diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/README.md b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/README.md
new file mode 100644
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--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/README.md
@@ -0,0 +1,465 @@
+
+
+# dclosestCentroids
+
+> Assign each data point in a double-precision floating-point input matrix to its closest centroid.
+
+
+
+
+
+
+
+
+
+
+
+## Usage
+
+```javascript
+var dclosestCentroids = require( '@stdlib/ml/strided/dkmeans-closest-centroids' );
+```
+
+
+
+#### dclosestCentroids( order, M, N, k, metric, X, LDX, C, LDC, out, so, counts, sco )
+
+
+
+Assigns each data point in a double-precision floating-point input matrix to its closest centroid.
+
+```javascript
+var Float64Array = require( '@stdlib/array/float64' );
+var Int32Array = require( '@stdlib/array/int32' );
+
+/*
+ X = [
+ [ 1.0, 1.0 ],
+ [ 5.0, 5.0 ],
+ [ 1.5, 1.5 ]
+ ]
+*/
+var X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0, 1.5, 1.5 ] );
+
+/*
+ C = [
+ [ 1.0, 1.0 ],
+ [ 5.0, 5.0 ]
+ ]
+*/
+var C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+
+var out = new Int32Array( 3 );
+var counts = new Int32Array( 2 );
+
+dclosestCentroids( 'row-major', 3, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 );
+// out => [ 0, 1, 0 ]
+```
+
+The function has the following parameters:
+
+- **order**: storage layout.
+- **M**: number of data points.
+- **N**: number of features.
+- **k**: number of centroids.
+- **metric**: distance [metric][@stdlib/ml/base/kmeans/metrics] used to compute the distance between a data point and a centroid. Must be one of `'sqeuclidean'`, `'cosine'`, `'cityblock'`, or `'correlation'`.
+- **X**: input data matrix stored as a [`Float64Array`][mdn-float64array].
+- **LDX**: stride of the first dimension of `X` (a.k.a., leading dimension of the matrix `X`).
+- **C**: centroid matrix stored as a [`Float64Array`][mdn-float64array].
+- **LDC**: stride of the first dimension of `C` (a.k.a., leading dimension of the matrix `C`).
+- **out**: output array stored as an [`Int32Array`][mdn-int32array] for storing the index of the closest centroid for each data point.
+- **so**: stride length of `out`.
+- **counts**: output array stored as an [`Int32Array`][mdn-int32array] for storing the number of data points assigned to each centroid. Should be initialized to zeros prior to invocation.
+- **sco**: stride length of `counts`.
+
+The function **mutates** `out` and `counts` in-place and returns `out`.
+
+```javascript
+var Float64Array = require( '@stdlib/array/float64' );
+var Int32Array = require( '@stdlib/array/int32' );
+
+/*
+ X = [
+ [ 1.0, 1.0 ],
+ [ 5.0, 5.0 ],
+ [ 1.5, 1.5 ]
+ ]
+*/
+var X = new Float64Array( [ 1.0, 5.0, 1.5, 1.0, 5.0, 1.5 ] );
+var C = new Float64Array( [ 1.0, 5.0, 1.0, 5.0 ] );
+
+var out = new Int32Array( 3 );
+var counts = new Int32Array( 2 );
+
+dclosestCentroids( 'column-major', 3, 2, 2, 'sqeuclidean', X, 3, C, 2, out, 1, counts, 1 );
+// out => [ 0, 1, 0 ]
+```
+
+
+
+#### dclosestCentroids.ndarray( M, N, k, metric, X, sx1, sx2, ox, C, sc1, sc2, oc, out, so, oo, counts, sco, ocounts )
+
+
+
+Assigns each data point in a double-precision floating-point input matrix to its closest centroid using alternative indexing semantics.
+
+```javascript
+var Float64Array = require( '@stdlib/array/float64' );
+var Int32Array = require( '@stdlib/array/int32' );
+
+var X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0, 1.5, 1.5 ] );
+var C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+
+var out = new Int32Array( 3 );
+var counts = new Int32Array( 2 );
+
+dclosestCentroids.ndarray( 3, 2, 2, 'sqeuclidean', X, 2, 1, 0, C, 2, 1, 0, out, 1, 0, counts, 1, 0 );
+// out => [ 0, 1, 0 ]
+```
+
+The function has the following parameters:
+
+- **M**: number of data points.
+- **N**: number of features.
+- **k**: number of centroids.
+- **metric**: distance [metric][@stdlib/ml/base/kmeans/metrics].
+- **X**: input data matrix stored as a [`Float64Array`][mdn-float64array].
+- **sx1**: stride of the first dimension of `X`.
+- **sx2**: stride of the second dimension of `X`.
+- **ox**: starting index for `X`.
+- **C**: centroid matrix stored as a [`Float64Array`][mdn-float64array].
+- **sc1**: stride of the first dimension of `C`.
+- **sc2**: stride of the second dimension of `C`.
+- **oc**: starting index for `C`.
+- **out**: output array stored as an [`Int32Array`][mdn-int32array].
+- **so**: stride length of `out`.
+- **oo**: starting index for `out`.
+- **counts**: output array stored as an [`Int32Array`][mdn-int32array]. Should be initialized to zeros prior to invocation.
+- **sco**: stride length of `counts`.
+- **ocounts**: starting index for `counts`.
+
+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.
+
+```javascript
+var Float64Array = require( '@stdlib/array/float64' );
+var Int32Array = require( '@stdlib/array/int32' );
+
+var X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0, 1.5, 1.5 ] );
+var C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+
+var out = new Int32Array( 3 );
+var counts = new Int32Array( 2 );
+
+// Traverse the data points in reverse order:
+dclosestCentroids.ndarray( 3, 2, 2, 'sqeuclidean', X, -2, 1, 4, C, 2, 1, 0, out, -1, 2, counts, 1, 0 );
+// out => [ 0, 1, 0 ]
+```
+
+
+
+
+
+
+
+
+
+## Notes
+
+- For each data point, the function writes the index of the closest centroid to the output array `out` and increments the corresponding element of the `counts` array. Accordingly, the `counts` array should be initialized to zeros prior to invocation.
+- If `M`, `N`, or `k` is less than `1`, both functions return `out` unchanged.
+
+
+
+
+
+
+
+
+
+## Examples
+
+
+
+
+
+```javascript
+var Float64Array = require( '@stdlib/array/float64' );
+var Int32Array = require( '@stdlib/array/int32' );
+var ndarray2array = require( '@stdlib/ndarray/base/to-array' );
+var shape2strides = require( '@stdlib/ndarray/base/shape2strides' );
+var dclosestCentroids = require( '@stdlib/ml/strided/dkmeans-closest-centroids' );
+
+var order = 'row-major';
+
+var shapeX = [ 4, 2 ];
+var stridesX = shape2strides( shapeX, order );
+var X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0, 1.5, 1.5, 4.5, 5.5 ] );
+console.log( ndarray2array( X, shapeX, stridesX, 0, order ) );
+
+var shapeC = [ 2, 2 ];
+var stridesC = shape2strides( shapeC, order );
+var C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+console.log( ndarray2array( C, shapeC, stridesC, 0, order ) );
+
+var out = new Int32Array( shapeX[ 0 ] );
+var counts = new Int32Array( shapeC[ 0 ] );
+
+dclosestCentroids( order, shapeX[ 0 ], shapeX[ 1 ], shapeC[ 0 ], 'sqeuclidean', X, stridesX[ 0 ], C, stridesC[ 0 ], out, 1, counts, 1 );
+
+console.log( 'out = %s', out.toString() );
+console.log( 'counts = %s', counts.toString() );
+```
+
+
+
+
+
+
+
+* * *
+
+
+
+## C APIs
+
+
+
+
+
+
+
+
+
+
+
+### Usage
+
+```c
+#include "stdlib/ml/strided/dkmeans_closest_centroids.h"
+```
+
+
+
+#### stdlib_strided_dclosest_centroids( order, M, N, k, metric, \*X, LDX, \*C, LDC, \*out, so, \*counts, sco )
+
+
+
+Assigns each data point in a double-precision floating-point input matrix to its closest centroid.
+
+```c
+#include "stdlib/blas/base/shared.h"
+#include "stdlib/ml/base/kmeans/metrics.h"
+#include
+
+const double X[] = { 1.0, 1.0, 5.0, 5.0, 1.5, 1.5 };
+const double C[] = { 1.0, 1.0, 5.0, 5.0 };
+
+int32_t out[ 3 ];
+int32_t counts[] = { 0, 0 };
+
+stdlib_strided_dclosest_centroids( CblasRowMajor, 3, 2, 2, STDLIB_ML_KMEANS_SQEUCLIDEAN, X, 2, C, 2, out, 1, counts, 1 );
+// out => { 0, 1, 0 }
+```
+
+The function accepts the following arguments:
+
+- **order**: `[in] CBLAS_LAYOUT` storage layout.
+- **M**: `[in] CBLAS_INT` number of data points.
+- **N**: `[in] CBLAS_INT` number of features.
+- **k**: `[in] CBLAS_INT` number of centroids.
+- **metric**: `[in] enum STDLIB_ML_KMEANS_METRIC` distance metric.
+- **X**: `[in] double*` input data matrix.
+- **LDX**: `[in] CBLAS_INT` stride of the first dimension of `X` (a.k.a., leading dimension of the matrix `X`).
+- **C**: `[in] double*` centroid matrix.
+- **LDC**: `[in] CBLAS_INT` stride of the first dimension of `C` (a.k.a., leading dimension of the matrix `C`).
+- **out**: `[out] int32_t*` output array for closest centroid indices.
+- **so**: `[in] CBLAS_INT` stride length for `out`.
+- **counts**: `[inout] int32_t*` output array for per-centroid assignment counts.
+- **sco**: `[in] CBLAS_INT` stride length for `counts`.
+
+```c
+int32_t * stdlib_strided_dclosest_centroids( const CBLAS_LAYOUT order, const CBLAS_INT M, const CBLAS_INT N, const CBLAS_INT k, const enum STDLIB_ML_KMEANS_METRIC metric, const double *X, const CBLAS_INT LDX, const double *C, const CBLAS_INT LDC, int32_t *out, const CBLAS_INT so, int32_t *counts, const CBLAS_INT sco );
+```
+
+
+
+#### stdlib_strided_dclosest_centroids_ndarray( M, N, k, metric, \*X, sx1, sx2, ox, \*C, sc1, sc2, oc, \*out, so, oo, \*counts, sco, oco )
+
+
+
+Assigns each data point in a double-precision floating-point input matrix to its closest centroid using alternative indexing semantics.
+
+```c
+#include "stdlib/ml/base/kmeans/metrics.h"
+#include
+
+const double X[] = { 1.0, 1.0, 5.0, 5.0, 1.5, 1.5 };
+const double C[] = { 1.0, 1.0, 5.0, 5.0 };
+
+int32_t out[ 3 ];
+int32_t counts[] = { 0, 0 };
+
+stdlib_strided_dclosest_centroids_ndarray( 3, 2, 2, STDLIB_ML_KMEANS_SQEUCLIDEAN, X, 2, 1, 0, C, 2, 1, 0, out, 1, 0, counts, 1, 0 );
+// out => { 0, 1, 0 }
+```
+
+The function accepts the following arguments:
+
+- **M**: `[in] CBLAS_INT` number of data points.
+- **N**: `[in] CBLAS_INT` number of features.
+- **k**: `[in] CBLAS_INT` number of centroids.
+- **metric**: `[in] enum STDLIB_ML_KMEANS_METRIC` distance metric.
+- **X**: `[in] double*` input data matrix.
+- **sx1**: `[in] CBLAS_INT` stride of the first dimension of `X`.
+- **sx2**: `[in] CBLAS_INT` stride of the second dimension of `X`.
+- **ox**: `[in] CBLAS_INT` index offset for `X`.
+- **C**: `[in] double*` centroid matrix.
+- **sc1**: `[in] CBLAS_INT` stride of the first dimension of `C`.
+- **sc2**: `[in] CBLAS_INT` stride of the second dimension of `C`.
+- **oc**: `[in] CBLAS_INT` index offset for `C`.
+- **out**: `[out] int32_t*` output array for closest centroid indices.
+- **so**: `[in] CBLAS_INT` stride length for `out`.
+- **oo**: `[in] CBLAS_INT` index offset for `out`.
+- **counts**: `[inout] int32_t*` output array for per-centroid assignment counts.
+- **sco**: `[in] CBLAS_INT` stride length for `counts`.
+- **oco**: `[in] CBLAS_INT` index offset for `counts`.
+
+```c
+int32_t * stdlib_strided_dclosest_centroids_ndarray( const CBLAS_INT M, const CBLAS_INT N, const CBLAS_INT k, const enum STDLIB_ML_KMEANS_METRIC metric, const double *X, const CBLAS_INT sx1, const CBLAS_INT sx2, const CBLAS_INT ox, const double *C, const CBLAS_INT sc1, const CBLAS_INT sc2, const CBLAS_INT oc, int32_t *out, const CBLAS_INT so, const CBLAS_INT oo, int32_t *counts, const CBLAS_INT sco, const CBLAS_INT oco );
+```
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+### Examples
+
+```c
+#include "stdlib/ml/strided/dkmeans_closest_centroids.h"
+#include "stdlib/ml/base/kmeans/metrics.h"
+#include "stdlib/blas/base/shared.h"
+#include
+#include
+
+int main( void ) {
+ // Define a collection of data points (row-major):
+ const double X[ 3*3 ] = {
+ 1.0, 2.0, 3.0,
+ 4.0, 5.0, 6.0,
+ 7.0, 8.0, 9.0
+ };
+
+ // Define a collection of centroids (row-major):
+ const double C[] = { 1.0, 1.0, 5.0, 5.0, 2.0, 2.0 };
+
+ // Specify the number of data points, features, and centroids:
+ const int M = 3;
+ const int N = 3;
+ const int k = 2;
+
+ // Allocate output arrays:
+ int32_t out[ 3 ];
+ int32_t counts[] = { 0, 0 };
+
+ // Assign each data point to its closest centroid:
+ stdlib_strided_dclosest_centroids( CblasRowMajor, M, N, k, STDLIB_ML_KMEANS_SQEUCLIDEAN, X, N, C, N, out, 1, counts, 1 );
+
+ // Print the results:
+ int i;
+ for ( i = 0; i < M; i++ ) {
+ printf( "out[ %i ] = %i\n", i, out[ i ] );
+ }
+ for ( i = 0; i < k; i++ ) {
+ printf( "counts[ %i ] = %i\n", i, counts[ i ] );
+ }
+
+ for ( i = 0; i < k; i++ ) {
+ counts[ i ] = 0;
+ }
+
+ // Assign each data point to its closest centroid using alternative indexing semantics:
+ stdlib_strided_dclosest_centroids_ndarray( M, N, k, STDLIB_ML_KMEANS_SQEUCLIDEAN, X, N, 1, 0, C, N, 1, 0, out, 1, 0, counts, 1, 0 );
+
+ // Print the results:
+ for ( i = 0; i < M; i++ ) {
+ printf( "out[ %i ] = %i\n", i, out[ i ] );
+ }
+ for ( i = 0; i < k; i++ ) {
+ printf( "counts[ %i ] = %i\n", i, counts[ i ] );
+ }
+}
+```
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+[mdn-typed-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray
+
+[mdn-float64array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Float64Array
+
+[mdn-int32array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Int32Array
+
+[@stdlib/ml/base/kmeans/metrics]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/ml/base/kmeans/metrics
+
+
+
+
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/benchmark/benchmark.js b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/benchmark/benchmark.js
new file mode 100644
index 000000000000..c96744d120d6
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/benchmark/benchmark.js
@@ -0,0 +1,134 @@
+/**
+* @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 bench = require( '@stdlib/bench' );
+var isnan = require( '@stdlib/math/base/assert/is-nan' );
+var uniform = require( '@stdlib/random/array/uniform' );
+var zeros = require( '@stdlib/array/zeros' );
+var pow = require( '@stdlib/math/base/special/pow' );
+var floor = require( '@stdlib/math/base/special/floor' );
+var format = require( '@stdlib/string/format' );
+var pkg = require( './../package.json' ).name;
+var dclosestCentroids = require( './../lib/dkmeans_closest_centroids.js' );
+
+
+// VARIABLES //
+
+var LAYOUTS = [
+ 'row-major',
+ 'column-major'
+];
+var K = 4; // number of centroids
+var options = {
+ 'dtype': 'float64'
+};
+
+
+// FUNCTIONS //
+
+/**
+* Creates a benchmark function.
