@stdlib/ndarray-base-max-view-buffer-index NPM

Max View Buffer Index

Compute the maximum linear index in an underlying data buffer accessible to an array view.

Installation

npm install @stdlib/ndarray-base-max-view-buffer-index

Usage

var maxViewBufferIndex = require( '@stdlib/ndarray-base-max-view-buffer-index' );

maxViewBufferIndex( shape, strides, offset )

Computes the maximum linear index in an underlying data buffer accessible to an array view.

// Array shape:
var shape = [ 2, 2 ];

// Stride array:
var strides = [ 2, 1 ];

// Index offset which specifies the location of the first indexed value:
var offset = 0;

var idx = maxViewBufferIndex( shape, strides, offset );
// returns 3

Examples

var discreteUniform = require( '@stdlib/random-base-discrete-uniform' );
var shape2strides = require( '@stdlib/ndarray-base-shape2strides' );
var strides2offset = require( '@stdlib/ndarray-base-strides2offset' );
var randu = require( '@stdlib/random-base-randu' );
var maxViewBufferIndex = require( '@stdlib/ndarray-base-max-view-buffer-index' );

var strides;
var offset;
var shape;
var idx;
var i;
var j;

shape = [ 0, 0, 0 ];

for ( i = 0; i < 100; i++ ) {
    // Generate a random array shape:
    shape[ 0 ] = discreteUniform( 1, 10 );
    shape[ 1 ] = discreteUniform( 1, 10 );
    shape[ 2 ] = discreteUniform( 1, 10 );

    // Generate strides:
    if ( randu() < 0.5 ) {
        strides = shape2strides( shape, 'row-major' );
    } else {
        strides = shape2strides( shape, 'column-major' );
    }
    j = discreteUniform( 0, shape.length-1 );
    strides[ j ] *= ( randu() < 0.5 ) ? -1 : 1;

    // Compute the index offset:
    offset = strides2offset( shape, strides );

    // Compute the maximum linear index:
    idx = maxViewBufferIndex( shape, strides, offset );
    console.log( 'Shape: %s. Strides: %s. Offset: %d. Max idx: %d.', shape.join( 'x' ), strides.join( ',' ), offset, idx );
}

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.