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Assign elements in an input ndarray to elements in an output ndarray.
To use in Observable,
assign = require( 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-base-assign@umd/browser.js' )
To vendor stdlib functionality and avoid installing dependency trees for Node.js, you can use the UMD server build:
var assign = require( 'path/to/vendor/umd/ndarray-base-assign/index.js' )
To include the bundle in a webpage,
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-base-assign@umd/browser.js"></script>
If no recognized module system is present, access bundle contents via the global scope:
<script type="text/javascript">
(function () {
window.assign;
})();
</script>
Assign elements in an input ndarray to elements in an output ndarray.
var Float64Array = require( '@stdlib/array-float64' );
// 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'
};
// Copy elements:
assign( [ x, y ] );
console.log( y.data );
// => <Float64Array>[ 2.0, 3.0, 6.0, 7.0, 10.0, 11.0 ]
The function accepts the following arguments:
- arrays: array-like object containing one input ndarray and one output ndarray.
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 assigning elements 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 shape2strides = require( '@stdlib/ndarray-base-shape2strides' );
var ndarray2array = require( '@stdlib/ndarray-base-to-array' );
var assign = require( '@stdlib/ndarray-base-assign' );
var N = 10;
var shape = [ 5, 2 ];
var x = {
'dtype': 'generic',
'data': filledarrayBy( N, 'generic', discreteUniform( -100, 100 ) ),
'shape': shape,
'strides': [ 2, 1 ],
'offset': 0,
'order': 'row-major'
};
var y = {
'dtype': 'generic',
'data': filledarray( 0, N, 'generic' ),
'shape': shape.slice(),
'strides': shape2strides( shape, 'column-major' ),
'offset': 0,
'order': 'column-major'
};
assign( [ x, y ] );
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>
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.