feat: add C implementation for stats/base/ndarray/scovarmtk

lib/node_modules/@stdlib/stats/base/ndarray/scovarmtk/README.md

@@ -186,6 +186,219 @@ 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/ndarray/scovarmtk.h"
+```
+
+#### stdlib_stats_scovarmtk( arrays )
+
+Computes the covariance of two one-dimensional single-precision floating-point ndarrays provided known means and using a one-pass textbook algorithm.
+
+```c
+#include "stdlib/ndarray/ctor.h"
+#include "stdlib/ndarray/dtypes.h"
+#include "stdlib/ndarray/index_modes.h"
+#include "stdlib/ndarray/orders.h"
+#include "stdlib/ndarray/base/bytes_per_element.h"
+#include <stdint.h>
+
+// Create input ndarrays:
+const float xdata[] = { 1.0f, -2.0f, 2.0f };
+const float ydata[] = { 2.0f, -2.0f, 1.0f };
+int64_t shape[] = { 3 };
+int64_t strides[] = { STDLIB_NDARRAY_FLOAT32_BYTES_PER_ELEMENT };
+int8_t submodes[] = { STDLIB_NDARRAY_INDEX_ERROR };
+
+struct ndarray *x = stdlib_ndarray_allocate( STDLIB_NDARRAY_FLOAT32, (uint8_t *)xdata, 1, shape, strides, 0, STDLIB_NDARRAY_ROW_MAJOR, STDLIB_NDARRAY_INDEX_ERROR, 1, submodes );
+struct ndarray *y = stdlib_ndarray_allocate( STDLIB_NDARRAY_FLOAT32, (uint8_t *)ydata, 1, shape, strides, 0, STDLIB_NDARRAY_ROW_MAJOR, STDLIB_NDARRAY_INDEX_ERROR, 1, submodes );
+
+// Create an ndarray for specifying the degrees of freedom adjustment:
+const float cdata[] = { 1.0f };
+int64_t cstrides[] = { 0 };
+struct ndarray *corr = stdlib_ndarray_allocate( STDLIB_NDARRAY_FLOAT32, (uint8_t *)cdata, 0, NULL, cstrides, 0, STDLIB_NDARRAY_ROW_MAJOR, STDLIB_NDARRAY_INDEX_ERROR, 1, submodes );
+
+// Create ndarrays for specifying the known means:
+const float mxdata[] = { 1.0f/3.0f };
+const float mydata[] = { 1.0f/3.0f };
+struct ndarray *meanx = stdlib_ndarray_allocate( STDLIB_NDARRAY_FLOAT32, (uint8_t *)mxdata, 0, NULL, cstrides, 0, STDLIB_NDARRAY_ROW_MAJOR, STDLIB_NDARRAY_INDEX_ERROR, 1, submodes );
+struct ndarray *meany = stdlib_ndarray_allocate( STDLIB_NDARRAY_FLOAT32, (uint8_t *)mydata, 0, NULL, cstrides, 0, STDLIB_NDARRAY_ROW_MAJOR, STDLIB_NDARRAY_INDEX_ERROR, 1, submodes );
+
+// Compute the result:
+const struct ndarray *arrays[] = { x, y, corr, meanx, meany };
+float v = stdlib_stats_scovarmtk( arrays );
+// returns ~3.8333f
+
+// Free allocated memory:
+stdlib_ndarray_free( x );
+stdlib_ndarray_free( y );
+stdlib_ndarray_free( corr );
+stdlib_ndarray_free( meanx );
+stdlib_ndarray_free( meany );
+```
+
+The function accepts the following arguments:
+
+-   **arrays**: `[in] struct ndarray**` list containing the following ndarrays:
+
+    -   `[in] struct ndarray*` first one-dimensional input ndarray.
+    -   `[in] struct ndarray*` second one-dimensional input ndarray.
+    -   `[in] struct ndarray*` a zero-dimensional ndarray specifying the 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 [covariance][covariance] according to `N-c` where `c` corresponds to the provided degrees of freedom adjustment and `N` corresponds to the number of elements in each input ndarray. When computing the population [covariance][covariance], setting this parameter to `0` is the standard choice (i.e., the provided arrays contain data constituting entire populations). When computing the unbiased sample [covariance][covariance], setting this parameter to `1` is the standard choice (i.e., the provided arrays contain data sampled from larger populations; this is commonly referred to as Bessel's correction).
+    -   `[in] struct ndarray*` a zero-dimensional ndarray specifying the mean of the first one-dimensional ndarray.
+    -   `[in] struct ndarray*` a zero-dimensional ndarray specifying the mean of the second one-dimensional ndarray.
+
+```c
+float stdlib_stats_scovarmtk( const struct ndarray *arrays[] );
+```
+
+</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/ndarray/scovarmtk.h"
