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unaryReduceStrided1d

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

Usage

import unaryReduceStrided1d from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-base-unary-reduce-strided1d@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-strided1d@deno/mod.js';

unaryReduceStrided1d( fcn, arrays, dims[, options] )

Performs a reduction over a list of specified dimensions in an input ndarray via a one-dimensional strided array reduction 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 ndarray2array from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-base-to-array@deno/mod.js';
import getStride from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-base-stride@deno/mod.js';
import getOffset from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-base-offset@deno/mod.js';
import getData from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-base-data-buffer@deno/mod.js';
import numelDimension from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-base-numel-dimension@deno/mod.js';
var gsum = require( 'https://cdn.jsdelivr.net/gh/stdlib-js/blas-ext-base-gsum' ).ndarray;

function wrapper( arrays ) {
    var x = arrays[ 0 ];
    return gsum( numelDimension( x, 0 ), getData( x ), getStride( x, 0 ), getOffset( x ) );
}

// 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( [ 0.0, 0.0, 0.0 ] );

// 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': 'float64',
    'data': ybuf,
    'shape': ysh,
    'strides': sy,
    'offset': oy,
    'order': 'row-major'
};

// Perform a reduction:
unaryReduceStrided1d( wrapper, [ x, y ], [ 2, 3 ] );

var arr = ndarray2array( y.data, y.shape, y.strides, y.offset, y.order );
// returns [ [ 10.0, 26.0, 42.0 ] ]

The function accepts the following arguments:

• fcn: function which will be applied to a one-dimensional 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).

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).

TODO: document factory method

Notes

• 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] )
  

  where

  • arrays: array containing a one-dimensional subarray of the input ndarray and any additional ndarray arguments as zero-dimensional ndarrays.
  • 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.

Examples

import discreteUniform from 'https://cdn.jsdelivr.net/gh/stdlib-js/random-array-discrete-uniform@deno/mod.js';
import zeros from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-base-zeros@deno/mod.js';
import ndarray2array from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-base-to-array@deno/mod.js';
import numelDimension from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-base-numel-dimension@deno/mod.js';
import getData from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-base-data-buffer@deno/mod.js';
import getStride from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-base-stride@deno/mod.js';
import getOffset from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-base-offset@deno/mod.js';
var gsum = require( 'https://cdn.jsdelivr.net/gh/stdlib-js/blas-ext-base-gsum' ).ndarray;
import unaryReduceStrided1d from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-base-unary-reduce-strided1d@deno/mod.js';

function wrapper( arrays ) {
    var x = arrays[ 0 ];
    return gsum( numelDimension( x, 0 ), getData( x ), getStride( x, 0 ), getOffset( x ) ); // eslint-disable-line max-len
}

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': zeros( 2 ),
    'shape': [ 1, 2 ],
    'strides': [ 2, 1 ],
    'offset': 0,
    'order': 'row-major'
};

unaryReduceStrided1d( wrapper, [ x, y ], [ 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 ) );

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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.

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License

See LICENSE.

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