Auto-generated commit

Author: stdlib-bot

.github/.keepalive

@@ -4,14 +4,25 @@
 
 <section class="release" id="unreleased">
 
-## Unreleased (2025-03-10)
+## Unreleased (2025-03-16)
+
+<section class="features">
+
+### Features
+
+-   [`c155eb0`](https://github.com/stdlib-js/stdlib/commit/c155eb0820f1938b7ff56a719b72a442ccc202a1) - add C ndarray interface and refactor implementation for `stats/base/dsvariance`  [(#5641)](https://github.com/stdlib-js/stdlib/pull/5641)
+
+</section>
+
+<!-- /.features -->
 
 <section class="commits">
 
 ### Commits
 
 <details>
 
+-   [`c155eb0`](https://github.com/stdlib-js/stdlib/commit/c155eb0820f1938b7ff56a719b72a442ccc202a1) - **feat:** add C ndarray interface and refactor implementation for `stats/base/dsvariance`  [(#5641)](https://github.com/stdlib-js/stdlib/pull/5641) _(by Prashant Kumar Yadav, stdlib-bot)_
 -   [`6d3a881`](https://github.com/stdlib-js/stdlib/commit/6d3a881e8df45f6c90f52af25666142603f59ca5) - **refactor:** update `stats/base/dsvariance` native addon from C++ to C [(#4868)](https://github.com/stdlib-js/stdlib/pull/4868) _(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)_

CHANGELOG.md

@@ -17,8 +17,10 @@ Aksshay Balasubramanian <[email protected]>
 Aleksandr <[email protected]>
 Ali Salesi <[email protected]>
 Aman Bhansali <[email protected]>
+AmanBhadkariya <[email protected]>
 Amit Jimiwal <[email protected]>
 Anshu Kumar <[email protected]>
+Anshu Kumar <[email protected]>
 Anudeep Sanapala <[email protected]>
 Athan Reines <[email protected]>
 Ayaka <[email protected]>
@@ -31,8 +33,10 @@ Dan Rose <[email protected]>
 Daniel Killenberger <[email protected]>
 Daniel Yu <[email protected]>
 Debashis Maharana <[email protected]>
+Deep Trivedi <[email protected]>
 Desh Deepak Kant <[email protected]>
 Dev Goel <[email protected]>
+Dhanyabad behera <[email protected]>
 Dhruv Arvind Singh <[email protected]>
 Dhruvil Mehta <[email protected]>
 Divyansh Seth <[email protected]>
@@ -47,12 +51,14 @@ Gautam sharma <[email protected]>
 Golden Kumar <[email protected]>
 Gunj Joshi <[email protected]>
 Gururaj Gurram <[email protected]>
+Haroon Rasheed <[email protected]>
 Harsh <[email protected]>
 HarshaNP <[email protected]>
 Harshita Kalani <[email protected]>
 Hemant M Mehta <[email protected]>
 Hridyanshu <[email protected]>
 Jaimin Godhani <[email protected]>
+Jaison D Souza <[email protected]>
 Jalaj Kumar <[email protected]>
 James Gelok <[email protected]>
 Jaysukh Makvana <[email protected]>
@@ -66,6 +72,7 @@ Justin Dennison <[email protected]>
 Justyn Shelby <[email protected]>
 Karan Anand <[email protected]>
 Karthik Prakash <[email protected]>
+Kaushikgtm <[email protected]>
 Kohantika Nath <[email protected]>
 Krishnam Agarwal <[email protected]>
 Krishnendu Das <[email protected]>
@@ -93,7 +100,7 @@ Oneday12323 <[email protected]>
 Ori Miles <[email protected]>
 Philipp Burckhardt <[email protected]>
 Prajwal Kulkarni <[email protected]>
-Pranav Goswami <[email protected]>
+Pranav Goswami <[email protected]>
 Pranjal Jha <[email protected]>
 Prashant Kumar Yadav <[email protected]>
 Pratik Singh <[email protected]>
@@ -113,6 +120,7 @@ Rutam Kathale <[email protected]>
 Ruthwik Chikoti <[email protected]>
 Ryan Seal <[email protected]>
 Rylan Yang <[email protected]>
+SHIVAM YADAV <[email protected]>
 Sai Srikar Dumpeti <[email protected]>
 Sarthak Paandey <[email protected]>
 Saurabh Singh <[email protected]>

CONTRIBUTORS

@@ -131,17 +131,16 @@ To view installation and usage instructions specific to each branch build, be su
 var dsvariance = require( '@stdlib/stats-base-dsvariance' );
 ```
 
-#### dsvariance( N, correction, x, stride )
+#### dsvariance( N, correction, x, strideX )
 
-Computes the [variance][variance] of a single-precision floating-point strided array `x` using extended accumulation and returning an extended precision result.
+Computes the [variance][variance] of a single-precision floating-point strided array using 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 = dsvariance( N, 1, x, 1 );
+var v = dsvariance( x.length, 1, x, 1 );
 // returns ~4.3333
 ```
 
