feat: add C ndarray interface and refactor implementation for stats/base/dvarmpn

lib/node_modules/@stdlib/stats/base/dvarmpn/README.md

@@ -98,7 +98,7 @@ The use of the term `n-1` is commonly referred to as Bessel's correction. Note,
 var dvarmpn = require( '@stdlib/stats/base/dvarmpn' );
 ```
 
-#### dvarmpn( N, mean, correction, x, stride )
+#### dvarmpn( N, mean, correction, x, strideX )
 
 Computes the [variance][variance] of a double-precision floating-point strided array `x` provided a known `mean` and using Neely's correction algorithm.
 
@@ -117,9 +117,9 @@ The function has the following parameters:
 -   **mean**: mean.
 -   **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 [`Float64Array`][@stdlib/array/float64].
--   **stride**: index increment for `x`.
+-   **strideX**: index increment 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 `strideX` parameters determine which elements in `x` are accessed at runtime. For example, to compute the [variance][variance] of every other element in `x`,
 
 ```javascript
 var Float64Array = require( '@stdlib/array/float64' );
@@ -149,7 +149,7 @@ var v = dvarmpn( N, 1.25, 1, x1, 2 );
 // returns 6.25
 ```
 
-#### dvarmpn.ndarray( N, mean, correction, x, stride, offset )
+#### dvarmpn.ndarray( N, mean, correction, x, strideX, offsetX )
 
 Computes the [variance][variance] of a double-precision floating-point strided array provided a known `mean` and using Neely's correction algorithm and alternative indexing semantics.
 
@@ -164,7 +164,7 @@ var v = dvarmpn.ndarray( x.length, 1.0/3.0, 1, x, 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 [variance][variance] for every other value in `x` starting from the second value
 
@@ -223,6 +223,127 @@ 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/dvarmpn.h"
+```
+
+#### stdlib_strided_dvarmpn( N, mean, correction, \*X, strideX )
+
+Computes the [variance][variance] of a double-precision floating-point strided array `x` provided a known `mean` and using Neely's correction algorithm.
+
+```c
+const double x[] = { 1.0, -2.0, 2.0 };
+
+double v = stdlib_strided_dvarmpn( x.length, 1.0/3.0, 1, x, 1 );
+// returns ~4.3333
+```
+
+The function accepts the following arguments:
+
+-   **N**: number of indexed elements.
+-   **mean**: mean.
+-   **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 [`Float64Array`][@stdlib/array/float64].
+-   **strideX**: index increment for `x`.
+
+```c
+double stdlib_strided_dvarmpn( const CBLAS_INT N, const double mean, const double correction, const double *X, const CBLAS_INT strideX );
+```
+
+#### stdlib_strided_dvarmpn_ndarray( N, mean, correction, \*X, strideX, offsetX )
+
+Computes the [variance][variance] of a double-precision floating-point strided array provided a known `mean` and using Neely's correction algorithm and alternative indexing semantics.
+
+```c
+const double x[] = { 1.0, -2.0, 2.0 };
+
+double v = stdlib_strided_dvarmpn_ndarray( x.length, 1.0/3.0, 1, x, 1, 0 );
+// returns ~4.3333
+```
+
+The function accepts the following arguments:
+
+-   **N**: number of indexed elements.
+-   **mean**: mean.
+-   **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 [`Float64Array`][@stdlib/array/float64].
+-   **strideX**: index increment for `x`.
+-   **offsetX**: `[in] CBLAS_INT` starting index for `X`.
+
+```c
+double stdlib_strided_dvarmpn_ndarray( const CBLAS_INT N, const double mean, const double correction, const double *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/dvarmpn.h"
+#include <stdio.h>
+
+int main( void ) {
+    // Create a strided array:
+    const double 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_dvarmpn( N, 4.5, 1, x, strideX );
+
+    // Print the result:
+    printf( "sample variance: %lf\n", v );
+}
+```
+
+</section>
+
+<!-- /.examples -->
+
+</section>
+
+<!-- /.c -->
+
 * * *
 
 <section class="references">

lib/node_modules/@stdlib/stats/base/dvarmpn/benchmark/benchmark.js

@@ -21,14 +21,20 @@
 // MODULES //
 
 var bench = require( '@stdlib/bench' );
-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 Float64Array = require( '@stdlib/array/float64' );
 var pkg = require( './../package.json' ).name;
 var dvarmpn = require( './../lib/dvarmpn.js' );
 
 
+// VARIABLES //
+
+var options = {
+	'dtype': 'float64'
+};
+
+
 // FUNCTIONS //
 
 /**
@@ -39,13 +45,7 @@ var dvarmpn = require( './../lib/dvarmpn.js' );
 * @returns {Function} benchmark function
 */
 function createBenchmark( len ) {
-	var x;
-	var i;
-
-	x = new Float64Array( len );
-	for ( i = 0; i < x.length; i++ ) {
-		x[ i ] = ( randu()*20.0 ) - 10.0;
-	}
+	var x = uniform( len, -100, 100, options );
 	return benchmark;
 
 	function benchmark( b ) {

lib/node_modules/@stdlib/stats/base/dvarmpn/benchmark/benchmark.native.js

@@ -22,10 +22,9 @@
 
