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feat: add 9d kernels
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‎lib/node_modules/@stdlib/ndarray/base/some-by/lib/9d.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 strides2order = require( '@stdlib/ndarray/base/strides2order' );
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var zeroTo = require( '@stdlib/array/base/zero-to' );
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var reverse = require( '@stdlib/array/base/reverse' );
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var take = require( '@stdlib/array/base/take-indexed' );
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// MAIN //
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/**
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* Tests whether at least `n` elements in an ndarray pass a test implemented by a predicate function.
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*
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* @private
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* @param {Object} x - object containing ndarray meta data
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* @param {ndarrayLike} x.ref - reference to the original ndarray-like object
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* @param {string} x.dtype - data type
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* @param {Collection} x.data - data buffer
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* @param {NonNegativeIntegerArray} x.shape - dimensions
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* @param {IntegerArray} x.strides - stride lengths
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* @param {NonNegativeInteger} x.offset - index offset
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* @param {string} x.order - specifies whether `x` is row-major (C-style) or column-major (Fortran-style)
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* @param {PositiveInteger} n - number of elements
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* @param {Function} predicate - predicate function
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* @param {*} thisArg - predicate function execution context
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* @returns {boolean} boolean indicating whether at least `n` elements pass a test
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*
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* @example
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* var Float64Array = require( '@stdlib/array/float64' );
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*
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* function predicate( value ) {
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* return value > 0.0;
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* }
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*
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* // Create a data buffer:
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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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*
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* // Define the shape of the input array:
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* var shape = [ 1, 1, 1, 1, 1, 1, 3, 1, 2 ];
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*
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* // Define the array strides:
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* var sx = [ 12, 12, 12, 12, 12, 12, 4, 4, 1 ];
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*
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* // Define the index offset:
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* var ox = 1;
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*
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* // Create the input ndarray-like object:
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* var x = {
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* 'ref': null,
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* 'dtype': 'float64',
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* 'data': xbuf,
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* 'shape': shape,
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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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* // Test elements:
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* var out = some9d( x, 4, predicate );
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* // returns true
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*/
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function some9d( x, n, predicate, thisArg ) {
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var count;
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var xbuf;
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var idx;
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var dx0;
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var dx1;
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var dx2;
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var dx3;
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var dx4;
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var dx5;
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var dx6;
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var dx7;
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var dx8;
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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 sx;
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var ix;
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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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// Note on variable naming convention: S#, dx#, i# where # corresponds to the loop number, with `0` being the innermost loop...
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// Extract loop variables for purposes of loop interchange: dimensions and loop offset (pointer) increments...
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sh = x.shape;
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sx = x.strides;
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idx = zeroTo( sh.length );
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if ( strides2order( sx ) === 1 ) {
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// For row-major ndarrays, the last dimensions have the fastest changing indices...
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S0 = sh[ 8 ];
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S1 = sh[ 7 ];
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S2 = sh[ 6 ];
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S3 = sh[ 5 ];
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S4 = sh[ 4 ];
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S5 = sh[ 3 ];
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S6 = sh[ 2 ];
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S7 = sh[ 1 ];
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S8 = sh[ 0 ];
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dx0 = sx[ 8 ]; // offset increment for innermost loop
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dx1 = sx[ 7 ] - ( S0*sx[8] );
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dx2 = sx[ 6 ] - ( S1*sx[7] );
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dx3 = sx[ 5 ] - ( S2*sx[6] );
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dx4 = sx[ 4 ] - ( S3*sx[5] );
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dx5 = sx[ 3 ] - ( S4*sx[4] );
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dx6 = sx[ 2 ] - ( S5*sx[3] );
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dx7 = sx[ 1 ] - ( S6*sx[2] );
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dx8 = sx[ 0 ] - ( S7*sx[1] ); // offset increment for outermost loop
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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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dx0 = sx[ 0 ]; // offset increment for innermost loop
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dx1 = sx[ 1 ] - ( S0*sx[0] );
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dx2 = sx[ 2 ] - ( S1*sx[1] );
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dx3 = sx[ 3 ] - ( S2*sx[2] );
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dx4 = sx[ 4 ] - ( S3*sx[3] );
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dx5 = sx[ 5 ] - ( S4*sx[4] );
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dx6 = sx[ 6 ] - ( S5*sx[5] );
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dx7 = sx[ 7 ] - ( S6*sx[6] );
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dx8 = sx[ 8 ] - ( S7*sx[7] ); // offset increment for outermost loop
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idx = reverse( idx );
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}
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// Set a pointer to the first indexed element:
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ix = x.offset;
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// Cache a reference to the input ndarray buffer:
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xbuf = x.data;
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// Initialize a counter:
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count = 0;
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// Iterate over the ndarray dimensions...
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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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if ( predicate.call( thisArg, xbuf[ ix ], take( [ i8, i7, i6, i5, i4, i3, i2, i1, i0 ], idx ), x.ref ) ) { // eslint-disable-line max-len
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count += 1;
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if ( count === n ) {
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return true;
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}
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}
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ix += dx0;
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}
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ix += dx1;
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}
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ix += dx2;
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}
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ix += dx3;
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}
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ix += dx4;
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}
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ix += dx5;
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}
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ix += dx6;
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}
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ix += dx7;
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}
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ix += dx8;
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}
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return false;
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}
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// EXPORTS //
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module.exports = some9d;

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