@stdlib/stats-base-smeanpw
v0.2.2
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Calculate the arithmetic mean of a single-precision floating-point strided array using pairwise summation.
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smeanpw
Calculate the arithmetic mean of a single-precision floating-point strided array using pairwise summation.
The arithmetic mean is defined as
Installation
npm install @stdlib/stats-base-smeanpw
Usage
var smeanpw = require( '@stdlib/stats-base-smeanpw' );
smeanpw( N, x, stride )
Computes the arithmetic mean of a single-precision floating-point strided array x
using pairwise summation.
var Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );
var N = x.length;
var v = smeanpw( N, x, 1 );
// returns ~0.3333
The function has the following parameters:
- N: number of indexed elements.
- x: input
Float32Array
. - 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
,
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 = smeanpw( N, x, 2 );
// returns 1.25
Note that indexing is relative to the first index. To introduce an offset, use typed array
views.
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 = smeanpw( N, x1, 2 );
// returns 1.25
smeanpw.ndarray( N, x, stride, offset )
Computes the arithmetic mean of a single-precision floating-point strided array using pairwise summation and alternative indexing semantics.
var Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );
var N = x.length;
var v = smeanpw.ndarray( N, x, 1, 0 );
// 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
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 = smeanpw.ndarray( N, x, 2, 1 );
// returns 1.25
Notes
- If
N <= 0
, both functions returnNaN
. - 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 randu = require( '@stdlib/random-base-randu' );
var round = require( '@stdlib/math-base-special-round' );
var Float32Array = require( '@stdlib/array-float32' );
var smeanpw = require( '@stdlib/stats-base-smeanpw' );
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 = smeanpw( x.length, x, 1 );
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.
See Also
@stdlib/stats-base/dmeanpw
: calculate the arithmetic mean of a double-precision floating-point strided array using pairwise summation.@stdlib/stats-base/meanpw
: calculate the arithmetic mean of a strided array using pairwise summation.@stdlib/stats-base/smean
: calculate the arithmetic mean of a single-precision floating-point strided array.
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.