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

Min View Buffer Index

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

Installation

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

Usage

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

minViewBufferIndex( shape, strides, offset )

Computes the minimum 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 = 13; // includes a view offset

var idx = minViewBufferIndex( shape, strides, offset );
// returns 10

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 minViewBufferIndex = require( '@stdlib/ndarray-base-min-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 ) + 25; // include view offset

    // Compute the minimum linear index:
    idx = minViewBufferIndex( shape, strides, offset );
    console.log( 'Shape: %s. Strides: %s. Offset: %d. Min 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.