About stdlib...

We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.

The library is fully decomposable, being architected in such a way that you can swap out and mix and match APIs and functionality to cater to your exact preferences and use cases.

When you use stdlib, you can be absolutely certain that you are using the most thorough, rigorous, well-written, studied, documented, tested, measured, and high-quality code out there.

To join us in bringing numerical computing to the web, get started by checking us out on GitHub, and please consider financially supporting stdlib. We greatly appreciate your continued support!

dasum

[![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url]

> Compute the sum of [absolute values][@stdlib/math/base/special/abs] ([_L1_ norm][l1norm]).

The [_L1_ norm][l1norm] is defined as

Installation

``bash npm install @stdlib/blas-base-dasum`

`Usage`

`javascript var dasum = require( '@stdlib/blas-base-dasum' );`

#### dasum( N, x, stride )

Computes the sum of [absolute values][@stdlib/math/base/special/abs].

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

var x = new Float64Array( [ -2.0, 1.0, 3.0, -5.0, 4.0, 0.0, -1.0, -3.0 ] );

var sum = dasum( x.length, x, 1 ); // returns 19.0`

The function has the following parameters:

- N: number of indexed elements. - x: input [Float64Array][mdn-float64array]. - stride: index increment.

The N and stride parameters determine which elements in x are used to compute the sum. For example, to sum every other value,

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

var x = new Float64Array( [ -2.0, 1.0, 3.0, -5.0, 4.0, 0.0, -1.0, -3.0 ] );

var sum = dasum( 4, x, 2 ); // returns 10.0`

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

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

// Initial array... var x0 = new Float64Array( [ 1.0, -2.0, 3.0, -4.0, 5.0, -6.0 ] );

// Create an offset view... var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element

// Sum every other value... var sum = dasum( 3, x1, 2 ); // returns 12.0`

If N is less than or equal to 0, the function returns 0.

#### dasum.ndarray( N, x, stride, offset )

Computes the sum of [absolute values][@stdlib/math/base/special/abs] using alternative indexing semantics.

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

var x = new Float64Array( [ -2.0, 1.0, 3.0, -5.0, 4.0, 0.0, -1.0, -3.0 ] );

var sum = dasum.ndarray( x.length, x, 1, 0 ); // returns 19.0`

The function has the following additional parameters:

- offset: starting index.

While [typed array][mdn-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 sum the last three elements,

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

var x = new Float64Array( [ 1.0, -2.0, 3.0, -4.0, 5.0, -6.0 ] );

var sum = dasum.ndarray( 3, x, 1, x.length-3 ); // returns 15.0

// Using a negative stride to sum from the last element: sum = dasum.ndarray( 3, x, -1, x.length-1 ); // returns 15.0`

`Notes`

- If N <= 0, the sum is 0. -dasum() corresponds to the [BLAS][blas] level 1 function [dasum][dasum].

`Examples`

`javascript var discreteUniform = require( '@stdlib/random-array-discrete-uniform' ); var dasum = require( '@stdlib/blas-base-dasum' );

var opts = { 'dtype': 'float64' }; var x = discreteUniform( 10, -100, 100, opts ); console.log( x );

var out = dasum( x.length, x, 1 ); console.log( out );`

*

`C APIs`

`$3`

`c #include "stdlib/blas/base/dasum.h"`

#### c_dasum( N, \*X, stride )

Computes the sum of absolute values.

`c const double x[] = { 1.0, 2.0, 3.0, 4.0 };

double v = c_dasum( 4, x, 1 ); // returns 10.0`

The function accepts the following arguments:

- N: [in] CBLAS_INTnumber of indexed elements. - X:[in] double*input array. - stride:[in] CBLAS_INT index increment for X.

`c double c_dasum( const CBLAS_INT N, const double *X, const CBLAS_INT stride );`

#### c_dasum_ndarray( N, \*X, stride, offset )

Computes the sum of absolute values using alternative indexing semantics.

`c const double x[] = { 1.0, 2.0, 3.0, 4.0 };

double v = c_dasum_ndarray( 4, x, -1, 3 ); // returns 10.0`

The function accepts the following arguments:

- N: [in] CBLAS_INTnumber of indexed elements. - X:[in] double*input array. - stride:[in] CBLAS_INT index increment for X. - offset:[in] CBLAS_INT starting index for X.

