@stdlib/stats-iter-mmax NPM

itermmax

Create an iterator which iteratively computes a moving maximum value.

Installation

npm install @stdlib/stats-iter-mmax

Usage

var itermmax = require( '@stdlib/stats-iter-mmax' );

itermmax( iterator, W )

Returns an iterator which iteratively computes a moving maximum value. The W parameter defines the number of iterated values over which to compute the moving maximum.

var array2iterator = require( '@stdlib/array-to-iterator' );

var arr = array2iterator( [ 2.0, 1.0, 3.0, -7.0, -5.0 ] );
var it = itermmax( arr, 3 );

// Fill the window...
var m = it.next().value; // [2.0]
// returns 2.0

m = it.next().value; // [2.0, 1.0]
// returns 2.0

m = it.next().value; // [2.0, 1.0, 3.0]
// returns 3.0

// Window begins sliding...
m = it.next().value; // [1.0, 3.0, -7.0]
// returns 3.0

m = it.next().value; // [3.0, -7.0, -5.0]
// returns 3.0

Notes

If an iterated value is non-numeric (including NaN), the function returns NaN for at least W-1 future invocations. If non-numeric iterated values are possible, you are advised to provide an iterator which type checks and handles non-numeric values accordingly.
As W values are needed to fill the window buffer, the first W-1 returned values are calculated from smaller sample sizes. Until the window is full, each returned value is calculated from all previously iterated values.

Examples

var runif = require( '@stdlib/random-iter-uniform' );
var itermmax = require( '@stdlib/stats-iter-mmax' );

// Create an iterator for generating uniformly distributed pseudorandom numbers:
var rand = runif( -10.0, 10.0, {
    'seed': 1234,
    'iter': 100
});

// Create an iterator for iteratively computing a moving maximum value:
var it = itermmax( rand, 3 );

// Perform manual iteration...
var v;
while ( true ) {
    v = it.next();
    if ( v.done ) {
        break;
    }
    if ( typeof v.value === 'number' ) {
        console.log( 'max: %d', v.value );
    }
}

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.