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Merged
merged 16 commits into from
Jun 6, 2025
Merged
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# unaryReduceStrided1dBy

> Perform a reduction over a list of specified dimensions in an input ndarray via a one-dimensional strided array reduction function accepting a callback and assign results to a provided output ndarray.

<section class="intro">

</section>

<!-- /.intro -->

<section class="usage">

## Usage

```javascript
var unaryReduceStrided1dBy = require( '@stdlib/ndarray/base/unary-reduce-strided1d-by' );
```

#### unaryReduceStrided1dBy( fcn, arrays, dims\[, options], clbk\[, thisArg] )

Performs a reduction over a list of specified dimensions in an input ndarray via a one-dimensional strided array reduction function accepting a callback and assigns results to a provided output ndarray.

<!-- eslint-disable max-len -->

```javascript
var Float64Array = require( '@stdlib/array/float64' );
var ndarray2array = require( '@stdlib/ndarray/base/to-array' );
var maxBy = require( '@stdlib/stats/base/ndarray/max-by' );

// Define a callback function:
function clbk( value ) {
return value * 2.0;
}

// 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( [ 0.0, 0.0, 0.0 ] );

// Define the array shapes:
var xsh = [ 1, 3, 2, 2 ];
var ysh = [ 1, 3 ];

// Define the array strides:
var sx = [ 12, 4, 2, 1 ];
var sy = [ 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': 'float64',
'data': ybuf,
'shape': ysh,
'strides': sy,
'offset': oy,
'order': 'row-major'
};

// Perform a reduction:
unaryReduceStrided1dBy( maxBy, [ x, y ], [ 2, 3 ], clbk );

var arr = ndarray2array( y.data, y.shape, y.strides, y.offset, y.order );
// returns [ [ 8.0, 16.0, 24.0 ] ]
```

The function accepts the following arguments:

- **fcn**: function which will be applied to a one-dimensional subarray and should reduce the subarray to a single scalar value.
- **arrays**: array-like object containing one input ndarray and one output ndarray, followed by any additional ndarray arguments.
- **dims**: list of dimensions over which to perform a reduction.
- **options**: function options which are passed through to `fcn` (_optional_).
- **clbk**: callback function.
- **thisArg**: callback execution context (_optional_).

Each provided ndarray should be an object with the following properties:

- **dtype**: data type.
- **data**: data buffer.
- **shape**: dimensions.
- **strides**: stride lengths.
- **offset**: index offset.
- **order**: specifies whether an ndarray is row-major (C-style) or column major (Fortran-style).

The invoked callback function is provided the following arguments:

- **value**: current array element.
- **indices**: current array element indices.
- **arr**: the input ndarray.

To set the callback execution context, provide a `thisArg`.

<!-- eslint-disable max-len -->

```javascript
var Float64Array = require( '@stdlib/array/float64' );
var ndarray2array = require( '@stdlib/ndarray/base/to-array' );
var maxBy = require( '@stdlib/stats/base/ndarray/max-by' );

// Define a callback function:
function clbk( value ) {
this.count += 1;
return value * 2.0;
}

// 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( [ 0.0, 0.0, 0.0 ] );

// Define the array shapes:
var xsh = [ 1, 3, 2, 2 ];
var ysh = [ 1, 3 ];

// Define the array strides:
var sx = [ 12, 4, 2, 1 ];
var sy = [ 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': 'float64',
'data': ybuf,
'shape': ysh,
'strides': sy,
'offset': oy,
'order': 'row-major'
};

// Define callback execution context:
var ctx = {
'count': 0
};

// Perform a reduction:
unaryReduceStrided1dBy( maxBy, [ x, y ], [ 2, 3 ], clbk, ctx );

var arr = ndarray2array( y.data, y.shape, y.strides, y.offset, y.order );
// returns [ [ 8.0, 16.0, 24.0 ] ]

var count = ctx.count;
// returns 12
```

#### TODO: document factory method

</section>

<!-- /.usage -->

<section class="notes">

## Notes

- 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.

- The reduction function is expected to have the following signature:

```text
fcn( arrays[, options], clbk[, thisArg ] )
```

where

- **arrays**: array containing a one-dimensional subarray of the input ndarray and any additional ndarray arguments as zero-dimensional ndarrays.
- **options**: function options (_optional_).
- **clbk**: callback function.
- **thisArg**: callback execution context (_optional_).

- 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.

