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Author: stdlib-bot

CHANGELOG.md

@@ -6,12 +6,23 @@
 
 ## Unreleased (2025-01-18)
 
+<section class="features">
+
+### Features
+
+-   [`5cfc390`](https://github.com/stdlib-js/stdlib/commit/5cfc390ccb133747ade74ea8b0c29d4e08fce639) - add C `ndarray` interface and refactor implementation for `stats/base/dsmeanpn` [(#4331)](https://github.com/stdlib-js/stdlib/pull/4331)
+
+</section>
+
+<!-- /.features -->
+
 <section class="commits">
 
 ### Commits
 
 <details>
 
+-   [`5cfc390`](https://github.com/stdlib-js/stdlib/commit/5cfc390ccb133747ade74ea8b0c29d4e08fce639) - **feat:** add C `ndarray` interface and refactor implementation for `stats/base/dsmeanpn` [(#4331)](https://github.com/stdlib-js/stdlib/pull/4331) _(by Neeraj Pathak, Athan Reines)_
 -   [`15c26dd`](https://github.com/stdlib-js/stdlib/commit/15c26dd8e62bcc6ef839f1c604e3fe537c4d3ca5) - **refactor:** update `stats/base/dsmeanpn` native addon from C++ to C [(#4723)](https://github.com/stdlib-js/stdlib/pull/4723) _(by Prashant Kumar Yadav)_
 -   [`62364f6`](https://github.com/stdlib-js/stdlib/commit/62364f62ea823a3b52c2ad25660ecd80c71f8f36) - **style:** fix C comment alignment _(by Philipp Burckhardt)_
 -   [`9e689ff`](https://github.com/stdlib-js/stdlib/commit/9e689ffcb7c6223afc521f1e574b42f10921cf5e) - **chore:** fix indentation in manifest.json files _(by Philipp Burckhardt)_
@@ -28,9 +39,10 @@
 
 ### Contributors
 
-A total of 3 people contributed to this release. Thank you to the following contributors:
+A total of 4 people contributed to this release. Thank you to the following contributors:
 
 -   Athan Reines
+-   Neeraj Pathak
 -   Philipp Burckhardt
 -   Prashant Kumar Yadav
 

README.md

@@ -84,36 +84,33 @@ To view installation and usage instructions specific to each branch build, be su
 var dsmeanpn = require( '@stdlib/stats-base-dsmeanpn' );
 ```
 
-#### dsmeanpn( N, x, stride )
+#### dsmeanpn( N, x, strideX )
 
 Computes the [arithmetic mean][arithmetic-mean] of a single-precision floating-point strided array `x` using a two-pass error correction algorithm with extended accumulation and returning an extended precision result.
 
 ```javascript
 var Float32Array = require( '@stdlib/array-float32' );
 
 var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );
-var N = x.length;
 
-var v = dsmeanpn( N, x, 1 );
+var v = dsmeanpn( x.length, x, 1 );
 // returns ~0.3333
 ```
 
 The function has the following parameters:
 
 -   **N**: number of indexed elements.
 -   **x**: input [`Float32Array`][@stdlib/array/float32].
--   **stride**: index increment for `x`.
+-   **strideX**: stride length for `x`.
 
-The `N` and `stride` parameters determine which elements in `x` are accessed at runtime. For example, to compute the [arithmetic mean][arithmetic-mean] 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 [arithmetic mean][arithmetic-mean] of every other element in `x`,
 
 ```javascript
 var Float32Array = require( '@stdlib/array-float32' );
-var floor = require( '@stdlib/math-base-special-floor' );
 
 var x = new Float32Array( [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ] );
-var N = floor( x.length / 2 );
 
-var v = dsmeanpn( N, x, 2 );
+var v = dsmeanpn( 4, x, 2 );
 // returns 1.25
 ```
 
@@ -123,45 +120,39 @@ Note that indexing is relative to the first index. To introduce an offset, use [
 
 ```javascript
 var Float32Array = require( '@stdlib/array-float32' );
-var floor = require( '@stdlib/math-base-special-floor' );
 
 var x0 = new Float32Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
 var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
 
