@stdlib/blas-sdot

Calculate the dot product of two single-precision floating-point vectors.

Usage no npm install needed!

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  import stdlibBlasSdot from 'https://cdn.skypack.dev/@stdlib/blas-sdot';
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README

sdot

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Calculate the dot product of two single-precision floating-point vectors.

The dot product (or scalar product) is defined as

Dot product definition.

Installation

npm install @stdlib/blas-sdot

Usage

var sdot = require( '@stdlib/blas-sdot' );

sdot( x, y )

Calculates the dot product of vectors x and y.

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

var x = array( new Float32Array( [ 4.0, 2.0, -3.0, 5.0, -1.0 ] ) );
var y = array( new Float32Array( [ 2.0, 6.0, -1.0, -4.0, 8.0 ] ) );

var z = sdot( x, y );
// returns -5.0

The function has the following parameters:

  • x: a 1-dimensional ndarray whose underlying data type is float32.
  • y: a 1-dimensional ndarray whose underlying data type is float32.

If provided empty vectors, the function returns 0.0.

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

var x = array( new Float32Array() );
var y = array( new Float32Array() );

var z = sdot( x, y );
// returns 0.0

Notes

  • sdot() provides a higher-level interface to the BLAS level 1 function sdot.

Examples

var discreteUniform = require( '@stdlib/random-base-discrete-uniform' );
var Float32Array = require( '@stdlib/array-float32' );
var array = require( '@stdlib/ndarray-array' );
var sdot = require( '@stdlib/blas-sdot' );

var x = array( new Float32Array( 10 ) );
var y = array( new Float32Array( 10 ) );

var rand1 = discreteUniform.factory( 0, 100 );
var rand2 = discreteUniform.factory( 0, 10 );

var i;
for ( i = 0; i < x.length; i++ ) {
    x.set( i, rand1() );
    y.set( i, rand2() );
}
console.log( x.toString() );
console.log( y.toString() );

var z = sdot( x, y );
console.log( z );

See Also

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.

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License

See LICENSE.

Copyright

Copyright © 2016-2022. The Stdlib Authors.