@stdlib/stats-incr-rmse

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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 in NaN, the accumulated value is NaN 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() );

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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.

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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

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