feat: add 3d kernel

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Author: kgryte

lib/node_modules/@stdlib/ndarray/base/unary-reduce-strided1d-to-struct/lib/3d.js

@@ -0,0 +1,268 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2025 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+'use strict';
+
+// MODULES //
+
+var copyIndexed = require( '@stdlib/array/base/copy-indexed' );
+var incrementOffsets = require( './increment_offsets.js' );
+var setViewOffsets = require( './set_view_offsets.js' );
+var offsets = require( './offsets.js' );
+
+
+// MAIN //
+
+/**
+* Performs a reduction over an input ndarray and assigns results to a provided output ndarray.
+*
+* @private
+* @param {Function} fcn - wrapper for a one-dimensional strided array reduction function
+* @param {Array<Object>} arrays - ndarrays
+* @param {Array<Object>} views - initialized ndarray-like objects representing sub-array views
+* @param {IntegerArray} strides - loop dimension strides for the input ndarray
+* @param {boolean} isRowMajor - boolean indicating if provided arrays are in row-major order
+* @param {Function} strategy - input ndarray reshape strategy
+* @param {Options} opts - function options
+* @returns {void}
+*
+* @example
+* var Float64Array = require( '@stdlib/array/float64' );
+* var ndarray2array = require( '@stdlib/ndarray/base/to-array' );
+* var Float64Results = require( '@stdlib/stats/base/ztest/one-sample/results/float64' );
+* var structFactory = require( '@stdlib/array/struct-factory' );
+* var ztest = require( '@stdlib/stats/base/ndarray/ztest' );
+*
+* var ResultsArray = structFactory( Float64Results );
+*
+* // 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 ResultsArray( 3 );
+*
+* // Define the array shapes:
+* var xsh = [ 1, 1, 3, 2, 2 ];
+* var ysh = [ 1, 1, 3 ];
+*
+* // Define the array strides:
+* var sx = [ 12, 12, 4, 2, 1 ];
+* var sy = [ 3, 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': Float64Results,
+*     'data': ybuf,
+*     'shape': ysh,
+*     'strides': sy,
+*     'offset': oy,
+*     'order': 'row-major'
+* };
+*
+* // Create additional parameter ndarray-like objects:
+* var alternative = {
+*    'dtype': 'generic',
+*    'data': [ 'two-sided' ],
+*    'shape': ysh,
+*    'strides': [ 0, 0, 0 ],
+*    'offset': 0,
+*    'order': 'row-major'
+};
+* var alpha = {
+*     'dtype': 'float64',
+*     'data': [ 0.05 ],
+*     'shape': ysh,
+*     'strides': [ 0, 0, 0 ],
+*     'offset': 0,
+*     'order': 'row-major'
+};
+* var mu = {
+*     'dtype': 'float64',
+*     'data': [ 0.0 ],
+*     'shape': ysh,
+*     'strides': [ 0, 0, 0 ],
+*     'offset': 0,
+*     'order': 'row-major'
+};
+* var sigma = {
+*     'dtype': 'float64',
+*     'data': [ 1.0 ],
+*     'shape': ysh,
+*     'strides': [ 0, 0, 0 ],
+*     'offset': 0,
+*     'order': 'row-major'
+* };
+*
+* // Initialize ndarray-like objects representing sub-array views:
+* var views = [
+*     {
+*         'dtype': x.dtype,
+*         'data': x.data,
+*         'shape': [ 2, 2 ],
+*         'strides': [ 2, 1 ],
+*         'offset': x.offset,
+*         'order': x.order
+*     },
+*     {
+*         'dtype': y.dtype,
+*         'data': y.data,
+*         'shape': [],
+*         'strides': [ 0 ],
+*         'offset': y.offset,
+*         'order': y.order
+*     },
+*     {
+*         'dtype': alternative.dtype,
+*         'data': alternative.data,
+*         'shape': [],
+*         'strides': [ 0 ],
+*         'offset': alternative.offset,
