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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.
import unaryReduceSubarrayBy from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-base-unary-reduce-subarray-by@deno/mod.js';You can also import the following named exports from the package:
import { factory } from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-base-unary-reduce-subarray-by@deno/mod.js';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.
import Float64Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float64@deno/mod.js';
import filled from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-base-filled@deno/mod.js';
import ndarray2array from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-base-to-array@deno/mod.js';
import everyBy from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-base-every-by@deno/mod.js';
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.
import Float64Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float64@deno/mod.js';
import filled from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-base-filled@deno/mod.js';
import ndarray2array from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-base-to-array@deno/mod.js';
import everyBy from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-base-every-by@deno/mod.js';
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.
import discreteUniform from 'https://cdn.jsdelivr.net/gh/stdlib-js/random-array-discrete-uniform@deno/mod.js';
import filled from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-base-filled@deno/mod.js';
import ndarray2array from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-base-to-array@deno/mod.js';
import everyBy from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-base-every-by@deno/mod.js';
import unaryReduceSubarrayBy from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-base-unary-reduce-subarray-by@deno/mod.js';
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 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.
