@stdlib/stats-strided-mean

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mean

Calculate the arithmetic mean of a strided array.

The arithmetic mean is defined as

Installation

npm install @stdlib/stats-strided-mean

Usage

var mean = require( '@stdlib/stats-strided-mean' );

mean( N, x, strideX )

Computes the arithmetic mean of a strided array.

var x = [ 1.0, -2.0, 2.0 ];

var v = mean( x.length, x, 1 );
// returns ~0.3333

The function has the following parameters:

• N: number of indexed elements.
• x: input Array or typed 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 ];

var v = mean( 4, x, 2 );
// returns 1.25

Note that indexing is relative to the first index. To introduce an offset, use typed array views.

var Float64Array = require( '@stdlib/array-float64' );

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 v = mean( 4, x1, 2 );
// returns 1.25

mean.ndarray( N, x, strideX, offsetX )

Computes the arithmetic mean of a strided array using alternative indexing semantics.

var x = [ 1.0, -2.0, 2.0 ];

var v = mean.ndarray( x.length, x, 1, 0 );
// returns ~0.33333

The 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 x starting from the second element

var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ];

var v = mean.ndarray( 4, x, 2, 1 );
// returns 1.25

Notes

• If N <= 0, both functions return NaN.
• Both functions support array-like objects having getter and setter accessors for array element access (e.g., @stdlib/array-base/accessor).
• Depending on the environment, the typed versions (dmean, smean, etc.) are likely to be significantly more performant.

Examples

var discreteUniform = require( '@stdlib/random-array-discrete-uniform' );
var mean = require( '@stdlib/stats-strided-mean' );

var x = discreteUniform( 10, -50, 50, {
    'dtype': 'float64'
});
console.log( x );

var v = mean( x.length, x, 1 );
console.log( v );

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See Also

• @stdlib/stats-strided/dmean: calculate the arithmetic mean of a double-precision floating-point strided array.
• @stdlib/stats-strided/nanmean: calculate the arithmetic mean of a strided array, ignoring NaN values.
• @stdlib/stats-strided/smean: calculate the arithmetic mean of a single-precision floating-point strided array.

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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

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License

See LICENSE.

Copyright

Copyright 2016-2026. The Stdlib Authors.