@stdlib/stats-incr-mmeanstdev
v0.2.2
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Compute a moving arithmetic mean and corrected sample standard deviation incrementally.
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incrmmeanstdev
Compute a moving arithmetic mean and corrected sample standard deviation incrementally.
For a window of size W
, the arithmetic mean is defined as
and the corrected sample standard deviation is defined as
Installation
npm install @stdlib/stats-incr-mmeanstdev
Usage
var incrmmeanstdev = require( '@stdlib/stats-incr-mmeanstdev' );
incrmmeanstdev( [out,] window )
Returns an accumulator function
which incrementally computes a moving arithmetic mean and corrected sample standard deviation. The window
parameter defines the number of values over which to compute the moving arithmetic mean and corrected sample standard deviation.
var accumulator = incrmmeanstdev( 3 );
By default, the returned accumulator function
returns the accumulated values as a two-element array
. To avoid unnecessary memory allocation, the function supports providing an output (destination) object.
var Float64Array = require( '@stdlib/array-float64' );
var accumulator = incrmmeanstdev( new Float64Array( 2 ), 3 );
accumulator( [x] )
If provided an input value x
, the accumulator function returns updated accumulated values. If not provided an input value x
, the accumulator function returns the current accumulated values.
var accumulator = incrmmeanstdev( 3 );
var out = accumulator();
// returns null
// Fill the window...
out = accumulator( 2.0 ); // [2.0]
// returns [ 2.0, 0.0 ]
out = accumulator( 1.0 ); // [2.0, 1.0]
// returns [ 1.5, ~0.71 ]
out = accumulator( 3.0 ); // [2.0, 1.0, 3.0]
// returns [ 2.0, 1.0 ]
// Window begins sliding...
out = accumulator( -7.0 ); // [1.0, 3.0, -7.0]
// returns [ -1.0, ~5.29 ]
out = accumulator( -5.0 ); // [3.0, -7.0, -5.0]
// returns [ -3.0, ~5.29 ]
out = accumulator();
// returns [ -3.0, ~5.29 ]
Notes
- Input values are not type checked. If provided
NaN
, the accumulated values areNaN
for at leastW-1
future invocations. If non-numeric inputs are possible, you are advised to type check and handle accordingly before passing the value to the accumulator function. - As
W
values are needed to fill the window buffer, the firstW-1
returned values are calculated from smaller sample sizes. Until the window is full, each returned value is calculated from all provided values.
Examples
var randu = require( '@stdlib/random-base-randu' );
var Float64Array = require( '@stdlib/array-float64' );
var ArrayBuffer = require( '@stdlib/array-buffer' );
var incrmmeanstdev = require( '@stdlib/stats-incr-mmeanstdev' );
var offset;
var acc;
var buf;
var out;
var ms;
var N;
var v;
var i;
var j;
// Define the number of accumulators:
N = 5;
// Create an array buffer for storing accumulator output:
buf = new ArrayBuffer( N*2*8 ); // 8 bytes per element
// Initialize accumulators:
acc = [];
for ( i = 0; i < N; i++ ) {
// Compute the byte offset:
offset = i * 2 * 8; // stride=2, bytes_per_element=8
// Create a new view for storing accumulated values:
out = new Float64Array( buf, offset, 2 );
// Initialize an accumulator which will write results to the view:
acc.push( incrmmeanstdev( out, 5 ) );
}
// Simulate data and update the moving sample means and standard deviations...
for ( i = 0; i < 100; i++ ) {
for ( j = 0; j < N; j++ ) {
v = randu() * 100.0 * (j+1);
acc[ j ]( v );
}
}
// Print the final results:
console.log( 'Mean\tStDev' );
for ( i = 0; i < N; i++ ) {
ms = acc[ i ]();
console.log( '%d\t%d', ms[ 0 ].toFixed( 3 ), ms[ 1 ].toFixed( 3 ) );
}
See Also
@stdlib/stats-incr/meanstdev
: compute an arithmetic mean and corrected sample standard deviation incrementally.@stdlib/stats-incr/mmean
: compute a moving arithmetic mean incrementally.@stdlib/stats-incr/mmeanvar
: compute a moving arithmetic mean and unbiased sample variance incrementally.@stdlib/stats-incr/mstdev
: compute a moving corrected sample standard deviation incrementally.
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 © 2016-2024. The Stdlib Authors.