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Perform a reduction over a list of specified dimensions in two input ndarrays via a one-dimensional strided array binary reduction function and assign results to a provided output ndarray.

npm install @stdlib/ndarray-base-binary-reduce-strided1d

Alternatively,

• To load the package in a website via a script tag without installation and bundlers, use the ES Module available on the esm branch (see README).
• If you are using Deno, visit the deno branch (see README for usage intructions).
• For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the umd branch (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 binaryReduceStrided1d = require( '@stdlib/ndarray-base-binary-reduce-strided1d' );

Performs a reduction over a list of specified dimensions in two input ndarrays via a one-dimensional strided array binary reduction function and assigns results to a provided output ndarray.

var Float64Array = require( '@stdlib/array-float64' );
var ndarray2array = require( '@stdlib/ndarray-base-to-array' );
var gdot = require( '@stdlib/blas-base-ndarray-gdot' );

// 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( [ 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 zbuf = new Float64Array( [ 0.0, 0.0, 0.0 ] );

// Define the array shapes:
var xsh = [ 1, 3, 2, 2 ];
var ysh = [ 1, 3, 2, 2 ];
var zsh = [ 1, 3 ];

// Define the array strides:
var sx = [ 12, 4, 2, 1 ];
var sy = [ 12, 4, 2, 1 ];
var sz = [ 3, 1 ];

// Define the index offsets:
var ox = 0;
var oy = 0;
var oz = 0;

// Create input ndarray-like objects:
var x = {
    'dtype': 'float64',
    'data': xbuf,
    'shape': xsh,
    'strides': sx,
    'offset': ox,
    'order': 'row-major'
};
var y = {
    'dtype': 'float64',
    'data': ybuf,
    'shape': ysh,
    'strides': sy,
    'offset': oy,
    'order': 'row-major'
};

// Create an output ndarray-like object:
var z = {
    'dtype': 'float64',
    'data': zbuf,
    'shape': zsh,
    'strides': sz,
    'offset': oz,
    'order': 'row-major'
};

// Perform a reduction:
binaryReduceStrided1d( gdot, [ x, y, z ], [ 2, 3 ] );

var arr = ndarray2array( z.data, z.shape, z.strides, z.offset, z.order );
// returns [ [ 30.0, 174.0, 446.0 ] ]

The function accepts the following arguments:

• fcn: function which will be applied to two one-dimensional subarrays and should reduce them to a single scalar value.
• arrays: array-like object containing two input ndarrays 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).

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 output ndarray is expected to have the same dimensions as the non-reduced dimensions of the input ndarrays.

• Any additional ndarray arguments are expected to have the same leading dimensions as the non-reduced dimensions of the input ndarrays.

• When calling the reduction function, any additional ndarray arguments are provided as k-dimensional subarrays, where k = M - N with M being the number of dimensions in an ndarray argument and N being the number of non-reduced dimensions in the input ndarrays. For example, if an input ndarrays have three dimensions, the number of reduced dimensions is two, and an additional ndarray argument has one dimension, thus matching the number of non-reduced dimensions in the input ndarrays, the reduction function is provided a zero-dimensional subarray as an additional ndarray argument. In the same scenario but where an additional ndarray argument has two dimensions, thus exceeding the number of non-reduced dimensions in the input ndarrays, the reduction function is provided a one-dimensional subarray as an additional ndarray argument.

• The reduction function is expected to have the following signature:

  fcn( arrays[, options] )
  

  where

  • arrays: array containing a one-dimensional subarray for each input ndarray and any additional ndarray arguments as subarrays.
  • options: function options (optional).

• 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 zeros = require( '@stdlib/array-base-zeros' );
var ndarray2array = require( '@stdlib/ndarray-base-to-array' );
var gdot = require( '@stdlib/blas-base-ndarray-gdot' );
var binaryReduceStrided1d = require( '@stdlib/ndarray-base-binary-reduce-strided1d' );

var N = 10;
var x = {
    'dtype': 'generic',
    'data': discreteUniform( N, -5, 5, {
        'dtype': 'generic'
    }),
    'shape': [ 1, 5, 2 ],
    'strides': [ 10, 2, 1 ],
    'offset': 0,
    'order': 'row-major'
};
var y = {
    'dtype': 'generic',
    'data': discreteUniform( N, -5, 5, {
        'dtype': 'generic'
    }),
    'shape': [ 1, 5, 2 ],
    'strides': [ 10, 2, 1 ],
    'offset': 0,
    'order': 'row-major'
};
var z = {
    'dtype': 'generic',
    'data': zeros( 2 ),
    'shape': [ 1, 2 ],
    'strides': [ 2, 1 ],
    'offset': 0,
    'order': 'row-major'
};

binaryReduceStrided1d( gdot, [ x, y, z ], [ 1 ] );

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 ) );
console.log( ndarray2array( z.data, z.shape, z.strides, z.offset, z.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.

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