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 8000 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!
Perform a reduction over a list of specified dimensions in an input ndarray according to a callback function and assign results to a provided output ndarray.
npm install @stdlib/ndarray-base-unary-reduce-subarray-byAlternatively,
- To load the package in a website via a
scripttag without installation and bundlers, use the ES Module available on theesmbranch (see README). - If you are using Deno, visit the
denobranch (see README for usage intructions). - For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the
umdbranch (see README).
The branches.md file summarizes the available branches and displays a diagram illustrating their relationships.
To view installation and usage instructions specific to each branch build, be sure to explicitly navigate to the respective README files on each branch, as linked to above.
var unaryReduceSubarrayBy = require( '@stdlib/ndarray-base-unary-reduce-subarray-by' );Performs a reduction over a list of specified dimensions in an input ndarray according to a callback function and assigns results to a provided output ndarray.
var Float64Array = require( '@stdlib/array-float64' );
var filled = require( '@stdlib/array-base-filled' );
var ndarray2array = require( '@stdlib/ndarray-base-to-array' );
var everyBy = require( '@stdlib/ndarray-base-every-by' );
function clbk( value ) {
return value > 0.0;
}
// Create data buffers:
var xbuf = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 0.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );
var ybuf = filled( false, 3 );
// Define the array shapes:
var xsh = [ 1, 3, 2, 2 ];
var ysh = [ 1, 3 ];
// Define the array strides:
var sx = [ 12, 4, 2, 1 ];
var sy = [ 3, 1 ];
// Define the index offsets:
var ox = 0;
var oy = 0;
// Create an input ndarray-like object:
var x = {
'dtype': 'float64',
'data': xbuf,
'shape': xsh,
'strides': sx,
'offset': ox,
'order': 'row-major'
};
// Create an output ndarray-like object:
var y = {
'dtype': 'generic',
'data': ybuf,
'shape': ysh,
'strides': sy,
'offset': oy,
'order': 'row-major'
};
// Perform a reduction:
unaryReduceSubarrayBy( everyBy, [ x, y ], [ 2, 3 ], clbk );
var arr = ndarray2array( y.data, y.shape, y.strides, y.offset, y.order );
// returns [ [ true, false, true ] ]The function accepts the following arguments:
- fcn: function which will be applied to a subarray and should reduce the subarray to a single scalar value.
- arrays: array-like object containing one input ndarray and one output ndarray, followed by any additional ndarray arguments.
- dims: list of dimensions over which to perform a reduction.
- options: function options which are passed through to
fcn(optional). - clbk: callback function.
- thisArg: callback execution context (optional).
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).
The invoked callback function is provided the following arguments:
- value: input array element.
- indices: current array element indices.
- arr: the input ndarray.
To set the callback execution context, provide a thisArg.
var Float64Array = require( '@stdlib/array-float64' );
var filled = require( '@stdlib/array-base-filled' );
var ndarray2array = require( '@stdlib/ndarray-base-to-array' );
var everyBy = require( '@stdlib/ndarray-base-every-by' );
function clbk( value ) {
this.count += 1;
return value > 0.0;
}
// Create data buffers:
var xbuf = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 0.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );
var ybuf = filled( false, 6 );
// Define the array shapes:
var xsh = [ 3, 2, 2 ];
var ysh = [ 3, 2 ];
// Define the array strides:
var sx = [ 4, 2, 1 ];
var sy = [ 2, 1 ];
// Define the index offsets:
var ox = 0;
var oy = 0;
// Create an input ndarray-like object:
var x = {
'dtype': 'float64',
'data': xbuf,
'shape': xsh,
'strides': sx,
'offset': ox,
'order': 'row-major'
};
// Create an output ndarray-like object:
var y = {
'dtype': 'generic',
'data': ybuf,
'shape': ysh,
'strides': sy,
'offset': oy,
'order': 'row-major'
};
var ctx = {
'count': 0
};
// Perform a reduction:
unaryReduceSubarrayBy( everyBy, [ x, y ], [ 1 ], clbk, ctx );
var arr = ndarray2array( y.data, y.shape, y.strides, y.offset, y.order );
// returns [ [ true, true ], [ true, false ], [ true, true ] ]
var count = ctx.count;
// returns 11-
The output ndarray and any additional ndarray arguments are expected to have the same dimensions as the non-reduced dimensions of the input ndarray. When calling the reduction function, any additional ndarray arguments are provided as zero-dimensional ndarray-like objects.
-
The reduction function is expected to have the following signature:
fcn( arrays[, options], wrappedCallback )where
- arrays: array containing a subarray of the input ndarray and any additional ndarray arguments as zero-dimensional ndarrays.
- options: function options (optional).
- wrappedCallback: callback function. This function is a wrapper around a provided
clbkargument.
-
For very high-dimensional ndarrays which are non-contiguous, one should consider copying the underlying data to contiguous memory before performing a reduction in order to achieve better performance.
var discreteUniform = require( '@stdlib/random-array-discrete-uniform' );
var filled = require( '@stdlib/array-base-filled' );
var ndarray2array = require( '@stdlib/ndarray-base-to-array' );
var everyBy = require( '@stdlib/ndarray-base-every-by' );
var unaryReduceSubarrayBy = require( '@stdlib/ndarray-base-unary-reduce-subarray-by' );
function clbk( value ) {
return value > -3;
}
var x = {
'dtype': 'generic',
'data': discreteUniform( 40, -5, 5, {
'dtype': 'generic'
}),
'shape': [ 2, 5, 2, 2 ],
'strides': [ 1, 2, 10, 20 ],
'offset': 0,
'order': 'column-major'
};
var y = {
'dtype': 'generic',
'data': filled( false, 10 ),
'shape': [ 2, 5 ],
'strides': [ 1, 2 ],
'offset': 0,
'order': 'column-major'
};
unaryReduceSubarrayBy( everyBy, [ x, y ], [ 2, 3 ], clbk );
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 ) );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 © 2016-2025. The Stdlib Authors.