+*
+* @private
+* @param {string} order - storage layout
+* @param {PositiveInteger} N - number of data points and features along each dimension
+* @returns {Function} benchmark function
+*/
+function createBenchmark( order, N ) {
+ var counts;
+ var LDX;
+ var LDC;
+ var out;
+ var X;
+ var C;
+
+ X = uniform( N*N, -10000.0, 10000.0, options );
+ C = uniform( K*N, -10000.0, 10000.0, options );
+ out = zeros( N, 'int32' );
+ counts = zeros( K, 'int32' );
+
+ if ( order === 'column-major' ) {
+ LDX = N;
+ LDC = K;
+ } else {
+ LDX = N;
+ LDC = N;
+ }
+ return benchmark;
+
+ /**
+ * Benchmark function.
+ *
+ * @private
+ * @param {Benchmark} b - benchmark instance
+ */
+ function benchmark( b ) {
+ var z;
+ var i;
+
+ b.tic();
+ for ( i = 0; i < b.iterations; i++ ) {
+ z = dclosestCentroids( order, N, N, K, 'sqeuclidean', X, LDX, C, LDC, out, 1, counts, 1 );
+ if ( isnan( z[ i%z.length ] ) ) {
+ b.fail( 'should not return NaN' );
+ }
+ }
+ b.toc();
+ if ( isnan( z[ i%z.length ] ) ) {
+ b.fail( 'should not return NaN' );
+ }
+ b.pass( 'benchmark finished' );
+ b.end();
+ }
+}
+
+
+// MAIN //
+
+/**
+* Main execution sequence.
+*
+* @private
+*/
+function main() {
+ var min;
+ var max;
+ var ord;
+ var N;
+ var f;
+ var i;
+ var k;
+
+ min = 1; // 10^min
+ max = 6; // 10^max
+
+ for ( k = 0; k < LAYOUTS.length; k++ ) {
+ ord = LAYOUTS[ k ];
+ for ( i = min; i <= max; i++ ) {
+ N = floor( pow( pow( 10, i ), 1.0/2.0 ) );
+ f = createBenchmark( ord, N );
+ bench( format( '%s:order=%s,size=%d', pkg, ord, N*N ), f );
+ }
+ }
+}
+
+main();
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/benchmark/benchmark.native.js b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/benchmark/benchmark.native.js
new file mode 100644
index 000000000000..aa77bc5c6a43
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/benchmark/benchmark.native.js
@@ -0,0 +1,139 @@
+/**
+* @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 isnan = require( '@stdlib/math/base/assert/is-nan' );
+var uniform = require( '@stdlib/random/array/uniform' );
+var zeros = require( '@stdlib/array/zeros' );
+var pow = require( '@stdlib/math/base/special/pow' );
+var floor = require( '@stdlib/math/base/special/floor' );
+var tryRequire = require( '@stdlib/utils/try-require' );
+var format = require( '@stdlib/string/format' );
+var pkg = require( './../package.json' ).name;
+
+
+// VARIABLES //
+
+var dclosestCentroids = tryRequire( resolve( __dirname, './../lib/dkmeans_closest_centroids.native.js' ) );
+var opts = {
+ 'skip': ( dclosestCentroids instanceof Error )
+};
+var LAYOUTS = [
+ 'row-major',
+ 'column-major'
+];
+var K = 4; // number of centroids
+var options = {
+ 'dtype': 'float64'
+};
+
+
+// FUNCTIONS //
+
+/**
+* Creates a benchmark function.
+*
+* @private
+* @param {string} order - storage layout
+* @param {PositiveInteger} N - number of data points and features along each dimension
+* @returns {Function} benchmark function
+*/
+function createBenchmark( order, N ) {
+ var counts;
+ var LDX;
+ var LDC;
+ var out;
+ var X;
+ var C;
+
+ X = uniform( N*N, -10000.0, 10000.0, options );
+ C = uniform( K*N, -10000.0, 10000.0, options );
+ out = zeros( N, 'int32' );
+ counts = zeros( K, 'int32' );
+
+ if ( order === 'column-major' ) {
+ LDX = N;
+ LDC = K;
+ } else {
+ LDX = N;
+ LDC = N;
+ }
+ return benchmark;
+
+ /**
+ * Benchmark function.
+ *
+ * @private
+ * @param {Benchmark} b - benchmark instance
+ */
+ function benchmark( b ) {
+ var z;
+ var i;
+
+ b.tic();
+ for ( i = 0; i < b.iterations; i++ ) {
+ z = dclosestCentroids( order, N, N, K, 'sqeuclidean', X, LDX, C, LDC, out, 1, counts, 1 );
+ if ( isnan( z[ i%z.length ] ) ) {
+ b.fail( 'should not return NaN' );
+ }
+ }
+ b.toc();
+ if ( isnan( z[ i%z.length ] ) ) {
+ b.fail( 'should not return NaN' );
+ }
+ b.pass( 'benchmark finished' );
+ b.end();
+ }
+}
+
+
+// MAIN //
+
+/**
+* Main execution sequence.
+*
+* @private
+*/
+function main() {
+ var min;
+ var max;
+ var ord;
+ var N;
+ var f;
+ var i;
+ var k;
+
+ min = 1; // 10^min
+ max = 6; // 10^max
+
+ for ( k = 0; k < LAYOUTS.length; k++ ) {
+ ord = LAYOUTS[ k ];
+ for ( i = min; i <= max; i++ ) {
+ N = floor( pow( pow( 10, i ), 1.0/2.0 ) );
+ f = createBenchmark( ord, N );
+ bench( format( '%s::native:order=%s,size=%d', pkg, ord, N*N ), opts, f );
+ }
+ }
+}
+
+main();
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/benchmark/benchmark.ndarray.js b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/benchmark/benchmark.ndarray.js
new file mode 100644
index 000000000000..7e51c34bdb5f
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/benchmark/benchmark.ndarray.js
@@ -0,0 +1,141 @@
+/**
+* @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 bench = require( '@stdlib/bench' );
+var isnan = require( '@stdlib/math/base/assert/is-nan' );
+var uniform = require( '@stdlib/random/array/uniform' );
+var zeros = require( '@stdlib/array/zeros' );
+var pow = require( '@stdlib/math/base/special/pow' );
+var floor = require( '@stdlib/math/base/special/floor' );
+var isColumnMajor = require( '@stdlib/ndarray/base/assert/is-column-major-string' );
+var format = require( '@stdlib/string/format' );
+var pkg = require( './../package.json' ).name;
+var dclosestCentroids = require( './../lib/ndarray.js' );
+
+
+// VARIABLES //
+
+var LAYOUTS = [
+ 'row-major',
+ 'column-major'
+];
+var K = 4; // number of centroids
+var options = {
+ 'dtype': 'float64'
+};
+
+
+// FUNCTIONS //
+
+/**
+* Creates a benchmark function.
+*
+* @private
+* @param {string} order - storage layout
+* @param {PositiveInteger} N - number of data points and features along each dimension
+* @returns {Function} benchmark function
+*/
+function createBenchmark( order, N ) {
+ var counts;
+ var out;
+ var sx1;
+ var sx2;
+ var sc1;
+ var sc2;
+ var X;
+ var C;
+
+ X = uniform( N*N, -10000.0, 10000.0, options );
+ C = uniform( K*N, -10000.0, 10000.0, options );
+ out = zeros( N, 'int32' );
+ counts = zeros( K, 'int32' );
+
+ if ( isColumnMajor( order ) ) {
+ sx1 = 1;
+ sx2 = N;
+ sc1 = 1;
+ sc2 = K;
+ } else { // order === 'row-major'
+ sx1 = N;
+ sx2 = 1;
+ sc1 = N;
+ sc2 = 1;
+ }
+ return benchmark;
+
+ /**
+ * Benchmark function.
+ *
+ * @private
+ * @param {Benchmark} b - benchmark instance
+ */
+ function benchmark( b ) {
+ var z;
+ var i;
+
+ b.tic();
+ for ( i = 0; i < b.iterations; i++ ) {
+ z = dclosestCentroids( N, N, K, 'sqeuclidean', X, sx1, sx2, 0, C, sc1, sc2, 0, out, 1, 0, counts, 1, 0 );
+ if ( isnan( z[ i%z.length ] ) ) {
+ b.fail( 'should not return NaN' );
+ }
+ }
+ b.toc();
+ if ( isnan( z[ i%z.length ] ) ) {
+ b.fail( 'should not return NaN' );
+ }
+ b.pass( 'benchmark finished' );
+ b.end();
+ }
+}
+
+
+// MAIN //
+
+/**
+* Main execution sequence.
+*
+* @private
+*/
+function main() {
+ var min;
+ var max;
+ var ord;
+ var N;
+ var f;
+ var i;
+ var k;
+
+ min = 1; // 10^min
+ max = 6; // 10^max
+
+ for ( k = 0; k < LAYOUTS.length; k++ ) {
+ ord = LAYOUTS[ k ];
+ for ( i = min; i <= max; i++ ) {
+ N = floor( pow( pow( 10, i ), 1.0/2.0 ) );
+ f = createBenchmark( ord, N );
+ bench( format( '%s:ndarray:order=%s,size=%d', pkg, ord, N*N ), f );
+ }
+ }
+}
+
+main();
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/benchmark/benchmark.ndarray.native.js b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/benchmark/benchmark.ndarray.native.js
new file mode 100644
index 000000000000..7fded2a5318f
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/benchmark/benchmark.ndarray.native.js
@@ -0,0 +1,146 @@
+/**
+* @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 isnan = require( '@stdlib/math/base/assert/is-nan' );
+var uniform = require( '@stdlib/random/array/uniform' );
+var zeros = require( '@stdlib/array/zeros' );
+var pow = require( '@stdlib/math/base/special/pow' );
+var floor = require( '@stdlib/math/base/special/floor' );
+var isColumnMajor = require( '@stdlib/ndarray/base/assert/is-column-major-string' );
+var tryRequire = require( '@stdlib/utils/try-require' );
+var format = require( '@stdlib/string/format' );
+var pkg = require( './../package.json' ).name;
+
+
+// VARIABLES //
+
+var dclosestCentroids = tryRequire( resolve( __dirname, './../lib/ndarray.native.js' ) );
+var opts = {
+ 'skip': ( dclosestCentroids instanceof Error )
+};
+var LAYOUTS = [
+ 'row-major',
+ 'column-major'
+];
+var K = 4; // number of centroids
+var options = {
+ 'dtype': 'float64'
+};
+
+
+// FUNCTIONS //
+
+/**
+* Creates a benchmark function.
+*
+* @private
+* @param {string} order - storage layout
+* @param {PositiveInteger} N - number of data points and features along each dimension
+* @returns {Function} benchmark function
+*/
+function createBenchmark( order, N ) {
+ var counts;
+ var out;
+ var sx1;
+ var sx2;
+ var sc1;
+ var sc2;
+ var X;
+ var C;
+
+ X = uniform( N*N, -10000.0, 10000.0, options );
+ C = uniform( K*N, -10000.0, 10000.0, options );
+ out = zeros( N, 'int32' );
+ counts = zeros( K, 'int32' );
+
+ if ( isColumnMajor( order ) ) {
+ sx1 = 1;
+ sx2 = N;
+ sc1 = 1;
+ sc2 = K;
+ } else { // order === 'row-major'
+ sx1 = N;
+ sx2 = 1;
+ sc1 = N;
+ sc2 = 1;
+ }
+ return benchmark;
+
+ /**
+ * Benchmark function.
+ *
+ * @private
+ * @param {Benchmark} b - benchmark instance
+ */
+ function benchmark( b ) {
+ var z;
+ var i;
+
+ b.tic();
+ for ( i = 0; i < b.iterations; i++ ) {
+ z = dclosestCentroids( N, N, K, 'sqeuclidean', X, sx1, sx2, 0, C, sc1, sc2, 0, out, 1, 0, counts, 1, 0 );
+ if ( isnan( z[ i%z.length ] ) ) {
+ b.fail( 'should not return NaN' );
+ }
+ }
+ b.toc();
+ if ( isnan( z[ i%z.length ] ) ) {
+ b.fail( 'should not return NaN' );
+ }
+ b.pass( 'benchmark finished' );
+ b.end();
+ }
+}
+
+
+// MAIN //
+
+/**
+* Main execution sequence.
+*
+* @private
+*/
+function main() {
+ var min;
+ var max;
+ var ord;
+ var N;
+ var f;
+ var i;
+ var k;
+
+ min = 1; // 10^min
+ max = 6; // 10^max
+
+ for ( k = 0; k < LAYOUTS.length; k++ ) {
+ ord = LAYOUTS[ k ];
+ for ( i = min; i <= max; i++ ) {
+ N = floor( pow( pow( 10, i ), 1.0/2.0 ) );
+ f = createBenchmark( ord, N );
+ bench( format( '%s::native:ndarray:order=%s,size=%d', pkg, ord, N*N ), opts, f );
+ }
+ }
+}
+
+main();
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/benchmark/c/Makefile b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/benchmark/c/Makefile
new file mode 100644
index 000000000000..0756dc7da20a
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/benchmark/c/Makefile
@@ -0,0 +1,146 @@
+#/
+# @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.
+#/
+
+# VARIABLES #
+
+ifndef VERBOSE
+ QUIET := @
+else
+ QUIET :=
+endif
+
+# Determine the OS ([1][1], [2][2]).
+#
+# [1]: https://en.wikipedia.org/wiki/Uname#Examples
+# [2]: http://stackoverflow.com/a/27776822/2225624
+OS ?= $(shell uname)
+ifneq (, $(findstring MINGW,$(OS)))
+ OS := WINNT
+else
+ifneq (, $(findstring MSYS,$(OS)))
+ OS := WINNT
+else
+ifneq (, $(findstring CYGWIN,$(OS)))
+ OS := WINNT
+else
+ifneq (, $(findstring Windows_NT,$(OS)))
+ OS := WINNT
+endif
+endif
+endif
+endif
+
+# Define the program used for compiling C source files:
+ifdef C_COMPILER
+ CC := $(C_COMPILER)
+else
+ CC := gcc
+endif
+
+# Define the command-line options when compiling C files:
+CFLAGS ?= \
+ -std=c99 \
+ -O3 \
+ -Wall \
+ -pedantic
+
+# Determine whether to generate position independent code ([1][1], [2][2]).
+#
+# [1]: https://gcc.gnu.org/onlinedocs/gcc/Code-Gen-Options.html#Code-Gen-Options
+# [2]: http://stackoverflow.com/questions/5311515/gcc-fpic-option
+ifeq ($(OS), WINNT)
+ fPIC ?=
+else
+ fPIC ?= -fPIC
+endif
+
+# List of includes (e.g., `-I /foo/bar -I /beep/boop/include`):
+INCLUDE ?=
+
+# List of source files:
+SOURCE_FILES ?=
+
+# List of libraries (e.g., `-lopenblas -lpthread`):
+LIBRARIES ?=
+
+# List of library paths (e.g., `-L /foo/bar -L /beep/boop`):
+LIBPATH ?=
+
+# List of C targets:
+c_targets := benchmark.length.out
+
+
+# RULES #
+
+#/
+# Compiles source files.
+#
+# @param {string} [C_COMPILER] - C compiler (e.g., `gcc`)
+# @param {string} [CFLAGS] - C compiler options
+# @param {(string|void)} [fPIC] - compiler flag determining whether to generate position independent code (e.g., `-fPIC`)
+# @param {string} [INCLUDE] - list of includes (e.g., `-I /foo/bar -I /beep/boop/include`)
+# @param {string} [SOURCE_FILES] - list of source files
+# @param {string} [LIBPATH] - list of library paths (e.g., `-L /foo/bar -L /beep/boop`)
+# @param {string} [LIBRARIES] - list of libraries (e.g., `-lopenblas -lpthread`)
+#
+# @example
+# make
+#
+# @example
+# make all
+#/
+all: $(c_targets)
+
+.PHONY: all
+
+#/
+# Compiles C source files.
