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Calculate the arithmetic mean of a strided array, ignoring
NaNvalues and using Welford's algorithm.
The arithmetic mean is defined as
import nanmeanwd from 'https://cdn.jsdelivr.net/gh/stdlib-js/stats-base-nanmeanwd@deno/mod.js';You can also import the following named exports from the package:
import { ndarray } from 'https://cdn.jsdelivr.net/gh/stdlib-js/stats-base-nanmeanwd@deno/mod.js';Computes the arithmetic mean of a strided array, ignoring NaN values and using Welford's algorithm.
var x = [ 1.0, -2.0, NaN, 2.0 ];
var v = nanmeanwd( x.length, x, 1 );
// returns ~0.3333The function has the following parameters:
- N: number of indexed elements.
- x: input
Arrayortyped array. - strideX: stride length for
x.
The N and stride parameters determine which elements in the strided array are accessed at runtime. For example, to compute the arithmetic mean of every other element in x,
var x = [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0, NaN ];
var v = nanmeanwd( 5, x, 2 );
// returns 1.25Note that indexing is relative to the first index. To introduce an offset, use typed array views.
import Float64Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float64@deno/mod.js';
var x0 = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN, NaN ] );
var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var v = nanmeanwd( 5, x1, 2 );
// returns 1.25Computes the arithmetic mean of a strided array, ignoring NaN values and using Welford's algorithm and alternative indexing semantics.
var x = [ 1.0, -2.0, NaN, 2.0 ];
var v = nanmeanwd.ndarray( x.length, x, 1, 0 );
// returns ~0.33333The function has the following additional parameters:
- offsetX: 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 element in the strided array starting from the second element
var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN, NaN ];
var v = nanmeanwd.ndarray( 5, x, 2, 1 );
// returns 1.25- If
N <= 0, both functions returnNaN. - Both functions support array-like objects having getter and setter accessors for array element access (e.g.,
@stdlib/array-base/accessor). - If every indexed element is
NaN, both functions returnNaN. - Depending on the environment, the typed versions (
dnanmeanwd,snanmeanwd, etc.) are likely to be significantly more performant.
import uniform from 'https://cdn.jsdelivr.net/gh/stdlib-js/random-base-uniform@deno/mod.js';
import filledarrayBy from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-filled-by@deno/mod.js';
import bernoulli from 'https://cdn.jsdelivr.net/gh/stdlib-js/random-base-bernoulli@deno/mod.js';
import nanmeanwd from 'https://cdn.jsdelivr.net/gh/stdlib-js/stats-base-nanmeanwd@deno/mod.js';
function rand() {
if ( bernoulli( 0.8 ) < 1 ) {
return NaN;
}
return uniform( -50.0, 50.0 );
}
var x = filledarrayBy( 10, 'float64', rand );
console.log( x );
var v = nanmeanwd( x.length, x, 1 );
console.log( v );- Welford, B. P. 1962. "Note on a Method for Calculating Corrected Sums of Squares and Products." Technometrics 4 (3). Taylor & Francis: 419–20. doi:10.1080/00401706.1962.10490022.
- van Reeken, A. J. 1968. "Letters to the Editor: Dealing with Neely's Algorithms." Communications of the ACM 11 (3): 149–50. doi:10.1145/362929.362961.
@stdlib/stats-strided/dnanmeanwd: calculate the arithmetic mean of a double-precision floating-point strided array, using Welford's algorithm and ignoring NaN values.@stdlib/stats-strided/meanwd: calculate the arithmetic mean of a strided array using Welford's algorithm.@stdlib/stats-base/nanmean: calculate the arithmetic mean of a strided array, ignoring NaN values.@stdlib/stats-strided/snanmeanwd: calculate the arithmetic mean of a single-precision floating-point strided array, ignoring NaN values and using Welford's algorithm.
This package is part of stdlib, a standard library 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.
See LICENSE.
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