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feat: add ndarray/base/unary-reduce-strided1d
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‎lib/node_modules/@stdlib/ndarray/base/unary-reduce-strided1d/README.md

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<!--
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@license Apache-2.0
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Copyright (c) 2025 The Stdlib Authors.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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# unaryReduceStrided1d
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> Perform a reduction over a list of specified dimensions in an input ndarray via a one-dimensional strided array reduction function and assign results to a provided output ndarray.
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<section class="intro">
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</section>
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<!-- /.intro -->
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<section class="usage">
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## Usage
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```javascript
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var unaryReduceStrided1d = require( '@stdlib/ndarray/base/unary-reduce-strided1d' );
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```
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#### unaryReduceStrided1d( fcn, arrays, dims\[, options] )
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Performs a reduction over a list of specified dimensions in an input ndarray via a one-dimensional strided array reduction function and assigns results to a provided output ndarray.
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<!-- eslint-disable max-len -->
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```javascript
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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 getStride = require( '@stdlib/ndarray/base/stride' );
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var getOffset = require( '@stdlib/ndarray/base/offset' );
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var getData = require( '@stdlib/ndarray/base/data-buffer' );
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var numelDimension = require( '@stdlib/ndarray/base/numel-dimension' );
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var gsum = require( '@stdlib/blas/ext/base/gsum' ).ndarray;
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function wrapper( arrays ) {
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var x = arrays[ 0 ];
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return gsum( numelDimension( x, 0 ), getData( x ), getStride( x, 0 ), getOffset( x ) );
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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 Float64Array( [ 0.0, 0.0, 0.0 ] );
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// Define the array shapes:
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var xsh = [ 1, 3, 2, 2 ];
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var ysh = [ 1, 3 ];
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// Define the array strides:
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var sx = [ 12, 4, 2, 1 ];
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var sy = [ 3, 1 ];
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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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// 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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// Create an output ndarray-like object:
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var y = {
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'dtype': 'float64',
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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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// Perform a reduction:
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unaryReduceStrided1d( wrapper, [ x, y ], [ 2, 3 ] );
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var arr = ndarray2array( y.data, y.shape, y.strides, y.offset, y.order );
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// returns [ [ 10.0, 26.0, 42.0 ] ]
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```
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The function accepts the following arguments:
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- **fcn**: function which will be applied to a one-dimensional subarray and should reduce the subarray to a single scalar value.
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- **arrays**: array-like object containing one input ndarray and one output ndarray, followed by any additional ndarray arguments.
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- **dims**: list of dimensions over which to perform a reduction.
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- **options**: function options which are passed through to `fcn` (_optional_).
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Each provided ndarray should be an object with the following properties:
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- **dtype**: data type.
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- **data**: data buffer.
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- **shape**: dimensions.
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- **strides**: stride lengths.
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- **offset**: index offset.
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- **order**: specifies whether an ndarray is row-major (C-style) or column major (Fortran-style).
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</section>
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<!-- /.usage -->
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<section class="notes">
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## Notes
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- The output ndarray and any additional ndarray arguments are expected to have the same dimensions as the non-reduced dimensions of the input ndarray. When calling the reduction function, any additional ndarray arguments are provided as zero-dimensional ndarray-like objects.
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- The reduction function is expected to have the following signature:
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```text
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fcn( arrays[, options] )
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```
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where
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- **arrays**: array containing a one-dimensional subarray of the input ndarray and any additional ndarray arguments as zero-dimensional ndarrays.
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- **options**: function options (_optional_).
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- For very high-dimensional ndarrays which are non-contiguous, one should consider copying the underlying data to contiguous memory before performing a reduction in order to achieve better performance.
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</section>
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<!-- /.notes -->
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<section class="examples">
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## Examples
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<!-- eslint no-undef: "error" -->
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```javascript
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var discreteUniform = require( '@stdlib/random/array/discrete-uniform' );
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var zeros = require( '@stdlib/array/base/zeros' );
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var ndarray2array = require( '@stdlib/ndarray/base/to-array' );
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var numelDimension = require( '@stdlib/ndarray/base/numel-dimension' );
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var getData = require( '@stdlib/ndarray/base/data-buffer' );
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var getStride = require( '@stdlib/ndarray/base/stride' );
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var getOffset = require( '@stdlib/ndarray/base/offset' );
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var gsum = require( '@stdlib/blas/ext/base/gsum' ).ndarray;
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var unaryReduceStrided1d = require( '@stdlib/ndarray/base/unary-reduce-strided1d' );
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function wrapper( arrays ) {
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var x = arrays[ 0 ];
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return gsum( numelDimension( x, 0 ), getData( x ), getStride( x, 0 ), getOffset( x ) ); // eslint-disable-line max-len
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}
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var N = 10;
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var x = {
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'dtype': 'generic',
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'data': discreteUniform( N, -5, 5, {
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'dtype': 'generic'
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}),
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'shape': [ 1, 5, 2 ],
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'strides': [ 10, 2, 1 ],
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'offset': 0,
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'order': 'row-major'
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};
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var y = {
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'dtype': 'generic',
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'data': zeros( 2 ),
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'shape': [ 1, 2 ],
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'strides': [ 2, 1 ],
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'offset': 0,
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'order': 'row-major'
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};
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unaryReduceStrided1d( wrapper, [ x, y ], [ 1 ] );
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console.log( ndarray2array( x.data, x.shape, x.strides, x.offset, x.order ) );
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console.log( ndarray2array( y.data, y.shape, y.strides, y.offset, y.order ) );
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```
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</section>
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<!-- /.examples -->
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<!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->
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<section class="related">
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</section>
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<!-- /.related -->
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<section class="links">
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</section>
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<!-- /.links -->

