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https://github.com/stdlib-js/stats-base-dsmeanpn

Calculate the arithmetic mean of a single-precision floating-point strided array using a two-pass error correction algorithm with extended accumulation and returning an extended precision result.
https://github.com/stdlib-js/stats-base-dsmeanpn

arithmetic-mean array average avg central-tendency float float32 javascript math mathematics mean node node-js nodejs statistics stats stdlib strided strided-array typed

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Calculate the arithmetic mean of a single-precision floating-point strided array using a two-pass error correction algorithm with extended accumulation and returning an extended precision result.

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README 

        



  

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# dsmeanpn



[![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url] 



> Calculate the [arithmetic mean][arithmetic-mean] of a single-precision floating-point strided array using a two-pass error correction algorithm with extended accumulation and returning an extended precision result.



The [arithmetic mean][arithmetic-mean] is defined as



```math

\mu = \frac{1}{n} \sum_{i=0}^{n-1} x_i

```



## Installation



```bash

npm install @stdlib/stats-base-dsmeanpn

```



Alternatively,



-   To load the package in a website via a `script` tag without installation and bundlers, use the [ES Module][es-module] available on the [`esm`][esm-url] branch (see [README][esm-readme]).

-   If you are using Deno, visit the [`deno`][deno-url] branch (see [README][deno-readme] for usage intructions).

-   For use in Observable, or in browser/node environments, use the [Universal Module Definition (UMD)][umd] build available on the [`umd`][umd-url] branch (see [README][umd-readme]).



The [branches.md][branches-url] 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



```javascript

var dsmeanpn = require( '@stdlib/stats-base-dsmeanpn' );

```



#### dsmeanpn( N, x, stride )



Computes the [arithmetic mean][arithmetic-mean] of a single-precision floating-point strided array `x` using a two-pass error correction algorithm with extended accumulation and returning an extended precision result.



```javascript

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



var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );

var N = x.length;



var v = dsmeanpn( N, x, 1 );

// returns ~0.3333

```



The function has the following parameters:



-   **N**: number of indexed elements.

-   **x**: input [`Float32Array`][@stdlib/array/float32].

-   **stride**: index increment for `x`.



The `N` and `stride` parameters determine which elements in `x` are accessed at runtime. For example, to compute the [arithmetic mean][arithmetic-mean] of every other element in `x`,



```javascript

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

var floor = require( '@stdlib/math-base-special-floor' );



var x = new Float32Array( [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ] );

var N = floor( x.length / 2 );



var v = dsmeanpn( N, x, 2 );

// returns 1.25

```



Note that indexing is relative to the first index. To introduce an offset, use [`typed array`][mdn-typed-array] views.



```javascript

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

var floor = require( '@stdlib/math-base-special-floor' );



var x0 = new Float32Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );

var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element



var N = floor( x0.length / 2 );



var v = dsmeanpn( N, x1, 2 );

// returns 1.25

```



#### dsmeanpn.ndarray( N, x, stride, offset )



Computes the [arithmetic mean][arithmetic-mean] of a single-precision floating-point strided array using a two-pass error correction algorithm with extended accumulation and alternative indexing semantics and returning an extended precision result.



```javascript

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



var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );

var N = x.length;



var v = dsmeanpn.ndarray( N, x, 1, 0 );

// returns ~0.33333

```



The function has the following additional parameters:



-   **offset**: starting index for `x`.



While [`typed array`][mdn-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 calculate the [arithmetic mean][arithmetic-mean] for every other value in `x` starting from the second value



```javascript

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

var floor = require( '@stdlib/math-base-special-floor' );



var x = new Float32Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );

var N = floor( x.length / 2 );



var v = dsmeanpn.ndarray( N, x, 2, 1 );

// returns 1.25

```



## Notes



-   If `N <= 0`, both functions return `NaN`.

-   Accumulated intermediate values are stored as double-precision floating-point numbers. 



## Examples



```javascript

var randu = require( '@stdlib/random-base-randu' );

var round = require( '@stdlib/math-base-special-round' );

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

var dsmeanpn = require( '@stdlib/stats-base-dsmeanpn' );



var x;

var i;



x = new Float32Array( 10 );

for ( i = 0; i < x.length; i++ ) {

    x[ i ] = round( (randu()*100.0) - 50.0 );

}

console.log( x );



var v = dsmeanpn( x.length, x, 1 );

console.log( v );

```



* * *



## References



-   Neely, Peter M. 1966. "Comparison of Several Algorithms for Computation of Means, Standard Deviations and Correlation Coefficients." _Communications of the ACM_ 9 (7). Association for Computing Machinery: 496–99. doi:[10.1145/365719.365958][@neely:1966a].

