npm package: @stdlib/ndarray-base-unary-reduce-strided1d-dispatch
Description: Constructor for performing a reduction on an input ndarray.
Author: The Stdlib Authors
Latest version: 0.0.1

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# UnaryStrided1dDispatch

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> Constructor for performing a reduction on an input ndarray.

<section class="installation">

## Installation

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

</section>

<section class="usage">

## Usage

```javascript
var UnaryStrided1dDispatch = require( '@stdlib/ndarray-base-unary-reduce-strided1d-dispatch' );
```

#### UnaryStrided1dDispatch( table, idtypes, odtypes, policies )

Constructor for performing a reduction on an input ndarray.

```javascript
var base = require( '@stdlib/stats-base-ndarray-max' );

var table = {
    'default': base
};

var dtypes = [ 'float64', 'float32', 'generic' ];
var policies = {
    'output': 'same',
    'casting': 'none'
};

var unary = new UnaryStrided1dDispatch( table, [ dtypes ], dtypes, policies );
```

The constructor has the following parameters:

-   **table**: strided reduction function dispatch table. Must have the following properties:

    -   **default**: default strided reduction function which should be invoked when provided ndarrays have data types which do not have a corresponding specialized implementation.

    A dispatch table may have the following additional properties:

    -   **types**: one-dimensional list of ndarray data types describing specialized input ndarray argument signatures. Only the input ndarray argument data types should be specified. Output ndarray and additional input ndarray argument data types should be omitted and are not considered during dispatch. The length of `types` must equal the number of strided functions specified by `fcns` (i.e., for every input ndarray data type, there must be a corresponding strided reduction function in `fcns`).
    -   **fcns**: list of strided reduction functions which are specific to specialized input ndarray argument signatures.

-   **idtypes**: list containing lists of supported input data types for each input ndarray argument.

-   **odtypes**: list of supported output data types.

-   **policies**: dispatch policies. Must have the following properties:

    -   **output**: output data type [policy][@stdlib/ndarray/output-dtype-policies].
    -   **casting**: input ndarray casting [policy][@stdlib/ndarray/input-casting-policies].

#### UnaryStrided1dDispatch.prototype.apply( x\[, ...args]\[, options] )

Performs a reduction on a provided input ndarray.

```javascript
var ndarray = require( '@stdlib/ndarray-base-ctor' );
var base = require( '@stdlib/stats-base-ndarray-max' );

var table = {
    'default': base
};

var dtypes = [ 'float64', 'float32', 'generic' ];
var policies = {
    'output': 'same',
    'casting': 'none'
};

var unary = new UnaryStrided1dDispatch( table, [ dtypes ], dtypes, policies );

var xbuf = [ -1.0, 2.0, -3.0 ];
var x = new ndarray( 'generic', xbuf, [ xbuf.length ], [ 1 ], 0, 'row-major' );

var y = unary.apply( x );
// returns <ndarray>

var v = y.get();
// returns 2.0
```

The method has the following parameters:

-   **x**: input ndarray.
-   **...args**: additional input ndarray arguments (_optional_).
-   **options**: function options (_optional_).

The method accepts the following options:

-   **dims**: list of dimensions over which to perform a reduction.
-   **dtype**: output ndarray data type. Setting this option overrides the output data type policy.
-   **keepdims**: boolean indicating whether the reduced dimensions should be included in the returned ndarray as singleton dimensions. Default: `false`.

By default, the method returns an ndarray having a data type determined by the output data type policy. To override the default behavior, set the `dtype` option.

```javascript
var ndarray = require( '@stdlib/ndarray-base-ctor' );
var base = require( '@stdlib/stats-base-ndarray-max' );
var getDType = require( '@stdlib/ndarray-dtype' );

var table = {
    'default': base
};

var dtypes = [ 'float64', 'float32', 'generic' ];
var policies = {
    'output': 'same',
    'casting': 'none'
};

var unary = new UnaryStrided1dDispatch( table, [ dtypes ], dtypes, policies );

var xbuf = [ -1.0, 2.0, -3.0 ];
var x = new ndarray( 'generic', xbuf, [ xbuf.length ], [ 1 ], 0, 'row-major' );

var y = unary.apply( x, {
    'dtype': 'float64'
});
// returns <ndarray>

var dt = getDType( y );
// returns 'float64'
```

