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Multiply a single-precision floating-point vector
xby a constantalpha.
npm install @stdlib/blas-base-sscalAlternatively,
- To load the package in a website via a
scripttag without installation and bundlers, use the ES Module available on theesmbranch (see README). - If you are using Deno, visit the
denobranch (see README for usage intructions). - For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the
umdbranch (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.
var sscal = require( '@stdlib/blas-base-sscal' );Multiplies a single-precision floating-point vector x by a constant alpha.
var Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ -2.0, 1.0, 3.0, -5.0, 4.0, 0.0, -1.0, -3.0 ] );
sscal( x.length, 5.0, x, 1 );
// x => <Float32Array>[ -10.0, 5.0, 15.0, -25.0, 20.0, 0.0, -5.0, -15.0 ]The function has the following parameters:
- N: number of indexed elements.
- alpha: scalar constant.
- x: input
Float32Array. - stride: index increment.
The N and stride parameters determine which elements in x are accessed at runtime. For example, to multiply every other value by a constant
var Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ -2.0, 1.0, 3.0, -5.0, 4.0, 0.0, -1.0, -3.0 ] );
sscal( 4, 5.0, x, 2 );
// x => <Float32Array>[ -10.0, 1.0, 15.0, -5.0, 20.0, 0.0, -5.0, -3.0 ]Note that indexing is relative to the first index. To introduce an offset, use typed array views.
var Float32Array = require( '@stdlib/array-float32' );
// Initial array:
var x0 = new Float32Array( [ 1.0, -2.0, 3.0, -4.0, 5.0, -6.0 ] );
// Create an offset view:
var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
// Scale every other value:
sscal( 3, 5.0, x1, 2 );
// x0 => <Float32Array>[ 1.0, -10.0, 3.0, -20.0, 5.0, -30.0 ]If either N or stride is less than or equal to 0, the function returns x unchanged.
Multiplies a single-precision floating-point vector x by a constant alpha using alternative indexing semantics.
var Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ -2.0, 1.0, 3.0, -5.0, 4.0, 0.0, -1.0, -3.0 ] );
sscal.ndarray( x.length, 5.0, x, 1, 0 );
// x => <Float32Array>[ -10.0, 5.0, 15.0, -25.0, 20.0, 0.0, -5.0, -15.0 ]The function has the following additional parameters:
- offset: 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 multiply the last three elements of x by a constant
var Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ 1.0, -2.0, 3.0, -4.0, 5.0, -6.0 ] );
sscal.ndarray( 3, 5.0, x, 1, x.length-3 );
// x => <Float32Array>[ 1.0, -2.0, 3.0, -20.0, 25.0, -30.0 ]var discreteUniform = require( '@stdlib/random-array-discrete-uniform' );
var sscal = require( '@stdlib/blas-base-sscal' );
var opts = {
'dtype': 'float32'
};
var x = discreteUniform( 10, -100, 100, opts );
console.log( x );
sscal( x.length, 5.0, x, 1 );
console.log( x );@stdlib/blas-base/daxpy: multiply a vector x by a constant and add the result to y.@stdlib/blas-base/dscal: multiply a double-precision floating-point vector by a constant.@stdlib/blas-base/gscal: multiply a vector by a constant.@stdlib/blas-base/saxpy: multiply a vector x by a constant and add the result to y.
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.
See LICENSE.
Copyright © 2016-2024. The Stdlib Authors.
