npm package: @stdlib/ndarray-base-binary-reduce-strided1d
Description: Perform a reduction over a list of specified dimensions in two input ndarrays via a one-dimensional strided array reduction function and assign results to a provided output ndarray.
Author: The Stdlib Authors
Latest version: 0.1.1

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# binaryReduceStrided1d

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

<section class="intro">

</section>

<!-- /.intro -->

<section class="installation">

## Installation

```bash
npm install @stdlib/ndarray-base-binary-reduce-strided1d
```

</section>

<section class="usage">

## Usage

```javascript
var binaryReduceStrided1d = require( '@stdlib/ndarray-base-binary-reduce-strided1d' );
```

#### binaryReduceStrided1d( fcn, arrays, dims\[, options] )

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.

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

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

The function accepts the following arguments:

-   **fcn**: function which will be applied to two one-dimensional subarrays and should reduce them to a single scalar value.
-   **arrays**: array-like object containing two input ndarrays 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_).

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).

#### 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 ndarrays. 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] )
    ```

    where

    -   **arrays**: array containing a one-dimensional subarray for each input ndarray and any additional ndarray arguments as zero-dimensional ndarrays.
    -   **options**: function options (_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 gdot = require( '@stdlib/blas-base-ndarray-gdot' );
var binaryReduceStrided1d = require( '@stdlib/ndarray-base-binary-reduce-strided1d' );

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

binaryReduceStrided1d( gdot, [ x, y, z ], [ 1 ] );

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

</section>

<!-- /.examples -->

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<section class="related">

</section>

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<section class="main-repo" >

* * *

## Notice

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For more information on the project, filing bug reports and feature requests, and guidance on how to develop [stdlib][stdlib], see the main project [repository][stdlib].

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## License

See [LICENSE][stdlib-license].


## Copyright

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