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feat: add 5d kernels
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‎lib/node_modules/@stdlib/ndarray/base/unary-reduce-subarray/lib/5d.js‎

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/**
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* @license Apache-2.0
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*
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* Copyright (c) 2025 The Stdlib Authors.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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'use strict';
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// MODULES //
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var isRowMajor = require( '@stdlib/ndarray/base/assert/is-row-major-string' );
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var incrementOffsets = require( './increment_offsets.js' );
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var setViewOffsets = require( './set_view_offsets.js' );
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var offsets = require( './offsets.js' );
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// MAIN //
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/**
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* Performs a reduction over an input ndarray and assigns results to a provided output ndarray.
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*
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* @private
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* @param {Function} fcn - reduction function
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* @param {Array<Object>} arrays - ndarrays
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* @param {Array<Object>} views - initialized ndarray-like objects representing sub-array views
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* @param {IntegerArray} strides - loop dimension strides for the input ndarray
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* @param {Options} opts - function options
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* @returns {void}
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*
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* @example
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* var Float64Array = require( '@stdlib/array/float64' );
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* var filled = require( '@stdlib/array/base/filled' );
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* var ndarray2array = require( '@stdlib/ndarray/base/to-array' );
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* var base = require( '@stdlib/ndarray/base/every' );
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*
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* // Create data buffers:
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* 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 ] );
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* var ybuf = filled( false, 3 );
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*
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* // Define the array shapes:
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* var xsh = [ 1, 1, 1, 1, 3, 2, 2 ];
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* var ysh = [ 1, 1, 1, 1, 3 ];
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*
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* // Define the array strides:
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* var sx = [ 12, 12, 12, 12, 4, 2, 1 ];
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* var sy = [ 3, 3, 3, 3, 1 ];
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*
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* // Define the index offsets:
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* var ox = 0;
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* var oy = 0;
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*
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* // Create an input ndarray-like object:
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* var x = {
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* 'dtype': 'float64',
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* 'data': xbuf,
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* 'shape': xsh,
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* 'strides': sx,
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* 'offset': ox,
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* 'order': 'row-major'
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* };
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*
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* // Create an output ndarray-like object:
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* var y = {
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* 'dtype': 'generic',
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* 'data': ybuf,
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* 'shape': ysh,
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* 'strides': sy,
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* 'offset': oy,
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* 'order': 'row-major'
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* };
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*
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* // Initialize ndarray-like objects representing sub-array views:
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* var views = [
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* {
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* 'dtype': x.dtype,
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* 'data': x.data,
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* 'shape': [ 2, 2 ],
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* 'strides': [ 2, 1 ],
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* 'offset': x.offset,
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* 'order': x.order
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* }
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* ];
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*
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* // Perform a reduction:
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* unary5d( base, [ x, y ], views, [ 12, 12, 12, 12, 4 ], {} );
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*
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* var arr = ndarray2array( y.data, y.shape, y.strides, y.offset, y.order );
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* // returns [ [ [ [ [ true, false, true ] ] ] ] ]
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*/
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function unary5d( fcn, arrays, views, strides, opts ) {
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var ybuf;
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var dv0;
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var dv1;
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var dv2;
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var dv3;
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var dv4;
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var sh;
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var S0;
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var S1;
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var S2;
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var S3;
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var S4;
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var sv;
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var iv;
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var i0;
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var i1;
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var i2;
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var i3;
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var i4;
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var y;
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var i;
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// Note on variable naming convention: S#, dv#, i# where # corresponds to the loop number, with `0` being the innermost loop...
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// Resolve the output ndarray and associated shape:
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y = arrays[ 1 ];
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sh = y.shape;
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// Extract loop variables for purposes of loop interchange: dimensions and loop offset (pointer) increments...
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if ( isRowMajor( y.order ) ) {
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// For row-major ndarrays, the last dimensions have the fastest changing indices...
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S0 = sh[ 4 ];
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S1 = sh[ 3 ];
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S2 = sh[ 2 ];
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S3 = sh[ 1 ];
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S4 = sh[ 0 ];
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dv0 = [ strides[4] ]; // offset increment for innermost loop
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dv1 = [ strides[3] - ( S0*strides[4] ) ];
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dv2 = [ strides[2] - ( S1*strides[3] ) ];
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dv3 = [ strides[1] - ( S2*strides[2] ) ];
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dv4 = [ strides[0] - ( S3*strides[1] ) ]; // offset increment for outermost loop
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for ( i = 1; i < arrays.length; i++ ) {
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sv = arrays[ i ].strides;
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dv0.push( sv[4] );
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dv1.push( sv[3] - ( S0*sv[4] ) );
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dv2.push( sv[2] - ( S1*sv[3] ) );
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dv3.push( sv[1] - ( S2*sv[2]) );
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dv4.push( sv[0] - ( S3*sv[1]) );
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}
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} else { // order === 'column-major'
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// For column-major ndarrays, the first dimensions have the fastest changing indices...
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S0 = sh[ 0 ];
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S1 = sh[ 1 ];
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S2 = sh[ 2 ];
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S3 = sh[ 3 ];
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S4 = sh[ 4 ];
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dv0 = [ strides[0] ]; // offset increment for innermost loop
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dv1 = [ strides[1] - ( S0*strides[0] ) ];
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dv2 = [ strides[2] - ( S1*strides[1] ) ];
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dv3 = [ strides[3] - ( S2*strides[2] ) ];
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dv4 = [ strides[4] - ( S3*strides[3] ) ]; // offset increment for outermost loop
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for ( i = 1; i < arrays.length; i++ ) {
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sv = arrays[ i ].strides;
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dv0.push( sv[0] );
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dv1.push( sv[1] - ( S0*sv[0] ) );
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dv2.push( sv[2] - ( S1*sv[1] ) );
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dv3.push( sv[3] - ( S2*sv[2] ) );
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dv4.push( sv[4] - ( S3*sv[3] ) );
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}
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}
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// Resolve a list of pointers to the first indexed elements in the respective ndarrays:
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iv = offsets( arrays );
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// Cache a reference to the output ndarray buffer:
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ybuf = y.data;
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// Iterate over the non-reduced ndarray dimensions...
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for ( i4 = 0; i4 < S4; i4++ ) {
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for ( i3 = 0; i3 < S3; i3++ ) {
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for ( i2 = 0; i2 < S2; i2++ ) {
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for ( i1 = 0; i1 < S1; i1++ ) {
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for ( i0 = 0; i0 < S0; i0++ ) {
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setViewOffsets( views, iv );
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ybuf[ iv[1] ] = fcn( views, opts );
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incrementOffsets( iv, dv0 );
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}
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incrementOffsets( iv, dv1 );
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}
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incrementOffsets( iv, dv2 );
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}
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incrementOffsets( iv, dv3 );
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}
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incrementOffsets( iv, dv4 );
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}
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}
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// EXPORTS //
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module.exports = unary5d;

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