feat: add ml/strided/dkmeans-distance

lib/node_modules/@stdlib/ml/strided/dkmeans-distance/README.md

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+<!--
+
+@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.
+
+-->
+
+# dkmeansDistance
+
+> Computes the distance between two double-precision floating-point strided arrays according to a specified distance metric.
+
+<section class="intro">
+
+</section>
+
+<!-- /.intro -->
+
+<section class="usage">
+
+## Usage
+
+```javascript
+var dkmeansDistance = require( '@stdlib/ml/strided/dkmeans-distance' );
+```
+
+#### dkmeansDistance( N, metric, x, strideX, y, strideY )
+
+Computes the distance between two double-precision floating-point strided arrays according to a specified distance metric.
+
+```javascript
+var Float64Array = require( '@stdlib/array/float64' );
+
+var x = new Float64Array( [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ] );
+var y = new Float64Array( [ 2.0, 1.0, 2.0, 1.0, -2.0, 2.0, 3.0, 4.0 ] );
+
+var z = dkmeansDistance( x.length, 'sqeuclidean', x, 1, y, 1 );
+// returns 72.0
+```
+
+The function has the following parameters:
+
+-   **N**: number of indexed elements.
+-   **metric**: distance [metric][@stdlib/ml/base/kmeans/metrics].
+-   **x**: input [`Float64Array`][@stdlib/array/float64].
+-   **strideX**: stride length of `x`.
+-   **y**: input [`Float64Array`][@stdlib/array/float64].
+-   **strideY**: stride length of `y`.
+
+The `N` and stride parameters determine which elements in the strided arrays are accessed at runtime. For example, to calculate the distance between every other element in `x` and the first `N` elements of `y` in reverse order,
+
+```javascript
+var Float64Array = require( '@stdlib/array/float64' );
+
+var x = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
+var y = new Float64Array( [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ] );
+
+var z = dkmeansDistance( 3, 'sqeuclidean', x, 2, y, -1 );
+// returns 20.0
+```
+
+Note that indexing is relative to the first index. To introduce an offset, use [`typed array`][mdn-typed-array] views.
+
+<!-- eslint-disable stdlib/capitalized-comments -->
+
+```javascript
+var Float64Array = require( '@stdlib/array/float64' );
+
+// Initial arrays...
+var x0 = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
+var y0 = new Float64Array( [ 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );
+
+// Create offset views...
+var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
+var y1 = new Float64Array( y0.buffer, y0.BYTES_PER_ELEMENT*3 ); // start at 4th element
+
+var z = dkmeansDistance( 3, 'sqeuclidean', x1, 1, y1, 1 );
+// returns 192.0
+```
+
+#### dkmeansDistance.ndarray( N, metric, x, strideX, offsetX, y, strideY, offsetY )
+
+Computes the distance between two double-precision floating-point strided arrays according to a specified distance metric using alternative indexing semantics.
+
+```javascript
+var Float64Array = require( '@stdlib/array/float64' );
+
+var x = new Float64Array( [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ] );
+var y = new Float64Array( [ 2.0, 1.0, 2.0, 1.0, -2.0, 2.0, 3.0, 4.0 ] );
+
+var z = dkmeansDistance.ndarray( x.length, 'sqeuclidean', x, 1, 0, y, 1, 0 );
+// returns 72.0
+```
+
+The function has the following additional parameters:
+
+-   **offsetX**: starting index for `x`.
+-   **offsetY**: starting index for `y`.
+
+While [`typed array`][mdn-typed-array] views mandate a view offset based on the underlying buffer, the offset parameters support indexing semantics based on starting indices. For example, to calculate the distance between every other element in `x` starting from the second element with the last 3 elements in `y` in reverse order
+
+```javascript
+var Float64Array = require( '@stdlib/array/float64' );
+
+var x = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
+var y = new Float64Array( [ 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );
+
+var z = dkmeansDistance.ndarray( 3, 'sqeuclidean', x, 2, 1, y, -1, y.length-1 );
+// returns 165.0
+```
+
+</section>
+
+<!-- /.usage -->
+
+<section class="notes">
+
+## Notes
+
+-   If `N <= 0`, both functions return `NaN`.
+
+</section>
+
+<!-- /.notes -->
+
+<section class="examples">
+
+## Examples
+
+<!-- eslint no-undef: "error" -->
+
+```javascript
+var discreteUniform = require( '@stdlib/random/array/discrete-uniform' );
+var dkmeansDistance = require( '@stdlib/ml/strided/dkmeans-distance' );
+
+var opts = {
+    'dtype': 'float64'
+};
+var x = discreteUniform( 10, 0, 100, opts );
+console.log( x );
+
+var y = discreteUniform( x.length, 0, 10, opts );
+console.log( y );
+
+var out = dkmeansDistance.ndarray( x.length, 'sqeuclidean', x, 1, 0, y, -1, y.length-1 );
+console.log( out );
+```
+
+</section>
+
+<!-- /.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/ml/strided/dkmeans_distance.h"
+```
+
+#### stdlib_strided_dkmeans_distance( N, metric, \*X, strideX, \*Y, strideY )
+
+Computes the distance between two double-precision floating-point strided arrays according to a specified distance metric.