-   Schubert, Erich, and Michael Gertz. 2018. "Numerically Stable Parallel Computation of (Co-)Variance." In _Proceedings of the 30th International Conference on Scientific and Statistical Database Management_. New York, NY, USA: Association for Computing Machinery. doi:[10.1145/3221269.3223036][@schubert:2018a].



* * *



## See Also



-   [`@stdlib/stats-base/dmeanpn`][@stdlib/stats/base/dmeanpn]: calculate the arithmetic mean of a double-precision floating-point strided array using a two-pass error correction algorithm.

-   [`@stdlib/stats-base/dsmean`][@stdlib/stats/base/dsmean]: calculate the arithmetic mean of a single-precision floating-point strided array using extended accumulation and returning an extended precision result.

-   [`@stdlib/stats-base/dsnanmeanpn`][@stdlib/stats/base/dsnanmeanpn]: calculate the arithmetic mean of a single-precision floating-point strided array, ignoring NaN values, using a two-pass error correction algorithm with extended accumulation, and returning an extended precision result.

-   [`@stdlib/stats-base/meanpn`][@stdlib/stats/base/meanpn]: calculate the arithmetic mean of a strided array using a two-pass error correction algorithm.

-   [`@stdlib/stats-base/smeanpn`][@stdlib/stats/base/smeanpn]: calculate the arithmetic mean of a single-precision floating-point strided array using a two-pass error correction algorithm.



* * *



## Notice



This package is part of [stdlib][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][stdlib], see the main project [repository][stdlib].



#### Community



[![Chat][chat-image]][chat-url]



---



## License



See [LICENSE][stdlib-license].



## Copyright



Copyright © 2016-2024. The Stdlib [Authors][stdlib-authors].



[npm-image]: http://img.shields.io/npm/v/@stdlib/stats-base-dsmeanpn.svg

[npm-url]: https://npmjs.org/package/@stdlib/stats-base-dsmeanpn



[test-image]: https://github.com/stdlib-js/stats-base-dsmeanpn/actions/workflows/test.yml/badge.svg?branch=main

[test-url]: https://github.com/stdlib-js/stats-base-dsmeanpn/actions/workflows/test.yml?query=branch:main



[coverage-image]: https://img.shields.io/codecov/c/github/stdlib-js/stats-base-dsmeanpn/main.svg

[coverage-url]: https://codecov.io/github/stdlib-js/stats-base-dsmeanpn?branch=main



[chat-image]: https://img.shields.io/gitter/room/stdlib-js/stdlib.svg

[chat-url]: https://app.gitter.im/#/room/#stdlib-js_stdlib:gitter.im



[stdlib]: https://github.com/stdlib-js/stdlib



[stdlib-authors]: https://github.com/stdlib-js/stdlib/graphs/contributors



[umd]: https://github.com/umdjs/umd

[es-module]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules



[deno-url]: https://github.com/stdlib-js/stats-base-dsmeanpn/tree/deno

[deno-readme]: https://github.com/stdlib-js/stats-base-dsmeanpn/blob/deno/README.md

[umd-url]: https://github.com/stdlib-js/stats-base-dsmeanpn/tree/umd

[umd-readme]: https://github.com/stdlib-js/stats-base-dsmeanpn/blob/umd/README.md

[esm-url]: https://github.com/stdlib-js/stats-base-dsmeanpn/tree/esm

[esm-readme]: https://github.com/stdlib-js/stats-base-dsmeanpn/blob/esm/README.md

[branches-url]: https://github.com/stdlib-js/stats-base-dsmeanpn/blob/main/branches.md



[stdlib-license]: https://raw.githubusercontent.com/stdlib-js/stats-base-dsmeanpn/main/LICENSE



[arithmetic-mean]: https://en.wikipedia.org/wiki/Arithmetic_mean



[@stdlib/array/float32]: https://github.com/stdlib-js/array-float32



[mdn-typed-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray



[@neely:1966a]: https://doi.org/10.1145/365719.365958



[@schubert:2018a]: https://doi.org/10.1145/3221269.3223036



[@stdlib/stats/base/dmeanpn]: https://github.com/stdlib-js/stats-base-dmeanpn



[@stdlib/stats/base/dsmean]: https://github.com/stdlib-js/stats-base-dsmean



[@stdlib/stats/base/dsnanmeanpn]: https://github.com/stdlib-js/stats-base-dsnanmeanpn



[@stdlib/stats/base/meanpn]: https://github.com/stdlib-js/stats-base-meanpn



[@stdlib/stats/base/smeanpn]: https://github.com/stdlib-js/stats-base-smeanpn