 var resolve = require( 'path' ).resolve;
 var bench = require( '@stdlib/bench' );
-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 Float64Array = require( '@stdlib/array/float64' );
 var tryRequire = require( '@stdlib/utils/try-require' );
 var pkg = require( './../package.json' ).name;
 
@@ -36,6 +35,9 @@ var dvarmpn = tryRequire( resolve( __dirname, './../lib/dvarmpn.native.js' ) );
 var opts = {
 	'skip': ( dvarmpn instanceof Error )
 };
+var options = {
+	'dtype': 'float64'
+};
 
 
 // FUNCTIONS //
@@ -48,13 +50,7 @@ var opts = {
 * @returns {Function} benchmark function
 */
 function createBenchmark( len ) {
-	var x;
-	var i;
-
-	x = new Float64Array( len );
-	for ( i = 0; i < x.length; i++ ) {
-		x[ i ] = ( randu()*20.0 ) - 10.0;
-	}
+	var x = uniform( len, -100, 100, options );
 	return benchmark;
 
 	function benchmark( b ) {

lib/node_modules/@stdlib/stats/base/dvarmpn/benchmark/benchmark.ndarray.js

@@ -21,14 +21,20 @@
 // MODULES //
 
 var bench = require( '@stdlib/bench' );
-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 Float64Array = require( '@stdlib/array/float64' );
 var pkg = require( './../package.json' ).name;
 var dvarmpn = require( './../lib/ndarray.js' );
 
 
+// VARIABLES //
+
+var options = {
+	'dtype': 'float64'
+};
+
+
 // FUNCTIONS //
 
 /**
@@ -39,13 +45,7 @@ var dvarmpn = require( './../lib/ndarray.js' );
 * @returns {Function} benchmark function
 */
 function createBenchmark( len ) {
-	var x;
-	var i;
-
-	x = new Float64Array( len );
-	for ( i = 0; i < x.length; i++ ) {
-		x[ i ] = ( randu()*20.0 ) - 10.0;
-	}
+	var x = uniform( len, -100, 100, options );
 	return benchmark;
 
 	function benchmark( b ) {

lib/node_modules/@stdlib/stats/base/dvarmpn/benchmark/benchmark.ndarray.native.js

@@ -22,10 +22,9 @@
 
 var resolve = require( 'path' ).resolve;
 var bench = require( '@stdlib/bench' );
-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 Float64Array = require( '@stdlib/array/float64' );
 var tryRequire = require( '@stdlib/utils/try-require' );
 var pkg = require( './../package.json' ).name;
 
@@ -36,6 +35,9 @@ var dvarmpn = tryRequire( resolve( __dirname, './../lib/ndarray.native.js' ) );
 var opts = {
 	'skip': ( dvarmpn instanceof Error )
 };
+var options = {
+	'dtype': 'float64'
+};
 
 
 // FUNCTIONS //
@@ -48,13 +50,7 @@ var opts = {
 * @returns {Function} benchmark function
 */
 function createBenchmark( len ) {
-	var x;
-	var i;
-
-	x = new Float64Array( len );
-	for ( i = 0; i < x.length; i++ ) {
-		x[ i ] = ( randu()*20.0 ) - 10.0;
-	}
+	var x = uniform( len, -100, 100, options );
 	return benchmark;
 
 	function benchmark( b ) {

lib/node_modules/@stdlib/stats/base/dvarmpn/benchmark/c/benchmark.length.c

@@ -94,7 +94,7 @@ static double rand_double( void ) {
 * @param len          array length
 * @return             elapsed time in seconds
 */
-static double benchmark( int iterations, int len ) {
+static double benchmark1( int iterations, int len ) {
 	double elapsed;
 	double x[ len ];
 	double v;
@@ -107,6 +107,7 @@ static double benchmark( int iterations, int len ) {
 	v = 0.0;
 	t = tic();
 	for ( i = 0; i < iterations; i++ ) {
+		// cppcheck-suppress uninitvar
 		v = stdlib_strided_dvarmpn( len, 0.0, 1, x, 1 );
 		if ( v != v ) {
 			printf( "should not return NaN\n" );
@@ -120,6 +121,40 @@ static double benchmark( int iterations, int len ) {
 	return elapsed;
 }
 
+/**
+* Runs a benchmark.
+*
+* @param iterations   number of iterations
+* @param len          array length
+* @return             elapsed time in seconds
+*/
+static double benchmark2( int iterations, int len ) {
+	double elapsed;
+	double x[ len ];
+	float v;
+	double t;
+	int i;
+
+	for ( i = 0; i < len; i++ ) {
+		x[ i ] = ( rand_double() * 20000.0 ) - 10000.0;
+	}
+	v = 0.0f;
+	t = tic();
+	for ( i = 0; i < iterations; i++ ) {
+		// cppcheck-suppress uninitvar
+		v = stdlib_strided_dvarmpn_ndarray( len, 0.0, 1, x, 1, 1 );
+		if ( v != v ) {
+			printf( "should not return NaN\n" );
+			break;
+		}
+	}
+	elapsed = tic() - t;
+	if ( v != v ) {
+		printf( "should not return NaN\n" );
+	}
+	return elapsed;
+}
+
 /**
 * Main execution sequence.
 */
@@ -142,7 +177,18 @@ int main( void ) {
 		for ( j = 0; j < REPEATS; j++ ) {
 			count += 1;
 			printf( "# c::%s:len=%d\n", NAME, len );
-			elapsed = benchmark( iter, len );
+			elapsed = benchmark1( iter, len );
+			print_results( iter, elapsed );
+			printf( "ok %d benchmark finished\n", count );
+		}
+	}
+	for ( i = MIN; i <= MAX - 3; i++ ) {
+		len = pow( 10, i );
+		iter = ITERATIONS;
+		for ( j = 0; j < REPEATS; j++ ) {
+			count += 1;
+			printf( "# c::%s:ndarray:len=%d\n", NAME, len );
+			elapsed = benchmark2( iter, len );
 			print_results( iter, elapsed );
 			printf( "ok %d benchmark finished\n", count );
 		}