About stdlib...
We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.
The library is fully decomposable, being architected in such a way that you can swap out and mix and match APIs and functionality to cater to your exact preferences and use cases.
When you use stdlib, you can be absolutely certain that you are using the most thorough, rigorous, well-written, studied, documented, tested, measured, and high-quality code out there.
To join us in bringing numerical computing to the web, get started by checking us out on GitHub, and please consider financially supporting stdlib. We greatly appreciate your continued support!
Apply a unary callback to elements in an input ndarray and assign results to elements in an output ndarray.
To use in Observable,
unary = require( 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-base-unary@umd/browser.js' )To vendor stdlib functionality and avoid installing dependency trees for Node.js, you can use the UMD server build:
var unary = require( 'path/to/vendor/umd/ndarray-base-unary/index.js' )To include the bundle in a webpage,
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-base-unary@umd/browser.js"></script>If no recognized module system is present, access bundle contents via the global scope:
<script type="text/javascript">
(function () {
window.unary;
})();
</script>Applies a unary callback to elements in an input ndarray and assigns results to elements in an output ndarray.
var Float64Array = require( '@stdlib/array-float64' );
function scale( x ) {
return x * 10.0;
}
// Create data buffers:
var xbuf = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );
var ybuf = new Float64Array( 6 );
// Define the shape of the input and output arrays:
var shape = [ 3, 1, 2 ];
// Define the array strides:
var sx = [ 4, 4, 1 ];
var sy = [ 2, 2, 1 ];
// Define the index offsets:
var ox = 1;
var oy = 0;
// Create the input and output ndarray-like objects:
var x = {
'dtype': 'float64',
'data': xbuf,
'shape': shape,
'strides': sx,
'offset': ox,
'order': 'row-major'
};
var y = {
'dtype': 'float64',
'data': ybuf,
'shape': shape,
'strides': sy,
'offset': oy,
'order': 'row-major'
};
// Apply the unary function:
unary( [ x, y ], scale );
console.log( y.data );
// => <Float64Array>[ 20.0, 30.0, 60.0, 70.0, 100.0, 110.0 ]The function accepts the following arguments:
- arrays: array-like object containing one input ndarray and one output ndarray.
- fcn: unary function to apply.
Each provided ndarray should be an object with the following properties:
- dtype: data type.
- data: data buffer.
- shape: dimensions.
- strides: stride lengths.
- offset: index offset.
- order: specifies whether an ndarray is row-major (C-style) or column major (Fortran-style).
- For very high-dimensional ndarrays which are non-contiguous, one should consider copying the underlying data to contiguous memory before applying a unary function in order to achieve better performance.
<!DOCTYPE html>
<html lang="en">
<body>
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/random-base-discrete-uniform@umd/browser.js"></script>
<script type="text/javascript">
(function () {.factory;
var filledarray = require( '@stdlib/array-filled' );
var filledarrayBy = require( '@stdlib/array-filled-by' );
var abs = require( '@stdlib/math-base-special-abs' );
var shape2strides = require( '@stdlib/ndarray-base-shape2strides' );
var ndarray2array = require( '@stdlib/ndarray-base-to-array' );
var unary = require( '@stdlib/ndarray-base-unary' );
var N = 10;
var x = {
'dtype': 'generic',
'data': filledarrayBy( N, 'generic', discreteUniform( -100, 100 ) ),
'shape': [ 5, 2 ],
'strides': [ 2, 1 ],
'offset': 0,
'order': 'row-major'
};
var y = {
'dtype': 'generic',
'data': filledarray( 0, N, 'generic' ),
'shape': x.shape.slice(),
'strides': shape2strides( x.shape, 'column-major' ),
'offset': 0,
'order': 'column-major'
};
unary( [ x, y ], abs );
console.log( ndarray2array( x.data, x.shape, x.strides, x.offset, x.order ) );
console.log( ndarray2array( y.data, y.shape, y.strides, y.offset, y.order ) );
})();
</script>
</body>
</html>@stdlib/ndarray-dispatch: create an ndarray function interface which performs multiple dispatch.
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.
See LICENSE.
Copyright © 2016-2024. The Stdlib Authors.