+*         'order': alternative.order
+*     },
+*     {
+*         'dtype': alpha.dtype,
+*         'data': alpha.data,
+*         'shape': [],
+*         'strides': [ 0 ],
+*         'offset': alpha.offset,
+*         'order': alpha.order
+*     },
+*     {
+*         'dtype': mu.dtype,
+*         'data': mu.data,
+*         'shape': [],
+*         'strides': [ 0 ],
+*         'offset': mu.offset,
+*         'order': mu.order
+*     },
+*     {
+*         'dtype': sigma.dtype,
+*         'data': sigma.data,
+*         'shape': [],
+*         'strides': [ 0 ],
+*         'offset': sigma.offset,
+*         'order': sigma.order
+*     }
+* ];
+*
+* // Define a reshape strategy:
+* function strategy( x ) {
+*     return {
+*         'dtype': x.dtype,
+*         'data': x.data,
+*         'shape': [ 4 ],
+*         'strides': [ 1 ],
+*         'offset': x.offset,
+*         'order': x.order
+*     };
+* }
+*
+* // Perform a reduction:
+* unary3d( ztest, [ x, y, alternative, alpha, mu, sigma ], views, [ 12, 12, 4 ], true, strategy, {} );
+*
+* var arr = ndarray2array( y.data, y.shape, y.strides, y.offset, y.order );
+* // returns [ [ [ <Float64Results>, <Float64Results>, <Float64Results> ] ] ]
+*/
+function unary3d( fcn, arrays, views, strides, isRowMajor, strategy, opts ) {
+	var dv0;
+	var dv1;
+	var dv2;
+	var sh;
+	var S0;
+	var S1;
+	var S2;
+	var sv;
+	var iv;
+	var i0;
+	var i1;
+	var i2;
+	var v;
+	var i;
+
+	// Note on variable naming convention: S#, dv#, i# where # corresponds to the loop number, with `0` being the innermost loop...
+
+	// Resolve the output shape:
+	sh = arrays[ 1 ].shape;
+
+	// Extract loop variables for purposes of loop interchange: dimensions and loop offset (pointer) increments...
+	if ( isRowMajor ) {
+		// For row-major ndarrays, the last dimensions have the fastest changing indices...
+		S0 = sh[ 2 ];
+		S1 = sh[ 1 ];
+		S2 = sh[ 0 ];
+		dv0 = [ strides[2] ];                     // offset increment for innermost loop
+		dv1 = [ strides[1] - ( S0*strides[2] ) ];
+		dv2 = [ strides[0] - ( S1*strides[1] ) ]; // offset increment for outermost loop
+		for ( i = 1; i < arrays.length; i++ ) {
+			sv = arrays[ i ].strides;
+			dv0.push( sv[2] );
+			dv1.push( sv[1] - ( S0*sv[2] ) );
+			dv2.push( sv[0] - ( S1*sv[1] ) );
+		}
+	} else { // order === 'column-major'
+		// For column-major ndarrays, the first dimensions have the fastest changing indices...
+		S0 = sh[ 0 ];
+		S1 = sh[ 1 ];
+		S2 = sh[ 2 ];
+		dv0 = [ strides[0] ];                     // offset increment for innermost loop
+		dv1 = [ strides[1] - ( S0*strides[0] ) ];
+		dv2 = [ strides[2] - ( S1*strides[1] ) ]; // offset increment for outermost loop
+		for ( i = 1; i < arrays.length; i++ ) {
+			sv = arrays[ i ].strides;
+			dv0.push( sv[0] );
+			dv1.push( sv[1] - ( S0*sv[0] ) );
+			dv2.push( sv[2] - ( S1*sv[1] ) );
+		}
+	}
+	// Resolve a list of pointers to the first indexed elements in the respective ndarrays:
+	iv = offsets( arrays );
+
+	// 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:
+	v = copyIndexed( views );
+
+	// Iterate over the non-reduced ndarray dimensions...
+	for ( i2 = 0; i2 < S2; i2++ ) {
+		for ( i1 = 0; i1 < S1; i1++ ) {
+			for ( i0 = 0; i0 < S0; i0++ ) {
+				setViewOffsets( views, iv );
+				v[ 0 ] = strategy( views[ 0 ] );
+				fcn( v, opts );
+				incrementOffsets( iv, dv0 );
+			}
+			incrementOffsets( iv, dv1 );
+		}
+		incrementOffsets( iv, dv2 );
+	}
+}
+
+
+// EXPORTS //
+
+module.exports = unary3d;