+#include "stdlib/ndarray/ctor.h"
+#include "stdlib/ndarray/dtypes.h"
+#include "stdlib/ndarray/index_modes.h"
+#include "stdlib/ndarray/orders.h"
+#include "stdlib/ndarray/base/bytes_per_element.h"
+#include <stdint.h>
+#include <stdlib.h>
+#include <stdio.h>
+
+int main( void ) {
+    // Create data buffers:
+    const float xdata[] = { 1.0f, -2.0f, 3.0f, -4.0f, 5.0f, -6.0f, 7.0f, -8.0f };
+    const float ydata[] = { 2.0f, -1.0f, 1.0f, -3.0f, 3.0f, -5.0f, 5.0f, -7.0f };
+
+    // Specify the number of array dimensions:
+    const int64_t ndims = 1;
+
+    // Specify the array shape:
+    int64_t shape[] = { 4 };
+
+    // Specify the array strides:
+    int64_t strides[] = { 2 * STDLIB_NDARRAY_FLOAT32_BYTES_PER_ELEMENT };
+
+    // Specify the byte offset:
+    const int64_t offset = 0;
+
+    // Specify the array order:
+    const enum STDLIB_NDARRAY_ORDER order = STDLIB_NDARRAY_ROW_MAJOR;
+
+    // Specify the index mode:
+    const enum STDLIB_NDARRAY_INDEX_MODE imode = STDLIB_NDARRAY_INDEX_ERROR;
+
+    // Specify the subscript index modes:
+    int8_t submodes[] = { STDLIB_NDARRAY_INDEX_ERROR };
+    const int64_t nsubmodes = 1;
+
+    // Create the first input ndarray:
+    struct ndarray *x = stdlib_ndarray_allocate( STDLIB_NDARRAY_FLOAT32, (uint8_t *)xdata, ndims, shape, strides, offset, order, imode, nsubmodes, submodes );
+    if ( x == NULL ) {
+        fprintf( stderr, "Error allocating memory.\n" );
+        exit( 1 );
+    }
+
+    // Create the second input ndarray:
+    struct ndarray *y = stdlib_ndarray_allocate( STDLIB_NDARRAY_FLOAT32, (uint8_t *)ydata, ndims, shape, strides, offset, order, imode, nsubmodes, submodes );
+    if ( y == NULL ) {
+        fprintf( stderr, "Error allocating memory.\n" );
+        exit( 1 );
+    }
+
+    // Create a data buffer for an ndarray specifying the degrees of freedom adjustment:
+    const float cdata[] = { 1.0f };
+
+    // Create data buffers for ndarrays specifying the means of the input ndarrays:
+    const float mxdata[] = { 4.0f };
+    const float mydata[] = { 2.75f };
+
+    // Specify the array strides for zero-dimensional ndarrays:
+    int64_t cstrides[] = { 0 };
+
+    // Create an ndarray for the degrees of freedom adjustment:
+    struct ndarray *corr = stdlib_ndarray_allocate( STDLIB_NDARRAY_FLOAT32, (uint8_t *)cdata, 0, NULL, cstrides, 0, order, imode, nsubmodes, submodes );
+    if ( corr == NULL ) {
+        fprintf( stderr, "Error allocating memory.\n" );
+        exit( 1 );
+    }
+
+    // Create an ndarray for the mean of the first input ndarray:
+    struct ndarray *meanx = stdlib_ndarray_allocate( STDLIB_NDARRAY_FLOAT32, (uint8_t *)mxdata, 0, NULL, cstrides, 0, order, imode, nsubmodes, submodes );
+    if ( meanx == NULL ) {
+        fprintf( stderr, "Error allocating memory.\n" );
+        exit( 1 );
+    }
+
+    // Create an ndarray for the mean of the second input ndarray:
+    struct ndarray *meany = stdlib_ndarray_allocate( STDLIB_NDARRAY_FLOAT32, (uint8_t *)mydata, 0, NULL, cstrides, 0, order, imode, nsubmodes, submodes );
+    if ( meany == NULL ) {
+        fprintf( stderr, "Error allocating memory.\n" );
+        exit( 1 );
+    }
+
+    // Define a list of ndarrays:
+    const struct ndarray *arrays[] = { x, y, corr, meanx, meany };
+
+    // Compute the result:
+    float v = stdlib_stats_scovarmtk( arrays );
+
+    // Print the result:
+    printf( "result: %f\n", v );
+
+    // Free allocated memory:
+    stdlib_ndarray_free( x );
+    stdlib_ndarray_free( y );
+    stdlib_ndarray_free( corr );
+    stdlib_ndarray_free( meanx );
+    stdlib_ndarray_free( meany );
+}
+```
+
+</section>
+
+<!-- /.examples -->
+
+</section>
+
+<!-- /.c -->
+
 <!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->
 