#### UnaryStrided1dDispatch.prototype.assign( x\[, ...args], out\[, options] )

Performs a reduction on a provided input ndarray and assigns results to a provided output ndarray.

```javascript
var base = require( '@stdlib/stats-base-ndarray-max' );
var dtypes = require( '@stdlib/ndarray-dtypes' );
var ndarray = require( '@stdlib/ndarray-base-ctor' );

var idt = dtypes( 'real_and_generic' );
var odt = idt;
var policies = {
    'output': 'same',
    'casting': 'none'
};

var table = {
    'default': base
};
var unary = new UnaryStrided1dDispatch( table, [ idt ], odt, policies );

var xbuf = [ -1.0, 2.0, -3.0 ];
var x = new ndarray( 'generic', xbuf, [ xbuf.length ], [ 1 ], 0, 'row-major' );

var ybuf = [ 0.0 ];
var y = new ndarray( 'generic', ybuf, [], [ 0 ], 0, 'row-major' );

var out = unary.assign( x, y );
// returns <ndarray>

var v = out.get();
// returns 2.0

var bool = ( out === y );
// returns true
```

The method has the following parameters:

-   **x**: input ndarray.
-   **args**: additional input ndarray arguments (_optional_).
-   **out**: output ndarray.
-   **options**: function options (_optional_).

The method accepts the following options:

-   **dims**: list of dimensions over which to perform a reduction.

</section>

<!-- /.usage -->

<section class="notes">

## Notes

-   A strided reduction function should have the following signature:

    ```text
    f( arrays )
    ```

    where

    -   **arrays**: array containing an input ndarray, followed by any additional ndarray arguments.

-   The output data type policy only applies to the `apply` method. For the `assign` method, the output ndarray is allowed to have any supported output data type.

</section>

<!-- /.notes -->

<section class="examples">

## Examples

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

```javascript
var dmax = require( '@stdlib/stats-base-ndarray-dmax' );
var smax = require( '@stdlib/stats-base-ndarray-smax' );
var base = require( '@stdlib/stats-base-ndarray-max' );
var uniform = require( '@stdlib/random-array-uniform' );
var dtypes = require( '@stdlib/ndarray-dtypes' );
var dtype = require( '@stdlib/ndarray-dtype' );
var ndarray2array = require( '@stdlib/ndarray-to-array' );
var ndarray = require( '@stdlib/ndarray-ctor' );
var UnaryStrided1dDispatch = require( '@stdlib/ndarray-base-unary-reduce-strided1d-dispatch' );

// Define the supported input and output data types:
var idt = dtypes( 'real_and_generic' );
var odt = dtypes( 'real_and_generic' );

// Define dispatch policies:
var policies = {
    'output': 'same',
    'casting': 'none'
};

// Define a dispatch table:
var table = {
    'types': [
        'float64', // input
        'float32'  // input
    ],
    'fcns': [
        dmax,
        smax
    ],
    'default': base
};

// Create an interface for performing a reduction:
var max = new UnaryStrided1dDispatch( table, [ idt ], odt, policies );

// Generate an array of random numbers:
var xbuf = uniform( 100, -1.0, 1.0, {
    'dtype': 'generic'
});

// Wrap in an ndarray:
var x = new ndarray( 'generic', xbuf, [ 10, 10 ], [ 10, 1 ], 0, 'row-major' );

// Perform a reduction:
var y = max.apply( x, {
    'dims': [ 0 ]
});

// Resolve the output array data type:
var dt = dtype( y );
console.log( dt );

// Print the results:
console.log( ndarray2array( y ) );
```

</section>

<!-- /.examples -->

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

<section class="related">

</section>

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---

## License

See [LICENSE][stdlib-license].


## Copyright

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