Skip to content

meanwd

Directory actions

More options

Directory actions

More options

Latest commit

 

History

History
 
 

meanwd

Folders and files

NameName
Last commit message
Last commit date

parent directory

..
benchmark
benchmark
 
 
docs
docs
 
 
examples
examples
 
 
lib
lib
 
 
test
test
 
 
README.md
README.md
 
 
package.json
package.json
 
 

README.md

meanwd

Compute the arithmetic mean along one or more ndarray dimensions using Welford's algorithm.

The arithmetic mean is defined as

$$\mu = \frac{1}{n} \sum_{i=0}^{n-1} x_i$$

Usage

var meanwd = require( '@stdlib/stats/meanwd' );

meanwd( x[, options] )

Computes the arithmetic mean along one or more ndarray dimensions using Welford's algorithm.

var array = require( '@stdlib/ndarray/array' );

var x = array( [ 1.0, 2.0, -2.0, 4.0 ] );

var y = meanwd( 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 = meanwd( x, {
    'dims': [ 0 ]
});
// returns <ndarray>[ -0.5, 3.0 ]

y = meanwd( x, {
    'dims': [ 1 ]
});
// returns <ndarray>[ 1.5, 1.0 ]

y = meanwd( 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 = meanwd( x, {
    'dims': [ 0 ],
    'keepdims': true
});
// returns <ndarray>[ [ -0.5, 3.0 ] ]

y = meanwd( x, {
    'dims': [ 1 ],
    'keepdims': true
});
// returns <ndarray>[ [ 1.5 ], [ 1.0 ] ]

y = meanwd( 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 = meanwd( x, {
    'dtype': 'float64'
});
// returns <ndarray>[ 1.25 ]

var dt = String( getDType( y ) );
// returns 'float64'

meanwd.assign( x, out[, options] )

Computes the arithmetic mean along one or more ndarray dimensions using Welford's algorithm and assigns 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 = meanwd.assign( x, y );
// returns <ndarray>[ 1.25 ]

var bool = ( out === y );
// returns true

The method has the following parameters:

  • x: input ndarray. Must have a real-valued or "generic" data type.
  • out: output ndarray.
  • options: function options (optional).

The method 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 keepdims option to true can 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 assign method, the output ndarray is allowed to have any supported output data type.

Examples

var uniform = require( '@stdlib/random/uniform' );
var getDType = require( '@stdlib/ndarray/dtype' );
var ndarray2array = require( '@stdlib/ndarray/to-array' );
var meanwd = require( '@stdlib/stats/meanwd' );

// Generate an array of random numbers:
var x = uniform( [ 5, 5 ], 0.0, 20.0 );
console.log( ndarray2array( x ) );

// Perform a reduction:
var y = meanwd( x, {
    'dims': [ 0 ]
});

// Resolve the output array data type:
var dt = getDType( y );
console.log( dt );

// Print the results:
console.log( ndarray2array( y ) );

References

  • Welford, B. P. 1962. "Note on a Method for Calculating Corrected Sums of Squares and Products." Technometrics 4 (3): 419–20. doi:10.1080/00401706.1962.10490022.