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* @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 ndarray2object = require( '@stdlib/ndarray/base/ndarraylike2object' );
var normalizeIndices = require( '@stdlib/ndarray/base/to-unique-normalized-indices' );
var indicesComplement = require( '@stdlib/array/base/indices-complement' );
var takeIndexed2 = require( '@stdlib/array/base/take-indexed2' );
var iterationOrder = require( '@stdlib/ndarray/base/iteration-order' );
var strides2order = require( '@stdlib/ndarray/base/strides2order' );
var anyIsEntryIn = require( '@stdlib/array/base/any-is-entry-in' );
var numel = require( '@stdlib/ndarray/base/numel' );
var join = require( '@stdlib/array/base/join' );
var format = require( '@stdlib/string/format' );
var initializeViews = require( './initialize_array_views.js' );
var reshapeStrategy = require( './reshape_strategy.js' );
var blockedaccessorbinary2d = require( './2d_blocked_accessors.js' );
var blockedaccessorbinary3d = require( './3d_blocked_accessors.js' );
var blockedaccessorbinary4d = require( './4d_blocked_accessors.js' );
var blockedaccessorbinary5d = require( './5d_blocked_accessors.js' );
var blockedaccessorbinary6d = require( './6d_blocked_accessors.js' );
var blockedaccessorbinary7d = require( './7d_blocked_accessors.js' );
var blockedaccessorbinary8d = require( './8d_blocked_accessors.js' );
var blockedaccessorbinary9d = require( './9d_blocked_accessors.js' );
var blockedaccessorbinary10d = require( './10d_blocked_accessors.js' );
var blockedbinary2d = require( './2d_blocked.js' );
var blockedbinary3d = require( './3d_blocked.js' );
var blockedbinary4d = require( './4d_blocked.js' );
var blockedbinary5d = require( './5d_blocked.js' );
var blockedbinary6d = require( './6d_blocked.js' );
var blockedbinary7d = require( './7d_blocked.js' );
var blockedbinary8d = require( './8d_blocked.js' );
var blockedbinary9d = require( './9d_blocked.js' );
var blockedbinary10d = require( './10d_blocked.js' );
var accessorbinary0d = require( './0d_accessors.js' );
var accessorbinary1d = require( './1d_accessors.js' );
var accessorbinary2d = require( './2d_accessors.js' );
var accessorbinary3d = require( './3d_accessors.js' );
var accessorbinary4d = require( './4d_accessors.js' );
var accessorbinary5d = require( './5d_accessors.js' );
var accessorbinary6d = require( './6d_accessors.js' );
var accessorbinary7d = require( './7d_accessors.js' );
var accessorbinary8d = require( './8d_accessors.js' );
var accessorbinary9d = require( './9d_accessors.js' );
var accessorbinary10d = require( './10d_accessors.js' );
var accessorbinarynd = require( './nd_accessors.js' );
var binary0d = require( './0d.js' );
var binary1d = require( './1d.js' );
var binary2d = require( './2d.js' );
var binary3d = require( './3d.js' );
var binary4d = require( './4d.js' );
var binary5d = require( './5d.js' );
var binary6d = require( './6d.js' );
var binary7d = require( './7d.js' );
var binary8d = require( './8d.js' );
var binary9d = require( './9d.js' );
var binary10d = require( './10d.js' );
var binarynd = require( './nd.js' );
// VARIABLES //
var BINARY = [
binary0d,
binary1d,
binary2d,
binary3d,
binary4d,
binary5d,
binary6d,
binary7d,
binary8d,
binary9d,
binary10d
];
var ACCESSOR_BINARY = [
accessorbinary0d,
accessorbinary1d,
accessorbinary2d,
accessorbinary3d,
accessorbinary4d,
accessorbinary5d,
accessorbinary6d,
accessorbinary7d,
accessorbinary8d,
accessorbinary9d,
accessorbinary10d
];
var BLOCKED_BINARY = [
blockedbinary2d, // 0
blockedbinary3d,
blockedbinary4d,
blockedbinary5d,
blockedbinary6d,
blockedbinary7d,
blockedbinary8d,
blockedbinary9d,
blockedbinary10d // 8
];
var BLOCKED_ACCESSOR_BINARY = [
blockedaccessorbinary2d, // 0
blockedaccessorbinary3d,
blockedaccessorbinary4d,
blockedaccessorbinary5d,
blockedaccessorbinary6d,
blockedaccessorbinary7d,
blockedaccessorbinary8d,
blockedaccessorbinary9d,
blockedaccessorbinary10d // 8
];
var MAX_DIMS = BINARY.length - 1;
// FUNCTIONS //
/**
* Returns a boolean indicating if at least one ndarray data buffer implements the accessor protocol.
