README.md

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dsapxsum

Add a scalar constant to each single-precision floating-point strided array element, and compute the sum using extended accumulation and returning an extended precision result.

Installation

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

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

dsapxsum( N, alpha, x, strideX )

Adds a scalar constant to each single-precision floating-point strided array element, and computes the sum using extended accumulation and returning an extended precision result.

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

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

var v = dsapxsum( x.length, 5.0, x, 1 );
// returns 16.0

The function has the following parameters:

• N: number of indexed elements.
• alpha: scalar constant.
• x: input Float32Array.
• strideX: stride length for x.

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

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

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

var v = dsapxsum( 4, 5.0, x, 2 );
// returns 25.0

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, 2.0, -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 = dsapxsum( 4, 5.0, x1, 2 );
// returns 25.0

dsapxsum.ndarray( N, alpha, x, strideX, offsetX )

Adds a scalar constant to each single-precision floating-point strided array element, and computes the sum using extended accumulation and alternative indexing semantics and returning an extended precision result.

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

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

var v = dsapxsum.ndarray( x.length, 5.0, x, 1, 0 );
// returns 16.0

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 access every other element starting from the second element:

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

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

var v = dsapxsum.ndarray( 4, 5.0, x, 2, 1 );
// returns 25.0

Notes

• If N <= 0, both functions return 0.0.
• Accumulated intermediate values are stored as double-precision floating-point numbers.

Examples

var discreteUniform = require( '@stdlib/random-array-discrete-uniform' );
var dsapxsum = require( '@stdlib/blas-ext-base-dsapxsum' );

var x = discreteUniform( 10.0, -100, 100, {
    'dtype': 'float32'
});
console.log( x );

var v = dsapxsum( x.length, 5.0, x, 1 );
console.log( v );

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C APIs

Usage

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

stdlib_strided_dsapxsum( N, alpha, *X, strideX )

Adds a scalar constant to each single-precision floating-point strided array element, and computes the sum using extended accumulation and returning an extended precision result.

const float x[] = { 1.0f, -2.0f, 2.0f };

double v = stdlib_strided_dsapxsum( 3, 5.0f, x, 1 );
// returns 16.0

The function accepts the following arguments:

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

double stdlib_strided_dsapxsum( const CBLAS_INT N, const float alpha, const float *X, const CBLAS_INT strideX );

stdlib_strided_dsapxsum_ndarray( N, alpha, *X, strideX, offsetX )

Adds a scalar constant to each single-precision floating-point strided array element, and computes the sum using extended accumulation and alternative indexing semantics and returning an extended precision result.

const float x[] = { 1.0f, -2.0f, 2.0f };

double v = stdlib_strided_dsapxsum_ndarray( 3, 5.0f, x, 1, 0 );
// returns 16.0

The function accepts the following arguments:

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

double stdlib_strided_dsapxsum_ndarray( const CBLAS_INT N, const float alpha, const float *X, const CBLAS_INT strideX, const CBLAS_INT offsetX );

Examples

#include "stdlib/blas/ext/base/dsapxsum.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 };

    // Specify the number of indexed elements:
    const int N = 8;

    // Specify a stride:
    const int strideX = 1;

    // Compute the sum:
    double v = stdlib_strided_dsapxsum( N, 5.0f, x, strideX );

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

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See Also

• @stdlib/blas-ext/base/dapxsum: adds a constant to each double-precision floating-point strided array element and computes the sum.
• @stdlib/blas-ext/base/dssum: calculate the sum of single-precision floating-point strided array elements using extended accumulation and returning an extended precision result.
• @stdlib/blas-ext/base/sapxsum: adds a constant to each single-precision floating-point strided array element and computes the sum.

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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.