meanpw

Compute the arithmetic mean of a one-dimensional ndarray using pairwise summation.

The arithmetic mean is defined as

Installation

npm install @stdlib/stats-base-ndarray-meanpw

Usage

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

meanpw( arrays )

Computes the arithmetic mean of a one-dimensional ndarray using pairwise summation.

var ndarray = require( '@stdlib/ndarray-base-ctor' );

var xbuf = [ 1.0, 3.0, 4.0, 2.0 ];
var x = new ndarray( 'generic', xbuf, [ 4 ], [ 1 ], 0, 'row-major' );

var v = meanpw( [ x ] );
// returns 2.5

The function has the following parameters:

• arrays: array-like object containing a one-dimensional input ndarray.

Notes

• If provided an empty one-dimensional ndarray, the function returns NaN.
• In general, pairwise summation is more numerically stable than ordinary recursive summation (i.e., "simple" summation), with slightly worse performance. While not the most numerically stable summation technique (e.g., compensated summation techniques such as the Kahan–Babuška-Neumaier algorithm are generally more numerically stable), pairwise summation strikes a reasonable balance between numerical stability and performance. If either numerical stability or performance is more desirable for your use case, consider alternative summation techniques.

Examples

var discreteUniform = require( '@stdlib/random-array-discrete-uniform' );
var ndarray = require( '@stdlib/ndarray-base-ctor' );
var ndarray2array = require( '@stdlib/ndarray-to-array' );
var meanpw = require( '@stdlib/stats-base-ndarray-meanpw' );

var xbuf = discreteUniform( 10, -50, 50, {
    'dtype': 'generic'
});
var x = new ndarray( 'generic', xbuf, [ xbuf.length ], [ 1 ], 0, 'row-major' );
console.log( ndarray2array( x ) );

var v = meanpw( [ x ] );
console.log( v );

References

• Higham, Nicholas J. 1993. "The Accuracy of Floating Point Summation." SIAM Journal on Scientific Computing 14 (4): 783–99. doi:10.1137/0914050.

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

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