*
* @private
* @param {ndarrayLike} x - first ndarray
* @param {ndarrayLike} y - second ndarray
* @param {ndarrayLike} z - third ndarray
* @returns {boolean} boolean indicating whether an ndarray data buffer implements the accessor protocol
*/
function hasAccessors( x, y, z ) {
return anyIsEntryIn( [ x, y, z ], 'accessorProtocol', true );
}
// MAIN //
/**
* Performs a reduction over a list of specified dimensions in two input ndarrays via a one-dimensional strided array binary reduction function and assigns results to a provided output ndarray.
*
* @private
* @param {Function} fcn - wrapper for a one-dimensional strided array reduction function
* @param {ArrayLikeObject<Object>} arrays - array-like object containing ndarrays
* @param {IntegerArray} dims - list of dimensions over which to perform a reduction
* @param {Options} [options] - function options
* @throws {Error} arrays must have the expected number of dimensions
* @throws {RangeError} dimension indices must not exceed input ndarray bounds
* @throws {RangeError} number of dimension indices must not exceed the number of input ndarray dimensions
* @throws {Error} must provide unique dimension indices
* @throws {Error} arrays must have the same loop dimension sizes
* @returns {void}
*
* @example
* var Float64Array = require( '@stdlib/array/float64' );
* var ndarray2array = require( '@stdlib/ndarray/base/to-array' );
* var getStride = require( '@stdlib/ndarray/base/stride' );
* var getOffset = require( '@stdlib/ndarray/base/offset' );
* var getData = require( '@stdlib/ndarray/base/data-buffer' );
* var numelDimension = require( '@stdlib/ndarray/base/numel-dimension' );
* var gdot = require( '@stdlib/blas/base/ndarray/gdot' );
*
* // 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( [ 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 zbuf = new Float64Array( [ 0.0, 0.0, 0.0 ] );
*
* // Define the array shapes:
* var xsh = [ 1, 3, 2, 2 ];
* var ysh = [ 1, 3, 2, 2 ];
* var zsh = [ 1, 3 ];
*
* // Define the array strides:
* var sx = [ 12, 4, 2, 1 ];
* var sy = [ 12, 4, 2, 1 ];
* var sz = [ 3, 1 ];
*
* // Define the index offsets:
* var ox = 0;
* var oy = 0;
* var oz = 0;
*
* // Create input ndarray-like objects:
* var x = {
* 'dtype': 'float64',
* 'data': xbuf,
* 'shape': xsh,
* 'strides': sx,
* 'offset': ox,
* 'order': 'row-major'
* };
* var y = {
* 'dtype': 'float64',
* 'data': ybuf,
* 'shape': ysh,
* 'strides': sy,
* 'offset': oy,
* 'order': 'row-major'
* };
*
* // Create an output ndarray-like object:
* var z = {
* 'dtype': 'float64',
* 'data': zbuf,
* 'shape': zsh,
* 'strides': sz,
* 'offset': oz,
* 'order': 'row-major'
* };
*
* // Perform a reduction:
* binaryReduceStrided1d( gdot, [ x, y, z ], [ 2, 3 ] );
*
* var arr = ndarray2array( z.data, z.shape, z.strides, z.offset, z.order );
* // returns [ [ 30.0, 174.0, 446.0 ] ]
*
* @example
* var Float64Array = require( '@stdlib/array/float64' );
* var getStride = require( '@stdlib/ndarray/base/stride' );
* var getOffset = require( '@stdlib/ndarray/base/offset' );
* var getData = require( '@stdlib/ndarray/base/data-buffer' );
* var numelDimension = require( '@stdlib/ndarray/base/numel-dimension' );
* var gdot = require( '@stdlib/blas/base/ndarray/gdot' );
*
