npm package: @stdlib/blas-ext-sum
Description: Compute the sum along one or more ndarray dimensions.
Author: The Stdlib Authors
Latest version: 0.1.1

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# sum

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> Compute the sum along one or more [ndarray][@stdlib/ndarray/ctor] dimensions.

<section class="installation">

## Installation

```bash
npm install @stdlib/blas-ext-sum
```

</section>

<section class="usage">

## Usage

```javascript
var sum = require( '@stdlib/blas-ext-sum' );
```

#### sum( x\[, options] )

Computes the sum along one or more [ndarray][@stdlib/ndarray/ctor] dimensions.

```javascript
var array = require( '@stdlib/ndarray-array' );

var x = array( [ -1.0, 2.0, -3.0 ] );

var y = sum( x );
// returns <ndarray>

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

The function has the following parameters:

-   **x**: input [ndarray][@stdlib/ndarray/ctor]. Must have a numeric or "generic" [data type][@stdlib/ndarray/dtypes].
-   **options**: function options (_optional_).

The function accepts the following options:

-   **dims**: list of dimensions over which to perform a reduction. If not provided, the function performs a reduction over all elements in a provided input [ndarray][@stdlib/ndarray/ctor].
-   **dtype**: output ndarray [data type][@stdlib/ndarray/dtypes]. Must be a numeric or "generic" [data type][@stdlib/ndarray/dtypes].
-   **keepdims**: boolean indicating whether the reduced dimensions should be included in the returned [ndarray][@stdlib/ndarray/ctor] as singleton dimensions. Default: `false`.

By default, the function performs a reduction over all elements in a provided input [ndarray][@stdlib/ndarray/ctor]. To perform a reduction over specific dimensions, provide a `dims` option.

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

var x = array( [ -1.0, 2.0, -3.0, 4.0 ], {
    'shape': [ 2, 2 ],
    'order': 'row-major'
});

var v = ndarray2array( x );
// returns [ [ -1.0, 2.0 ], [ -3.0, 4.0 ] ]

var y = sum( x, {
    'dims': [ 0 ]
});
// returns <ndarray>

v = ndarray2array( y );
// returns [ -4.0, 6.0 ]

y = sum( x, {
    'dims': [ 1 ]
});
// returns <ndarray>

v = ndarray2array( y );
// returns [ 1.0, 1.0 ]

y = sum( x, {
    'dims': [ 0, 1 ]
});
// returns <ndarray>

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

By default, the function excludes reduced dimensions from the output [ndarray][@stdlib/ndarray/ctor]. To include the reduced dimensions as singleton dimensions, set the `keepdims` option to `true`.

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

var x = array( [ -1.0, 2.0, -3.0, 4.0 ], {
    'shape': [ 2, 2 ],
    'order': 'row-major'
});

var v = ndarray2array( x );
// returns [ [ -1.0, 2.0 ], [ -3.0, 4.0 ] ]

var y = sum( x, {
    'dims': [ 0 ],
    'keepdims': true
});
// returns <ndarray>

v = ndarray2array( y );
// returns [ [ -4.0, 6.0 ] ]

y = sum( x, {
    'dims': [ 1 ],
    'keepdims': true
});
// returns <ndarray>

v = ndarray2array( y );
// returns [ [ 1.0 ], [ 1.0 ] ]

y = sum( x, {
    'dims': [ 0, 1 ],
    'keepdims': true
});
// returns <ndarray>

v = ndarray2array( y );
// returns [ [ 2.0 ] ]
```

By default, the function returns an [ndarray][@stdlib/ndarray/ctor] having a [data type][@stdlib/ndarray/dtypes] determined by the function's output data type [policy][@stdlib/ndarray/output-dtype-policies]. To override the default behavior, set the `dtype` option.

```javascript
var getDType = require( '@stdlib/ndarray-dtype' );
var array = require( '@stdlib/ndarray-array' );

var x = array( [ -1.0, 2.0, -3.0 ], {
    'dtype': 'generic'
});

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

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

#### sum.assign( x, out\[, options] )

Computes the sum along one or more [ndarray][@stdlib/ndarray/ctor] dimensions and assigns results to a provided output [ndarray][@stdlib/ndarray/ctor].

```javascript
var array = require( '@stdlib/ndarray-array' );
var zeros = require( '@stdlib/ndarray-zeros' );

var x = array( [ -1.0, 2.0, -3.0 ] );
var y = zeros( [] );

var out = sum.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][@stdlib/ndarray/ctor]. Must have a numeric or generic [data type][@stdlib/ndarray/dtypes].
-   **out**: output [ndarray][@stdlib/ndarray/ctor].
-   **options**: function options (_optional_).

The method accepts the following options:

-   **dims**: list of dimensions over which to perform a reduction. If not provided, the function performs a reduction over all elements in a provided input [ndarray][@stdlib/ndarray/ctor].

</section>

<!-- /.usage -->

<section class="notes">

## Notes

-   Setting the `keepdims` option to `true` can be useful when wanting to ensure that the output [ndarray][@stdlib/ndarray/ctor] is [broadcast-compatible][@stdlib/ndarray/base/broadcast-shapes] with ndarrays having the same shape as the input [ndarray][@stdlib/ndarray/ctor].
-   The output data type [policy][@stdlib/ndarray/output-dtype-policies] only applies to the main function and specifies that, by default, in order to avoid issues arising from integer overflow, the function must return an [ndarray][@stdlib/ndarray/ctor] having a [data type][@stdlib/ndarray/dtypes] amenable to accumulation. This means that, for integer data types having small value ranges (e.g., `int8`, `uint8`, etc), the main function returns an [ndarray][@stdlib/ndarray/ctor] having at least a 32-bit integer data type. By default, if an input [ndarray][@stdlib/ndarray/ctor] has a floating-point data type, the main function returns an [ndarray][@stdlib/ndarray/ctor] having the same data type. For the `assign` method, the output [ndarray][@stdlib/ndarray/ctor] is allowed to have any supported output [data type][@stdlib/ndarray/dtypes].
-   When summing a large number of lower precision floating-point numbers (e.g., as found in an [ndarray][@stdlib/ndarray/ctor] having a `'float32'` data type), the accumulated numerical error can become significant. In such cases, casting the input [ndarray][@stdlib/ndarray/ctor] to a higher precision floating-point data type, such as `'float64'`, prior to computation is advisable.

</section>

<!-- /.notes -->

<section class="examples">

## Examples

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

```javascript
var discreteUniform = require( '@stdlib/random-array-discrete-uniform' );
var getDType = require( '@stdlib/ndarray-dtype' );
var ndarray2array = require( '@stdlib/ndarray-to-array' );
var ndarray = require( '@stdlib/ndarray-ctor' );
var sum = require( '@stdlib/blas-ext-sum' );

// Generate an array of random numbers:
var xbuf = discreteUniform( 25, 0, 20, {
    'dtype': 'generic'
});

// Wrap in an ndarray:
var x = new ndarray( 'generic', xbuf, [ 5, 5 ], [ 5, 1 ], 0, 'row-major' );
console.log( ndarray2array( x ) );

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

// Resolve the output array data type:
var dt = getDType( 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>

<!-- /.related -->

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<section class="main-repo" >

* * *

## Notice

This package is part of [stdlib][stdlib], a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.

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].

#### Community

[![Chat][chat-image]][chat-url]

---

## License

See [LICENSE][stdlib-license].


## Copyright

Copyright &copy; 2016-2026. The Stdlib [Authors][stdlib-authors].

</section>

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[@stdlib/ndarray/ctor]: https://www.npmjs.com/package/@stdlib/ndarray-ctor

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