Calculate the arithmetic mean of a strided array using a second-order iterative Kahan–Babuška algorithm.
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Calculate the [arithmetic mean][arithmetic-mean] of a strided array using a second-order iterative Kahan–Babuška algorithm.
math
\mu = \frac{1}{n} \sum_{i=0}^{n-1} x_ibash
npm install @stdlib/stats-base-meankbn2script tag without installation and bundlers, use the [ES Module][es-module] available on the [esm][esm-url] branch (see [README][esm-readme]).deno][deno-url] branch (see [README][deno-readme] for usage intructions).umd][umd-url] branch (see [README][umd-readme]).javascript
var meankbn2 = require( '@stdlib/stats-base-meankbn2' );x using a second-order iterative Kahan–Babuška algorithm.javascript
var x = [ 1.0, -2.0, 2.0 ];
var N = x.length;
var v = meankbn2( N, x, 1 );
// returns ~0.3333Array][mdn-array] or [typed array][mdn-typed-array].x.N and stride parameters determine which elements in x are accessed at runtime. For example, to compute the [arithmetic mean][arithmetic-mean] of every other element in x,javascript
var floor = require( '@stdlib/math-base-special-floor' );
var x = [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ];
var N = floor( x.length / 2 );
var v = meankbn2( N, x, 2 );
// returns 1.25typed array][mdn-typed-array] views.javascript
var Float64Array = require( '@stdlib/array-float64' );
var floor = require( '@stdlib/math-base-special-floor' );
var x0 = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var N = floor( x0.length / 2 );
var v = meankbn2( N, x1, 2 );
// returns 1.25javascript
var x = [ 1.0, -2.0, 2.0 ];
var N = x.length;
var v = meankbn2.ndarray( N, x, 1, 0 );
// returns ~0.33333x.typed array][mdn-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][arithmetic-mean] for every other value in x starting from the second valuejavascript
var floor = require( '@stdlib/math-base-special-floor' );
var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ];
var N = floor( x.length / 2 );
var v = meankbn2.ndarray( N, x, 2, 1 );
// returns 1.25N <= 0, both functions return NaN.dmeankbn2][@stdlib/stats/strided/dmeankbn2], [smeankbn2][@stdlib/stats/base/smeankbn2], etc.) are likely to be significantly more performant.javascript
var randu = require( '@stdlib/random-base-randu' );
var round = require( '@stdlib/math-base-special-round' );
var Float64Array = require( '@stdlib/array-float64' );
var meankbn2 = require( '@stdlib/stats-base-meankbn2' );
var x;
var i;
x = new Float64Array( 10 );
for ( i = 0; i < x.length; i++ ) {
x[ i ] = round( (randu()*100.0) - 50.0 );
}
console.log( x );
var v = meankbn2( x.length, x, 1 );
console.log( v );