`c double c_dasum_ndarray( const CBLAS_INT N, const double *X, const CBLAS_INT stride, const CBLAS_INT offset );`

`$3`

`c #include "stdlib/blas/base/dasum.h" #include

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 };

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

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

// Compute the sum of absolute values: double sum = c_dasum( N, x, strideX );

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

// Compute the sum of absolute values: sum = c_dasum_ndarray( N, x, -strideX, N-1 );

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

*

`Notice`

This package is part of [stdlib][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][stdlib], see the main project [repository][stdlib].

#### Community

[![Chat][chat-image]][chat-url]

---

`License`

See [LICENSE][stdlib-license].

`Copyright`

[npm-image]: http://img.shields.io/npm/v/@stdlib/blas-base-dasum.svg [npm-url]: https://npmjs.org/package/@stdlib/blas-base-dasum

[test-image]: https://github.com/stdlib-js/blas-base-dasum/actions/workflows/test.yml/badge.svg?branch=v0.4.1 [test-url]: https://github.com/stdlib-js/blas-base-dasum/actions/workflows/test.yml?query=branch:v0.4.1

[coverage-image]: https://img.shields.io/codecov/c/github/stdlib-js/blas-base-dasum/main.svg [coverage-url]: https://codecov.io/github/stdlib-js/blas-base-dasum?branch=main

[chat-image]: https://img.shields.io/badge/zulip-join_chat-brightgreen.svg [chat-url]: https://stdlib.zulipchat.com

[stdlib]: https://github.com/stdlib-js/stdlib

[stdlib-authors]: https://github.com/stdlib-js/stdlib/graphs/contributors

[umd]: https://github.com/umdjs/umd [es-module]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules

[deno-url]: https://github.com/stdlib-js/blas-base-dasum/tree/deno [deno-readme]: https://github.com/stdlib-js/blas-base-dasum/blob/deno/README.md [umd-url]: https://github.com/stdlib-js/blas-base-dasum/tree/umd [umd-readme]: https://github.com/stdlib-js/blas-base-dasum/blob/umd/README.md [esm-url]: https://github.com/stdlib-js/blas-base-dasum/tree/esm [esm-readme]: https://github.com/stdlib-js/blas-base-dasum/blob/esm/README.md [branches-url]: https://github.com/stdlib-js/blas-base-dasum/blob/main/branches.md

[stdlib-license]: https://raw.githubusercontent.com/stdlib-js/blas-base-dasum/main/LICENSE

[blas]: http://www.netlib.org/blas

[dasum]: http://www.netlib.org/lapack/explore-html/de/da4/group__double__blas__level1.html

[mdn-float64array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Float64Array

[mdn-typed-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray

[l1norm]: https://en.wikipedia.org/wiki/Norm_%28mathematics%29

[@stdlib/math/base/special/abs]: https://www.npmjs.com/package/@stdlib/math-base-special-abs

[@stdlib/blas/base/daxpy]: https://www.npmjs.com/package/@stdlib/blas-base-daxpy

[@stdlib/blas/base/gasum]: https://www.npmjs.com/package/@stdlib/blas-base-gasum

[@stdlib/blas/base/sasum]: https://www.npmjs.com/package/@stdlib/blas-base-sasum

[@stdlib/blas/ext/base/dsum]: https://www.npmjs.com/package/@stdlib/blas-ext-base-dsum


  
    About stdlib...
  

  We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.

  The library is fully decomposable, being architected in such a way that you can swap out and mix and match APIs and functionality to cater to your exact preferences and use cases.

  When you use stdlib, you can be absolutely certain that you are using the most thorough, rigorous, well-written, studied, documented, tested, measured, and high-quality code out there.

  To join us in bringing numerical computing to the web, get started by checking us out on GitHub, and please consider financially supporting stdlib. We greatly appreciate your continued support!

`dasum`

[![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url]

> Compute the sum of [absolute values][@stdlib/math/base/special/abs] ([_L1_ norm][l1norm]).