@@ -150,18 +149,16 @@ The function has the following parameters:
 -   **N**: number of indexed elements.
 -   **correction**: degrees of freedom adjustment. Setting this parameter to a value other than `0` has the effect of adjusting the divisor during the calculation of the [variance][variance] according to `N-c` where `c` corresponds to the provided degrees of freedom adjustment. When computing the [variance][variance] of a population, setting this parameter to `0` is the standard choice (i.e., the provided array contains data constituting an entire population). When computing the unbiased sample [variance][variance], setting this parameter to `1` is the standard choice (i.e., the provided array contains data sampled from a larger population; this is commonly referred to as Bessel's correction).
 -   **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 [variance][variance] 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 [variance][variance] 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 = dsvariance( N, 1, x, 2 );
+var v = dsvariance( 4, 1, x, 2 );
 // returns 6.25
 ```
 
@@ -171,45 +168,39 @@ 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 = dsvariance( N, 1, x1, 2 );
+var v = dsvariance( 4, 1, x1, 2 );
 // returns 6.25
 ```
 
-#### dsvariance.ndarray( N, correction, x, stride, offset )
+#### dsvariance.ndarray( N, correction, x, strideX, offsetX )
 
 Computes the [variance][variance] of a single-precision floating-point strided array using 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 = dsvariance.ndarray( N, 1, x, 1, 0 );
+var v = dsvariance.ndarray( x.length, 1, x, 1, 0 );
 // returns ~4.33333
 ```
 
 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 [variance][variance] 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 [variance][variance] 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 = dsvariance.ndarray( N, 1, x, 2, 1 );
+var v = dsvariance.ndarray( 4, 1, x, 2, 1 );
 // returns 6.25
 ```
 
@@ -223,7 +214,7 @@ var v = dsvariance.ndarray( N, 1, x, 2, 1 );
 
 -   If `N <= 0`, both functions return `NaN`.
 -   If `N - c` is less than or equal to `0` (where `c` corresponds to the provided degrees of freedom adjustment), 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>
 
@@ -236,18 +227,12 @@ var v = dsvariance.ndarray( N, 1, 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 dsvariance = require( '@stdlib/stats-base-dsvariance' );
 
-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 = dsvariance( x.length, 1, x, 1 );
@@ -258,6 +243,127 @@ 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/dsvariance.h"
+```
+
+#### stdlib_strided_dsvariance( N, correction, \*X, strideX )
+
+Computes the [variance][variance] of a single-precision floating-point strided array using 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_dsvariance( 8, 1.0f, x, 1 );
+// returns 6.0
+```
+
+The function accepts the following arguments:
+
+-   **N**: `[in] CBLAS_INT` number of indexed elements.
+-   **correction**: `[in] float` degrees of freedom adjustment. Setting this parameter to a value other than `0` has the effect of adjusting the divisor during the calculation of the [variance][variance] according to `N-c` where `c` corresponds to the provided degrees of freedom adjustment. When computing the [variance][variance] of a population, setting this parameter to `0` is the standard choice (i.e., the provided array contains data constituting an entire population). When computing the unbiased sample [variance][variance], setting this parameter to `1` is the standard choice (i.e., the provided array contains data sampled from a larger population; this is commonly referred to as Bessel's correction).
+-   **X**: `[in] float*` input array.
+-   **strideX**: `[in] CBLAS_INT` stride length for `X`.
+
+```c
+double stdlib_strided_dsvariance( const CBLAS_INT N, const float correction, const float *X, const CBLAS_INT strideX );
+```
+
+#### stdlib_strided_dsvariance_ndarray( N, correction, \*X, strideX, offsetX )
+
+Computes the [variance][variance] of a single-precision floating-point strided array using 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_dsvariance_ndarray( 4, 1.0f, x, 2, 0 );
+// returns ~6.666667
+```
+
+The function accepts the following arguments:
+
+-   **N**: `[in] CBLAS_INT` number of indexed elements.
+-   **correction**: `[in] float` degrees of freedom adjustment. Setting this parameter to a value other than `0` has the effect of adjusting the divisor during the calculation of the [variance][variance] according to `N-c` where `c` corresponds to the provided degrees of freedom adjustment. When computing the [variance][variance] of a population, setting this parameter to `0` is the standard choice (i.e., the provided array contains data constituting an entire population). When computing the unbiased sample [variance][variance], setting this parameter to `1` is the standard choice (i.e., the provided array contains data sampled from a larger population; this is commonly referred to as Bessel's correction).
+-   **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_dsvariance_ndarray( const CBLAS_INT N, const float correction, 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/dsvariance.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 variance:
+    double v = stdlib_strided_dsvariance( N, 1.0f, x, strideX );
+
+    // Print the result:
+    printf( "sample variance: %lf\n", v );
+}
+```
+
+</section>
+
+<!-- /.examples -->
+
+</section>
+
+<!-- /.c -->
+
+* * *
+
 <section class="references">
 
 </section>

README.md

@@ -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 dsvariance = require( './../lib/dsvariance.js' );
 
 
+// VARIABLES //
+
+var options = {
+	'dtype': 'float32'
+};
+
+
 // FUNCTIONS //
 
 /**
@@ -39,13 +45,7 @@ var dsvariance = require( './../lib/dsvariance.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 ) {