* // 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( [ 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 zbuf = new Float64Array( [ 0.0 ] );
*
* // Define the array shapes:
* var xsh = [ 1, 3, 2, 2 ];
* var ysh = [ 1, 3, 2, 2 ];
* var zsh = [];
*
* // Define the array strides:
* var sx = [ 12, 4, 2, 1 ];
* var sy = [ 12, 4, 2, 1 ];
* var sz = [ 0 ];
*
* // Define the index offsets:
* var ox = 0;
* var oy = 0;
* var oz = 0;
*
* // Create input ndarray-like objects:
* var x = {
* 'dtype': 'float64',
* 'data': xbuf,
* 'shape': xsh,
* 'strides': sx,
* 'offset': ox,
* 'order': 'row-major'
* };
* var y = {
* 'dtype': 'float64',
* 'data': ybuf,
* 'shape': ysh,
* 'strides': sy,
* 'offset': oy,
* 'order': 'row-major'
* };
*
* // Create an output ndarray-like object:
* var z = {
* 'dtype': 'float64',
* 'data': zbuf,
* 'shape': zsh,
* 'strides': sz,
* 'offset': oz,
* 'order': 'row-major'
* };
*
* // Perform a reduction:
* binaryReduceStrided1d( gdot, [ x, y, z ], [ 0, 1, 2, 3 ] );
*
* var v = z.data;
* // returns <Float64Array>[ 650.0 ]
*
* @example
* var Float64Array = require( '@stdlib/array/float64' );
* var ndarray2array = require( '@stdlib/ndarray/base/to-array' );
* var getStride = require( '@stdlib/ndarray/base/stride' );
* var getOffset = require( '@stdlib/ndarray/base/offset' );
* var getData = require( '@stdlib/ndarray/base/data-buffer' );
* var numelDimension = require( '@stdlib/ndarray/base/numel-dimension' );
* var gdot = require( '@stdlib/blas/base/ndarray/gdot' );
*
* // 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( [ 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 zbuf = new Float64Array( [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] );
*
* // Define the array shapes:
* var xsh = [ 3, 2, 2 ];
* var ysh = [ 3, 2, 2 ];
* var zsh = [ 3, 2, 2 ];
*
* // Define the array strides:
* var sx = [ 4, 2, 1 ];
* var sy = [ 4, 2, 1 ];
* var sz = [ 4, 2, 1 ];
*
* // Define the index offsets:
* var ox = 0;
* var oy = 0;
* var oz = 0;
*
* // Create input ndarray-like objects:
* var x = {
* 'dtype': 'float64',
* 'data': xbuf,
* 'shape': xsh,
* 'strides': sx,
* 'offset': ox,
* 'order': 'row-major'
* };
* var y = {
* 'dtype': 'float64',
* 'data': ybuf,
* 'shape': ysh,
* 'strides': sy,
* 'offset': oy,
* 'order': 'row-major'
* };
*
* // Create an output ndarray-like object:
* var z = {
* 'dtype': 'float64',
* 'data': zbuf,
* 'shape': zsh,
* 'strides': sz,
* 'offset': oz,
* 'order': 'row-major'
* };
*
* // Perform a reduction:
* binaryReduceStrided1d( gdot, [ x, y, z ], [] );
*
* var arr = ndarray2array( z.data, z.shape, z.strides, z.offset, z.order );
* // returns [ [ [ 1.0, 4.0 ], [ 9.0, 16.0 ] ], [ [ 25.0, 36.0 ], [ 49.0, 64.0 ] ], [ [ 81.0, 100.0 ], [ 121.0, 144.0 ] ] ]
*/
function binaryReduceStrided1d( fcn, arrays, dims, options ) { // eslint-disable-line max-statements
var strategy1;
var strategy2;
var views;
var ndims;
var ldims;
var opts;
var ordx;
var ordy;
var tmpx;
var tmpy;
var arr;
var len;
var shx;
var shy;
var shc;
var shl;
var iox;
var ioy;
var ioz;
var scx;
var scy;
var slx;
var sly;
var sz;
var ns;
var d;
var s;
var N;
var M;
var K;
var x;
var y;
var z;
var i;
var j;
if ( arguments.length > 3 ) {
opts = options;
} else {
opts = {};
}
// Standardize ndarray meta data...