+#
+# @private
+# @param {string} CC - C compiler (e.g., `gcc`)
+# @param {string} CFLAGS - C compiler options
+# @param {(string|void)} fPIC - compiler flag determining whether to generate position independent code (e.g., `-fPIC`)
+# @param {string} INCLUDE - list of includes (e.g., `-I /foo/bar`)
+# @param {string} SOURCE_FILES - list of source files
+# @param {string} LIBPATH - list of library paths (e.g., `-L /foo/bar`)
+# @param {string} LIBRARIES - list of libraries (e.g., `-lopenblas`)
+#/
+$(c_targets): %.out: %.c
+ $(QUIET) $(CC) $(CFLAGS) $(fPIC) $(INCLUDE) -o $@ $(SOURCE_FILES) $< $(LIBPATH) -lm $(LIBRARIES)
+
+#/
+# Runs compiled benchmarks.
+#
+# @example
+# make run
+#/
+run: $(c_targets)
+ $(QUIET) ./$<
+
+.PHONY: run
+
+#/
+# Removes generated files.
+#
+# @example
+# make clean
+#/
+clean:
+ $(QUIET) -rm -f *.o *.out
+
+.PHONY: clean
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/benchmark/c/benchmark.length.c b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/benchmark/c/benchmark.length.c
new file mode 100644
index 000000000000..4db30fccecba
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/benchmark/c/benchmark.length.c
@@ -0,0 +1,225 @@
+/**
+* @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.
+*/
+
+#include "stdlib/ml/strided/dkmeans_closest_centroids.h"
+#include "stdlib/ml/base/kmeans/metrics.h"
+#include "stdlib/blas/base/shared.h"
+#include
+#include
+#include
+#include
+#include
+#include
+
+#define NAME "dclosest_centroids"
+#define ITERATIONS 10000000
+#define REPEATS 3
+#define MIN 1
+#define MAX 6
+#define K 4
+
+/**
+* Prints the TAP version.
+*/
+static void print_version( void ) {
+ printf( "TAP version 13\n" );
+}
+
+/**
+* Prints the TAP summary.
+*
+* @param total total number of tests
+* @param passing total number of passing tests
+*/
+static void print_summary( int total, int passing ) {
+ printf( "#\n" );
+ printf( "1..%d\n", total ); // TAP plan
+ printf( "# total %d\n", total );
+ printf( "# pass %d\n", passing );
+ printf( "#\n" );
+ printf( "# ok\n" );
+}
+
+/**
+* Prints benchmarks results.
+*
+* @param iterations number of iterations
+* @param elapsed elapsed time in seconds
+*/
+static void print_results( int iterations, double elapsed ) {
+ double rate = (double)iterations / elapsed;
+ printf( " ---\n" );
+ printf( " iterations: %d\n", iterations );
+ printf( " elapsed: %0.9f\n", elapsed );
+ printf( " rate: %0.9f\n", rate );
+ printf( " ...\n" );
+}
+
+/**
+* Returns a clock time.
+*
+* @return clock time
+*/
+static double tic( void ) {
+ struct timeval now;
+ gettimeofday( &now, NULL );
+ return (double)now.tv_sec + (double)now.tv_usec/1.0e6;
+}
+
+/**
+* Generates a random number on the interval [min,max).
+*
+* @param min minimum value (inclusive)
+* @param max maximum value (exclusive)
+* @return random number
+*/
+static double random_uniform( const double min, const double max ) {
+ double v = (double)rand() / ( (double)RAND_MAX + 1.0 );
+ return min + ( v*(max-min) );
+}
+
+/**
+* Runs a benchmark.
+*
+* @param iterations number of iterations
+* @param N number of data points and features along each dimension
+* @return elapsed time in seconds
+*/
+static double benchmark1( int iterations, int N ) {
+ int32_t *counts;
+ double elapsed;
+ int32_t *out;
+ double *X;
+ double *C;
+ double t;
+ int i;
+
+ X = (double *)malloc( ( N*N ) * sizeof( double ) );
+ C = (double *)malloc( ( K*N ) * sizeof( double ) );
+ out = (int32_t *)malloc( N * sizeof( int32_t ) );
+ counts = (int32_t *)calloc( K, sizeof( int32_t ) );
+ for ( i = 0; i < N*N; i++ ) {
+ X[ i ] = random_uniform( -10000.0, 10000.0 );
+ }
+ for ( i = 0; i < K*N; i++ ) {
+ C[ i ] = random_uniform( -10000.0, 10000.0 );
+ }
+ t = tic();
+ for ( i = 0; i < iterations; i++ ) {
+ // cppcheck-suppress uninitvar
+ stdlib_strided_dclosest_centroids( CblasRowMajor, N, N, K, STDLIB_ML_KMEANS_SQEUCLIDEAN, X, N, C, N, out, 1, counts, 1 );
+ if ( out[ i%N ] < 0 ) {
+ printf( "unexpected result\n" );
+ break;
+ }
+ }
+ elapsed = tic() - t;
+ if ( out[ i%N ] < 0 ) {
+ printf( "unexpected result\n" );
+ }
+ free( X );
+ free( C );
+ free( out );
+ free( counts );
+ return elapsed;
+}
+
+/**
+* Runs a benchmark.
+*
+* @param iterations number of iterations
+* @param N number of data points and features along each dimension
+* @return elapsed time in seconds
+*/
+static double benchmark2( int iterations, int N ) {
+ int32_t *counts;
+ double elapsed;
+ int32_t *out;
+ double *X;
+ double *C;
+ double t;
+ int i;
+
+ X = (double *)malloc( ( N*N ) * sizeof( double ) );
+ C = (double *)malloc( ( K*N ) * sizeof( double ) );
+ out = (int32_t *)malloc( N * sizeof( int32_t ) );
+ counts = (int32_t *)calloc( K, sizeof( int32_t ) );
+ for ( i = 0; i < N*N; i++ ) {
+ X[ i ] = random_uniform( -10000.0, 10000.0 );
+ }
+ for ( i = 0; i < K*N; i++ ) {
+ C[ i ] = random_uniform( -10000.0, 10000.0 );
+ }
+ t = tic();
+ for ( i = 0; i < iterations; i++ ) {
+ // cppcheck-suppress uninitvar
+ stdlib_strided_dclosest_centroids_ndarray( N, N, K, STDLIB_ML_KMEANS_SQEUCLIDEAN, X, N, 1, 0, C, N, 1, 0, out, 1, 0, counts, 1, 0 );
+ if ( out[ i%N ] < 0 ) {
+ printf( "unexpected result\n" );
+ break;
+ }
+ }
+ elapsed = tic() - t;
+ if ( out[ i%N ] < 0 ) {
+ printf( "unexpected result\n" );
+ }
+ free( X );
+ free( C );
+ free( out );
+ free( counts );
+ return elapsed;
+}
+
+/**
+* Main execution sequence.
+*/
+int main( void ) {
+ double elapsed;
+ int count;
+ int iter;
+ int len;
+ int N;
+ int i;
+ int j;
+
+ // Use the current time to seed the random number generator:
+ srand( time( NULL ) );
+
+ print_version();
+ count = 0;
+ for ( i = MIN; i <= MAX; i++ ) {
+ len = pow( 10, i );
+ N = (int)sqrt( (double)len );
+ iter = ITERATIONS / pow( 10, i-1 );
+ for ( j = 0; j < REPEATS; j++ ) {
+ count += 1;
+ printf( "# c::native::%s:order=row-major,size=%d\n", NAME, N*N );
+ elapsed = benchmark1( iter, N );
+ print_results( iter, elapsed );
+ printf( "ok %d benchmark finished\n", count );
+ }
+ for ( j = 0; j < REPEATS; j++ ) {
+ count += 1;
+ printf( "# c::native::%s:ndarray:order=row-major,size=%d\n", NAME, N*N );
+ elapsed = benchmark2( iter, N );
+ print_results( iter, elapsed );
+ printf( "ok %d benchmark finished\n", count );
+ }
+ }
+ print_summary( count, count );
+}
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/binding.gyp b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/binding.gyp
new file mode 100644
index 000000000000..60dce9d0b31a
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/binding.gyp
@@ -0,0 +1,265 @@
+# @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.
+
+# A `.gyp` file for building a Node.js native add-on.
+#
+# [1]: https://gyp.gsrc.io/docs/InputFormatReference.md
+# [2]: https://gyp.gsrc.io/docs/UserDocumentation.md
+{
+ # List of files to include in this file:
+ 'includes': [
+ './include.gypi',
+ ],
+
+ # Define variables to be used throughout the configuration for all targets:
+ 'variables': {
+ # Target name should match the add-on export name:
+ 'addon_target_name%': 'addon',
+
+ # Fortran compiler (to override -Dfortran_compiler=):
+ 'fortran_compiler%': 'gfortran',
+
+ # Fortran compiler flags:
+ 'fflags': [
+ # Specify the Fortran standard to which a program is expected to conform:
+ '-std=f95',
+
+ # Indicate that the layout is free-form source code:
+ '-ffree-form',
+
+ # Aggressive optimization:
+ '-O3',
+
+ # Enable commonly used warning options:
+ '-Wall',
+
+ # Warn if source code contains problematic language features:
+ '-Wextra',
+
+ # Warn if a procedure is called without an explicit interface:
+ '-Wimplicit-interface',
+
+ # Do not transform names of entities specified in Fortran source files by appending underscores (i.e., don't mangle names, thus allowing easier usage in C wrappers):
+ '-fno-underscoring',
+
+ # Warn if source code contains Fortran 95 extensions and C-language constructs:
+ '-pedantic',
+
+ # Compile but do not link (output is an object file):
+ '-c',
+ ],
+
+ # Set variables based on the host OS:
+ 'conditions': [
+ [
+ 'OS=="win"',
+ {
+ # Define the object file suffix:
+ 'obj': 'obj',
+ },
+ {
+ # Define the object file suffix:
+ 'obj': 'o',
+ }
+ ], # end condition (OS=="win")
+ ], # end conditions
+ }, # end variables
+
+ # Define compile targets:
+ 'targets': [
+
+ # Target to generate an add-on:
+ {
+ # The target name should match the add-on export name:
+ 'target_name': '<(addon_target_name)',
+
+ # Define dependencies:
+ 'dependencies': [],
+
+ # Define directories which contain relevant include headers:
+ 'include_dirs': [
+ # Local include directory:
+ '<@(include_dirs)',
+ ],
+
+ # List of source files:
+ 'sources': [
+ '<@(src_files)',
+ ],
+
+ # Settings which should be applied when a target's object files are used as linker input:
+ 'link_settings': {
+ # Define libraries:
+ 'libraries': [
+ '<@(libraries)',
+ ],
+
+ # Define library directories:
+ 'library_dirs': [
+ '<@(library_dirs)',
+ ],
+ },
+
+ # C/C++ compiler flags:
+ 'cflags': [
+ # Enable commonly used warning options:
+ '-Wall',
+
+ # Aggressive optimization:
+ '-O3',
+ ],
+
+ # C specific compiler flags:
+ 'cflags_c': [
+ # Specify the C standard to which a program is expected to conform:
+ '-std=c99',
+ ],
+
+ # C++ specific compiler flags:
+ 'cflags_cpp': [
+ # Specify the C++ standard to which a program is expected to conform:
+ '-std=c++11',
+ ],
+
+ # Linker flags:
+ 'ldflags': [],
+
+ # Apply conditions based on the host OS:
+ 'conditions': [
+ [
+ 'OS=="mac"',
+ {
+ # Linker flags:
+ 'ldflags': [
+ '-undefined dynamic_lookup',
+ '-Wl,-no-pie',
+ '-Wl,-search_paths_first',
+ ],
+ },
+ ], # end condition (OS=="mac")
+ [
+ 'OS!="win"',
+ {
+ # C/C++ flags:
+ 'cflags': [
+ # Generate platform-independent code:
+ '-fPIC',
+ ],
+ },
+ ], # end condition (OS!="win")
+ ], # end conditions
+
+ # Define custom build actions for particular inputs:
+ 'rules': [
+ {
+ # Define a rule for processing Fortran files:
+ 'extension': 'f',
+
+ # Define the pathnames to be used as inputs when performing processing:
+ 'inputs': [
+ # Full path of the current input:
+ '<(RULE_INPUT_PATH)'
+ ],
+
+ # Define the outputs produced during processing:
+ 'outputs': [
+ # Store an output object file in a directory for placing intermediate results (only accessible within a single target):
+ '<(INTERMEDIATE_DIR)/<(RULE_INPUT_ROOT).<(obj)'
+ ],
+
+ # Define the rule for compiling Fortran based on the host OS:
+ 'conditions': [
+ [
+ 'OS=="win"',
+
+ # Rule to compile Fortran on Windows:
+ {
+ 'rule_name': 'compile_fortran_windows',
+ 'message': 'Compiling Fortran file <(RULE_INPUT_PATH) on Windows...',
+
+ 'process_outputs_as_sources': 0,
+
+ # Define the command-line invocation:
+ 'action': [
+ '<(fortran_compiler)',
+ '<@(fflags)',
+ '<@(_inputs)',
+ '-o',
+ '<@(_outputs)',
+ ],
+ },
+
+ # Rule to compile Fortran on non-Windows:
+ {
+ 'rule_name': 'compile_fortran_linux',
+ 'message': 'Compiling Fortran file <(RULE_INPUT_PATH) on Linux...',
+
+ 'process_outputs_as_sources': 1,
+
+ # Define the command-line invocation:
+ 'action': [
+ '<(fortran_compiler)',
+ '<@(fflags)',
+ '-fPIC', # generate platform-independent code
+ '<@(_inputs)',
+ '-o',
+ '<@(_outputs)',
+ ],
+ }
+ ], # end condition (OS=="win")
+ ], # end conditions
+ }, # end rule (extension=="f")
+ ], # end rules
+ }, # end target <(addon_target_name)
+
+ # Target to copy a generated add-on to a standard location:
+ {
+ 'target_name': 'copy_addon',
+
+ # Declare that the output of this target is not linked:
+ 'type': 'none',
+
+ # Define dependencies:
+ 'dependencies': [
+ # Require that the add-on be generated before building this target:
+ '<(addon_target_name)',
+ ],
+
+ # Define a list of actions:
+ 'actions': [
+ {
+ 'action_name': 'copy_addon',
+ 'message': 'Copying addon...',
+
+ # Explicitly list the inputs in the command-line invocation below:
+ 'inputs': [],
+
+ # Declare the expected outputs:
+ 'outputs': [
+ '<(addon_output_dir)/<(addon_target_name).node',
+ ],
+
+ # Define the command-line invocation:
+ 'action': [
+ 'cp',
+ '<(PRODUCT_DIR)/<(addon_target_name).node',
+ '<(addon_output_dir)/<(addon_target_name).node',
+ ],
+ },
+ ], # end actions
+ }, # end target copy_addon
+ ], # end targets
+}
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/docs/repl.txt b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/docs/repl.txt
new file mode 100644
index 000000000000..012ce0193687
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/docs/repl.txt
@@ -0,0 +1,154 @@
+
+{{alias}}( order, M, N, k, metric, X, LDX, C, LDC, out, so, counts, sco )
+ Assigns each data point in a double-precision floating-point input matrix to
+ its closest centroid.
+
+ Indexing is relative to the first index. To introduce an offset, use typed
+ array views.
+
+ If `M`, `N`, or `k` is less than one, the function returns `out` unchanged.
+
+ Parameters
+ ----------
+ order: string
+ Row-major (C-style) or column-major (Fortran-style) order. Must be
+ either 'row-major' or 'column-major'.
+
+ M: integer
+ Number of data points.
+
+ N: integer
+ Number of features.
+
+ k: integer
+ Number of centroids.
+
+ metric: string
+ Distance metric.
+
+ X: Float64Array
+ Input data matrix.
+
+ LDX: integer
+ Stride of the first dimension of `X` (a.k.a., leading dimension of the
+ matrix `X`).
+
+ C: Float64Array
+ Centroid matrix.
+
+ LDC: integer
+ Stride of the first dimension of `C` (a.k.a., leading dimension of the
+ matrix `C`).
+
+ out: Int32Array
+ Output array for closest centroid indices.
+
+ so: integer
+ Stride length for `out`.
+
+ counts: Int32Array
+ Output array for per-centroid assignment counts.
+
+ sco: integer
+ Stride length for `counts`.
