npm's pretty magical

# npm

## @stdlib/stats-base-cumax

0.0.9 • Public • Published

# cumax

Calculate the cumulative maximum of a strided array.

## Installation

npm install @stdlib/stats-base-cumax

## Usage

var cumax = require( '@stdlib/stats-base-cumax' );

#### cumax( N, x, strideX, y, strideY )

Computes the cumulative maximum of a strided array.

var x = [ 1.0, -2.0, 2.0 ];
var y = [ 0.0, 0.0, 0.0 ];

cumax( x.length, x, 1, y, 1 );
// y => [ 1.0, 1.0, 2.0 ]

The function has the following parameters:

The N and stride parameters determine which elements in x and y are accessed at runtime. For example, to compute the cumulative maximum of every other element in x,

var x = [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ];
var y = [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ];

var v = cumax( 4, x, 2, y, 1 );
// y => [ 1.0, 2.0, 2.0, 4.0, 0.0, 0.0, 0.0, 0.0 ]

Note that indexing is relative to the first index. To introduce an offset, use typed array views.

var Float64Array = require( '@stdlib/array-float64' );

// Initial arrays...
var x0 = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
var y0 = new Float64Array( x0.length );

// Create offset views...
var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var y1 = new Float64Array( y0.buffer, y0.BYTES_PER_ELEMENT*3 ); // start at 4th element

cumax( 4, x1, -2, y1, 1 );
// y0 => <Float64Array>[ 0.0, 0.0, 0.0, 4.0, 4.0, 4.0, 4.0, 0.0 ]

#### cumax.ndarray( N, x, strideX, offsetX, y, strideY, offsetY )

Computes the cumulative maximum of a strided array using alternative indexing semantics.

var x = [ 1.0, -2.0, 2.0 ];
var y = [ 0.0, 0.0, 0.0 ];

cumax.ndarray( x.length, x, 1, 0, y, 1, 0 );
// y => [ 1.0, 1.0, 2.0 ]

The function has the following additional parameters:

• offsetX: starting index for x.
• offsetY: starting index for y.

While typed array views mandate a view offset based on the underlying buffer, offsetX and offsetY parameters support indexing semantics based on a starting indices. For example, to calculate the cumulative maximum of every other value in x starting from the second value and to store in the last N elements of y starting from the last element

var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ];
var y = [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ];

cumax.ndarray( 4, x, 2, 1, y, -1, y.length-1 );
// y => [ 0.0, 0.0, 0.0, 0.0, 4.0, 2.0, 1.0, 1.0 ]

## Notes

• If N <= 0, both functions return y unchanged.
• Depending on the environment, the typed versions (dcumax, scumax, etc.) are likely to be significantly more performant.

## Examples

var randu = require( '@stdlib/random-base-randu' );
var round = require( '@stdlib/math-base-special-round' );
var Float64Array = require( '@stdlib/array-float64' );
var cumax = require( '@stdlib/stats-base-cumax' );

var y;
var x;
var i;

x = new Float64Array( 10 );
y = new Float64Array( x.length );
for ( i = 0; i < x.length; i++ ) {
x[ i ] = round( randu()*100.0 );
}
console.log( x );
console.log( y );

cumax( x.length, x, 1, y, -1 );
console.log( y );

## 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.

### Install

npm i @stdlib/stats-base-cumax

### Repository

github.com/stdlib-js/stats-base-cumax

stdlib.io

77

0.0.9

Apache-2.0

43.8 kB

11