N = arrays.length;
arr = [];
for ( i = 0; i < N; i++ ) {
arr.push( ndarray2object( arrays[ i ] ) );
}
// Cache references to the input and output arrays:
x = arr[ 0 ];
y = arr[ 1 ];
z = arr[ 2 ];
// Resolve the number of input array dimensions:
shx = x.shape;
shy = y.shape;
ndims = shx.length;
// Verify that both input arrays have the same shape:
if ( shx.length !== shy.length ) {
throw new Error( format( 'invalid argument. Input arrays must have the same number of dimensions. First array dimensions: %d. Second array dimensions: %d.', shx.length, shy.length ) );
}
for ( i = 0; i < ndims; i++ ) {
if ( shx[ i ] !== shy[ i ] ) {
throw new Error( format( 'invalid argument. Input arrays must have the same shape. First array shape: [%s]. Second array shape: [%s].', join( shx, ',' ), join( shy, ',' ) ) );
}
}
// Verify that we've been provided a list of unique dimension indices...
M = dims.length;
d = normalizeIndices( dims, ndims-1 );
if ( d === null ) {
throw new RangeError( format( 'invalid argument. Third argument contains an out-of-bounds dimension index. Value: [%s].', join( dims, ',' ) ) );
}
d.sort();
if ( d.length !== M ) {
throw new Error( format( 'invalid argument. Third argument must contain a list of unique dimension indices. Value: [%s].', join( dims, ',' ) ) );
}
// Check whether we've been provided a valid number of dimensions to reduce...
if ( M > ndims ) {
throw new RangeError( format( 'invalid argument. Number of specified dimensions cannot exceed the number of dimensions in the input array. Number of dimensions: %d. Value: [%s].', ndims, join( dims, ',' ) ) );
}
// Verify that provided ndarrays have the expected number of dimensions...
K = ndims - M;
if ( z.shape.length !== K ) {
throw new Error( format( 'invalid argument. Output array must have the same number of non-reduced dimensions as input arrays. Input array shape: [%s]. Number of non-reduced dimensions: %d. Output array shape: [%s].', join( shx, ',' ), K, join( z.shape, ',' ) ) );
}
// Resolve the non-reduced ("loop") dimensions and associated strides:
ldims = indicesComplement( shx.length, d );
tmpx = takeIndexed2( shx, x.strides, ldims );
tmpy = takeIndexed2( shy, y.strides, ldims );
shl = tmpx[ 0 ]; // tmpx[ 0 ] == tmpy[ 0 ]
slx = tmpx[ 1 ];
sly = tmpy[ 1 ];
// Resolve the reduced ("core") dimensions and associated strides:
tmpx = takeIndexed2( shx, x.strides, d );
tmpy = takeIndexed2( shy, y.strides, d );
shc = tmpx[ 0 ]; // tmpx[ 0 ] == tmpy[ 0 ]
scx = tmpx[ 1 ];
scy = tmpy[ 1 ];
// Verify that the provided arrays have the same loop dimensions...
len = 1; // number of elements
ns = 0; // number of singleton dimensions
for ( i = 0; i < K; i++ ) {
s = shl[ i ];
for ( j = 2; j < N; j++ ) {
if ( s !== arr[ j ].shape[ i ] ) {
throw new Error( format( 'invalid argument. Non-reduced dimensions must be consistent across all provided arrays. Input array shape: [%s]. Non-reduced dimension indices: [%s]. Non-reduced dimensions: [%s]. Array shape: [%s] (index: %d).', join( shx, ',' ), join( ldims, ',' ), join( shl, ',' ), join( arr[ j ].shape, ',' ), j ) );
}
}
// Note that, if one of the dimensions is `0`, the length will be `0`...
len *= s;
// Check whether the current dimension is a singleton dimension...
if ( s === 1 ) {
ns += 1;
}
}
// Check whether we were provided empty ndarrays...
if ( len === 0 || ( shc.length && numel( shc ) === 0 ) ) {
return;
}
// Initialize ndarray-like objects for representing sub-array views...