+
+ Returns
+ -------
+ out: Int32Array
+ Output array.
+
+ Examples
+ --------
+ > var X = new {{alias:@stdlib/array/float64}}( [ 1.0, 1.0, 5.0, 5.0, 1.5,
+ ... 1.5 ] );
+ > var C = new {{alias:@stdlib/array/float64}}( [ 1.0, 1.0, 5.0, 5.0 ] );
+ > var out = new {{alias:@stdlib/array/int32}}( 3 );
+ > var counts = new {{alias:@stdlib/array/int32}}( 2 );
+ > {{alias}}( 'row-major', 3, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1,
+ ... counts, 1 );
+ > out
+ [ 0, 1, 0 ]
+
+
+{{alias}}.ndarray( M,N,k,metric,X,sx1,sx2,ox,C,sc1,sc2,oc,out,so,oo,co,sco,oco )
+ Assigns each data point in a double-precision floating-point input matrix to
+ its closest centroid using alternative indexing semantics.
+
+ While typed array views mandate a view offset based on the underlying
+ buffer, offset parameters support indexing semantics based on starting
+ indices.
+
+ Parameters
+ ----------
+ M: integer
+ Number of data points.
+
+ N: integer
+ Number of features.
+
+ k: integer
+ Number of centroids.
+
+ metric: string
+ Distance metric.
+
+ X: Float64Array
+ Input data matrix.
+
+ sx1: integer
+ Stride of the first dimension of `X`.
+
+ sx2: integer
+ Stride of the second dimension of `X`.
+
+ ox: integer
+ Starting index for `X`.
+
+ C: Float64Array
+ Centroid matrix.
+
+ sc1: integer
+ Stride of the first dimension of `C`.
+
+ sc2: integer
+ Stride of the second dimension of `C`.
+
+ oc: integer
+ Starting index for `C`.
+
+ out: Int32Array
+ Output array for closest centroid indices.
+
+ so: integer
+ Stride length for `out`.
+
+ oo: integer
+ Starting index for `out`.
+
+ co: Int32Array
+ Output array for per-centroid assignment counts.
+
+ sco: integer
+ Stride length for `co`.
+
+ oco: integer
+ Starting index for `co`.
+
+ Returns
+ -------
+ out: Int32Array
+ Output array.
+
+ Examples
+ --------
+ > var X = new {{alias:@stdlib/array/float64}}( [ 1.0, 1.0, 5.0, 5.0, 1.5, 1.5 ] );
+ > var C = new {{alias:@stdlib/array/float64}}( [ 1.0, 1.0, 5.0, 5.0 ] );
+ > var out = new {{alias:@stdlib/array/int32}}( 3 );
+ > var co = new {{alias:@stdlib/array/int32}}( 2 );
+ > {{alias}}.ndarray( 3, 2, 2, 'sqeuclidean', X, 2, 1, 0, C, 2, 1, 0, out,
+ ... 1, 0, co, 1, 0 );
+ > out
+ [ 0, 1, 0 ]
+
+ See Also
+ --------
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/docs/types/index.d.ts b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/docs/types/index.d.ts
new file mode 100644
index 000000000000..908e6d9cc89e
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/docs/types/index.d.ts
@@ -0,0 +1,150 @@
+/*
+* @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.
+*/
+
+// TypeScript Version: 4.1
+
+///
+
+import { Layout } from '@stdlib/types/blas';
+
+/**
+* Interface describing `dclosestCentroids`.
+*/
+interface Routine {
+ /**
+ * Assigns each data point in a double-precision floating-point input matrix to its closest centroid.
+ *
+ * @param order - storage layout
+ * @param M - number of data points
+ * @param N - number of features
+ * @param k - number of centroids
+ * @param metric - distance metric
+ * @param X - input data matrix
+ * @param LDX - stride of the first dimension of `X` (a.k.a., leading dimension of the matrix `X`)
+ * @param C - centroid matrix
+ * @param LDC - stride of the first dimension of `C` (a.k.a., leading dimension of the matrix `C`)
+ * @param out - output array for closest centroid indices
+ * @param so - stride length for `out`
+ * @param counts - output array for per-centroid assignment counts
+ * @param sco - stride length for `counts`
+ * @returns output array
+ *
+ * @example
+ * var Float64Array = require( '@stdlib/array/float64' );
+ * var Int32Array = require( '@stdlib/array/int32' );
+ *
+ * var X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0, 1.5, 1.5 ] );
+ * var C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ *
+ * var out = new Int32Array( 3 );
+ * var counts = new Int32Array( 2 );
+ *
+ * dclosestCentroids( 'row-major', 3, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 );
+ * // out => [ 0, 1, 0 ]
+ */
+ ( order: Layout, M: number, N: number, k: number, metric: string, X: Float64Array, LDX: number, C: Float64Array, LDC: number, out: Int32Array, so: number, counts: Int32Array, sco: number ): Int32Array;
+
+ /**
+ * Assigns each data point in a double-precision floating-point input matrix to its closest centroid using alternative indexing semantics.
+ *
+ * @param M - number of data points
+ * @param N - number of features
+ * @param k - number of centroids
+ * @param metric - distance metric
+ * @param X - input data matrix
+ * @param sx1 - stride of the first dimension of `X`
+ * @param sx2 - stride of the second dimension of `X`
+ * @param ox - starting index for `X`
+ * @param C - centroid matrix
+ * @param sc1 - stride of the first dimension of `C`
+ * @param sc2 - stride of the second dimension of `C`
+ * @param oc - starting index for `C`
+ * @param out - output array for closest centroid indices
+ * @param so - stride length for `out`
+ * @param oo - starting index for `out`
+ * @param counts - output array for per-centroid assignment counts
+ * @param sco - stride length for `counts`
+ * @param oco - starting index for `counts`
+ * @returns output array
+ *
+ * @example
+ * var Float64Array = require( '@stdlib/array/float64' );
+ * var Int32Array = require( '@stdlib/array/int32' );
+ *
+ * var X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0, 1.5, 1.5 ] );
+ * var C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ *
+ * var out = new Int32Array( 3 );
+ * var counts = new Int32Array( 2 );
+ *
+ * dclosestCentroids.ndarray( 3, 2, 2, 'sqeuclidean', X, 2, 1, 0, C, 2, 1, 0, out, 1, 0, counts, 1, 0 );
+ * // out => [ 0, 1, 0 ]
+ */
+ ndarray( M: number, N: number, k: number, metric: string, X: Float64Array, sx1: number, sx2: number, ox: number, C: Float64Array, sc1: number, sc2: number, oc: number, out: Int32Array, so: number, oo: number, counts: Int32Array, sco: number, oco: number ): Int32Array;
+}
+
+/**
+* Assigns each data point in a double-precision floating-point input matrix to its closest centroid.
+*
+* @param order - storage layout
+* @param M - number of data points
+* @param N - number of features
+* @param k - number of centroids
+* @param metric - distance metric
+* @param X - input data matrix
+* @param LDX - stride of the first dimension of `X` (a.k.a., leading dimension of the matrix `X`)
+* @param C - centroid matrix
+* @param LDC - stride of the first dimension of `C` (a.k.a., leading dimension of the matrix `C`)
+* @param out - output array for closest centroid indices
+* @param so - stride length for `out`
+* @param counts - output array for per-centroid assignment counts
+* @param sco - stride length for `counts`
+* @returns output array
+*
+* @example
+* var Float64Array = require( '@stdlib/array/float64' );
+* var Int32Array = require( '@stdlib/array/int32' );
+*
+* var X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0, 1.5, 1.5 ] );
+* var C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+*
+* var out = new Int32Array( 3 );
+* var counts = new Int32Array( 2 );
+*
+* dclosestCentroids( 'row-major', 3, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 );
+* // out => [ 0, 1, 0 ]
+*
+* @example
+* var Float64Array = require( '@stdlib/array/float64' );
+* var Int32Array = require( '@stdlib/array/int32' );
+*
+* var X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0, 1.5, 1.5 ] );
+* var C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+*
+* var out = new Int32Array( 3 );
+* var counts = new Int32Array( 2 );
+*
+* dclosestCentroids.ndarray( 3, 2, 2, 'sqeuclidean', X, 2, 1, 0, C, 2, 1, 0, out, 1, 0, counts, 1, 0 );
+* // out => [ 0, 1, 0 ]
+*/
+declare var dclosestCentroids: Routine;
+
+
+// EXPORTS //
+
+export = dclosestCentroids;
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/docs/types/test.ts b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/docs/types/test.ts
new file mode 100644
index 000000000000..baaa08fad91c
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/docs/types/test.ts
@@ -0,0 +1,360 @@
+/*
+* @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.
+*/
+
+import dclosestCentroids = require( './index' );
+
+
+// TESTS //
+
+// The function returns an Int32Array...
+{
+ const X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const out = new Int32Array( 2 );
+ const counts = new Int32Array( 2 );
+
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 ); // $ExpectType Int32Array
+}
+
+// The compiler throws an error if the function is provided a first argument which is not a valid order...
+{
+ const X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const out = new Int32Array( 2 );
+ const counts = new Int32Array( 2 );
+
+ dclosestCentroids( 5, 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( true, 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( false, 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( null, 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( void 0, 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( [], 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( {}, 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( ( x: number ): number => x, 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+}
+
+// The compiler throws an error if the function is provided a second argument which is not a number...
+{
+ const X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const out = new Int32Array( 2 );
+ const counts = new Int32Array( 2 );
+
+ dclosestCentroids( 'row-major', '5', 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', true, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', false, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', null, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', void 0, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', [], 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', {}, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', ( x: number ): number => x, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+}
+
+// The compiler throws an error if the function is provided a third argument which is not a number...
+{
+ const X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const out = new Int32Array( 2 );
+ const counts = new Int32Array( 2 );
+
+ dclosestCentroids( 'row-major', 2, '5', 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, true, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, false, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, null, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, void 0, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, [], 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, {}, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, ( x: number ): number => x, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+}
+
+// The compiler throws an error if the function is provided a fourth argument which is not a number...
+{
+ const X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const out = new Int32Array( 2 );
+ const counts = new Int32Array( 2 );
+
+ dclosestCentroids( 'row-major', 2, 2, '5', 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, true, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, false, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, null, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, void 0, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, [], 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, {}, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, ( x: number ): number => x, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+}
+
+// The compiler throws an error if the function is provided a fifth argument which is not a string...
+{
+ const X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const out = new Int32Array( 2 );
+ const counts = new Int32Array( 2 );
+
+ dclosestCentroids( 'row-major', 2, 2, 2, 5, X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, true, X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, false, X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, null, X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, void 0, X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, [], X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, {}, X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, ( x: number ): number => x, X, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+}
+
+// The compiler throws an error if the function is provided a sixth argument which is not a Float64Array...
+{
+ const C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const out = new Int32Array( 2 );
+ const counts = new Int32Array( 2 );
+
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', 5, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', true, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', false, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', null, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', void 0, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', {}, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', ( x: number ): number => x, 2, C, 2, out, 1, counts, 1 ); // $ExpectError
+}
+
+// The compiler throws an error if the function is provided a seventh argument which is not a number...
+{
+ const X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const out = new Int32Array( 2 );
+ const counts = new Int32Array( 2 );
+
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, '5', C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, true, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, false, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, null, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, void 0, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, [], C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, {}, C, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, ( x: number ): number => x, C, 2, out, 1, counts, 1 ); // $ExpectError
+}
+
+// The compiler throws an error if the function is provided an eighth argument which is not a Float64Array...
+{
+ const X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const out = new Int32Array( 2 );
+ const counts = new Int32Array( 2 );
+
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, 5, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, true, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, false, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, null, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, void 0, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, {}, 2, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, ( x: number ): number => x, 2, out, 1, counts, 1 ); // $ExpectError
+}
+
+// The compiler throws an error if the function is provided a ninth argument which is not a number...
+{
+ const X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const out = new Int32Array( 2 );
+ const counts = new Int32Array( 2 );
+
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, '5', out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, true, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, false, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, null, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, void 0, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, [], out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, {}, out, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, ( x: number ): number => x, out, 1, counts, 1 ); // $ExpectError
+}
+
+// The compiler throws an error if the function is provided a tenth argument which is not an Int32Array...
+{
+ const X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const counts = new Int32Array( 2 );
+
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, 5, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, true, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, false, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, null, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, void 0, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, {}, 1, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, ( x: number ): number => x, 1, counts, 1 ); // $ExpectError
+}
+
+// The compiler throws an error if the function is provided an eleventh argument which is not a number...
+{
+ const X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const out = new Int32Array( 2 );
+ const counts = new Int32Array( 2 );
+
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, '5', counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, true, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, false, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, null, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, void 0, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, [], counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, {}, counts, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, ( x: number ): number => x, counts, 1 ); // $ExpectError
+}
+
+// The compiler throws an error if the function is provided a twelfth argument which is not an Int32Array...
+{
+ const X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const out = new Int32Array( 2 );
+
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, 5, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, true, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, false, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, null, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, void 0, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, {}, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, ( x: number ): number => x, 1 ); // $ExpectError
+}
+
+// The compiler throws an error if the function is provided a thirteenth argument which is not a number...
+{
+ const X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const out = new Int32Array( 2 );
+ const counts = new Int32Array( 2 );
+
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, '5' ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, true ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, false ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, null ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, void 0 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, [] ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, {} ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, ( x: number ): number => x ); // $ExpectError
+}
+
+// The compiler throws an error if the function is provided an unsupported number of arguments...
+{
+ const X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const out = new Int32Array( 2 );
+ const counts = new Int32Array( 2 );
+
+ dclosestCentroids(); // $ExpectError
+ dclosestCentroids( 'row-major' ); // $ExpectError
+ dclosestCentroids( 'row-major', 2 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean' ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1 ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts ); // $ExpectError
+ dclosestCentroids( 'row-major', 2, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1, 0 ); // $ExpectError
+}
+
+// Attached to main export is an `ndarray` method which returns an Int32Array...
+{
+ const X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const out = new Int32Array( 2 );
+ const counts = new Int32Array( 2 );
+
+ dclosestCentroids.ndarray( 2, 2, 2, 'sqeuclidean', X, 2, 1, 0, C, 2, 1, 0, out, 1, 0, counts, 1, 0 ); // $ExpectType Int32Array
+}
+
+// The compiler throws an error if the `ndarray` method is provided a fourth argument which is not a string...
+{
+ const X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const out = new Int32Array( 2 );
+ const counts = new Int32Array( 2 );
+
+ dclosestCentroids.ndarray( 2, 2, 2, 5, X, 2, 1, 0, C, 2, 1, 0, out, 1, 0, counts, 1, 0 ); // $ExpectError
+ dclosestCentroids.ndarray( 2, 2, 2, true, X, 2, 1, 0, C, 2, 1, 0, out, 1, 0, counts, 1, 0 ); // $ExpectError
+ dclosestCentroids.ndarray( 2, 2, 2, null, X, 2, 1, 0, C, 2, 1, 0, out, 1, 0, counts, 1, 0 ); // $ExpectError
+ dclosestCentroids.ndarray( 2, 2, 2, {}, X, 2, 1, 0, C, 2, 1, 0, out, 1, 0, counts, 1, 0 ); // $ExpectError
+ dclosestCentroids.ndarray( 2, 2, 2, ( x: number ): number => x, X, 2, 1, 0, C, 2, 1, 0, out, 1, 0, counts, 1, 0 ); // $ExpectError
+}
+
+// The compiler throws an error if the `ndarray` method is provided a fifth argument which is not a Float64Array...
+{
+ const C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const out = new Int32Array( 2 );
+ const counts = new Int32Array( 2 );
+
+ dclosestCentroids.ndarray( 2, 2, 2, 'sqeuclidean', 5, 2, 1, 0, C, 2, 1, 0, out, 1, 0, counts, 1, 0 ); // $ExpectError
+ dclosestCentroids.ndarray( 2, 2, 2, 'sqeuclidean', true, 2, 1, 0, C, 2, 1, 0, out, 1, 0, counts, 1, 0 ); // $ExpectError
+ dclosestCentroids.ndarray( 2, 2, 2, 'sqeuclidean', null, 2, 1, 0, C, 2, 1, 0, out, 1, 0, counts, 1, 0 ); // $ExpectError
+ dclosestCentroids.ndarray( 2, 2, 2, 'sqeuclidean', {}, 2, 1, 0, C, 2, 1, 0, out, 1, 0, counts, 1, 0 ); // $ExpectError
+ dclosestCentroids.ndarray( 2, 2, 2, 'sqeuclidean', ( x: number ): number => x, 2, 1, 0, C, 2, 1, 0, out, 1, 0, counts, 1, 0 ); // $ExpectError
+}
+
+// The compiler throws an error if the `ndarray` method is provided a ninth argument which is not a Float64Array...
