@stdlib/stats-incr-rmse
v0.2.2
Published
Compute the root mean squared error (RMSE) incrementally.
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incrrmse
Compute the root mean squared error (RMSE) incrementally.
The root mean squared error (also known as the root mean square error (RMSE) and root mean square deviation (RMSD)) is defined as
Installation
npm install @stdlib/stats-incr-rmse
Usage
var incrrmse = require( '@stdlib/stats-incr-rmse' );
incrrmse()
Returns an accumulator function
which incrementally computes the root mean squared error.
var accumulator = incrrmse();
accumulator( [x, y] )
If provided input values x
and y
, the accumulator function returns an updated root mean squared error. If not provided input values x
and y
, the accumulator function returns the current root mean squared error.
var accumulator = incrrmse();
var r = accumulator( 2.0, 3.0 );
// returns 1.0
r = accumulator( -1.0, -4.0 );
// returns ~2.24
r = accumulator( -3.0, 5.0 );
// returns ~4.97
r = accumulator();
// returns ~4.97
Notes
- Input values are not type checked. If provided
NaN
or a value which, when used in computations, results inNaN
, the accumulated value isNaN
for all 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.
Examples
var randu = require( '@stdlib/random-base-randu' );
var incrrmse = require( '@stdlib/stats-incr-rmse' );
var accumulator;
var v1;
var v2;
var i;
// Initialize an accumulator:
accumulator = incrrmse();
// For each simulated datum, update the root mean squared error...
for ( i = 0; i < 100; i++ ) {
v1 = ( randu()*100.0 ) - 50.0;
v2 = ( randu()*100.0 ) - 50.0;
accumulator( v1, v2 );
}
console.log( accumulator() );
See Also
@stdlib/stats-incr/mrmse
: compute a moving root mean squared error (RMSE) incrementally.@stdlib/stats-incr/mse
: compute the mean squared error (MSE) incrementally.@stdlib/stats-incr/rss
: compute the residual sum of squares (RSS) 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.