Calculate the arithmetic mean of a single-precision floating-point strided array using ordinary recursive summation with extended accumulation.
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Calculate the arithmetic mean of a single-precision floating-point strided array using ordinary recursive summation with extended accumulation.
The arithmetic mean is defined as
\mu = \frac{1}{n} \sum_{i=0}^{n-1} x_i
npm install @stdlib/stats-base-sdsmeanors
Alternatively,
script
tag without installation and bundlers, use the ES Module available on the esm
branch (see README).deno
branch (see README for usage intructions).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.
var sdsmeanors = require( '@stdlib/stats-base-sdsmeanors' );
Computes the arithmetic mean of a single-precision floating-point strided array x
using ordinary recursive summation with extended accumulation.
var Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );
var N = x.length;
var v = sdsmeanors( N, x, 1 );
// returns ~0.3333
The function has the following parameters:
Float32Array
.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 = sdsmeanors( 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 = sdsmeanors( N, x1, 2 );
// returns 1.25
Computes the arithmetic mean of a single-precision floating-point strided array using ordinary recursive summation with extended accumulation 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 = sdsmeanors.ndarray( N, x, 1, 0 );
// returns ~0.33333
The function has the following additional parameters:
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 = sdsmeanors.ndarray( N, x, 2, 1 );
// returns 1.25
N <= 0
, both functions return NaN
.var randu = require( '@stdlib/random-base-randu' );
var round = require( '@stdlib/math-base-special-round' );
var Float32Array = require( '@stdlib/array-float32' );
var sdsmeanors = require( '@stdlib/stats-base-sdsmeanors' );
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 = sdsmeanors( x.length, x, 1 );
console.log( v );
@stdlib/stats-base/sdsmean
: calculate the arithmetic mean of a single-precision floating-point strided array using extended accumulation.
@stdlib/stats-base/sdsnanmeanors
: calculate the arithmetic mean of a single-precision floating-point strided array, ignoring NaN values and using ordinary recursive summation with extended accumulation.
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.
See LICENSE.
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