+{
+ const X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const out = new Int32Array( 2 );
+ const counts = new Int32Array( 2 );
+
+ dclosestCentroids.ndarray( 2, 2, 2, 'sqeuclidean', X, 2, 1, 0, 5, 2, 1, 0, out, 1, 0, counts, 1, 0 ); // $ExpectError
+ dclosestCentroids.ndarray( 2, 2, 2, 'sqeuclidean', X, 2, 1, 0, true, 2, 1, 0, out, 1, 0, counts, 1, 0 ); // $ExpectError
+ dclosestCentroids.ndarray( 2, 2, 2, 'sqeuclidean', X, 2, 1, 0, null, 2, 1, 0, out, 1, 0, counts, 1, 0 ); // $ExpectError
+ dclosestCentroids.ndarray( 2, 2, 2, 'sqeuclidean', X, 2, 1, 0, {}, 2, 1, 0, out, 1, 0, counts, 1, 0 ); // $ExpectError
+ dclosestCentroids.ndarray( 2, 2, 2, 'sqeuclidean', X, 2, 1, 0, ( x: number ): number => x, 2, 1, 0, out, 1, 0, counts, 1, 0 ); // $ExpectError
+}
+
+// The compiler throws an error if the `ndarray` method is provided a thirteenth argument which is not an Int32Array...
+{
+ const X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const counts = new Int32Array( 2 );
+
+ dclosestCentroids.ndarray( 2, 2, 2, 'sqeuclidean', X, 2, 1, 0, C, 2, 1, 0, 5, 1, 0, counts, 1, 0 ); // $ExpectError
+ dclosestCentroids.ndarray( 2, 2, 2, 'sqeuclidean', X, 2, 1, 0, C, 2, 1, 0, true, 1, 0, counts, 1, 0 ); // $ExpectError
+ dclosestCentroids.ndarray( 2, 2, 2, 'sqeuclidean', X, 2, 1, 0, C, 2, 1, 0, null, 1, 0, counts, 1, 0 ); // $ExpectError
+ dclosestCentroids.ndarray( 2, 2, 2, 'sqeuclidean', X, 2, 1, 0, C, 2, 1, 0, {}, 1, 0, counts, 1, 0 ); // $ExpectError
+ dclosestCentroids.ndarray( 2, 2, 2, 'sqeuclidean', X, 2, 1, 0, C, 2, 1, 0, ( x: number ): number => x, 1, 0, counts, 1, 0 ); // $ExpectError
+}
+
+// The compiler throws an error if the `ndarray` method is provided a sixteenth argument which is not an Int32Array...
+{
+ const X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const out = new Int32Array( 2 );
+
+ dclosestCentroids.ndarray( 2, 2, 2, 'sqeuclidean', X, 2, 1, 0, C, 2, 1, 0, out, 1, 0, 5, 1, 0 ); // $ExpectError
+ dclosestCentroids.ndarray( 2, 2, 2, 'sqeuclidean', X, 2, 1, 0, C, 2, 1, 0, out, 1, 0, true, 1, 0 ); // $ExpectError
+ dclosestCentroids.ndarray( 2, 2, 2, 'sqeuclidean', X, 2, 1, 0, C, 2, 1, 0, out, 1, 0, null, 1, 0 ); // $ExpectError
+ dclosestCentroids.ndarray( 2, 2, 2, 'sqeuclidean', X, 2, 1, 0, C, 2, 1, 0, out, 1, 0, {}, 1, 0 ); // $ExpectError
+ dclosestCentroids.ndarray( 2, 2, 2, 'sqeuclidean', X, 2, 1, 0, C, 2, 1, 0, out, 1, 0, ( x: number ): number => x, 1, 0 ); // $ExpectError
+}
+
+// The compiler throws an error if the `ndarray` method is provided an unsupported number of arguments...
+{
+ const X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+ const out = new Int32Array( 2 );
+ const counts = new Int32Array( 2 );
+
+ dclosestCentroids.ndarray(); // $ExpectError
+ dclosestCentroids.ndarray( 2 ); // $ExpectError
+ dclosestCentroids.ndarray( 2, 2 ); // $ExpectError
+ dclosestCentroids.ndarray( 2, 2, 2 ); // $ExpectError
+ dclosestCentroids.ndarray( 2, 2, 2, 'sqeuclidean' ); // $ExpectError
+ dclosestCentroids.ndarray( 2, 2, 2, 'sqeuclidean', X ); // $ExpectError
+ dclosestCentroids.ndarray( 2, 2, 2, 'sqeuclidean', X, 2, 1, 0, C, 2, 1, 0, out, 1, 0, counts, 1, 0, 0 ); // $ExpectError
+}
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/examples/c/Makefile b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/examples/c/Makefile
new file mode 100644
index 000000000000..c8f8e9a1517b
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/examples/c/Makefile
@@ -0,0 +1,146 @@
+#/
+# @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.
+#/
+
+# VARIABLES #
+
+ifndef VERBOSE
+ QUIET := @
+else
+ QUIET :=
+endif
+
+# Determine the OS ([1][1], [2][2]).
+#
+# [1]: https://en.wikipedia.org/wiki/Uname#Examples
+# [2]: http://stackoverflow.com/a/27776822/2225624
+OS ?= $(shell uname)
+ifneq (, $(findstring MINGW,$(OS)))
+ OS := WINNT
+else
+ifneq (, $(findstring MSYS,$(OS)))
+ OS := WINNT
+else
+ifneq (, $(findstring CYGWIN,$(OS)))
+ OS := WINNT
+else
+ifneq (, $(findstring Windows_NT,$(OS)))
+ OS := WINNT
+endif
+endif
+endif
+endif
+
+# Define the program used for compiling C source files:
+ifdef C_COMPILER
+ CC := $(C_COMPILER)
+else
+ CC := gcc
+endif
+
+# Define the command-line options when compiling C files:
+CFLAGS ?= \
+ -std=c99 \
+ -O3 \
+ -Wall \
+ -pedantic
+
+# Determine whether to generate position independent code ([1][1], [2][2]).
+#
+# [1]: https://gcc.gnu.org/onlinedocs/gcc/Code-Gen-Options.html#Code-Gen-Options
+# [2]: http://stackoverflow.com/questions/5311515/gcc-fpic-option
+ifeq ($(OS), WINNT)
+ fPIC ?=
+else
+ fPIC ?= -fPIC
+endif
+
+# List of includes (e.g., `-I /foo/bar -I /beep/boop/include`):
+INCLUDE ?=
+
+# List of source files:
+SOURCE_FILES ?=
+
+# List of libraries (e.g., `-lopenblas -lpthread`):
+LIBRARIES ?=
+
+# List of library paths (e.g., `-L /foo/bar -L /beep/boop`):
+LIBPATH ?=
+
+# List of C targets:
+c_targets := example.out
+
+
+# RULES #
+
+#/
+# Compiles source files.
+#
+# @param {string} [C_COMPILER] - C compiler (e.g., `gcc`)
+# @param {string} [CFLAGS] - C compiler options
+# @param {(string|void)} [fPIC] - compiler flag determining whether to generate position independent code (e.g., `-fPIC`)
+# @param {string} [INCLUDE] - list of includes (e.g., `-I /foo/bar -I /beep/boop/include`)
+# @param {string} [SOURCE_FILES] - list of source files
+# @param {string} [LIBPATH] - list of library paths (e.g., `-L /foo/bar -L /beep/boop`)
+# @param {string} [LIBRARIES] - list of libraries (e.g., `-lopenblas -lpthread`)
+#
+# @example
+# make
+#
+# @example
+# make all
+#/
+all: $(c_targets)
+
+.PHONY: all
+
+#/
+# Compiles C source files.
+#
+# @private
+# @param {string} CC - C compiler (e.g., `gcc`)
+# @param {string} CFLAGS - C compiler options
+# @param {(string|void)} fPIC - compiler flag determining whether to generate position independent code (e.g., `-fPIC`)
+# @param {string} INCLUDE - list of includes (e.g., `-I /foo/bar`)
+# @param {string} SOURCE_FILES - list of source files
+# @param {string} LIBPATH - list of library paths (e.g., `-L /foo/bar`)
+# @param {string} LIBRARIES - list of libraries (e.g., `-lopenblas`)
+#/
+$(c_targets): %.out: %.c
+ $(QUIET) $(CC) $(CFLAGS) $(fPIC) $(INCLUDE) -o $@ $(SOURCE_FILES) $< $(LIBPATH) -lm $(LIBRARIES)
+
+#/
+# Runs compiled examples.
+#
+# @example
+# make run
+#/
+run: $(c_targets)
+ $(QUIET) ./$<
+
+.PHONY: run
+
+#/
+# Removes generated files.
+#
+# @example
+# make clean
+#/
+clean:
+ $(QUIET) -rm -f *.o *.out
+
+.PHONY: clean
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/examples/c/example.c b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/examples/c/example.c
new file mode 100644
index 000000000000..9ac3045365ce
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/examples/c/example.c
@@ -0,0 +1,71 @@
+/**
+* @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.
+*/
+
+#include "stdlib/ml/strided/dkmeans_closest_centroids.h"
+#include "stdlib/ml/base/kmeans/metrics.h"
+#include "stdlib/blas/base/shared.h"
+#include
+#include
+
+int main( void ) {
+ // Define a collection of data points (row-major):
+ const double X[ 3*3 ] = {
+ 1.0, 2.0, 3.0,
+ 4.0, 5.0, 6.0,
+ 7.0, 8.0, 9.0
+ };
+
+ // Define a collection of centroids (row-major):
+ const double C[] = { 1.0, 1.0, 5.0, 5.0, 2.0, 2.0 };
+
+ // Specify the number of data points, features, and centroids:
+ const int M = 3;
+ const int N = 3;
+ const int k = 2;
+
+ // Allocate output arrays:
+ int32_t out[ 3 ];
+ int32_t counts[] = { 0, 0 };
+
+ // Assign each data point to its closest centroid:
+ stdlib_strided_dclosest_centroids( CblasRowMajor, M, N, k, STDLIB_ML_KMEANS_SQEUCLIDEAN, X, N, C, N, out, 1, counts, 1 );
+
+ // Print the results:
+ int i;
+ for ( i = 0; i < M; i++ ) {
+ printf( "out[ %i ] = %i\n", i, out[ i ] );
+ }
+ for ( i = 0; i < k; i++ ) {
+ printf( "counts[ %i ] = %i\n", i, counts[ i ] );
+ }
+
+ for ( i = 0; i < k; i++ ) {
+ counts[ i ] = 0;
+ }
+
+ // Assign each data point to its closest centroid using alternative indexing semantics:
+ stdlib_strided_dclosest_centroids_ndarray( M, N, k, STDLIB_ML_KMEANS_SQEUCLIDEAN, X, N, 1, 0, C, N, 1, 0, out, 1, 0, counts, 1, 0 );
+
+ // Print the results:
+ for ( i = 0; i < M; i++ ) {
+ printf( "out[ %i ] = %i\n", i, out[ i ] );
+ }
+ for ( i = 0; i < k; i++ ) {
+ printf( "counts[ %i ] = %i\n", i, counts[ i ] );
+ }
+}
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/examples/index.js b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/examples/index.js
new file mode 100644
index 000000000000..9de9ba7e30ee
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/examples/index.js
@@ -0,0 +1,45 @@
+/**
+* @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';
+
+var Float64Array = require( '@stdlib/array/float64' );
+var Int32Array = require( '@stdlib/array/int32' );
+var ndarray2array = require( '@stdlib/ndarray/base/to-array' );
+var shape2strides = require( '@stdlib/ndarray/base/shape2strides' );
+var dclosestCentroids = require( './../lib' );
+
+var order = 'row-major';
+
+var shapeX = [ 4, 2 ];
+var stridesX = shape2strides( shapeX, order );
+var X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0, 1.5, 1.5, 4.5, 5.5 ] );
+console.log( ndarray2array( X, shapeX, stridesX, 0, order ) );
+
+var shapeC = [ 2, 2 ];
+var stridesC = shape2strides( shapeC, order );
+var C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+console.log( ndarray2array( C, shapeC, stridesC, 0, order ) );
+
+var out = new Int32Array( shapeX[ 0 ] );
+var counts = new Int32Array( shapeC[ 0 ] );
+
+dclosestCentroids( order, shapeX[ 0 ], shapeX[ 1 ], shapeC[ 0 ], 'sqeuclidean', X, stridesX[ 0 ], C, stridesC[ 0 ], out, 1, counts, 1 );
+
+console.log( 'out = %s', out.toString() );
+console.log( 'counts = %s', counts.toString() );
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/include.gypi b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/include.gypi
new file mode 100644
index 000000000000..dcb556d250e8
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/include.gypi
@@ -0,0 +1,70 @@
+# @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.
+
+# A GYP include file for building a Node.js native add-on.
+#
+# Note that nesting variables is required due to how GYP processes a configuration. Any variables defined within a nested 'variables' section is defined in the outer scope. Thus, conditions in the outer variable scope are free to use these variables without running into "variable undefined" errors.
+#
+# Main documentation:
+#
+# [1]: https://gyp.gsrc.io/docs/InputFormatReference.md
+# [2]: https://gyp.gsrc.io/docs/UserDocumentation.md
+#
+# Variable nesting hacks:
+#
+# [3]: https://chromium.googlesource.com/external/skia/gyp/+/master/common_variables.gypi
+# [4]: https://src.chromium.org/viewvc/chrome/trunk/src/build/common.gypi?revision=127004
+{
+ # Define variables to be used throughout the configuration for all targets:
+ 'variables': {
+ 'variables': {
+ # Host BLAS library (to override -Dblas=):
+ 'blas%': '',
+
+ # Path to BLAS library (to override -Dblas_dir=):
+ 'blas_dir%': '',
+ }, # end variables
+
+ # Source directory:
+ 'src_dir': './src',
+
+ # Include directories:
+ 'include_dirs': [
+ '<@(blas_dir)',
+ '
+
+/*
+* If C++, prevent name mangling so that the compiler emits a binary file having undecorated names, thus mirroring the behavior of a C compiler.
+*/
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+* Assigns each data point in a double-precision floating-point input matrix to its closest centroid.
+*/
+void API_SUFFIX(stdlib_strided_dclosest_centroids)( const CBLAS_LAYOUT order, const CBLAS_INT M, const CBLAS_INT N, const CBLAS_INT k, const enum STDLIB_ML_KMEANS_METRIC metric, const double *X, const CBLAS_INT LDX, const double *C, const CBLAS_INT LDC, CBLAS_INT *out, const CBLAS_INT so, CBLAS_INT *counts, const CBLAS_INT sco );
+
+/**
+* Assigns each data point in a double-precision floating-point input matrix to its closest centroid using alternative indexing semantics.
+*/
+void API_SUFFIX(stdlib_strided_dclosest_centroids_ndarray)( const CBLAS_INT M, const CBLAS_INT N, const CBLAS_INT k, const enum STDLIB_ML_KMEANS_METRIC metric, const double *X, const CBLAS_INT sx1, const CBLAS_INT sx2, const CBLAS_INT ox, const double *C, const CBLAS_INT sc1, const CBLAS_INT sc2, const CBLAS_INT oc, CBLAS_INT *out, const CBLAS_INT so, const CBLAS_INT oo, CBLAS_INT *counts, const CBLAS_INT sco, const CBLAS_INT oco );
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif // !STDLIB_ML_STRIDED_DKMEANS_CLOSEST_CENTROIDS_H
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/lib/base.js b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/lib/base.js
new file mode 100644
index 000000000000..fc747e689ec1
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/lib/base.js
@@ -0,0 +1,111 @@
+/**
+* @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 dsquaredEuclidean = require( '@stdlib/stats/strided/distances/dsquared-euclidean' ).ndarray;
+var dcorrelation = require( '@stdlib/stats/strided/distances/dcorrelation' ).ndarray;
+var dcityblock = require( '@stdlib/stats/strided/distances/dcityblock' ).ndarray;
+var dcosine = require( '@stdlib/stats/strided/distances/dcosine-distance' ).ndarray;
+
+
+// MAIN //
+
+/**
+* Assigns each data point in a double-precision floating-point input matrix to its closest centroid.