-var N = floor( x0.length / 2 );
-
-var v = dsmeanpn( N, x1, 2 );
+var v = dsmeanpn( 4, x1, 2 );
 // returns 1.25
 ```
 
-#### dsmeanpn.ndarray( N, x, stride, offset )
+#### dsmeanpn.ndarray( N, x, strideX, offsetX )
 
 Computes the [arithmetic mean][arithmetic-mean] of a single-precision floating-point strided array using a two-pass error correction algorithm 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, 2.0 ] );
-var N = x.length;
 
-var v = dsmeanpn.ndarray( N, x, 1, 0 );
+var v = dsmeanpn.ndarray( x.length, x, 1, 0 );
 // returns ~0.33333
 ```
 
 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 [arithmetic mean][arithmetic-mean] for 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 [arithmetic mean][arithmetic-mean] for every other element in `x` starting from the second element
 
 ```javascript
 var Float32Array = require( '@stdlib/array-float32' );
-var floor = require( '@stdlib/math-base-special-floor' );
 
 var x = new Float32Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
-var N = floor( x.length / 2 );
 
-var v = dsmeanpn.ndarray( N, x, 2, 1 );
+var v = dsmeanpn.ndarray( 4, x, 2, 1 );
 // returns 1.25
 ```
 
@@ -174,7 +165,7 @@ var v = dsmeanpn.ndarray( N, x, 2, 1 );
 ## Notes
 
 -   If `N <= 0`, both functions return `NaN`.
--   Accumulated intermediate values are stored as double-precision floating-point numbers. 
+-   Accumulated intermediate values are stored as double-precision floating-point numbers.
 
