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feat: add remaining kernels
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‎lib/node_modules/@stdlib/ndarray/base/unary-reduce-strided1d-to-struct/lib/10d.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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/* eslint-disable max-depth */
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'use strict';
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// MODULES //
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var copyIndexed = require( '@stdlib/array/base/copy-indexed' );
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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 - wrapper for a one-dimensional strided array 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 {boolean} isRowMajor - boolean indicating if provided arrays are in row-major order
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* @param {Function} strategy - input ndarray reshape strategy
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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 ndarray2array = require( '@stdlib/ndarray/base/to-array' );
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* var Float64Results = require( '@stdlib/stats/base/ztest/one-sample/results/float64' );
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* var structFactory = require( '@stdlib/array/struct-factory' );
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* var ztest = require( '@stdlib/stats/base/ndarray/ztest' );
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*
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* var ResultsArray = structFactory( Float64Results );
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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, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );
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* var ybuf = new ResultsArray( 3 );
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*
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* // Define the array shapes:
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* var xsh = [ 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 2, 2 ];
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* var ysh = [ 1, 1, 1, 1, 1, 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, 12, 12, 12, 12, 12, 4, 2, 1 ];
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* var sy = [ 3, 3, 3, 3, 3, 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': Float64Results,
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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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* // Create additional parameter ndarray-like objects:
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* var alternative = {
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* 'dtype': 'generic',
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* 'data': [ 'two-sided' ],
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* 'shape': ysh,
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* 'strides': [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ],
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* 'offset': 0,
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* 'order': 'row-major'
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};
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* var alpha = {
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* 'dtype': 'float64',
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* 'data': [ 0.05 ],
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* 'shape': ysh,
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* 'strides': [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ],
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* 'offset': 0,
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* 'order': 'row-major'
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};
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* var mu = {
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* 'dtype': 'float64',
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* 'data': [ 0.0 ],
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* 'shape': ysh,
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* 'strides': [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ],
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* 'offset': 0,
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* 'order': 'row-major'
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};
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* var sigma = {
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* 'dtype': 'float64',
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* 'data': [ 1.0 ],
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* 'shape': ysh,
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* 'strides': [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ],
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* 'offset': 0,
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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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* 'dtype': y.dtype,
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* 'data': y.data,
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* 'shape': [],
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* 'strides': [ 0 ],
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* 'offset': y.offset,
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* 'order': y.order
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* },
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* {
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* 'dtype': alternative.dtype,
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* 'data': alternative.data,
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* 'shape': [],
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* 'strides': [ 0 ],
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* 'offset': alternative.offset,
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* 'order': alternative.order
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* },
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* {
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* 'dtype': alpha.dtype,
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* 'data': alpha.data,
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* 'shape': [],
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* 'strides': [ 0 ],
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* 'offset': alpha.offset,
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* 'order': alpha.order
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* },
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* {
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* 'dtype': mu.dtype,
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* 'data': mu.data,
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* 'shape': [],
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* 'strides': [ 0 ],
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* 'offset': mu.offset,
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* 'order': mu.order
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* },
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* {
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* 'dtype': sigma.dtype,
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* 'data': sigma.data,
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* 'shape': [],
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* 'strides': [ 0 ],
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* 'offset': sigma.offset,
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* 'order': sigma.order
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* }
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* ];
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*
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* // Define a reshape strategy:
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* function strategy( x ) {
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* return {
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* 'dtype': x.dtype,
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* 'data': x.data,
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* 'shape': [ 4 ],
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* 'strides': [ 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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* unary10d( ztest, [ x, y, alternative, alpha, mu, sigma ], views, [ 12, 12, 12, 12, 12, 12, 12, 12, 12, 4 ], true, strategy, {} );
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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 [ [ [ [ [ [ [ [ [ [ <Float64Results>, <Float64Results>, <Float64Results> ] ] ] ] ] ] ] ] ] ]
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*/
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function unary10d( fcn, arrays, views, strides, isRowMajor, strategy, opts ) { // eslint-disable-line max-statements
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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 dv5;
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var dv6;
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var dv7;
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var dv8;
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var dv9;
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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 S5;
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var S6;
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var S7;
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var S8;
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var S9;
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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 i5;
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var i6;
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var i7;
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var i8;
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var i9;
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var v;
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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 shape:
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sh = arrays[ 1 ].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 ) {
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// For row-major ndarrays, the last dimensions have the fastest changing indices...