+*
+* @private
+* @param {NonNegativeInteger} M - number of data points
+* @param {NonNegativeInteger} N - number of features
+* @param {NonNegativeInteger} k - number of centroids
+* @param {string} metric - distance metric
+* @param {Float64Array} X - input data matrix
+* @param {integer} sx1 - stride of the first dimension of `X`
+* @param {integer} sx2 - stride of the second dimension of `X`
+* @param {NonNegativeInteger} ox - index offset for `X`
+* @param {Float64Array} C - centroid matrix
+* @param {integer} sc1 - stride of the first dimension of `C`
+* @param {integer} sc2 - stride of the second dimension of `C`
+* @param {NonNegativeInteger} oc - index offset for `C`
+* @param {Int32Array} out - output array for closest centroid indices
+* @param {integer} so - stride length for `out`
+* @param {NonNegativeInteger} oo - index offset for `out`
+* @param {Int32Array} counts - output array for per-centroid assignment counts
+* @param {integer} sco - stride length for `counts`
+* @param {NonNegativeInteger} oco - index offset for `counts`
+* @returns {Int32Array} output array
+*
+* @example
+* var Float64Array = require( '@stdlib/array/float64' );
+* var Int32Array = require( '@stdlib/array/int32' );
+*
+* var X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0, 1.5, 1.5 ] );
+* var C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+*
+* var out = new Int32Array( 3 );
+* var counts = new Int32Array( 2 );
+*
+* closestCentroids( 3, 2, 2, 'sqeuclidean', X, 2, 1, 0, C, 2, 1, 0, out, 1, 0, counts, 1, 0 );
+* // out => [ 0, 1, 0 ]
+*/
+function closestCentroids( M, N, k, metric, X, sx1, sx2, ox, C, sc1, sc2, oc, out, so, oo, counts, sco, oco ) { // eslint-disable-line max-len, max-params
+ var bestDist;
+ var best;
+ var xidx;
+ var oidx;
+ var dist;
+ var d;
+ var i;
+ var j;
+
+ if ( metric === 'sqeuclidean' ) {
+ dist = dsquaredEuclidean;
+ } else if ( metric === 'correlation' ) {
+ dist = dcorrelation;
+ } else if ( metric === 'cityblock' ) {
+ dist = dcityblock;
+ } else {
+ dist = dcosine;
+ }
+
+ xidx = ox;
+ for ( i = 0; i < M; i++ ) {
+ oidx = oc;
+ best = 0;
+ bestDist = dist( N, X, sx2, xidx, C, sc2, oidx );
+
+ oidx += sc1; // move to the next centroid
+ for ( j = 1; j < k; j++ ) {
+ d = dist( N, X, sx2, xidx, C, sc2, oidx );
+ if ( d < bestDist ) {
+ bestDist = d;
+ best = j;
+ }
+ oidx += sc1;
+ }
+
+ out[ oo + ( i*so ) ] = best;
+ counts[ oco + ( sco*best ) ] += 1;
+ xidx += sx1;
+ }
+ return out;
+}
+
+module.exports = closestCentroids;
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/lib/dkmeans_closest_centroids.js b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/lib/dkmeans_closest_centroids.js
new file mode 100644
index 000000000000..8ab083aa1da0
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/lib/dkmeans_closest_centroids.js
@@ -0,0 +1,119 @@
+/**
+* @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 isLayout = require( '@stdlib/blas/base/assert/is-layout' );
+var isRowMajor = require( '@stdlib/ndarray/base/assert/is-row-major-string' );
+var isColumnMajor = require( '@stdlib/ndarray/base/assert/is-column-major-string' );
+var resolveMetricStr = require( '@stdlib/ml/base/kmeans/metric-resolve-str' );
+var stride2offset = require( '@stdlib/strided/base/stride2offset' );
+var max = require( '@stdlib/math/base/special/fast/max' );
+var format = require( '@stdlib/string/format' );
+var ndarray = require( './ndarray.js' );
+
+
+// MAIN //
+
+/**
+* Assigns each data point in a double-precision floating-point input matrix to its closest centroid.
+*
+* @param {string} order - storage layout
+* @param {NonNegativeInteger} M - number of data points
+* @param {NonNegativeInteger} N - number of features
+* @param {NonNegativeInteger} k - number of centroids
+* @param {string} metric - distance metric
+* @param {Float64Array} X - input data matrix
+* @param {integer} LDX - stride of the first dimension of `X` (a.k.a., leading dimension of the matrix `X`)
+* @param {Float64Array} C - centroid matrix
+* @param {integer} LDC - stride of the first dimension of `C` (a.k.a., leading dimension of the matrix `C`)
+* @param {Int32Array} out - output array for closest centroid indices
+* @param {integer} so - stride length for `out`
+* @param {Int32Array} counts - output array for per-centroid assignment counts
+* @param {integer} sco - stride length for `counts`
+* @throws {TypeError} first argument must be a valid order
+* @throws {TypeError} fifth argument must be a supported distance metric
+* @throws {RangeError} seventh argument must be greater than or equal to max(1,N) (row-major) or max(1,M) (column-major)
+* @throws {RangeError} ninth argument must be greater than or equal to max(1,N) (row-major) or max(1,k) (column-major)
+* @returns {Int32Array} output array
+*
+* @example
+* var Float64Array = require( '@stdlib/array/float64' );
+* var Int32Array = require( '@stdlib/array/int32' );
+*
+* var X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0, 1.5, 1.5 ] );
+* var C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+*
+* var out = new Int32Array( 3 );
+* var counts = new Int32Array( 2 );
+*
+* dclosestCentroids( 'row-major', 3, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 );
+* // out => [ 0, 1, 0 ]
+*/
+function dclosestCentroids( order, M, N, k, metric, X, LDX, C, LDC, out, so, counts, sco ) { // eslint-disable-line max-len, max-params
+ var sx1;
+ var sx2;
+ var sc1;
+ var sc2;
+ var sx;
+ var sc;
+
+ if ( !isLayout( order ) ) {
+ throw new TypeError( format( 'invalid argument. First argument must be a valid order. Value: `%s`.', order ) );
+ }
+ if ( isRowMajor( order ) ) {
+ sx = N;
+ sc = N;
+ } else {
+ sx = M;
+ sc = k;
+ }
+ if ( resolveMetricStr( metric ) === null ) {
+ throw new TypeError( format( 'invalid argument. Fifth argument must be a supported distance metric. Value: `%s`.', metric ) );
+ }
+ if ( LDX < max( 1, sx ) ) {
+ throw new RangeError( format( 'invalid argument. Seventh argument must be greater than or equal to max(1,%d). Value: `%d`.', sx, LDX ) );
+ }
+ if ( LDC < max( 1, sc ) ) {
+ throw new RangeError( format( 'invalid argument. Ninth argument must be greater than or equal to max(1,%d). Value: `%d`.', sc, LDC ) );
+ }
+ if ( k < 1 || M < 1 || N < 1 ) {
+ return out;
+ }
+ if ( isColumnMajor( order ) ) {
+ sx1 = 1;
+ sx2 = LDX;
+
+ sc1 = 1;
+ sc2 = LDC;
+ } else { // order === 'row-major'
+ sx1 = LDX;
+ sx2 = 1;
+
+ sc1 = LDC;
+ sc2 = 1;
+ }
+ return ndarray( M, N, k, metric, X, sx1, sx2, 0, C, sc1, sc2, 0, out, so, stride2offset( M, so ), counts, sco, stride2offset( k, sco ) ); // eslint-disable-line max-len
+}
+
+
+// EXPORTS //
+
+module.exports = dclosestCentroids;
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/lib/dkmeans_closest_centroids.native.js b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/lib/dkmeans_closest_centroids.native.js
new file mode 100644
index 000000000000..0a913832493c
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/lib/dkmeans_closest_centroids.native.js
@@ -0,0 +1,103 @@
+/**
+* @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 isLayout = require( '@stdlib/blas/base/assert/is-layout' );
+var isRowMajor = require( '@stdlib/ndarray/base/assert/is-row-major-string' );
+var resolveMetricStr = require( '@stdlib/ml/base/kmeans/metric-resolve-str' );
+var resolveMetricEnum = require( '@stdlib/ml/base/kmeans/metric-resolve-enum' );
+var resolveOrder = require( '@stdlib/blas/base/layout-resolve-enum' );
+var max = require( '@stdlib/math/base/special/fast/max' );
+var format = require( '@stdlib/string/format' );
+var addon = require( './../src/addon.node' );
+
+
+// MAIN //
+
+/**
+* Assigns each data point in a double-precision floating-point input matrix to its closest centroid.
+*
+* @param {string} order - storage layout
+* @param {NonNegativeInteger} M - number of data points
+* @param {NonNegativeInteger} N - number of features
+* @param {NonNegativeInteger} k - number of centroids
+* @param {string} metric - distance metric
+* @param {Float64Array} X - input data matrix
+* @param {integer} LDX - stride of the first dimension of `X` (a.k.a., leading dimension of the matrix `X`)
+* @param {Float64Array} C - centroid matrix
+* @param {integer} LDC - stride of the first dimension of `C` (a.k.a., leading dimension of the matrix `C`)
+* @param {Int32Array} out - output array for closest centroid indices
+* @param {integer} so - stride length for `out`
+* @param {Int32Array} counts - output array for per-centroid assignment counts
+* @param {integer} sco - stride length for `counts`
+* @throws {TypeError} first argument must be a valid order
+* @throws {TypeError} fifth argument must be a supported distance metric
+* @throws {RangeError} seventh argument must be greater than or equal to max(1,N) (row-major) or max(1,M) (column-major)
+* @throws {RangeError} ninth argument must be greater than or equal to max(1,N) (row-major) or max(1,k) (column-major)
+* @returns {Int32Array} output array
+*
+* @example
+* var Float64Array = require( '@stdlib/array/float64' );
+* var Int32Array = require( '@stdlib/array/int32' );
+*
+* var X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0, 1.5, 1.5 ] );
+* var C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+*
+* var out = new Int32Array( 3 );
+* var counts = new Int32Array( 2 );
+*
+* dclosestCentroids( 'row-major', 3, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 );
+* // out => [ 0, 1, 0 ]
+*/
+function dclosestCentroids( order, M, N, k, metric, X, LDX, C, LDC, out, so, counts, sco ) { // eslint-disable-line max-len, max-params
+ var sx;
+ var sc;
+
+ if ( !isLayout( order ) ) {
+ throw new TypeError( format( 'invalid argument. First argument must be a valid order. Value: `%s`.', order ) );
+ }
+ if ( isRowMajor( order ) ) {
+ sx = N;
+ sc = N;
+ } else {
+ sx = M;
+ sc = k;
+ }
+ if ( resolveMetricStr( metric ) === null ) {
+ throw new TypeError( format( 'invalid argument. Fifth argument must be a supported distance metric. Value: `%s`.', metric ) );
+ }
+ if ( LDX < max( 1, sx ) ) {
+ throw new RangeError( format( 'invalid argument. Seventh argument must be greater than or equal to max(1,%d). Value: `%d`.', sx, LDX ) );
+ }
+ if ( LDC < max( 1, sc ) ) {
+ throw new RangeError( format( 'invalid argument. Ninth argument must be greater than or equal to max(1,%d). Value: `%d`.', sc, LDC ) );
+ }
+ if ( k < 1 || M < 1 || N < 1 ) {
+ return out;
+ }
+ addon( resolveOrder( order ), M, N, k, resolveMetricEnum( metric ), X, LDX, C, LDC, out, so, counts, sco ); // eslint-disable-line max-len
+ return out;
+}
+
+
+// EXPORTS //
+
+module.exports = dclosestCentroids;
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/lib/index.js b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/lib/index.js
new file mode 100644
index 000000000000..87643f4f6f85
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/lib/index.js
@@ -0,0 +1,78 @@
+/**
+* @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';
+
+/**
+* Assign each data point in a double-precision floating-point input matrix to its closest centroid.
+*
+* @module @stdlib/ml/strided/dkmeans-closest-centroids
+*
+* @example
+* var Float64Array = require( '@stdlib/array/float64' );
+* var Int32Array = require( '@stdlib/array/int32' );
+* var dclosestCentroids = require( '@stdlib/ml/strided/dkmeans-closest-centroids' );
+*
+* var X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0, 1.5, 1.5 ] );
+* var C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+*
+* var out = new Int32Array( 3 );
+* var counts = new Int32Array( 2 );
+*
+* dclosestCentroids( 'row-major', 3, 2, 2, 'sqeuclidean', X, 2, C, 2, out, 1, counts, 1 );
+* // out => [ 0, 1, 0 ]
+*
+* @example
+* var Float64Array = require( '@stdlib/array/float64' );
+* var Int32Array = require( '@stdlib/array/int32' );
+* var dclosestCentroids = require( '@stdlib/ml/strided/dkmeans-closest-centroids' );
+*
+* var X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0, 1.5, 1.5 ] );
+* var C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+*
+* var out = new Int32Array( 3 );
+* var counts = new Int32Array( 2 );
+*
+* dclosestCentroids.ndarray( 3, 2, 2, 'sqeuclidean', X, 2, 1, 0, C, 2, 1, 0, out, 1, 0, counts, 1, 0 );
+* // out => [ 0, 1, 0 ]
+*/
+
+// MODULES //
+
+var join = require( 'path' ).join;
+var tryRequire = require( '@stdlib/utils/try-require' );
+var isError = require( '@stdlib/assert/is-error' );
+var main = require( './main.js' );
+
+
+// MAIN //
+
+var dclosestCentroids;
+var tmp = tryRequire( join( __dirname, './native.js' ) );
+if ( isError( tmp ) ) {
+ dclosestCentroids = main;
+} else {
+ dclosestCentroids = tmp;
+}
+
+
+// EXPORTS //
+
+module.exports = dclosestCentroids;
+
+// exports: { "ndarray": "dclosestCentroids.ndarray" }
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/lib/main.js b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/lib/main.js
new file mode 100644
index 000000000000..2728bf7a2f13
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/lib/main.js
@@ -0,0 +1,35 @@
+/**
+* @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 setReadOnly = require( '@stdlib/utils/define-nonenumerable-read-only-property' );
+var dclosestCentroids = require( './dkmeans_closest_centroids.js' );
+var ndarray = require( './ndarray.js' );
+
+
+// MAIN //
+
+setReadOnly( dclosestCentroids, 'ndarray', ndarray );
+
+
+// EXPORTS //
+
+module.exports = dclosestCentroids;
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/lib/native.js b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/lib/native.js
new file mode 100644
index 000000000000..ce44a736d7e2
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/lib/native.js
@@ -0,0 +1,35 @@
+/**
+* @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 setReadOnly = require( '@stdlib/utils/define-nonenumerable-read-only-property' );
+var dclosestCentroids = require( './dkmeans_closest_centroids.native.js' );
+var ndarray = require( './ndarray.native.js' );
+
+
+// MAIN //
+
+setReadOnly( dclosestCentroids, 'ndarray', ndarray );
+
+
+// EXPORTS //
+
+module.exports = dclosestCentroids;
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/lib/ndarray.js b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/lib/ndarray.js
new file mode 100644
index 000000000000..0431496ef459
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/lib/ndarray.js
@@ -0,0 +1,81 @@
+/**
+* @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 resolveMetricStr = require( '@stdlib/ml/base/kmeans/metric-resolve-str' );
+var format = require( '@stdlib/string/format' );
+var base = require( './base.js' );
+
+
+// MAIN //
+
+/**
+* Assigns each data point in a double-precision floating-point input matrix to its closest centroid using alternative indexing semantics.