+
+```c
+#include "stdlib/ml/base/kmeans/metrics.h"
+
+const double x[] = { 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 };
+const double y[] = { 2.0, 1.0, 2.0, 1.0, -2.0, 2.0, 3.0, 4.0 };
+
+double v = stdlib_strided_dkmeans_distance( 8, STDLIB_ML_KMEANS_SQEUCLIDEAN, x, 1, y, 1 );
+// returns 72.0
+```
+
+The function accepts the following arguments:
+
+-   **N**: `[in] CBLAS_INT` number of indexed elements.
+-   **metric**: `[in] enum STDLIB_ML_KMEANS_METRIC` distance [metric][@stdlib/ml/base/kmeans/metrics]. 
+-   **X**: `[in] double*` first input array.
+-   **strideX**: `[in] CBLAS_INT` stride length of `X`.
+-   **Y**: `[in] double*` second input array.
+-   **strideY**: `[in] CBLAS_INT` stride length of `Y`.
+
+```c
+double stdlib_strided_dkmeans_distance( const CBLAS_INT N, const enum STDLIB_ML_KMEANS_METRIC, const double *X, const CBLAS_INT strideX, const double *Y, const CBLAS_INT strideY );
+```
+
+<!--lint disable maximum-heading-length-->
+
+#### stdlib_strided_dkmeans_distance_ndarray( N, metric, \*X, strideX, offsetX, \*Y, strideY, offsetY )
+
+<!--lint enable maximum-heading-length-->
+
+Computes the distance between two double-precision floating-point strided arrays according to a specified distance metric using alternative indexing semantics.
+
+```c
+#include "stdlib/ml/base/kmeans/metrics.h"
+
+const double x[] = { 4.0, 2.0, -3.0, 5.0, -1.0 };
+const double y[] = { 2.0, 6.0, -1.0, -4.0, 8.0 };
+
+double v = stdlib_strided_dkmeans_distance_ndarray( 5, STDLIB_ML_KMEANS_SQEUCLIDEAN, x, -1, 4, y, -1, 4 );
+// returns 186.0
+```
+
+The function accepts the following arguments:
+
+-   **N**: `[in] CBLAS_INT` number of indexed elements.
+-   **metric**: `[in] enum STDLIB_ML_KMEANS_METRIC` distance [metric][@stdlib/ml/base/kmeans/metrics].
+-   **X**: `[in] double*` first input array.
+-   **strideX**: `[in] CBLAS_INT` stride length of `X`.
+-   **offsetX**: `[in] CBLAS_INT` starting index for `X`.
+-   **Y**: `[in] double*` second input array.
+-   **strideY**: `[in] CBLAS_INT` stride length of `Y`.
+-   **offsetY**: `[in] CBLAS_INT` starting index for `Y`.
+
+```c
+double stdlib_strided_dkmeans_distance_ndarray( const CBLAS_INT N, const enum STDLIB_ML_KMEANS_METRIC, const double *X, const CBLAS_INT strideX, const CBLAS_INT offsetX, const double *Y, const CBLAS_INT strideY, const CBLAS_INT offsetY );
+```
+
+</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/ml/strided/dkmeans_distance.h"
+#include "stdlib/ml/base/kmeans/metrics.h"
+#include <stdio.h>
+
+int main( void ) {
+    // Create strided arrays:
+    const double x[] = { 1.0, -2.0, 3.0, -4.0, 5.0, -6.0, 7.0, -8.0 };
+    const double y[] = { 1.0, -2.0, 3.0, -4.0, 5.0, -6.0, 7.0, -8.0 };
+
+    // Specify the number of elements:
+    const int N = 8;
+
+    // Specify strides:
+    const int strideX = 1;
+    const int strideY = -1;
+
+    // Compute the distance between `x` and `y`:
+    double d = stdlib_strided_dkmeans_distance( N, STDLIB_ML_KMEANS_SQEUCLIDEAN, x, strideX, y, strideY );
+
+    // Print the result:
+    printf( "Distance: %lf\n", d );
+
+    // Compute the distance between `x` and `y` with offsets:
+    d = stdlib_strided_dkmeans_distance_ndarray( N, STDLIB_ML_KMEANS_SQEUCLIDEAN, x, strideX, 0, y, strideY, N-1 );
+
+    // Print the result:
+    printf( "Distance: %lf\n", d );
+}
+```
+
+</section>
+
+<!-- /.examples -->
+
+</section>
+
+<!-- /.c -->
+
+<!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->
+
+<section class="related">
+
+</section>
+
+<!-- /.related -->
+
+<!-- Section for all links. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
+
+<section class="links">
+
+[@stdlib/array/float64]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/array/float64
+
+[mdn-typed-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray
+
+[@stdlib/ml/base/kmeans/metrics]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/ml/base/kmeans/metrics
+
+<!-- <related-links> -->
+
+<!-- </related-links> -->
+
+</section>
+
+<!-- /.links -->