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S0 = sh[ 9 ];
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S1 = sh[ 8 ];
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S2 = sh[ 7 ];
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S3 = sh[ 6 ];
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S4 = sh[ 5 ];
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S5 = sh[ 4 ];
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S6 = sh[ 3 ];
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S7 = sh[ 2 ];
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S8 = sh[ 1 ];
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S9 = sh[ 0 ];
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dv0 = [ strides[9] ]; // offset increment for innermost loop
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dv1 = [ strides[8] - ( S0*strides[9] ) ];
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dv2 = [ strides[7] - ( S1*strides[8] ) ];
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dv3 = [ strides[6] - ( S2*strides[7] ) ];
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dv4 = [ strides[5] - ( S3*strides[6] ) ];
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dv5 = [ strides[4] - ( S4*strides[5] ) ];
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dv6 = [ strides[3] - ( S5*strides[4] ) ];
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dv7 = [ strides[2] - ( S6*strides[3] ) ];
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dv8 = [ strides[1] - ( S7*strides[2] ) ];
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dv9 = [ strides[0] - ( S8*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[9] );
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dv1.push( sv[8] - ( S0*sv[9] ) );
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dv2.push( sv[7] - ( S1*sv[8] ) );
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dv3.push( sv[6] - ( S2*sv[7] ) );
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dv4.push( sv[5] - ( S3*sv[6] ) );
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dv5.push( sv[4] - ( S4*sv[5] ) );
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dv6.push( sv[3] - ( S5*sv[4] ) );
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dv7.push( sv[2] - ( S6*sv[3] ) );
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dv8.push( sv[1] - ( S7*sv[2] ) );
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dv9.push( sv[0] - ( S8*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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S5 = sh[ 5 ];
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S6 = sh[ 6 ];
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S7 = sh[ 7 ];
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S8 = sh[ 8 ];
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S9 = sh[ 9 ];
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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] ) ];
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dv5 = [ strides[5] - ( S4*strides[4] ) ];
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dv6 = [ strides[6] - ( S5*strides[5] ) ];
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dv7 = [ strides[7] - ( S6*strides[6] ) ];
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dv8 = [ strides[8] - ( S7*strides[7] ) ];
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dv9 = [ strides[9] - ( S8*strides[8] ) ]; // 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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dv5.push( sv[5] - ( S4*sv[4] ) );
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dv6.push( sv[6] - ( S5*sv[5] ) );
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dv7.push( sv[7] - ( S6*sv[6] ) );
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dv8.push( sv[8] - ( S7*sv[7] ) );
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dv9.push( sv[9] - ( S8*sv[8] ) );
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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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// Shallow copy the list of views to an internal array so that we can update with reshaped views without impacting the original list of views:
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v = copyIndexed( views );
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// Iterate over the non-reduced ndarray dimensions...
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for ( i9 = 0; i9 < S9; i9++ ) {
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for ( i8 = 0; i8 < S8; i8++ ) {
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for ( i7 = 0; i7 < S7; i7++ ) {
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for ( i6 = 0; i6 < S6; i6++ ) {
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for ( i5 = 0; i5 < S5; i5++ ) {
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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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v[ 0 ] = strategy( views[ 0 ] );
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fcn( v, 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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incrementOffsets( iv, dv5 );
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}
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incrementOffsets( iv, dv6 );
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}
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incrementOffsets( iv, dv7 );
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}
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incrementOffsets( iv, dv8 );
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}
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incrementOffsets( iv, dv9 );
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}
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}
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// EXPORTS //
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module.exports = unary10d;

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