README.md

dmeanpw

Compute the arithmetic mean of a one-dimensional double-precision floating-point ndarray using pairwise summation.

The arithmetic mean is defined as

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

Usage

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

dmeanpw( arrays )

Computes the arithmetic mean of a one-dimensional double-precision floating-point ndarray using pairwise summation.

var Float64Array = require( '@stdlib/array/float64' );
var ndarray = require( '@stdlib/ndarray/base/ctor' );

var xbuf = new Float64Array( [ 1.0, 3.0, 4.0, 2.0 ] );
var x = new ndarray( 'float64', xbuf, [ 4 ], [ 1 ], 0, 'row-major' );

var v = dmeanpw( [ 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 dmeanpw = require( '@stdlib/stats/base/ndarray/dmeanpw' );

var xbuf = discreteUniform( 10, -50, 50, {
    'dtype': 'float64'
});
var x = new ndarray( 'float64', xbuf, [ xbuf.length ], [ 1 ], 0, 'row-major' );
console.log( ndarray2array( x ) );

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

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References

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