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feat: add C ndarray API and refactor blas/ext/base/dsnansumpw
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PR-URL: stdlib-js#3262

Co-authored-by: Philipp Burckhardt <[email protected]>
Reviewed-by: Philipp Burckhardt <[email protected]>
Signed-off-by: Muhammad Haris <[email protected]>
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headlessNode and Planeshifter authored Nov 26, 2024
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135 changes: 124 additions & 11 deletions lib/node_modules/@stdlib/blas/ext/base/dsnansumpw/README.md
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Expand Up @@ -36,27 +36,26 @@ limitations under the License.
var dsnansumpw = require( '@stdlib/blas/ext/base/dsnansumpw' );
```

#### dsnansumpw( N, x, stride )
#### dsnansumpw( N, x, strideX )

Computes the sum of single-precision floating-point strided array elements, ignoring `NaN` values, using pairwise summation with extended accumulation, and returning an extended precision result.

```javascript
var Float32Array = require( '@stdlib/array/float32' );

var x = new Float32Array( [ 1.0, -2.0, NaN, 2.0 ] );
var N = x.length;

var v = dsnansumpw( N, x, 1 );
var v = dsnansumpw( x.length, x, 1 );
// returns 1.0
```

The function has the following parameters:

- **N**: number of indexed elements.
- **x**: input [`Float32Array`][@stdlib/array/float32].
- **stride**: index increment for `x`.
- **stride**: stride length for `x`.

The `N` and `stride` parameters determine which elements in the strided array are accessed at runtime. For example, to compute the sum of every other element in `x`,
The `N` and stride parameters determine which elements in the strided array are accessed at runtime. For example, to compute the sum of every other element:

```javascript
var Float32Array = require( '@stdlib/array/float32' );
Expand All @@ -81,25 +80,24 @@ var v = dsnansumpw( 4, x1, 2 );
// returns 5.0
```

#### dsnansumpw.ndarray( N, x, stride, offset )
#### dsnansumpw.ndarray( N, x, strideX, offsetX )

Computes the sum of single-precision floating-point strided array elements, ignoring `NaN` values and using pairwise summation with extended accumulation and alternative indexing semantics.
Computes the sum of single-precision floating-point strided array elements, ignoring `NaN` values, using pairwise summation with extended accumulation and alternative indexing semantics, and returning an extended precision result.

```javascript
var Float32Array = require( '@stdlib/array/float32' );

var x = new Float32Array( [ 1.0, -2.0, NaN, 2.0 ] );
var N = x.length;

var v = dsnansumpw.ndarray( N, x, 1, 0 );
var v = dsnansumpw.ndarray( x.length, x, 1, 0 );
// returns 1.0
```

The function has the following additional parameters:

- **offset**: starting index for `x`.
- **offsetX**: starting index for `x`.

While [`typed array`][mdn-typed-array] views mandate a view offset based on the underlying `buffer`, the `offset` parameter supports indexing semantics based on a starting index. For example, to calculate the sum of every other value in `x` starting from the second value
While [`typed array`][mdn-typed-array] views mandate a view offset based on the underlying `buffer`, the offset parameter supports indexing semantics based on a starting index. For example, to calculate the sum of every other element starting from the second element:

```javascript
var Float32Array = require( '@stdlib/array/float32' );
Expand Down Expand Up @@ -155,8 +153,123 @@ console.log( v );

<!-- /.examples -->

<!-- C interface documentation. -->

* * *

<section class="c">

## C APIs

<!-- Section to include introductory text. Make sure to keep an empty line after the intro `section` element and another before the `/section` close. -->

<section class="intro">

</section>

<!-- /.intro -->

<!-- C usage documentation. -->

<section class="usage">

### Usage

```c
#include "stdlib/blas/ext/base/dsnansumpw.h"
```

#### stdlib_strided_dsnansumpw( N, \*X, strideX )

Computes the sum of single-precision floating-point strided array elements, ignoring `NaN` values, using pairwise summation with extended accumulation, and returning an extended precision result.

```c
const float x[] = { 1.0f, -2.0f, 0.0f/0.0f, 2.0f };

double v = stdlib_strided_dsnansumpw( 4, x, 1 );
// returns 1.0
```
The function accepts the following arguments:
- **N**: `[in] CBLAS_INT` number of indexed elements.
- **X**: `[in] float*` input array.
- **strideX**: `[in] CBLAS_INT` stride length for `X`.
```c
double stdlib_strided_dsnansumpw( const CBLAS_INT N, const float *X, const CBLAS_INT strideX );
```