</section>

<!-- /.notes -->

<section class="examples">

## Examples

<!-- eslint no-undef: "error" -->

```javascript
var discreteUniform = require( '@stdlib/random/array/discrete-uniform' );
var zeros = require( '@stdlib/array/base/zeros' );
var ndarray2array = require( '@stdlib/ndarray/base/to-array' );
var maxBy = require( '@stdlib/stats/base/ndarray/max-by' );
var unaryReduceStrided1dBy = require( '@stdlib/ndarray/base/unary-reduce-strided1d-by' );

function clbk( value ) {
return value * 2.0;
}

var N = 10;
var x = {
'dtype': 'generic',
'data': discreteUniform( N, -5, 5, {
'dtype': 'generic'
}),
'shape': [ 1, 5, 2 ],
'strides': [ 10, 2, 1 ],
'offset': 0,
'order': 'row-major'
};
var y = {
'dtype': 'generic',
'data': zeros( 2 ),
'shape': [ 1, 5 ],
'strides': [ 5, 1 ],
'offset': 0,
'order': 'row-major'
};

unaryReduceStrided1dBy( maxBy, [ x, y ], [ 2 ], clbk );

console.log( ndarray2array( x.data, x.shape, x.strides, x.offset, x.order ) );
console.log( ndarray2array( y.data, y.shape, y.strides, y.offset, y.order ) );
```

</section>

<!-- /.examples -->

<!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->

<section class="related">

</section>

<!-- /.related -->

<section class="links">

</section>

<!-- /.links -->
Original file line number Diff line number Diff line change
@@ -0,0 +1,106 @@

{{alias}}( fcn, arrays, dims[, options], clbk[, thisArg] )
Performs a reduction over a list of specified dimensions in an input ndarray
via a one-dimensional strided array reduction function accepting a callback
and assigns results to a provided output ndarray.

Each provided "ndarray" should be an object with the following properties:

- dtype: data type.
- data: data buffer.
- shape: dimensions.
- strides: stride lengths.
- offset: index offset.
- order: specifies whether an ndarray is row-major (C-style) or column-major
(Fortran-style).

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.

Parameters
----------
fcn: Function
Function which will be applied to a one-dimensional subarray and should
reduce the subarray to a single scalar value. The function should have
the following signature:

fcn( arrays[, options], clbk[, thisArg] )

where

- arrays: array containing a one-dimensional subarray of the input
ndarray and any additional ndarray arguments as zero-dimensional
ndarrays.
- options: function options.
- clbk: callback function.
- thisArg: callback execution context.

arrays: ArrayLikeObject<ndarray>
Array-like object containing one input ndarray and one output ndarray,
followed by any additional ndarray arguments.

dims: Array<integer>
List of dimensions over which to perform a reduction.

options: Object (optional)
Function options.

clbk: Function
Callback function.

thisArg: any (optional)
Callback execution context.

Examples
--------
// Define ndarray data and meta data...
> var xbuf = new {{alias:@stdlib/array/float64}}( [ 1.0, 2.0, 3.0, 4.0 ] );
> var ybuf = new {{alias:@stdlib/array/float64}}( [ 0.0 ] );
> var dtype = 'float64';
> var shx = [ 2, 2 ];
> var shy = [];
> var sx = [ 2, 1 ];
> var sy = [ 0 ];
> var ox = 0;
> var oy = 0;
> var order = 'row-major';

// Define a callback function...
> function clbk( value ) { return value * 2.0; };

// Define a wrapper for a statistical function...
> function fcn( arrays, cb, ctx ) {
... var x = arrays[ 0 ];
... var N = x.shape[ 0 ];
... var d = x.data;
... var s = x.strides[ 0 ];
... var o = x.offset;
... return {{alias:@stdlib/stats/base/max-by}}.ndarray( N, d, s, o, cb, ctx );
... };

// Using minimal ndarray-like objects...
> x = {
... 'dtype': dtype,
... 'data': xbuf,
... 'shape': shx,
... 'strides': sx,
... 'offset': ox,
... 'order': order
... };
> y = {
... 'dtype': dtype,
... 'data': ybuf,
... 'shape': shy,
... 'strides': sy,
... 'offset': oy,
... 'order': order
... };
> {{alias}}( fcn, [ x, y ], [ 0, 1 ], clbk );
> y.data
<Float64Array>[ 8.0 ]

See Also
--------

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