+*
+* @param {NonNegativeInteger} M - number of data points
+* @param {NonNegativeInteger} N - number of features
+* @param {NonNegativeInteger} k - number of centroids
+* @param {string} metric - distance metric
+* @param {Float64Array} X - input data matrix
+* @param {integer} sx1 - stride of the first dimension of `X`
+* @param {integer} sx2 - stride of the second dimension of `X`
+* @param {NonNegativeInteger} ox - index offset for `X`
+* @param {Float64Array} C - centroid matrix
+* @param {integer} sc1 - stride of the first dimension of `C`
+* @param {integer} sc2 - stride of the second dimension of `C`
+* @param {NonNegativeInteger} oc - index offset for `C`
+* @param {Int32Array} out - output array for closest centroid indices
+* @param {integer} so - stride length for `out`
+* @param {NonNegativeInteger} oo - index offset for `out`
+* @param {Int32Array} counts - output array for per-centroid assignment counts
+* @param {integer} sco - stride length for `counts`
+* @param {NonNegativeInteger} oco - index offset for `counts`
+* @throws {TypeError} fourth argument must be a supported distance metric
+* @returns {Int32Array} output array
+*
+* @example
+* var Float64Array = require( '@stdlib/array/float64' );
+* var Int32Array = require( '@stdlib/array/int32' );
+*
+* var X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0, 1.5, 1.5 ] );
+* var C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+*
+* var out = new Int32Array( 3 );
+* var counts = new Int32Array( 2 );
+*
+* dclosestCentroids( 3, 2, 2, 'sqeuclidean', X, 2, 1, 0, C, 2, 1, 0, out, 1, 0, counts, 1, 0 );
+* // out => [ 0, 1, 0 ]
+*/
+function dclosestCentroids( M, N, k, metric, X, sx1, sx2, ox, C, sc1, sc2, oc, out, so, oo, counts, sco, oco ) { // eslint-disable-line max-len, max-params
+ var m = resolveMetricStr( metric );
+ if ( m === null ) {
+ throw new TypeError( format( 'invalid argument. Fourth argument must be a supported distance metric. Value: `%s`.', metric ) );
+ }
+ if ( k < 1 || M < 1 || N < 1 ) {
+ return out;
+ }
+ return base( M, N, k, m, X, sx1, sx2, ox, C, sc1, sc2, oc, out, so, oo, counts, sco, oco ); // eslint-disable-line max-len
+}
+
+
+// EXPORTS //
+
+module.exports = dclosestCentroids;
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/lib/ndarray.native.js b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/lib/ndarray.native.js
new file mode 100644
index 000000000000..553cc86d6fc2
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/lib/ndarray.native.js
@@ -0,0 +1,82 @@
+/**
+* @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 resolveMetricStr = require( '@stdlib/ml/base/kmeans/metric-resolve-str' );
+var resolveMetricEnum = require( '@stdlib/ml/base/kmeans/metric-resolve-enum' );
+var format = require( '@stdlib/string/format' );
+var addon = require( './../src/addon.node' );
+
+
+// MAIN //
+
+/**
+* Assigns each data point in a double-precision floating-point input matrix to its closest centroid using alternative indexing semantics.
+*
+* @param {NonNegativeInteger} M - number of data points
+* @param {NonNegativeInteger} N - number of features
+* @param {NonNegativeInteger} k - number of centroids
+* @param {string} metric - distance metric
+* @param {Float64Array} X - input data matrix
+* @param {integer} sx1 - stride of the first dimension of `X`
+* @param {integer} sx2 - stride of the second dimension of `X`
+* @param {NonNegativeInteger} ox - index offset for `X`
+* @param {Float64Array} C - centroid matrix
+* @param {integer} sc1 - stride of the first dimension of `C`
+* @param {integer} sc2 - stride of the second dimension of `C`
+* @param {NonNegativeInteger} oc - index offset for `C`
+* @param {Int32Array} out - output array for closest centroid indices
+* @param {integer} so - stride length for `out`
+* @param {NonNegativeInteger} oo - index offset for `out`
+* @param {Int32Array} counts - output array for per-centroid assignment counts
+* @param {integer} sco - stride length for `counts`
+* @param {NonNegativeInteger} oco - index offset for `counts`
+* @throws {TypeError} fourth argument must be a supported distance metric
+* @returns {Int32Array} output array
+*
+* @example
+* var Float64Array = require( '@stdlib/array/float64' );
+* var Int32Array = require( '@stdlib/array/int32' );
+*
+* var X = new Float64Array( [ 1.0, 1.0, 5.0, 5.0, 1.5, 1.5 ] );
+* var C = new Float64Array( [ 1.0, 1.0, 5.0, 5.0 ] );
+*
+* var out = new Int32Array( 3 );
+* var counts = new Int32Array( 2 );
+*
+* dclosestCentroids( 3, 2, 2, 'sqeuclidean', X, 2, 1, 0, C, 2, 1, 0, out, 1, 0, counts, 1, 0 );
+* // out => [ 0, 1, 0 ]
+*/
+function dclosestCentroids( M, N, k, metric, X, sx1, sx2, ox, C, sc1, sc2, oc, out, so, oo, counts, sco, oco ) { // eslint-disable-line max-len, max-params
+ if ( resolveMetricStr( metric ) === null ) {
+ throw new TypeError( format( 'invalid argument. Fourth argument must be a supported distance metric. Value: `%s`.', metric ) );
+ }
+ if ( k < 1 || M < 1 || N < 1 ) {
+ return out;
+ }
+ addon.ndarray( M, N, k, resolveMetricEnum( metric ), X, sx1, sx2, ox, C, sc1, sc2, oc, out, so, oo, counts, sco, oco ); // eslint-disable-line max-len
+ return out;
+}
+
+
+// EXPORTS //
+
+module.exports = dclosestCentroids;
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/manifest.json b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/manifest.json
new file mode 100644
index 000000000000..ed7c833db019
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/manifest.json
@@ -0,0 +1,95 @@
+{
+ "options": {
+ "task": "build"
+ },
+ "fields": [
+ {
+ "field": "src",
+ "resolve": true,
+ "relative": true
+ },
+ {
+ "field": "include",
+ "resolve": true,
+ "relative": true
+ },
+ {
+ "field": "libraries",
+ "resolve": false,
+ "relative": false
+ },
+ {
+ "field": "libpath",
+ "resolve": true,
+ "relative": false
+ }
+ ],
+ "confs": [
+ {
+ "task": "build",
+ "src": [
+ "./src/main.c"
+ ],
+ "include": [
+ "./include"
+ ],
+ "libraries": [],
+ "libpath": [],
+ "dependencies": [
+ "@stdlib/blas/base/shared",
+ "@stdlib/strided/base/stride2offset",
+ "@stdlib/ml/base/kmeans/metrics",
+ "@stdlib/stats/strided/distances/dsquared-euclidean",
+ "@stdlib/stats/strided/distances/dcorrelation",
+ "@stdlib/stats/strided/distances/dcosine-distance",
+ "@stdlib/stats/strided/distances/dcityblock",
+ "@stdlib/napi/export",
+ "@stdlib/napi/argv",
+ "@stdlib/napi/argv-int32",
+ "@stdlib/napi/argv-int64",
+ "@stdlib/napi/argv-strided-int32array",
+ "@stdlib/napi/argv-strided-float64array2d"
+ ]
+ },
+ {
+ "task": "benchmark",
+ "src": [
+ "./src/main.c"
+ ],
+ "include": [
+ "./include"
+ ],
+ "libraries": [],
+ "libpath": [],
+ "dependencies": [
+ "@stdlib/blas/base/shared",
+ "@stdlib/strided/base/stride2offset",
+ "@stdlib/ml/base/kmeans/metrics",
+ "@stdlib/stats/strided/distances/dsquared-euclidean",
+ "@stdlib/stats/strided/distances/dcorrelation",
+ "@stdlib/stats/strided/distances/dcosine-distance",
+ "@stdlib/stats/strided/distances/dcityblock"
+ ]
+ },
+ {
+ "task": "examples",
+ "src": [
+ "./src/main.c"
+ ],
+ "include": [
+ "./include"
+ ],
+ "libraries": [],
+ "libpath": [],
+ "dependencies": [
+ "@stdlib/blas/base/shared",
+ "@stdlib/strided/base/stride2offset",
+ "@stdlib/ml/base/kmeans/metrics",
+ "@stdlib/stats/strided/distances/dsquared-euclidean",
+ "@stdlib/stats/strided/distances/dcorrelation",
+ "@stdlib/stats/strided/distances/dcosine-distance",
+ "@stdlib/stats/strided/distances/dcityblock"
+ ]
+ }
+ ]
+}
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/package.json b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/package.json
new file mode 100644
index 000000000000..17599c728bb7
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/package.json
@@ -0,0 +1,78 @@
+{
+ "name": "@stdlib/ml/strided/dkmeans-closest-centroids",
+ "version": "0.0.0",
+ "description": "Assign each data point in a double-precision floating-point input matrix to its closest centroid.",
+ "license": "Apache-2.0",
+ "author": {
+ "name": "The Stdlib Authors",
+ "url": "https://github.com/stdlib-js/stdlib/graphs/contributors"
+ },
+ "contributors": [
+ {
+ "name": "The Stdlib Authors",
+ "url": "https://github.com/stdlib-js/stdlib/graphs/contributors"
+ }
+ ],
+ "main": "./lib",
+ "browser": "./lib/main.js",
+ "gypfile": true,
+ "directories": {
+ "benchmark": "./benchmark",
+ "doc": "./docs",
+ "example": "./examples",
+ "include": "./include",
+ "lib": "./lib",
+ "src": "./src",
+ "test": "./test"
+ },
+ "types": "./docs/types",
+ "scripts": {},
+ "homepage": "https://github.com/stdlib-js/stdlib",
+ "repository": {
+ "type": "git",
+ "url": "git://github.com/stdlib-js/stdlib.git"
+ },
+ "bugs": {
+ "url": "https://github.com/stdlib-js/stdlib/issues"
+ },
+ "dependencies": {},
+ "devDependencies": {},
+ "engines": {
+ "node": ">=0.10.0",
+ "npm": ">2.7.0"
+ },
+ "os": [
+ "aix",
+ "darwin",
+ "freebsd",
+ "linux",
+ "macos",
+ "openbsd",
+ "sunos",
+ "win32",
+ "windows"
+ ],
+ "keywords": [
+ "stdlib",
+ "ml",
+ "machine learning",
+ "kmeans",
+ "k-means",
+ "clustering",
+ "cluster",
+ "centroid",
+ "closest",
+ "nearest",
+ "assign",
+ "distance",
+ "metric",
+ "matrix",
+ "strided",
+ "array",
+ "ndarray",
+ "double",
+ "float",
+ "float64",
+ "float64array"
+ ]
+}
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/src/Makefile b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/src/Makefile
new file mode 100644
index 000000000000..2caf905cedbe
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/src/Makefile
@@ -0,0 +1,70 @@
+#/
+# @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.
+#/
+
+# VARIABLES #
+
+ifndef VERBOSE
+ QUIET := @
+else
+ QUIET :=
+endif
+
+# Determine the OS ([1][1], [2][2]).
+#
+# [1]: https://en.wikipedia.org/wiki/Uname#Examples
+# [2]: http://stackoverflow.com/a/27776822/2225624
+OS ?= $(shell uname)
+ifneq (, $(findstring MINGW,$(OS)))
+ OS := WINNT
+else
+ifneq (, $(findstring MSYS,$(OS)))
+ OS := WINNT
+else
+ifneq (, $(findstring CYGWIN,$(OS)))
+ OS := WINNT
+else
+ifneq (, $(findstring Windows_NT,$(OS)))
+ OS := WINNT
+endif
+endif
+endif
+endif
+
+
+# RULES #
+
+#/
+# Removes generated files for building an add-on.
+#
+# @example
+# make clean-addon
+#/
+clean-addon:
+ $(QUIET) -rm -f *.o *.node
+
+.PHONY: clean-addon
+
+#/
+# Removes generated files.
+#
+# @example
+# make clean
+#/
+clean: clean-addon
+
+.PHONY: clean
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/src/addon.c b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/src/addon.c
new file mode 100644
index 000000000000..7f15d669b3c5
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/src/addon.c
@@ -0,0 +1,112 @@
+/**
+* @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.
+*/
+
+#include "stdlib/ml/strided/dkmeans_closest_centroids.h"
+#include "stdlib/blas/base/shared.h"
+#include "stdlib/napi/export.h"
+#include "stdlib/napi/argv.h"
+#include "stdlib/napi/argv_int64.h"
+#include "stdlib/napi/argv_int32.h"
+#include "stdlib/napi/argv_strided_int32array.h"
+#include "stdlib/napi/argv_strided_float64array2d.h"
+#include
+
+/**
+* Receives JavaScript callback invocation data.
+*
+* @param env environment under which the function is invoked
+* @param info callback data
+* @return Node-API value
+*/
+static napi_value addon( napi_env env, napi_callback_info info ) {
+ CBLAS_INT sx1;
+ CBLAS_INT sx2;
+ CBLAS_INT sc1;
+ CBLAS_INT sc2;
+
+ STDLIB_NAPI_ARGV( env, info, argv, argc, 13 );
+
+ STDLIB_NAPI_ARGV_INT32( env, order, argv, 0 );
+ STDLIB_NAPI_ARGV_INT32( env, metric, argv, 4 );
+
+ STDLIB_NAPI_ARGV_INT64( env, M, argv, 1 );
+ STDLIB_NAPI_ARGV_INT64( env, N, argv, 2 );
+ STDLIB_NAPI_ARGV_INT64( env, k, argv, 3 );
+ STDLIB_NAPI_ARGV_INT64( env, so, argv, 10 );
+ STDLIB_NAPI_ARGV_INT64( env, sco, argv, 12 );
+ STDLIB_NAPI_ARGV_INT64( env, LDX, argv, 6 );
+ STDLIB_NAPI_ARGV_INT64( env, LDC, argv, 8 );
+
+ if ( order == CblasColMajor ) {
+ sx1 = 1;
+ sc1 = 1;
+ sx2 = LDX;
+ sc2 = LDC;
+ } else { // order == CblasRowMajor
+ sx1 = LDX;
+ sc1 = LDC;
+ sx2 = 1;
+ sc2 = 1;
+ }
+ STDLIB_NAPI_ARGV_STRIDED_INT32ARRAY( env, out, M, so, argv, 9 );
+ STDLIB_NAPI_ARGV_STRIDED_INT32ARRAY( env, counts, k, sco, argv, 11 );
+ STDLIB_NAPI_ARGV_STRIDED_FLOAT64ARRAY2D( env, X, M, N, sx1, sx2, argv, 5 );
+ STDLIB_NAPI_ARGV_STRIDED_FLOAT64ARRAY2D( env, C, k, N, sc1, sc2, argv, 7 );
+
+ API_SUFFIX(stdlib_strided_dclosest_centroids)( order, M, N, k, metric, X, LDX, C, LDC, out, so, counts, sco );
+
+ return NULL;
+}
+
+/**
+* Receives JavaScript callback invocation data.
+*
+* @param env environment under which the function is invoked
+* @param info callback data
+* @return Node-API value
+*/
+static napi_value addon_method( napi_env env, napi_callback_info info ) {
+ STDLIB_NAPI_ARGV( env, info, argv, argc, 18 );
+
+ STDLIB_NAPI_ARGV_INT32( env, metric, argv, 3 );
+
+ STDLIB_NAPI_ARGV_INT64( env, M, argv, 0 );
+ STDLIB_NAPI_ARGV_INT64( env, N, argv, 1 );
+ STDLIB_NAPI_ARGV_INT64( env, k, argv, 2 );
+ STDLIB_NAPI_ARGV_INT64( env, sx1, argv, 5 );
+ STDLIB_NAPI_ARGV_INT64( env, sx2, argv, 6 );
+ STDLIB_NAPI_ARGV_INT64( env, ox, argv, 7 );
+ STDLIB_NAPI_ARGV_INT64( env, sc1, argv, 9 );
+ STDLIB_NAPI_ARGV_INT64( env, sc2, argv, 10 );
+ STDLIB_NAPI_ARGV_INT64( env, oc, argv, 11 );
+ STDLIB_NAPI_ARGV_INT64( env, so, argv, 13 );
+ STDLIB_NAPI_ARGV_INT64( env, oo, argv, 14 );
+ STDLIB_NAPI_ARGV_INT64( env, sco, argv, 16 );
+ STDLIB_NAPI_ARGV_INT64( env, oco, argv, 17 );
+
+ STDLIB_NAPI_ARGV_STRIDED_INT32ARRAY( env, out, M, so, argv, 12 );
+ STDLIB_NAPI_ARGV_STRIDED_INT32ARRAY( env, counts, k, sco, argv, 15 );
+ STDLIB_NAPI_ARGV_STRIDED_FLOAT64ARRAY2D( env, X, M, N, sx1, sx2, argv, 4 );
+ STDLIB_NAPI_ARGV_STRIDED_FLOAT64ARRAY2D( env, C, k, N, sc1, sc2, argv, 8 );
+
+ API_SUFFIX(stdlib_strided_dclosest_centroids_ndarray)( M, N, k, metric, X, sx1, sx2, ox, C, sc1, sc2, oc, out, so, oo, counts, sco, oco );
+
+ return NULL;
+}
+
+STDLIB_NAPI_MODULE_EXPORT_FCN_WITH_METHOD( addon, "ndarray", addon_method )
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/src/main.c b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/src/main.c
new file mode 100644
index 000000000000..9741730266b4
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/src/main.c
@@ -0,0 +1,137 @@
+/**
+* @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.