 </section>
 
@@ -187,18 +178,12 @@ var v = dsmeanpn.ndarray( N, x, 2, 1 );
 <!-- eslint no-undef: "error" -->
 
 ```javascript
-var randu = require( '@stdlib/random-base-randu' );
-var round = require( '@stdlib/math-base-special-round' );
-var Float32Array = require( '@stdlib/array-float32' );
+var discreteUniform = require( '@stdlib/random-array-discrete-uniform' );
 var dsmeanpn = require( '@stdlib/stats-base-dsmeanpn' );
 
-var x;
-var i;
-
-x = new Float32Array( 10 );
-for ( i = 0; i < x.length; i++ ) {
-    x[ i ] = round( (randu()*100.0) - 50.0 );
-}
+var x = discreteUniform( 10, -50, 50, {
+    'dtype': 'float32'
+});
 console.log( x );
 
 var v = dsmeanpn( x.length, x, 1 );
@@ -209,6 +194,123 @@ 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/stats/base/dsmeanpn.h"
+```
+
+#### stdlib_strided_dsmeanpn( N, \*X, strideX )
+
+Computes the arithmetic mean of a single-precision floating-point strided array using a two-pass error correction algorithm with extended accumulation and returning an extended precision result.
+
+```c
+const float x[] = { 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f };
+
+double v = stdlib_strided_dsmeanpn( 4, x, 2 );
+// returns 4.0
+```
+
+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_dsmeanpn( const CBLAS_INT N, const float *X, const CBLAS_INT strideX );
+```
+
+#### stdlib_strided_dsmeanpn_ndarray( N, \*X, strideX, offsetX )
+
+Computes the arithmetic mean of a single-precision floating-point strided array using a two-pass error correction algorithm with extended accumulation and alternative indexing semantics and returning an extended precision result.
+
+```c
+const float x[] = { 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f };
+
+double v = stdlib_strided_dsmeanpn_ndarray( 4, x, 2, 0 );
+// returns 4.0
+```
+
+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_dsmeanpn_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/stats/base/dsmeanpn.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 };
+
+    // Specify the number of elements:
+    const int N = 4;
+
+    // Specify the stride length:
+    const int strideX = 2;
+
+    // Compute the arithmetic mean:
+    double v = stdlib_strided_dsmeanpn( N, x, strideX );
+
+    // Print the result:
+    printf( "mean: %lf\n", v );
+}
+```
+
+</section>
+
+<!-- /.examples -->
+
+</section>
+
+<!-- /.c -->
+
 * * *
 
 <section class="references">

benchmark/benchmark.js

@@ -21,14 +21,20 @@
 // MODULES //
 
 var bench = require( '@stdlib/bench-harness' );
-var randu = require( '@stdlib/random-base-randu' );
+var uniform = require( '@stdlib/random-array-uniform' );
 var isnan = require( '@stdlib/math-base-assert-is-nan' );
 var pow = require( '@stdlib/math-base-special-pow' );
-var Float32Array = require( '@stdlib/array-float32' );
 var pkg = require( './../package.json' ).name;
 var dsmeanpn = require( './../lib/dsmeanpn.js' );
 
 
+// VARIABLES //
+
+var options = {
+	'dtype': 'float32'
+};
+
+
 // FUNCTIONS //
 
 /**
@@ -39,13 +45,7 @@ var dsmeanpn = require( './../lib/dsmeanpn.js' );
 * @returns {Function} benchmark function
 */
 function createBenchmark( len ) {
-	var x;
-	var i;
-
-	x = new Float32Array( len );
-	for ( i = 0; i < x.length; i++ ) {
-		x[ i ] = ( randu()*20.0 ) - 10.0;
-	}
+	var x = uniform( len, -10.0, 10.0, options );
 	return benchmark;
 
 	function benchmark( b ) {

benchmark/benchmark.native.js

@@ -22,10 +22,9 @@
 
 var resolve = require( 'path' ).resolve;
 var bench = require( '@stdlib/bench-harness' );
-var randu = require( '@stdlib/random-base-randu' );
+var uniform = require( '@stdlib/random-array-uniform' );
 var isnan = require( '@stdlib/math-base-assert-is-nan' );
 var pow = require( '@stdlib/math-base-special-pow' );
-var Float32Array = require( '@stdlib/array-float32' );
 var tryRequire = require( '@stdlib/utils-try-require' );
 var pkg = require( './../package.json' ).name;
 
@@ -36,6 +35,9 @@ var dsmeanpn = tryRequire( resolve( __dirname, './../lib/dsmeanpn.native.js' ) )
 var opts = {
 	'skip': ( dsmeanpn instanceof Error )
 };
+var options = {
+	'dtype': 'float32'
+};
 
 
 // FUNCTIONS //
@@ -48,13 +50,7 @@ var opts = {
 * @returns {Function} benchmark function
 */
 function createBenchmark( len ) {
-	var x;
-	var i;
-
-	x = new Float32Array( len );
-	for ( i = 0; i < x.length; i++ ) {
-		x[ i ] = ( randu()*20.0 ) - 10.0;
-	}
+	var x = uniform( len, -10.0, 10.0, options );
 	return benchmark;
 
 	function benchmark( b ) {

benchmark/benchmark.ndarray.js

@@ -21,14 +21,20 @@
 // MODULES //
 
 var bench = require( '@stdlib/bench-harness' );
-var randu = require( '@stdlib/random-base-randu' );
+var uniform = require( '@stdlib/random-array-uniform' );
 var isnan = require( '@stdlib/math-base-assert-is-nan' );
 var pow = require( '@stdlib/math-base-special-pow' );
-var Float32Array = require( '@stdlib/array-float32' );
 var pkg = require( './../package.json' ).name;
 var dsmeanpn = require( './../lib/ndarray.js' );
 
 
+// VARIABLES //
+
+var options = {
+	'dtype': 'float32'
+};
+
+
 // FUNCTIONS //
 
 /**
@@ -39,13 +45,7 @@ var dsmeanpn = require( './../lib/ndarray.js' );
 * @returns {Function} benchmark function
 */
 function createBenchmark( len ) {
-	var x;
-	var i;
-
-	x = new Float32Array( len );
-	for ( i = 0; i < x.length; i++ ) {
-		x[ i ] = ( randu()*20.0 ) - 10.0;
-	}
+	var x = uniform( len, -10.0, 10.0, options );
 	return benchmark;
 
 	function benchmark( b ) {