 <section class="related">

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

@@ -27,7 +27,7 @@ var pow = require( '@stdlib/math/base/special/pow' );
 var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
 var format = require( '@stdlib/string/format' );
 var pkg = require( './../package.json' ).name;
-var scovarmtk = require( './../lib' );
+var scovarmtk = require( './../lib/main.js' );
 
 
 // VARIABLES //

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

@@ -0,0 +1,114 @@
+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2026 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+'use strict';
+
+// MODULES //
+
+var resolve = require( 'path' ).resolve;
+var bench = require( '@stdlib/bench' );
+var uniform = require( '@stdlib/random/uniform' );
+var isnanf = require( '@stdlib/math/base/assert/is-nanf' );
+var pow = require( '@stdlib/math/base/special/pow' );
+var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
+var format = require( '@stdlib/string/format' );
+var tryRequire = require( '@stdlib/utils/try-require' );
+var pkg = require( './../package.json' ).name;
+
+
+// VARIABLES //
+
+var scovarmtk = tryRequire( resolve( __dirname, './../lib/native.js' ) );
+var opts = {
+	'skip': ( scovarmtk instanceof Error )
+};
+var options = {
+	'dtype': 'float32'
+};
+
+var correction = scalar2ndarray( 1.0, options );
+var meanx = scalar2ndarray( 0.0, options );
+var meany = scalar2ndarray( 0.0, options );
+
+
+// FUNCTIONS //
+
+/**
+* Creates a benchmark function.
+*
+* @private
+* @param {PositiveInteger} len - array length
+* @returns {Function} benchmark function
+*/
+function createBenchmark( len ) {
+	var x = uniform( [ len ], -10.0, 10.0, options );
+	var y = uniform( [ len ], -10.0, 10.0, options );
+	return benchmark;
+
+	/**
+	* Benchmark function.
+	*
+	* @private
+	* @param {Benchmark} b - benchmark instance
+	*/
+	function benchmark( b ) {
+		var v;
+		var i;
+
+		b.tic();
+		for ( i = 0; i < b.iterations; i++ ) {
+			v = scovarmtk( [ x, y, correction, meanx, meany ] );
+			if ( isnanf( v ) ) {
+				b.fail( 'should not return NaN' );
+			}
+		}
+		b.toc();
+		if ( isnanf( v ) ) {
+			b.fail( 'should not return NaN' );
+		}
+		b.pass( 'benchmark finished' );
+		b.end();
+	}
+}
+
+
+// MAIN //
+
+/**
+* Main execution sequence.
+*
+* @private
+*/
+function main() {
+	var len;
+	var min;
+	var max;
+	var f;
+	var i;
+
+	min = 1; // 10^min
+	max = 6; // 10^max
+
+	for ( i = min; i <= max; i++ ) {
+		len = pow( 10, i );
+		f = createBenchmark( len );
+		bench( format( '%s::native:len=%d', pkg, len ), opts, f );
+	}
+}
+
+main();