@stdlib/strided-base-quaternary NPM

Quaternary

Apply a quaternary callback to strided input array elements and assign results to elements in a strided output array.

Installation

npm install @stdlib/strided-base-quaternary

Usage

var quaternary = require( '@stdlib/strided-base-quaternary' );

quaternary( arrays, shape, strides, fcn )

Applies a quaternary callback to strided input array elements and assigns results to elements in a strided output array.

var add = require( '@stdlib/math-base-ops-add4' );
var Float64Array = require( '@stdlib/array-float64' );

var x = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0 ] );
var y = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0 ] );
var z = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0 ] );
var w = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0 ] );
var u = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0 ] );

quaternary( [ x, y, z, w, u ], [ x.length ], [ 1, 1, 1, 1, 1 ], add );
// u => <Float64Array>[ 4.0, 8.0, 12.0, 16.0, 20.0 ]

The function accepts the following arguments:

arrays: array-like object containing four strided input arrays and one strided output array.
shape: array-like object containing a single element, the number of indexed elements.
strides: array-like object containing the stride lengths for the strided input and output arrays.
fcn: quaternary function to apply.

The shape and strides parameters determine which elements in the strided input and output arrays are accessed at runtime. For example, to index every other value in the strided input arrays and to index the first N elements of the strided output array in reverse order,

var add = require( '@stdlib/math-base-ops-add4' );
var Float64Array = require( '@stdlib/array-float64' );

var x = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0 ] );
var y = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0 ] );
var z = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0 ] );
var w = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0 ] );
var u = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0 ] );

quaternary( [ x, y, z, w, u ], [ 3 ], [ 2, 2, 2, 2, -1 ], add );
// u => <Float64Array>[ 20.0, 12.0, 4.0, 0.0, 0.0 ]

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

var add = require( '@stdlib/math-base-ops-add4' );
var Float64Array = require( '@stdlib/array-float64' );

// Initial arrays...
var x0 = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
var y0 = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
var z0 = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
var w0 = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
var u0 = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );

// 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*1 ); // start at 2nd element
var z1 = new Float64Array( z0.buffer, z0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var w1 = new Float64Array( w0.buffer, w0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var u1 = new Float64Array( u0.buffer, u0.BYTES_PER_ELEMENT*3 ); // start at 4th element

quaternary( [ x1, y1, z1, w1, u1 ], [ 3 ], [ -2, -2, -2, -2, 1 ], add );
// u0 => <Float64Array>[ 0.0, 0.0, 0.0, 24.0, 16.0, 8.0 ]

quaternary.ndarray( arrays, shape, strides, offsets, fcn )

Applies a quaternary callback to strided input array elements and assigns results to elements in a strided output array using alternative indexing semantics.

var add = require( '@stdlib/math-base-ops-add4' );
var Float64Array = require( '@stdlib/array-float64' );

var x = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0 ] );
var y = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0 ] );
var z = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0 ] );
var w = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0 ] );
var u = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0 ] );

quaternary.ndarray( [ x, y, z, w, u ], [ x.length ], [ 1, 1, 1, 1, 1 ], [ 0, 0, 0, 0, 0 ], add );
// u => <Float64Array>[ 4.0, 8.0, 12.0, 16.0, 20.0 ]

The function accepts the following additional arguments:

offsets: array-like object containing the starting indices (i.e., index offsets) for the strided input and output arrays.

While typed array views mandate a view offset based on the underlying buffer, the offsets parameter supports indexing semantics based on starting indices. For example, to index every other value in the strided input arrays starting from the second value and to index the last N elements in the strided output array,

var add = require( '@stdlib/math-base-ops-add4' );
var Float64Array = require( '@stdlib/array-float64' );

var x = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
var y = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
var z = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
var w = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
var u = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );

quaternary.ndarray( [ x, y, z, w, u ], [ 3 ], [ 2, 2, 2, 2, -1 ], [ 1, 1, 1, 1, u.length-1 ], add );
// u => <Float64Array>[ 0.0, 0.0, 0.0, 24.0, 16.0, 8.0 ]

Examples

var discreteUniform = require( '@stdlib/random-base-discrete-uniform' ).factory;
var filledarray = require( '@stdlib/array-filled' );
var filledarrayBy = require( '@stdlib/array-filled-by' );
var add = require( '@stdlib/math-base-ops-add4' );
var quaternary = require( '@stdlib/strided-base-quaternary' );

var N = 10;

var x = filledarrayBy( N, 'generic', discreteUniform( -100, 100 ) );
console.log( x );

var y = filledarrayBy( N, 'generic', discreteUniform( -100, 100 ) );
console.log( y );

var z = filledarrayBy( N, 'generic', discreteUniform( -100, 100 ) );
console.log( z );

var w = filledarrayBy( N, 'generic', discreteUniform( -100, 100 ) );
console.log( w );

var u = filledarray( 0.0, N, 'generic' );
console.log( u );

var shape = [ N ];
var strides = [ 1, 1, 1, 1, -1 ];
var offsets = [ 0, 0, 0, 0, N-1 ];

quaternary.ndarray( [ x, y, z, w, u ], shape, strides, offsets, add );
console.log( u );

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.