#### stdlib_strided_dsnansumpw_ndarray( N, \*X, strideX, offsetX )

Computes the sum of single-precision floating-point strided array elements, ignoring `NaN` values, using pairwise summation with extended accumulation and alternative indexing semantics, and returning an extended precision result.

```c
const float x[] = { 1.0f, -2.0f, 0.0f/0.0f, 2.0f };

double v = stdlib_strided_dsnansumpw_ndarray( 4, x, 1, 0 );
// returns 1.0
```
The function accepts the following arguments:
- **N**: `[in] CBLAS_INT` number of indexed elements.
- **X**: `[in] float*` input array.
- **strideX**: `[in] CBLAS_INT` stride length for `X`.
- **offsetX**: `[in] CBLAS_INT` starting index for `X`.
```c
double stdlib_strided_dsnansumpw_ndarray( const CBLAS_INT N, const float *X, const CBLAS_INT strideX, const CBLAS_INT offsetX );
```

</section>

<!-- /.usage -->

<!-- C API usage notes. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->

<section class="notes">

</section>

<!-- /.notes -->

<!-- C API usage examples. -->

<section class="examples">

### Examples

```c
#include "stdlib/blas/ext/base/dsnansumpw.h"
#include <stdio.h>

int main( void ) {
// Create a strided array:
const float x[] = { 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 0.0f/0.0f, 0.0f/0.0f };

// Specify the number of elements:
const int N = 5;

// Specify the stride length:
const int strideX = 2;

// Compute the sum:
double v = stdlib_strided_dsnansumpw( N, x, strideX );

// Print the result:
printf( "sum: %lf\n", v );
}
```
</section>
<!-- /.examples -->
</section>
<!-- /.c -->
<section class="references">
## References
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Expand Up @@ -32,6 +32,19 @@ var dsnansumpw = require( './../lib/dsnansumpw.js' );

// FUNCTIONS //

/**
* Returns a random number.
*
* @private
* @returns {number} random number
*/
function rand() {
if ( bernoulli( 0.8 ) > 0 ) {
return NaN;
}
return uniform( -10, 10 );
}

/**
* Creates a benchmark function.
*
Expand All @@ -43,13 +56,6 @@ function createBenchmark( len ) {
var x = filledarrayBy( len, 'float32', rand );
return benchmark;

function rand() {
if ( bernoulli( 0.8 ) > 0 ) {
return NaN;
}
return uniform( -10, 10 );
}

function benchmark( b ) {
var v;
var i;
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Expand Up @@ -41,6 +41,19 @@ var opts = {

// FUNCTIONS //

/**
* Returns a random number.
*
* @private
* @returns {number} random number
*/
function rand() {
if ( bernoulli( 0.8 ) > 0 ) {
return NaN;
}
return uniform( -10, 10 );
}

/**
* Creates a benchmark function.
*
Expand All @@ -52,13 +65,6 @@ function createBenchmark( len ) {
var x = filledarrayBy( len, 'float32', rand );
return benchmark;

function rand() {
if ( bernoulli( 0.8 ) > 0 ) {
return NaN;
}
return uniform( -10, 10 );
}

function benchmark( b ) {
var v;
var i;
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Expand Up @@ -32,6 +32,19 @@ var dsnansumpw = require( './../lib/ndarray.js' );

// FUNCTIONS //

/**
* Returns a random number.
*
* @private
* @returns {number} random number
*/
function rand() {
if ( bernoulli( 0.8 ) > 0 ) {
return NaN;
}
return uniform( -10, 10 );
}

/**
* Creates a benchmark function.
*
Expand All @@ -43,13 +56,6 @@ function createBenchmark( len ) {
var x = filledarrayBy( len, 'float32', rand );
return benchmark;

function rand() {
if ( bernoulli( 0.8 ) > 0 ) {
return NaN;
}
return uniform( -10, 10 );
}

function benchmark( b ) {
var v;
var i;
Expand Down
Original file line number Diff line number Diff line change
Expand Up @@ -41,6 +41,19 @@ var opts = {

// FUNCTIONS //

/**
* Returns a random number.
*
* @private
* @returns {number} random number
*/
function rand() {
if ( bernoulli( 0.8 ) > 0 ) {
return NaN;
}
return uniform( -10, 10 );
}

/**
* Creates a benchmark function.
*
Expand All @@ -52,13 +65,6 @@ function createBenchmark( len ) {
var x = filledarrayBy( len, 'float32', rand );
return benchmark;

function rand() {
if ( bernoulli( 0.8 ) > 0 ) {
return NaN;
}
return uniform( -10, 10 );
}

function benchmark( b ) {
var v;
var i;
Expand Down
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