snansumkbn

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

Usage

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

snansumkbn( N, x, strideX )

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

var Float32Array = require( '@stdlib/array/float32' );

var x = new Float32Array( [ 1.0, -2.0, NaN, 2.0 ] );

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

The function has the following parameters:

• N: number of indexed elements.
• x: input Float32Array.
• stride: stride length.

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 Float32Array = require( '@stdlib/array/float32' );

var x = new Float32Array( [ 1.0, 2.0, NaN, -7.0, NaN, 3.0, 4.0, 2.0 ] );

var v = snansumkbn( 4, x, 2 );
// returns 5.0

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

var Float32Array = require( '@stdlib/array/float32' );

var x0 = new Float32Array( [ 2.0, 1.0, NaN, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element

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

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

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

var Float32Array = require( '@stdlib/array/float32' );

var x = new Float32Array( [ 1.0, -2.0, NaN, 2.0 ] );

var v = snansumkbn.ndarray( 4, x, 1, 0 );
// returns 1.0

The function has the following additional parameters:

• offsetX: starting index.

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 Float32Array = require( '@stdlib/array/float32' );

var x = new Float32Array( [ 2.0, 1.0, NaN, -2.0, -2.0, 2.0, 3.0, 4.0 ] );

var v = snansumkbn.ndarray( 4, x, 2, 1 );
// returns 5.0

Notes

• If N <= 0, both functions return 0.0.

Examples

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

function rand() {
    if ( bernoulli( 0.5 ) < 1 ) {
        return discreteUniform( 0, 100 );
    }
    return NaN;
}

var x = filledarrayBy( 10, 'float32', rand );
console.log( x );

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

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C APIs

Usage

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

stdlib_strided_snansumkbn( N, *X, strideX )

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

const float x[] = { 1.0f, 2.0f, 0.0f/0.0f, 4.0f };

float v = stdlib_strided_snansumkbn( 4, x, 1 );
// returns 7.0f

The function accepts the following arguments:

• N: [in] CBLAS_INT number of indexed elements.
• X: [in] float* input array.
• strideX: [in] CBLAS_INT stride length.

float stdlib_strided_snansumkbn( const CBLAS_INT N, const float *X, const CBLAS_INT strideX );

stdlib_strided_snansumkbn_ndarray( N, *X, strideX, offsetX )

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

const float x[] = { 1.0f, 2.0f, 0.0f/0.0f, 4.0f };

float v = stdlib_strided_snansumkbn_ndarray( 4, x, 1, 0 );
// returns 7.0f

The function accepts the following arguments:

• N: [in] CBLAS_INT number of indexed elements.
• X: [in] float* input array.
• strideX: [in] CBLAS_INT stride length.
• offsetX: [in] CBLAS_INT starting index.

float stdlib_strided_snansumkbn_ndarray( const CBLAS_INT N, const float *X, const CBLAS_INT strideX, const CBLAS_INT offsetX );

Examples

#include "stdlib/blas/ext/base/snansumkbn.h"
#include <stdio.h>

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

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

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

    // Compute the sum:
    float v = stdlib_strided_snansumkbn( N, x, strideX );

    // Print the result:
    printf( "Sum: %f\n", v );
}

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/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/gnansumkbn: calculate the sum of strided array elements, ignoring NaN values and using an improved Kahan–Babuška algorithm.
• @stdlib/blas/ext/base/snansum: calculate the sum of single-precision floating-point strided array elements, ignoring NaN values.
• @stdlib/blas/ext/base/snansumkbn2: calculate the sum of single-precision floating-point strided array elements, ignoring NaN values and using a second-order iterative Kahan–Babuška algorithm.
• @stdlib/blas/ext/base/snansumors: calculate the sum of single-precision floating-point strided array elements, ignoring NaN values and using ordinary recursive summation.
• @stdlib/blas/ext/base/snansumpw: calculate the sum of single-precision floating-point strided array elements, ignoring NaN values and using pairwise summation.
• @stdlib/blas/ext/base/ssumkbn: calculate the sum of single-precision floating-point strided array elements using an improved Kahan–Babuška algorithm.