‎lib/node_modules/@stdlib/ndarray/base/unary-reduce-strided1d/docs/repl.txt

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{{alias}}( fcn, arrays, dims[, options] )
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Performs a reduction over a list of specified dimensions in an input ndarray
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via a one-dimensional strided array reduction function and assigns results
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to a provided output ndarray.
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Each provided "ndarray" should be an object with the following properties:
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- dtype: data type.
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- data: data buffer.
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- shape: dimensions.
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- strides: stride lengths.
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- offset: index offset.
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- order: specifies whether an ndarray is row-major (C-style) or column-major
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(Fortran-style).
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The output ndarray and any additional ndarray arguments are expected to have
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the same dimensions as the non-reduced dimensions of the input ndarray. When
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calling the reduction function, any additional ndarray arguments are
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provided as zero-dimensional ndarray-like objects.
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Parameters
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----------
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fcn: Function
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Function which will be applied to a one-dimensional subarray and should
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reduce the subarray to a single scalar value. The function should have
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the following signature:
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fcn( arrays[, options] )
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where
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- arrays: array containing a one-dimensional subarray of the input
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ndarray and any additional ndarray arguments as zero-dimensional
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ndarrays.
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- options: function options.
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arrays: ArrayLikeObject<ndarray>
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Array-like object containing one input ndarray and one output ndarray,
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followed by any additional ndarray arguments.
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dims: Array<integer>
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List of dimensions over which to perform a reduction.
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options: Object (optional)
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Function options.
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Examples
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--------
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// Define ndarray data and meta data...
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> var xbuf = new {{alias:@stdlib/array/float64}}( [ 1.0, 2.0, 3.0, 4.0 ] );
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> var ybuf = new {{alias:@stdlib/array/float64}}( [ 0.0 ] );
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> var dtype = 'float64';
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> var shx = [ 2, 2 ];
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> var shy = [];
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> var sx = [ 2, 1 ];
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> var sy = [ 0 ];
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> var ox = 0;
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> var oy = 0;
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> var order = 'row-major';
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// Define a wrapper for an extended BLAS function...
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> function fcn( arrays ) {
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... var x = arrays[ 0 ];
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... var N = x.shape[ 0 ];
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... var d = x.data;
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... var s = x.strides[ 0 ];
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... var o = x.offset;
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... return {{alias:@stdlib/blas/ext/base/gsum}}.ndarray( N, d, s, o );
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... };
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// Using minimal ndarray-like objects...
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> x = {
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... 'dtype': dtype,
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... 'data': xbuf,
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... 'shape': shx,
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... 'strides': sx,
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... 'offset': ox,
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... 'order': order
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... };
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> y = {
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... 'dtype': dtype,
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... 'data': ybuf,
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... 'shape': shy,
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... 'strides': sy,
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... 'offset': oy,
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... 'order': order
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... };
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> {{alias}}( fcn, [ x, y ], [ 0, 1 ] );
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> y.data
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<Float64Array>[ 10.0 ]
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See Also
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--------
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‎lib/node_modules/@stdlib/ndarray/base/unary-reduce-strided1d/examples/index.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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'use strict';
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var discreteUniform = require( '@stdlib/random/array/discrete-uniform' );
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var zeros = require( '@stdlib/array/base/zeros' );
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var ndarray2array = require( '@stdlib/ndarray/base/to-array' );
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var numelDimension = require( '@stdlib/ndarray/base/numel-dimension' );
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var getData = require( '@stdlib/ndarray/base/data-buffer' );
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var getStride = require( '@stdlib/ndarray/base/stride' );
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var getOffset = require( '@stdlib/ndarray/base/offset' );
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var gsum = require( '@stdlib/blas/ext/base/gsum' ).ndarray;
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var unaryReduceStrided1d = require( './../lib' );
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function wrapper( arrays ) {
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var x = arrays[ 0 ];
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return gsum( numelDimension( x, 0 ), getData( x ), getStride( x, 0 ), getOffset( x ) ); // eslint-disable-line max-len
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}
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var N = 10;
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var x = {
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'dtype': 'generic',
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'data': discreteUniform( N, -5, 5, {
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'dtype': 'generic'
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}),
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'shape': [ 1, 5, 2 ],
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'strides': [ 10, 2, 1 ],
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'offset': 0,
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'order': 'row-major'
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};
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var y = {
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'dtype': 'generic',
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'data': zeros( 2 ),
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'shape': [ 1, 2 ],
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'strides': [ 2, 1 ],
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'offset': 0,
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'order': 'row-major'
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};
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unaryReduceStrided1d( wrapper, [ x, y ], [ 1 ] );
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console.log( ndarray2array( x.data, x.shape, x.strides, x.offset, x.order ) );
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console.log( ndarray2array( y.data, y.shape, y.strides, y.offset, y.order ) );

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