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!

incrmgmean

Compute a moving geometric mean incrementally.

The geometric mean is defined as the nth root of a product of n numbers.

$$\biggl( \prod_{i=0}^{n-1} \biggr)^{\frac{1}{n}} = \sqrt[n]{x_0 x_1 \cdots x_{n-1}}$$

Installation

npm install @stdlib/stats-incr-mgmean

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 incrmgmean = require( '@stdlib/stats-incr-mgmean' );

incrmgmean( window )

Returns an accumulator function which incrementally computes a moving geometric mean. The window parameter defines the number of values over which to compute the moving geometric mean.

var accumulator = incrmgmean( 3 );

accumulator( [x] )

If provided an input value x, the accumulator function returns an updated geometric mean. If not provided an input value x, the accumulator function returns the current geometric-mean.

var accumulator = incrmgmean( 3 );

var v = accumulator();
// returns null

// Fill the window...
v = accumulator( 2.0 ); // [2.0]
// returns 2.0

v = accumulator( 1.0 ); // [2.0, 1.0]
// returns ~1.41

v = accumulator( 3.0 ); // [2.0, 1.0, 3.0]
// returns ~1.82

// Window begins sliding...
v = accumulator( 7.0 ); // [1.0, 3.0, 7.0]
// returns ~2.76

v = accumulator( 5.0 ); // [3.0, 7.0, 5.0]
// returns ~4.72

v = accumulator();
// returns ~4.72

Notes

• Input values are not type checked. If provided NaN or a value which, when used in computations, results in NaN, the accumulated value is NaN for at least W-1 future invocations. If non-numeric inputs are possible, you are advised to type check and handle accordingly before passing the value to the accumulator function.
• As W values are needed to fill the window buffer, the first W-1 returned values are calculated from smaller sample sizes. Until the window is full, each returned value is calculated from all provided values.

Examples

var randu = require( '@stdlib/random-base-randu' );
var incrmgmean = require( '@stdlib/stats-incr-mgmean' );

var accumulator;
var v;
var i;

// Initialize an accumulator:
accumulator = incrmgmean( 5 );

// For each simulated datum, update the moving geometric mean...
for ( i = 0; i < 100; i++ ) {
    v = randu() * 100.0;
    accumulator( v );
}
console.log( accumulator() );

---

See Also

• @stdlib/stats-incr/gmean: compute a geometric mean incrementally.
• @stdlib/stats-incr/mhmean: compute a moving harmonic mean incrementally.
• @stdlib/stats-incr/mmean: compute a moving arithmetic mean incrementally.

---

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

---

License

See LICENSE.

Copyright

Copyright © 2016-2024. The Stdlib Authors.