function () {
    window.dnannsumkbn;
})();
</script>

dnannsumkbn( N, x, strideX, out, strideOut )

Computes the sum of double-precision floating-point strided array elements, ignoring NaN values and using an improved Kahan–Babuška algorithm.

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

var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );
var out = new Float64Array( 2 );

var v = dnannsumkbn( x.length, x, 1, out, 1 );
// returns <Float64Array>[ 1.0, 3 ]

The function has the following parameters:

  • N: number of indexed elements.
  • x: input Float64Array.
  • strideX: stride length for x.
  • out: output Float64Array whose first element is the sum and whose second element is the number of non-NaN elements.
  • strideOut: stride length 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 Float64Array = require( '@stdlib/array-float64' );

var x = new Float64Array( [ 1.0, 2.0, NaN, -7.0, NaN, 3.0, 4.0, 2.0 ] );
var out = new Float64Array( 2 );

var v = dnannsumkbn( 4, x, 2, out, 1 );
// returns <Float64Array>[ 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 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 v = dnannsumkbn( 4, x1, 2, out1, 1 );
// returns <Float64Array>[ 5.0, 4 ]

dnannsumkbn.ndarray( N, x, strideX, offsetX, out, strideOut, offsetOut )

Computes the sum of double-precision floating-point strided array elements, ignoring NaN values and using an improved Kahan–Babuška algorithm and alternative indexing semantics.

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

var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );
var out = new Float64Array( 2 );

var v = dnannsumkbn.ndarray( x.length, x, 1, 0, out, 1, 0 );
// returns <Float64Array>[ 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, offset parameters support indexing semantics based on starting indices. For example, to calculate the sum of every other element starting from the second element:

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

var x = new Float64Array( [ 2.0, 1.0, NaN, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
var out = new Float64Array( 4 );

var v = dnannsumkbn.ndarray( 4, 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 to 0.0.

Examples

<!DOCTYPE html>
<html lang="en">
<body>
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/random-base-bernoulli@umd/browser.js"></script>
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/random-base-discrete-uniform@umd/browser.js"></script>
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/array-filled-by@umd/browser.js"></script>
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/array-float64@umd/browser.js"></script>
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/blas-ext-base-dnannsumkbn@umd/browser.js"></script>
<script type="text/javascript">
(function () {

function rand() {
    if ( bernoulli( 0.8 ) > 0 ) {
        return discreteUniform( 0, 100 );
    }
    return NaN;
}

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

var out = new Float64Array( 2 );
dnannsumkbn( x.length, x, 1, out, 1 );
console.log( out );

})();
</script>
</body>
</html>

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/dnannsum: calculate the sum of double-precision floating-point strided array elements, ignoring NaN values.
  • @stdlib/blas-ext/base/dnannsumkbn2: calculate the sum of double-precision floating-point strided array elements, ignoring NaN values and using a second-order iterative Kahan–Babuška algorithm.
  • @stdlib/blas-ext/base/dnannsumors: calculate the sum of double-precision floating-point strided array elements, ignoring NaN values and using ordinary recursive summation.
  • @stdlib/blas-ext/base/dnannsumpw: calculate the sum of double-precision floating-point strided array elements, ignoring NaN values and using pairwise summation.
  • @stdlib/blas-ext/base/dsumkbn: calculate the sum of double-precision floating-point strided array elements using an improved Kahan–Babuška algorithm.
  • @stdlib/blas-ext/base/gnannsumkbn: calculate the sum of 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.

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License

See LICENSE.

Copyright

Copyright © 2016-2024. The Stdlib Authors.