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dnannsumkbn

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

Installation

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

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

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

var bernoulli = require( '@stdlib/random-base-bernoulli' );
var discreteUniform = require( '@stdlib/random-base-discrete-uniform' );
var filledarrayBy = require( '@stdlib/array-filled-by' );
var Float64Array = require( '@stdlib/array-float64' );
var dnannsumkbn = require( '@stdlib/blas-ext-base-dnannsumkbn' );

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 );

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C APIs

Usage

#include "stdlib/blas/ext/base/dnannsumkbn.h"

stdlib_strided_dnannsumkbn( N, *X, strideX, *n )

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

#include "stdlib/blas/base/shared.h"

const double x[] = { 1.0, 2.0, 0.0/0.0, 4.0 };
CBLAS_INT n = 0;

double v = stdlib_strided_dnannsumkbn( 4, x, 1, &n );
// returns 7.0

The function accepts the following arguments:

• N: [in] CBLAS_INT number of indexed elements.
• X: [in] double* input array.
• strideX: [in] CBLAS_INT stride length for X.
• n: [out] CBLAS_INT* pointer for storing the number of non-NaN elements.

double stdlib_strided_dnannsumkbn( const CBLAS_INT N, const double *X, const CBLAS_INT strideX, CBLAS_INT *n );

stdlib_strided_dnannsumkbn_ndarray( N, *X, strideX, offsetX, *n )

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.

#include "stdlib/blas/base/shared.h"

const double x[] = { 1.0, 2.0, 0.0/0.0, 4.0 };
CBLAS_INT n = 0; 

double v = stdlib_strided_dnannsumkbn_ndarray( 4, x, 1, 0, &n );
// returns 7.0

The function accepts the following arguments:

• N: [in] CBLAS_INT number of indexed elements.
• X: [in] double* input array.
• strideX: [in] CBLAS_INT stride length for X.
• offsetX: [in] CBLAS_INT starting index for X.
• n: [out] CBLAS_INT* pointer for storing the number of non-NaN elements.

double stdlib_strided_dnannsumkbn_ndarray( const CBLAS_INT N, const double *X, const CBLAS_INT strideX, const CBLAS_INT offsetX, CBLAS_INT *n );

Examples

#include "stdlib/blas/ext/base/dnannsumkbn.h"
#include "stdlib/blase/base/shared.h"
#include <stdio.h>

int main( void ) {
    // Create a strided array:
    const double x[] = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 0.0/0.0, 0.0/0.0 };

    // Specify the number of elements:
    const int N = 5;

    // Specify the stride length:
    const int strideX = 2;

    // Initialize a variable for storing the number of non-NaN elements:
    CBLAS_INT n = 0;

    // Compute the sum:
    double v = stdlib_strided_dnannsumkbn( N, x, strideX, &n );

    // Print the result:
    printf( "sum: %lf\n", v );
    printf( "n: %"CBLAS_IFMT"\n", n );
}

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.

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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.

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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

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License

See LICENSE.

Copyright

Copyright © 2016-2024. The Stdlib Authors.