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@stdlib/stats-base-meanpn

Calculate the arithmetic mean of a strided array using a two-pass error correction algorithm.

0.2.2

Apache-2.0

JavaScript

2.96 kB

1,882,225 4.4

Installations

npm install @stdlib/stats-base-meanpn

Pull Requests

Open

0

Total

0

Closed

0

Issues

Open

0

Total

0

Closed

0

Releases

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Developer

stdlib-js

Developer Guide

BETA

Module System

CommonJS

Min. Node Version

>=0.10.0

Typescript Support

Yes

Node Version

16.20.2

NPM Version

8.19.4 Statistics

1 Stars

62 Commits

3 Watching

5 Branches

10 Contributors

Updated on 01 Nov 2024

Bundle Size

8.89 kB

Minified

2.96 kB

Minified + Gzipped

Bundlephobia

Languages

JavaScript (100%)

Total Downloads

Cumulative downloads

Total Downloads

1,882,225

Last day

23%

6,454

Compared to previous day

Last week

19.7%

36,678

Compared to previous week

Last month

-2%

145,393

Compared to previous month

Last year

742.1%

1,663,065

Compared to previous year

Daily Downloads

Weekly Downloads

Monthly Downloads

Yearly Downloads

Dependencies

@stdlib/blas-ext-base-gapxsumpw @stdlib/blas-ext-base-gsumpw @stdlib/utils-define-nonenumerable-read-only-property

Versions

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!

meanpn

Calculate the arithmetic mean of a strided array using a two-pass error correction algorithm.

The arithmetic mean is defined as

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

Installation

1npm install @stdlib/stats-base-meanpn

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

1var meanpn = require( '@stdlib/stats-base-meanpn' );

meanpn( N, x, stride )

Computes the arithmetic mean of a strided array x using a two-pass error correction algorithm.

1var x = [ 1.0, -2.0, 2.0 ];
2var N = x.length;
3
4var v = meanpn( N, x, 1 );
5// returns ~0.3333

The function has the following parameters:

N: number of indexed elements.
x: input Array or typed array.
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 of every other element in x,

1var floor = require( '@stdlib/math-base-special-floor' );
2
3var x = [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ];
4var N = floor( x.length / 2 );
5
6var v = meanpn( N, x, 2 );
7// returns 1.25

Note that indexing is relative to the first index. To introduce an offset, use typed array views.

1var Float64Array = require( '@stdlib/array-float64' );
2var floor = require( '@stdlib/math-base-special-floor' );
3
4var x0 = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
5var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
6
7var N = floor( x0.length / 2 );
8
9var v = meanpn( N, x1, 2 );
10// returns 1.25

meanpn.ndarray( N, x, stride, offset )

Computes the arithmetic mean of a strided array using a two-pass error correction algorithm and alternative indexing semantics.

1var x = [ 1.0, -2.0, 2.0 ];
2var N = x.length;
3
4var v = meanpn.ndarray( N, x, 1, 0 );
5// returns ~0.33333

The function has the following additional parameters:

offset: starting index for x.

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 calculate the arithmetic mean for every other value in x starting from the second value

1var floor = require( '@stdlib/math-base-special-floor' );
2
3var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ];
4var N = floor( x.length / 2 );
5
6var v = meanpn.ndarray( N, x, 2, 1 );
7// returns 1.25

Notes

If N <= 0, both functions return NaN.
Depending on the environment, the typed versions (dmeanpn, smeanpn, etc.) are likely to be significantly more performant.

Examples

1var randu = require( '@stdlib/random-base-randu' );
2var round = require( '@stdlib/math-base-special-round' );
3var Float64Array = require( '@stdlib/array-float64' );
4var meanpn = require( '@stdlib/stats-base-meanpn' );
5
6var x;
7var i;
8
9x = new Float64Array( 10 );
10for ( i = 0; i < x.length; i++ ) {
11    x[ i ] = round( (randu()*100.0) - 50.0 );
12}
13console.log( x );
14
15var v = meanpn( x.length, x, 1 );
16console.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.
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.

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

No vulnerabilities found.

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3

Pinned-Dependencies

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Reason

dependency not pinned by hash detected -- score normalized to 3

Details

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Info: 31 out of 31 GitHub-owned GitHubAction dependencies pinned
Info: 18 out of 23 third-party GitHubAction dependencies pinned
Info: 0 out of 31 npmCommand dependencies pinned

2

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Score

4.4

/10

Last Scanned on 2024-11-18

The Open Source Security Foundation is a cross-industry collaboration to improve the security of open source software (OSS). The Scorecard provides security health metrics for open source projects.

Learn More

@stdlib/stats-base-meanpn

Installations

Pull Requests

0

0

0

Issues

0

0

0

Releases

Unable to fetch releases

Developer

Developer Guide

CommonJS

>=0.10.0

Yes

16.20.2

8.19.4

Statistics

Bundle Size

8.89 kB

2.96 kB

Languages

Total Downloads

1,882,225

Daily Downloads

Weekly Downloads

Monthly Downloads

Yearly Downloads

Dependencies

meanpn

Installation

Usage

meanpn( N, x, stride )

meanpn.ndarray( N, x, stride, offset )

Notes

Examples

References

See Also

Notice

Community

License

Copyright

10

10

10

10

9

3

2

0

0

0

0

0

0

4.4

/10