Skip to content

Compute the L2-norm of a complex single-precision floating-point vector.

License

Notifications You must be signed in to change notification settings

stdlib-js/blas-base-scnrm2

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

13 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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!

scnrm2

NPM version Build Status Coverage Status

Compute the L2-norm of a complex single-precision floating-point vector.

Installation

npm install @stdlib/blas-base-scnrm2

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 scnrm2 = require( '@stdlib/blas-base-scnrm2' );

scnrm2( N, cx, strideX )

Computes the L2-norm of a complex single-precision floating-point vector.

var Complex64Array = require( '@stdlib/array-complex64' );

var cx = new Complex64Array( [ 0.3, 0.1, 0.5, 0.0, 0.0, 0.5, 0.0, 0.2 ] );

var norm = scnrm2( 4, cx, 1 );
// returns ~0.8

The function has the following parameters:

  • N: number of indexed elements.
  • cx: input Complex64Array.
  • strideX: index increment for cx.

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

var Complex64Array = require( '@stdlib/array-complex64' );

var cx = new Complex64Array( [ -2.0, 1.0, 3.0, -5.0, 4.0, 0.0, -1.0, -3.0 ] );

var norm = scnrm2( 2, cx, 2 );
// returns ~4.6

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

var Complex64Array = require( '@stdlib/array-complex64' );

// Initial array:
var cx0 = new Complex64Array( [ 1.0, -2.0, 3.0, -4.0, 5.0, -6.0 ] );

// Create an offset view:
var cx1 = new Complex64Array( cx0.buffer, cx0.BYTES_PER_ELEMENT*1 ); // start at 2nd element

// Compute the L2-norm:
var norm = scnrm2( 2, cx1, 1 );
// returns ~9.3

scnrm2.ndarray( N, cx, strideX, offset )

Computes the L2-norm of a complex single-precision floating-point vector using alternative indexing semantics.

var Complex64Array = require( '@stdlib/array-complex64' );

var cx = new Complex64Array( [ 0.3, 0.1, 0.5, 0.0, 0.0, 0.5, 0.0, 0.2 ] );

var norm = scnrm2.ndarray( 4, cx, 1, 0 );
// returns ~0.8

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 start from the second index,

var Complex64Array = require( '@stdlib/array-complex64' );

var cx = new Complex64Array( [ 1.0, -2.0, 3.0, -4.0, 5.0, -6.0 ] );

var norm = scnrm2.ndarray( 2, cx, 1, 1 );
// returns ~9.3

Notes

  • If N <= 0, both functions return 0.0.
  • scnrm2() corresponds to the BLAS level 1 function scnrm2.

Examples

var discreteUniform = require( '@stdlib/random-base-discrete-uniform' );
var filledarrayBy = require( '@stdlib/array-filled-by' );
var Complex64 = require( '@stdlib/complex-float32-ctor' );
var scnrm2 = require( '@stdlib/blas-base-scnrm2' );

function rand() {
    return new Complex64( discreteUniform( 0, 10 ), discreteUniform( -5, 5 ) );
}

var cx = filledarrayBy( 10, 'complex64', rand );
console.log( cx.toString() );

// Compute the L2-norm:
var norm = scnrm2( cx.length, cx, 1 );
console.log( norm );

C APIs

Usage

#include "stdlib/blas/base/scnrm2.h"

c_scnrm2( N, *CX, strideX )

Computes the L2-norm of a complex single-precision floating-point vector.

const float cx[] = { 0.3f, 0.1f, 0.5f, 0.0f, 0.0f, 0.5f, 0.0f, 0.2f };

float norm = c_scnrm2( 4, (void *)cx, 1 );
// returns 0.8

The function accepts the following arguments:

  • N: [in] CBLAS_INT number of indexed elements.
  • CX: [in] void* input array.
  • strideX: [in] CBLAS_INT index increment for CX.
float c_scnrm2( const CBLAS_INT N, const void *CX, const CBLAS_INT strideX );

Examples

#include "stdlib/blas/base/scnrm2.h"
#include <stdio.h>

int main( void ) {
    // Create a strided array of interleaved real and imaginary components:
    const float cx[] = { 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f };

    // Specify the number of elements:
    const int N = 4;

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

    // Compute the L2-norm:
    c_scnrm2( N, (void *)cx, strideX );

    // Print the result:
    printf( "L2-norm: %f\n", norm );
}

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

Chat


License

See LICENSE.

Copyright

Copyright © 2016-2024. The Stdlib Authors.