views = [
{
'dtype': x.dtype,
'data': x.data,
'shape': shc,
'strides': scx,
'offset': x.offset,
'order': x.order
},
{
'dtype': y.dtype,
'data': y.data,
'shape': shc,
'strides': scy,
'offset': y.offset,
'order': y.order
}
];
initializeViews( arr, views );
// Determine the strategy for reshaping sub-array views of the input array prior to performing a reduction:
strategy1 = reshapeStrategy( views[ 0 ] );
strategy2 = reshapeStrategy( views[ 1 ] );
// Determine whether we can avoid iteration altogether...
if ( K === 0 ) {
if ( hasAccessors( x, y, z ) ) {
return ACCESSOR_BINARY[ K ]( fcn, arr, strategy1, strategy2, opts );
}
return BINARY[ K ]( fcn, arr, strategy1, strategy2, opts );
}
// Determine whether we only have one loop dimension and can thus readily perform one-dimensional iteration...
if ( K === 1 ) {
if ( hasAccessors( x, y, z ) ) {
return ACCESSOR_BINARY[ K ]( fcn, arr, views, slx, sly, strategy1, strategy2, opts ); // eslint-disable-line max-len
}
return BINARY[ K ]( fcn, arr, views, slx, sly, strategy1, strategy2, opts ); // eslint-disable-line max-len
}
sz = z.strides;
// Determine whether the loop dimensions have only **one** non-singleton dimension (e.g., shape=[10,1,1,1]) so that we can treat loop iteration as being equivalent to one-dimensional iteration...
if ( ns === K-1 ) {
// Get the index of the non-singleton dimension...
for ( i = 0; i < K; i++ ) {
if ( shl[ i ] !== 1 ) {
break;
}
}
z.shape = [ shl[i] ];
for ( j = 0; j < N; j++ ) {
arr[ j ].strides = [ arr[j].strides[i] ];
}
slx = [ slx[i] ];
sly = [ sly[i] ];
if ( hasAccessors( x, y, z ) ) {
return ACCESSOR_BINARY[ 1 ]( fcn, arr, views, slx, sly, strategy1, strategy2, opts ); // eslint-disable-line max-len
}
return BINARY[ 1 ]( fcn, arr, views, slx, sly, strategy1, strategy2, opts ); // eslint-disable-line max-len
}
iox = iterationOrder( slx ); // +/-1
ioy = iterationOrder( sly ); // +/-1
ioz = iterationOrder( sz ); // +/-1
// Determine whether we can avoid blocked iteration...
ordx = strides2order( slx );
ordy = strides2order( sly );
if ( iox !== 0 && ioy !== 0 && ioz !== 0 && ordx === strides2order( sz ) && ordy === strides2order( sz ) && K <= MAX_DIMS ) { // eslint-disable-line max-len
// So long as iteration for each respective array always moves in the same direction (i.e., no mixed sign strides) and the memory layouts are the same, we can leverage cache-optimal (i.e., normal) nested loops without resorting to blocked iteration...
if ( hasAccessors( x, y, z ) ) {
return ACCESSOR_BINARY[ K ]( fcn, arr, views, slx, sly, ordx === 1, strategy1, strategy2, opts ); // eslint-disable-line max-len
}
return BINARY[ K ]( fcn, arr, views, slx, sly, ordx === 1, strategy1, strategy2, opts ); // eslint-disable-line max-len
}
// At this point, we're either dealing with non-contiguous n-dimensional arrays, high dimensional n-dimensional arrays, and/or arrays having differing memory layouts, so our only hope is that we can still perform blocked iteration...
// Determine whether we can perform blocked iteration...
if ( K <= MAX_DIMS ) {
if ( hasAccessors( x, y, z ) ) {
return BLOCKED_ACCESSOR_BINARY[ K-2 ]( fcn, arr, views, slx, sly, strategy1, strategy2, opts ); // eslint-disable-line max-len
}
return BLOCKED_BINARY[ K-2 ]( fcn, arr, views, slx, sly, strategy1, strategy2, opts ); // eslint-disable-line max-len
}
// Fall-through to linear view iteration without regard for how data is stored in memory (i.e., take the slow path)...
if ( hasAccessors( x, y, z ) ) {
return accessorbinarynd( fcn, arr, views, strategy1, strategy2, opts );
}
binarynd( fcn, arr, views, strategy1, strategy2, opts );
}
// EXPORTS //
module.exports = binaryReduceStrided1d;
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