npm package: @stdlib/blas-ext-base-ndarray-scusumors
Description: Compute the cumulative sum of a one-dimensional single-precision floating-point ndarray using ordinary recursive summation.
Author: The Stdlib Authors
Latest version: 0.1.1

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# scusumors

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> Compute the cumulative sum of a one-dimensional single-precision floating-point ndarray using ordinary recursive summation.

<section class="intro">

</section>

<!-- /.intro -->

<section class="installation">

## Installation

```bash
npm install @stdlib/blas-ext-base-ndarray-scusumors
```

</section>

<section class="usage">

## Usage

```javascript
var scusumors = require( '@stdlib/blas-ext-base-ndarray-scusumors' );
```

#### scusumors( arrays )

Computes the cumulative sum of a one-dimensional single-precision floating-point ndarray using ordinary recursive summation.

```javascript
var Float32Array = require( '@stdlib/array-float32' );
var ndarray2array = require( '@stdlib/ndarray-to-array' );
var scalar2ndarray = require( '@stdlib/ndarray-base-from-scalar' );
var ndarray = require( '@stdlib/ndarray-base-ctor' );

var xbuf = new Float32Array( [ 1.0, 3.0, 4.0, 2.0 ] );
var x = new ndarray( 'float32', xbuf, [ 4 ], [ 1 ], 0, 'row-major' );

var ybuf = new Float32Array( [ 0.0, 0.0, 0.0, 0.0 ] );
var y = new ndarray( 'float32', ybuf, [ 4 ], [ 1 ], 0, 'row-major' );

var initial = scalar2ndarray( 0.0, 'float32', 'row-major' );

var v = scusumors( [ x, y, initial ] );
// returns <ndarray>

var bool = ( v === y );
// returns true

var arr = ndarray2array( v );
// returns [ 1.0, 4.0, 8.0, 10.0 ]
```

The function has the following parameters:

-   **arrays**: array-like object containing a one-dimensional input ndarray, a one-dimensional output ndarray, and a zero-dimensional ndarray containing the initial sum.

</section>

<!-- /.usage -->

<section class="notes">

## Notes

-   If provided an empty one-dimensional input ndarray, the function returns the output ndarray unchanged.
-   Ordinary recursive summation (i.e., a "simple" sum) is performant, but can incur significant numerical error. If performance is paramount and error tolerated, using ordinary recursive summation is acceptable; in all other cases, exercise due caution.

</section>

<!-- /.notes -->

<section class="examples">

## Examples

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

```javascript
var discreteUniform = require( '@stdlib/random-array-discrete-uniform' );
var ndarray = require( '@stdlib/ndarray-base-ctor' );
var zerosLike = require( '@stdlib/ndarray-zeros-like' );
var scalar2ndarray = require( '@stdlib/ndarray-from-scalar' );
var ndarray2array = require( '@stdlib/ndarray-to-array' );
var scusumors = require( '@stdlib/blas-ext-base-ndarray-scusumors' );

var xbuf = discreteUniform( 10, -50, 50, {
    'dtype': 'float32'
});
var x = new ndarray( 'float32', xbuf, [ xbuf.length ], [ 1 ], 0, 'row-major' );
console.log( ndarray2array( x ) );

var y = zerosLike( x );
console.log( ndarray2array( y ) );

var initial = scalar2ndarray( 100.0, {
    'dtype': 'float32'
});

var v = scusumors( [ x, y, initial ] );
console.log( ndarray2array( v ) );
```

</section>

<!-- /.examples -->

<section class="references">

</section>

<!-- /.references -->

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<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

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---

## License

See [LICENSE][stdlib-license].


## Copyright

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

</section>

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[umd]: https://github.com/umdjs/umd
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[deno-url]: https://github.com/stdlib-js/blas-ext-base-ndarray-scusumors/tree/deno
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