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dsnannsumors

Calculate the sum of single-precision floating-point strided array elements, ignoring NaN values, using ordinary recursive summation with extended accumulation, and returning an extended precision result.

Installation

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

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

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

Computes the sum of single-precision floating-point strided array elements, ignoring NaN values, using ordinary recursive summation with extended accumulation, and returning an extended precision result.

var Float32Array = require( '@stdlib/array-float32' );
var Float64Array = require( '@stdlib/array-float64' );

var x = new Float32Array( [ 1.0, -2.0, NaN, 2.0 ] );
var out = new Float64Array( 2 );

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

The function has the following parameters:

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

var Float32Array = require( '@stdlib/array-float32' );
var Float64Array = require( '@stdlib/array-float64' );

var x = new Float32Array( [ 1.0, 2.0, NaN, -7.0, NaN, 3.0, 4.0, 2.0 ] );
var out = new Float64Array( 2 );

var v = dsnannsumors( 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 Float32Array = require( '@stdlib/array-float32' );
var Float64Array = require( '@stdlib/array-float64' );

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 out0 = new Float64Array( 4 );
var out1 = new Float64Array( out0.buffer, out0.BYTES_PER_ELEMENT*2 ); // start at 3rd element

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

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

Computes the sum of single-precision floating-point strided array elements, ignoring NaN values, using ordinary recursive summation with extended accumulation and alternative indexing semantics, and returning an extended precision result.

var Float32Array = require( '@stdlib/array-float32' );
var Float64Array = require( '@stdlib/array-float64' );

var x = new Float32Array( [ 1.0, -2.0, NaN, 2.0 ] );
var out = new Float64Array( 2 );

var v = dsnannsumors.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, the 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 Float32Array = require( '@stdlib/array-float32' );
var Float64Array = require( '@stdlib/array-float64' );

var x = new Float32Array( [ 2.0, 1.0, NaN, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
var out = new Float64Array( 4 );

var v = dsnannsumors.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.
• Accumulated intermediate values are stored as double-precision floating-point numbers.

Examples

var discreteUniform = require( '@stdlib/random-base-discrete-uniform' );
var bernoulli = require( '@stdlib/random-base-bernoulli' );
var filledarrayBy = require( '@stdlib/array-filled-by' );
var Float32Array = require( '@stdlib/array-float32' );
var Float64Array = require( '@stdlib/array-float64' );
var dsnannsumors = require( '@stdlib/blas-ext-base-dsnannsumors' );

function rand() {
    if ( bernoulli( 0.5 ) < 0.2 ) {
        return NaN;
    }
    return discreteUniform( 0, 100 );
}

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

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

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C APIs

Usage

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

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

Computes the sum of single-precision floating-point strided array elements, ignoring NaN values, using ordinary recursive summation with extended accumulation, and returning an extended precision result.

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

const float x[] = { 1.0f, -2.0f, 0.0f/0.0f, 2.0f };
CBLAS_INT n = 0;

double v = stdlib_strided_dsnannsumors( 4, x, 1, &n );
// returns 1.0

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 for X.
• n: [out] CBLAS_INT* pointer for storing the number of non-NaN elements.

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

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

Computes the sum of single-precision floating-point strided array elements, ignoring NaN values, using ordinary recursive summation with extended accumulation and alternative indexing semantics, and returning an extended precision result.

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

const float x[] = { 1.0f, -2.0f, 0.0f/0.0f, 2.0f };
CBLAS_INT n = 0;

double v = stdlib_strided_dsnannsumors_ndarray( 4, x, 1, 0, &n );
// returns 1.0

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

Examples

#include "stdlib/blas/ext/base/dsnannsumors.h"
#include "stdlib/blas/base/shared.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;

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

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

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

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See Also

• @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/dsnansumors: calculate the sum of single-precision floating-point strided array elements, ignoring NaN values, using ordinary recursive summation with extended accumulation, and returning an extended precision result.
• @stdlib/blas-ext/base/dssumors: calculate the sum of single-precision floating-point strided array elements using ordinary recursive summation with extended accumulation and returning an extended precision result.

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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.