itersumabs2

Compute the sum of squared absolute values for all iterated values.

The sum of squared absolute values is defined as

s = \sum_{i=0}^{n-1} x_i^2

Installation

npm install @stdlib/stats-iter-sumabs2

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 itersumabs2 = require( '@stdlib/stats-iter-sumabs2' );

itersumabs2( iterator )

Computes the sum of squared absolute values for all iterated values.

var array2iterator = require( '@stdlib/array-to-iterator' );

var arr = array2iterator( [ -1.0, 2.0, -3.0, 4.0 ] );

var s = itersumabs2( arr );
// returns 30.0

Notes

If an iterated value is non-numeric (including NaN), the returned iterator returns NaN. If non-numeric iterated values are possible, you are advised to provide an iterator which type checks and handles non-numeric values accordingly.
For iterators which can generate many values or which may output large numbers, care should be taken to prevent overflow.

Examples

var runif = require( '@stdlib/random-iter-uniform' );
var itersumabs2 = require( '@stdlib/stats-iter-sumabs2' );

// Create an iterator for generating uniformly distributed pseudorandom numbers:
var rand = runif( -10.0, 10.0, {
    'seed': 1234,
    'iter': 100
});

// Compute the sum of squared absolute values:
var s = itersumabs2( rand );
// returns <number>

console.log( 'sumabs2: %d.', s );

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.