@stdlib/stats-meanors
v0.1.1
Published
Compute the arithmetic mean along one or more ndarray dimensions using ordinary recursive summation.
Readme
meanors
Compute the arithmetic mean along one or more ndarray dimensions using ordinary recursive summation.
The arithmetic mean is defined as
Installation
npm install @stdlib/stats-meanorsUsage
var meanors = require( '@stdlib/stats-meanors' );meanors( x[, options] )
Computes the arithmetic mean along one or more ndarray dimensions using ordinary recursive summation.
var array = require( '@stdlib/ndarray-array' );
var x = array( [ 1.0, 2.0, -2.0, 4.0 ] );
var y = meanors( x );
// returns <ndarray>[ 1.25 ]The function has the following parameters:
- x: input ndarray. Must have a real-valued or "generic" data type.
- options: function options (optional).
The function accepts the following options:
- dims: list of dimensions over which to perform a reduction. If not provided, the function performs a reduction over all elements in a provided input ndarray.
- dtype: output ndarray data type. Must be a real-valued floating-point or "generic" data type.
- keepdims: boolean indicating whether the reduced dimensions should be included in the returned ndarray as singleton dimensions. Default:
false.
By default, the function performs a reduction over all elements in a provided input ndarray. To perform a reduction over specific dimensions, provide a dims option.
var array = require( '@stdlib/ndarray-array' );
var x = array( [ 1.0, 2.0, -2.0, 4.0 ], {
'shape': [ 2, 2 ],
'order': 'row-major'
});
// returns <ndarray>[ [ 1.0, 2.0 ], [ -2.0, 4.0 ] ]
var y = meanors( x, {
'dims': [ 0 ]
});
// returns <ndarray>[ -0.5, 3.0 ]
y = meanors( x, {
'dims': [ 1 ]
});
// returns <ndarray>[ 1.5, 1.0 ]
y = meanors( x, {
'dims': [ 0, 1 ]
});
// returns <ndarray>[ 1.25 ]By default, the function excludes reduced dimensions from the output ndarray. To include the reduced dimensions as singleton dimensions, set the keepdims option to true.
var array = require( '@stdlib/ndarray-array' );
var x = array( [ 1.0, 2.0, -2.0, 4.0 ], {
'shape': [ 2, 2 ],
'order': 'row-major'
});
// returns <ndarray>[ [ 1.0, 2.0 ], [ -2.0, 4.0 ] ]
var y = meanors( x, {
'dims': [ 0 ],
'keepdims': true
});
// returns <ndarray>[ [ -0.5, 3.0 ] ]
y = meanors( x, {
'dims': [ 1 ],
'keepdims': true
});
// returns <ndarray>[ [ 1.5 ], [ 1.0 ] ]
y = meanors( x, {
'dims': [ 0, 1 ],
'keepdims': true
});
// returns <ndarray>[ [ 1.25 ] ]By default, the function returns an ndarray having a data type determined by the function's output data type policy. To override the default behavior, set the dtype option.
var getDType = require( '@stdlib/ndarray-dtype' );
var array = require( '@stdlib/ndarray-array' );
var x = array( [ 1.0, 2.0, -2.0, 4.0 ], {
'dtype': 'generic'
});
var y = meanors( x, {
'dtype': 'float64'
});
// returns <ndarray>
var dt = String( getDType( y ) );
// returns 'float64'meanors.assign( x, out[, options] )
Computes the arithmetic mean along one or more ndarray dimensions using ordinary recursive summation and assigns the results to a provided output ndarray.
var array = require( '@stdlib/ndarray-array' );
var zeros = require( '@stdlib/ndarray-zeros' );
var x = array( [ 1.0, 2.0, -2.0, 4.0 ] );
var y = zeros( [] );
var out = meanors.assign( x, y );
// returns <ndarray>[ 1.25 ]
var bool = ( out === y );
// returns trueThe function has the following parameters:
The function accepts the following options:
- dims: list of dimensions over which to perform a reduction. If not provided, the function performs a reduction over all elements in a provided input ndarray.
Notes
- Setting the
keepdimsoption totruecan be useful when wanting to ensure that the output ndarray is broadcast-compatible with ndarrays having the same shape as the input ndarray. - The output data type policy only applies to the main function and specifies that, by default, the function must return an ndarray having a real-valued floating-point or "generic" data type. For the
assignmethod, the output ndarray is allowed to have any supported output data type. - Ordinary recursive summation (i.e., a "simple" sum) is performant, but can incur significant numerical error. If performance is paramount and error tolerated, using ordinary recursive summation to compute an arithmetic mean is acceptable; in all other cases, exercise due caution.
Examples
var uniform = require( '@stdlib/random-uniform' );
var getDType = require( '@stdlib/ndarray-dtype' );
var ndarray2array = require( '@stdlib/ndarray-to-array' );
var meanors = require( '@stdlib/stats-meanors' );
// Generate an array of random numbers:
var x = uniform( [ 5, 5 ], 0.0, 20.0 );
console.log( ndarray2array( x ) );
// Perform a reduction:
var y = meanors( x, {
'dims': [ 0 ]
});
// Resolve the output array data type:
var dt = getDType( y );
console.log( dt );
// Print the results:
console.log( ndarray2array( y ) );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 © 2016-2026. The Stdlib Authors.