The [_L1_ norm][l1norm] is defined as

`Installation`

``bash npm install @stdlib/blas-base-dasum `

`Usage`

`javascript var dasum = require( '@stdlib/blas-base-dasum' ); `

#### dasum( N, x, stride )

Computes the sum of [absolute values][@stdlib/math/base/special/abs].

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

var x = new Float64Array( [ -2.0, 1.0, 3.0, -5.0, 4.0, 0.0, -1.0, -3.0 ] );

var sum = dasum( x.length, x, 1 ); // returns 19.0 `

The function has the following parameters:

- N: number of indexed elements. - x: input [Float64Array][mdn-float64array]. - stride: index increment.

The N and stride parameters determine which elements in x are used to compute the sum. For example, to sum every other value,

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

var x = new Float64Array( [ -2.0, 1.0, 3.0, -5.0, 4.0, 0.0, -1.0, -3.0 ] );

var sum = dasum( 4, x, 2 ); // returns 10.0 `

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

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

// Initial array... var x0 = new Float64Array( [ 1.0, -2.0, 3.0, -4.0, 5.0, -6.0 ] );

// Create an offset view... var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element

// Sum every other value... var sum = dasum( 3, x1, 2 ); // returns 12.0 `

If N is less than or equal to 0, the function returns 0.

#### dasum.ndarray( N, x, stride, offset )

Computes the sum of [absolute values][@stdlib/math/base/special/abs] using alternative indexing semantics.

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

var x = new Float64Array( [ -2.0, 1.0, 3.0, -5.0, 4.0, 0.0, -1.0, -3.0 ] );

var sum = dasum.ndarray( x.length, x, 1, 0 ); // returns 19.0 `

The function has the following additional parameters:

- offset: starting index.

While [typed array][mdn-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 sum the last three elements,

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

var x = new Float64Array( [ 1.0, -2.0, 3.0, -4.0, 5.0, -6.0 ] );

var sum = dasum.ndarray( 3, x, 1, x.length-3 ); // returns 15.0

// Using a negative stride to sum from the last element: sum = dasum.ndarray( 3, x, -1, x.length-1 ); // returns 15.0 `

`Notes`

- If N <= 0, the sum is 0. - dasum() corresponds to the [BLAS][blas] level 1 function [dasum][dasum].

`Examples`

`javascript var discreteUniform = require( '@stdlib/random-array-discrete-uniform' ); var dasum = require( '@stdlib/blas-base-dasum' );

var opts = { 'dtype': 'float64' }; var x = discreteUniform( 10, -100, 100, opts ); console.log( x );

var out = dasum( x.length, x, 1 ); console.log( out ); `

*

`C APIs`

`$3`

`c #include "stdlib/blas/base/dasum.h" `

#### c_dasum( N, \*X, stride )

Computes the sum of absolute values.

`c const double x[] = { 1.0, 2.0, 3.0, 4.0 };

double v = c_dasum( 4, x, 1 ); // returns 10.0 `

The function accepts the following arguments:

- N: [in] CBLAS_INT number of indexed elements. - X: [in] double* input array. - stride: [in] CBLAS_INT index increment for X.

`c double c_dasum( const CBLAS_INT N, const double *X, const CBLAS_INT stride ); `

#### c_dasum_ndarray( N, \*X, stride, offset )

Computes the sum of absolute values using alternative indexing semantics.

`c const double x[] = { 1.0, 2.0, 3.0, 4.0 };

double v = c_dasum_ndarray( 4, x, -1, 3 ); // returns 10.0 `

The function accepts the following arguments:

- N: [in] CBLAS_INT number of indexed elements. - X: [in] double* input array. - stride: [in] CBLAS_INT index increment for X. - offset: [in] CBLAS_INT starting index for X.

`c double c_dasum_ndarray( const CBLAS_INT N, const double *X, const CBLAS_INT stride, const CBLAS_INT offset ); `

`$3`

`c #include "stdlib/blas/base/dasum.h" #include

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 };

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

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

// Compute the sum of absolute values: double sum = c_dasum( N, x, strideX );

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

// Compute the sum of absolute values: sum = c_dasum_ndarray( N, x, -strideX, N-1 );

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