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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 iterationOrder = require( '@stdlib/ndarray/base/iteration-order' );
var minmaxViewBufferIndex = require( '@stdlib/ndarray/base/minmax-view-buffer-index' ).assign;
var ndarraylike2object = require( '@stdlib/ndarray/base/ndarraylike2object' );
var assign = require( '@stdlib/ndarray/base/assign' );
var ndarraylike2ndarray = require( '@stdlib/ndarray/base/ndarraylike2ndarray' );
var emptyLike = require( '@stdlib/ndarray/base/empty-like' );
// FUNCTIONS //
/**
* Returns an input ndarray.
*
* @private
* @param {ndarrayLike} x - input ndarray
* @returns {ndarrayLike} input ndarray
*/
function identity( x ) {
return x;
}
/**
* Broadcasts a zero-dimensional ndarray to a one-dimensional ndarray view containing a single element.
*
* @private
* @param {ndarrayLike} x - input ndarray
* @returns {ndarrayLike} broadcasted ndarray view
*/
function broadcast( x ) {
// NOTE: the following properties must be set in the exact same order as in `x` in order to ensure that the returned object has the same hidden shape as the input ndarray-like object...
return {
'dtype': x.dtype,
'data': x.data,
'shape': [ 1 ],
'strides': [ 0 ],
'offset': x.offset,
'order': x.order
};
}
/**
* Returns a function which returns an ndarray view in which the singleton dimensions are removed from an input ndarray having only a single non-singleton dimension.
*
* @private
* @param {ndarrayLike} arr - original ndarray
* @param {NonNegativeInteger} index - index of the non-singleton dimension
* @returns {Function} function for returning an ndarray view
*/
function squeeze( arr, index ) {
var sh = [ arr.shape[ index ] ];
var sx = [ arr.strides[ index ] ];
return reshape;
/**
* Returns an ndarray view in which the singleton dimensions are removed from an input ndarray having only a single non-singleton dimension.
*
* @private
* @param {ndarrayLike} x - input ndarray
* @returns {ndarrayLike} a squeezed ndarray view
*/
function reshape( x ) {
// NOTE: the following properties must be set in the exact same order as in `arr` in order to ensure that the returned object has the same hidden shape as the input ndarray-like object...
return {
'dtype': x.dtype,
'data': x.data,
'shape': sh,
'strides': sx,
'offset': x.offset,
'order': x.order
};
}
}
/**
* Returns a function which returns a one-dimensional ndarray view of a contiguous input ndarray having more than one dimension.
*
* @private
* @param {NonNegativeInteger} len - number of elements in an ndarray
* @param {integer} iox - iteration order
* @returns {Function} function for returning a one-dimensional ndarray view
*/
function contiguous( len, iox ) {
var xmmv;
var ind;
var sh;
var sx;
// Resolve the index of the min/max view buffer element which is the first indexed element...
if ( iox === 1 ) {
ind = 0;
} else {
ind = 1;
}
// Initialize an array for storing the min/max view buffer elements:
xmmv = [ 0, 0 ]; // [ min, max ]
// Initialize the output one-dimensional view's shape and strides:
sh = [ len ];
sx = [ iox ];
return reshape;
/**
* Returns a one-dimensional ndarray view of a contiguous input ndarray having more than one dimension.
*
* @private
* @param {ndarrayLike} x - input ndarray
* @returns {ndarrayLike} a one-dimensional ndarray view
*/
function reshape( x ) {
// Resolve the minimum and maximum linear indices in the underlying data buffer which are accessible to the input ndarray view:
minmaxViewBufferIndex( x.shape, x.strides, x.offset, xmmv );
// NOTE: the following properties must be set in the exact same order as in `x` in order to ensure that the returned object has the same hidden shape as the input ndarray-like object...
return {
'dtype': x.dtype,
'data': x.data,
'shape': sh,
'strides': sx,
'offset': xmmv[ ind ], // the index of the first indexed element
'order': x.order
};
}
}
/**
* Returns a function which copies an input ndarray to a contiguous ndarray workspace.
*
* @private
* @param {NonNegativeInteger} len - number of elements in an ndarray
* @param {ndarrayLike} workspace - ndarray workspace
* @returns {Function} function which copies an input ndarray to a contiguous ndarray workspace
*/
function copy( len, workspace ) {
// NOTE: the following properties must be set in the exact same order as in the input ndarray-like object in order to ensure that the returned object has the same hidden shape...
var view = {
'dtype': workspace.dtype,
'data': workspace.data,
'shape': [ len ],
'strides': [ 1 ],
'offset': workspace.offset,
'order': workspace.order
};
return reshape;
/**
* Copies an input ndarray to a contiguous ndarray workspace and returns a one-dimensional workspace view.
*
* @private
* @param {ndarrayLike} x - input ndarray
* @returns {ndarrayLike} one-dimensional workspace view
*/
function reshape( x ) {
assign( [ x, workspace ] );
return view;
}
}
// MAIN //
/**
* Returns a function for reshaping input ndarrays which have the same data type, shape, and strides as a provided ndarray.
*
* @private
* @param {ndarrayLike} x - input ndarray
* @param {string} x.dtype - input ndarray data type
* @param {Collection} x.data - input ndarray data buffer
* @param {NonNegativeIntegerArray} x.shape - input ndarray shape
* @param {IntegerArray} x.strides - input ndarray strides
* @param {NonNegativeInteger} x.offset - input ndarray index offset
* @param {string} x.order - input ndarray memory layout
* @returns {Function} function implementing a reshape strategy
*/
function strategy( x ) {
var ndims;
var xmmv;
var len;
var iox;
var sh;
var ns;
var i;
// Resolve the number of array dimensions:
sh = x.shape;
ndims = sh.length;
// Check whether the ndarray is zero-dimensional...
if ( ndims === 0 ) {
return broadcast;
}
// Check whether the ndarray is already one-dimensional...
if ( ndims === 1 ) {
return identity;
}
// Determine the number of singleton dimensions...
len = 1; // number of elements
ns = 0; // number of singleton dimensions
for ( i = 0; i < ndims; i++ ) {
// Check whether the current dimension is a singleton dimension...
if ( sh[ i ] === 1 ) {
ns += 1;
}
len *= sh[ i ];
}
// Determine whether the ndarray has only **one** non-singleton dimension (e.g., ndims=4, shape=[10,1,1,1]) so that we can simply create an ndarray view without the singleton dimensions...
if ( ns === ndims-1 ) {
// Get the index of the non-singleton dimension...
for ( i = 0; i < ndims; i++ ) {
if ( sh[ i ] !== 1 ) {
break;
}
}
return squeeze( x, i );
}
iox = iterationOrder( x.strides ); // +/-1
// Determine whether we can avoid copying data...
if ( iox !== 0 ) {
// Determine the minimum and maximum linear indices which are accessible by the ndarray view:
xmmv = minmaxViewBufferIndex( sh, x.strides, x.offset, [ 0, 0 ] );
// Determine whether we can ignore shape (and strides) and create a new one-dimensional ndarray view...
if ( len === ( xmmv[1]-xmmv[0]+1 ) ) {
return contiguous( len, iox );
}
// The ndarray is non-contiguous, so we cannot directly interpret as a one-dimensional ndarray...
// Fall-through to copying to a workspace ndarray...
}
// At this point, we're dealing with a non-contiguous multi-dimensional ndarray, so we need to copy to a contiguous workspace:
return copy( len, ndarraylike2object( emptyLike( ndarraylike2ndarray( x ) ) ) ); // eslint-disable-line max-len
}
// EXPORTS //
module.exports = strategy;
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