+*/
+
+#include "stdlib/ml/strided/dkmeans_closest_centroids.h"
+#include "stdlib/strided/base/stride2offset.h"
+#include "stdlib/blas/base/shared.h"
+#include "stdlib/ml/base/kmeans/metrics.h"
+#include "stdlib/stats/strided/distances/dsquared_euclidean.h"
+#include "stdlib/stats/strided/distances/dcorrelation.h"
+#include "stdlib/stats/strided/distances/dcosine_distance.h"
+#include "stdlib/stats/strided/distances/dcityblock.h"
+#include
+
+/**
+* Data type to store distance metric function.
+*/
+typedef double func_type( const CBLAS_INT N, const double *X, const CBLAS_INT sx, const CBLAS_INT ox, const double *Y, const CBLAS_INT strideY, const CBLAS_INT offsetY );
+
+/**
+* Assigns each data point in a double-precision floating-point input matrix to its closest centroid.
+*
+* @param order storage layout
+* @param M number of data points
+* @param N number of features
+* @param k number of centroids
+* @param metric distance metric
+* @param X input data matrix
+* @param LDX stride of the first dimension of `X` (a.k.a., leading dimension of the matrix `X`)
+* @param C centroid matrix
+* @param LDC stride of the first dimension of `C` (a.k.a., leading dimension of the matrix `C`)
+* @param out output array for closest centroid indices
+* @param so stride length for `out`
+* @param counts output array for per-centroid assignment counts
+* @param sco stride length for `counts`
+*/
+void API_SUFFIX(stdlib_strided_dclosest_centroids)( const CBLAS_LAYOUT order, const CBLAS_INT M, const CBLAS_INT N, const CBLAS_INT k, const enum STDLIB_ML_KMEANS_METRIC metric, const double *X, const CBLAS_INT LDX, const double *C, const CBLAS_INT LDC, CBLAS_INT *out, const CBLAS_INT so, CBLAS_INT *counts, const CBLAS_INT sco ) {
+ CBLAS_INT sx1;
+ CBLAS_INT sx2;
+ CBLAS_INT sc1;
+ CBLAS_INT sc2;
+ CBLAS_INT oco;
+ CBLAS_INT oo;
+
+ if ( order == CblasColMajor ) {
+ sx1 = 1;
+ sc1 = 1;
+ sx2 = LDX;
+ sc2 = LDC;
+ } else { // order == CblasRowMajor
+ sx1 = LDX;
+ sc1 = LDC;
+ sx2 = 1;
+ sc2 = 1;
+ }
+ oo = stdlib_strided_stride2offset( M, so );
+ oco = stdlib_strided_stride2offset( k, sco );
+ API_SUFFIX(stdlib_strided_dclosest_centroids_ndarray)( M, N, k, metric, X, sx1, sx2, 0, C, sc1, sc2, 0, out, so, oo, counts, sco, oco );
+}
+
+/**
+* Assigns each data point in a double-precision floating-point input matrix to its closest centroid using alternative indexing semantics.
+*
+* @param M number of data points
+* @param N number of features
+* @param k number of centroids
+* @param metric distance metric
+* @param X input data matrix
+* @param sx1 stride of the first dimension of `X`
+* @param sx2 stride of the second dimension of `X`
+* @param ox index offset for `X`
+* @param C centroid matrix
+* @param sc1 stride of the first dimension of `C`
+* @param sc2 stride of the second dimension of `C`
+* @param oc index offset for `C`
+* @param out output array for closest centroid indices
+* @param so stride length for `out`
+* @param oo index offset for `out`
+* @param counts output array for per-centroid assignment counts
+* @param sco stride length for `counts`
+* @param oco index offset for `counts`
+*/
+void API_SUFFIX(stdlib_strided_dclosest_centroids_ndarray)( const CBLAS_INT M, const CBLAS_INT N, const CBLAS_INT k, const enum STDLIB_ML_KMEANS_METRIC metric, const double *X, const CBLAS_INT sx1, const CBLAS_INT sx2, const CBLAS_INT ox, const double *C, const CBLAS_INT sc1, const CBLAS_INT sc2, const CBLAS_INT oc, CBLAS_INT *out, const CBLAS_INT so, const CBLAS_INT oo, CBLAS_INT *counts, const CBLAS_INT sco, const CBLAS_INT oco ) {
+ double bestDist;
+ CBLAS_INT best;
+ CBLAS_INT xidx;
+ CBLAS_INT oidx;
+ func_type *f;
+ CBLAS_INT i;
+ CBLAS_INT j;
+ double d;
+
+ if ( metric == STDLIB_ML_KMEANS_SQEUCLIDEAN ) {
+ f = (func_type *) stdlib_strided_dsquared_euclidean_ndarray;
+ } else if ( metric == STDLIB_ML_KMEANS_CORRELATION ) {
+ f = (func_type *) stdlib_strided_dcorrelation_ndarray;
+ } else if ( metric == STDLIB_ML_KMEANS_CITYBLOCK ) {
+ f = (func_type *) stdlib_strided_dcityblock_ndarray;
+ } else {
+ f = (func_type *) stdlib_strided_dcosine_distance_ndarray;
+ }
+
+ xidx = ox;
+ for ( i = 0; i < M; i++ ) {
+ oidx = oc;
+ best = 0;
+ bestDist = f( N, X, sx2, xidx, C, sc2, oidx );
+
+ oidx += sc1; // move to the next centroid
+ for ( j = 1; j < k; j++ ) {
+ d = f( N, X, sx2, xidx, C, sc2, oidx );
+ if ( d < bestDist ) {
+ bestDist = d;
+ best = j;
+ }
+ oidx += sc1;
+ }
+
+ out[ oo + ( i*so ) ] = best;
+ counts[ oco + ( sco*best ) ] += 1;
+ xidx += sx1;
+ }
+}
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/fixtures/column_major.json b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/fixtures/column_major.json
new file mode 100644
index 000000000000..8b14ec02faef
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/fixtures/column_major.json
@@ -0,0 +1,33 @@
+{
+ "order": "column-major",
+ "metric": "sqeuclidean",
+ "M": 4,
+ "N": 2,
+ "k": 2,
+ "X": [ 1.0, 8.0, 2.0, 0.0, 2.0, 9.0, 3.0, 1.0 ],
+ "strideX1": 1,
+ "strideX2": 4,
+ "offsetX": 0,
+ "LDX": 4,
+ "X_mat": [
+ [ 1.0, 2.0 ],
+ [ 8.0, 9.0 ],
+ [ 2.0, 3.0 ],
+ [ 0.0, 1.0 ]
+ ],
+ "C": [ 1.0, 8.0, 2.0, 9.0 ],
+ "strideC1": 1,
+ "strideC2": 2,
+ "offsetC": 0,
+ "LDC": 2,
+ "C_mat": [
+ [ 1.0, 2.0 ],
+ [ 8.0, 9.0 ]
+ ],
+ "strideO": 1,
+ "offsetO": 0,
+ "strideCounts": 1,
+ "offsetCounts": 0,
+ "expected_out": [ 0, 1, 0, 0 ],
+ "expected_counts": [ 3, 1 ]
+}
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/fixtures/large-strides/column_major.json b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/fixtures/large-strides/column_major.json
new file mode 100644
index 000000000000..1d3b83ce994a
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/fixtures/large-strides/column_major.json
@@ -0,0 +1,31 @@
+{
+ "order": "column-major",
+ "metric": "sqeuclidean",
+ "M": 4,
+ "N": 2,
+ "k": 2,
+ "X": [ 1.0, 99.0, 8.0, 99.0, 2.0, 99.0, 0.0, 99.0, 2.0, 99.0, 9.0, 99.0, 3.0, 99.0, 1.0, 99.0 ],
+ "strideX1": 2,
+ "strideX2": 8,
+ "offsetX": 0,
+ "X_mat": [
+ [ 1.0, 2.0 ],
+ [ 8.0, 9.0 ],
+ [ 2.0, 3.0 ],
+ [ 0.0, 1.0 ]
+ ],
+ "C": [ 1.0, 99.0, 8.0, 99.0, 2.0, 99.0, 9.0, 99.0 ],
+ "strideC1": 2,
+ "strideC2": 4,
+ "offsetC": 0,
+ "C_mat": [
+ [ 1.0, 2.0 ],
+ [ 8.0, 9.0 ]
+ ],
+ "strideO": 1,
+ "offsetO": 0,
+ "strideCounts": 1,
+ "offsetCounts": 0,
+ "expected_out": [ 0, 1, 0, 0 ],
+ "expected_counts": [ 3, 1 ]
+}
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/fixtures/large-strides/row_major.json b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/fixtures/large-strides/row_major.json
new file mode 100644
index 000000000000..98930240b080
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/fixtures/large-strides/row_major.json
@@ -0,0 +1,31 @@
+{
+ "order": "row-major",
+ "metric": "sqeuclidean",
+ "M": 4,
+ "N": 2,
+ "k": 2,
+ "X": [ 1.0, 99.0, 2.0, 99.0, 8.0, 99.0, 9.0, 99.0, 2.0, 99.0, 3.0, 99.0, 0.0, 99.0, 1.0, 99.0 ],
+ "strideX1": 4,
+ "strideX2": 2,
+ "offsetX": 0,
+ "X_mat": [
+ [ 1.0, 2.0 ],
+ [ 8.0, 9.0 ],
+ [ 2.0, 3.0 ],
+ [ 0.0, 1.0 ]
+ ],
+ "C": [ 1.0, 99.0, 2.0, 99.0, 8.0, 99.0, 9.0, 99.0 ],
+ "strideC1": 4,
+ "strideC2": 2,
+ "offsetC": 0,
+ "C_mat": [
+ [ 1.0, 2.0 ],
+ [ 8.0, 9.0 ]
+ ],
+ "strideO": 1,
+ "offsetO": 0,
+ "strideCounts": 1,
+ "offsetCounts": 0,
+ "expected_out": [ 0, 1, 0, 0 ],
+ "expected_counts": [ 3, 1 ]
+}
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/fixtures/mixed-strides/column_major.json b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/fixtures/mixed-strides/column_major.json
new file mode 100644
index 000000000000..4a904bcc1bfc
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/fixtures/mixed-strides/column_major.json
@@ -0,0 +1,31 @@
+{
+ "order": "column-major",
+ "metric": "sqeuclidean",
+ "M": 4,
+ "N": 2,
+ "k": 2,
+ "X": [ 1.0, 8.0, 2.0, 0.0, 2.0, 9.0, 3.0, 1.0 ],
+ "strideX1": 1,
+ "strideX2": -4,
+ "offsetX": 4,
+ "X_mat": [
+ [ 1.0, 2.0 ],
+ [ 8.0, 9.0 ],
+ [ 2.0, 3.0 ],
+ [ 0.0, 1.0 ]
+ ],
+ "C": [ 1.0, 8.0, 2.0, 9.0 ],
+ "strideC1": 1,
+ "strideC2": -2,
+ "offsetC": 2,
+ "C_mat": [
+ [ 1.0, 2.0 ],
+ [ 8.0, 9.0 ]
+ ],
+ "strideO": 1,
+ "offsetO": 0,
+ "strideCounts": 1,
+ "offsetCounts": 0,
+ "expected_out": [ 0, 1, 0, 0 ],
+ "expected_counts": [ 3, 1 ]
+}
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/fixtures/mixed-strides/row_major.json b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/fixtures/mixed-strides/row_major.json
new file mode 100644
index 000000000000..7fd093f2f6d8
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/fixtures/mixed-strides/row_major.json
@@ -0,0 +1,31 @@
+{
+ "order": "row-major",
+ "metric": "sqeuclidean",
+ "M": 4,
+ "N": 2,
+ "k": 2,
+ "X": [ 1.0, 2.0, 8.0, 9.0, 2.0, 3.0, 0.0, 1.0 ],
+ "strideX1": -2,
+ "strideX2": 1,
+ "offsetX": 6,
+ "X_mat": [
+ [ 1.0, 2.0 ],
+ [ 8.0, 9.0 ],
+ [ 2.0, 3.0 ],
+ [ 0.0, 1.0 ]
+ ],
+ "C": [ 1.0, 2.0, 8.0, 9.0 ],
+ "strideC1": 2,
+ "strideC2": 1,
+ "offsetC": 0,
+ "C_mat": [
+ [ 1.0, 2.0 ],
+ [ 8.0, 9.0 ]
+ ],
+ "strideO": 1,
+ "offsetO": 0,
+ "strideCounts": 1,
+ "offsetCounts": 0,
+ "expected_out": [ 0, 0, 1, 0 ],
+ "expected_counts": [ 3, 1 ]
+}
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/fixtures/negative-strides/column_major.json b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/fixtures/negative-strides/column_major.json
new file mode 100644
index 000000000000..39551a2510d5
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/fixtures/negative-strides/column_major.json
@@ -0,0 +1,31 @@
+{
+ "order": "column-major",
+ "metric": "sqeuclidean",
+ "M": 4,
+ "N": 2,
+ "k": 2,
+ "X": [ 1.0, 8.0, 2.0, 0.0, 2.0, 9.0, 3.0, 1.0 ],
+ "strideX1": -1,
+ "strideX2": 4,
+ "offsetX": 3,
+ "X_mat": [
+ [ 1.0, 2.0 ],
+ [ 8.0, 9.0 ],
+ [ 2.0, 3.0 ],
+ [ 0.0, 1.0 ]
+ ],
+ "C": [ 1.0, 8.0, 2.0, 9.0 ],
+ "strideC1": 1,
+ "strideC2": 2,
+ "offsetC": 0,
+ "C_mat": [
+ [ 1.0, 2.0 ],
+ [ 8.0, 9.0 ]
+ ],
+ "strideO": -1,
+ "offsetO": 3,
+ "strideCounts": 1,
+ "offsetCounts": 0,
+ "expected_out": [ 0, 1, 0, 0 ],
+ "expected_counts": [ 3, 1 ]
+}
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/fixtures/negative-strides/row_major.json b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/fixtures/negative-strides/row_major.json
new file mode 100644
index 000000000000..1d10ac41404c
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/fixtures/negative-strides/row_major.json
@@ -0,0 +1,31 @@
+{
+ "order": "row-major",
+ "metric": "sqeuclidean",
+ "M": 4,
+ "N": 2,
+ "k": 2,
+ "X": [ 1.0, 2.0, 8.0, 9.0, 2.0, 3.0, 0.0, 1.0 ],
+ "strideX1": -2,
+ "strideX2": 1,
+ "offsetX": 6,
+ "X_mat": [
+ [ 1.0, 2.0 ],
+ [ 8.0, 9.0 ],
+ [ 2.0, 3.0 ],
+ [ 0.0, 1.0 ]
+ ],
+ "C": [ 1.0, 2.0, 8.0, 9.0 ],
+ "strideC1": 2,
+ "strideC2": 1,
+ "offsetC": 0,
+ "C_mat": [
+ [ 1.0, 2.0 ],
+ [ 8.0, 9.0 ]
+ ],
+ "strideO": -1,
+ "offsetO": 3,
+ "strideCounts": 1,
+ "offsetCounts": 0,
+ "expected_out": [ 0, 1, 0, 0 ],
+ "expected_counts": [ 3, 1 ]
+}
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/fixtures/offsets/column_major.json b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/fixtures/offsets/column_major.json
new file mode 100644
index 000000000000..33f798fe7360
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/fixtures/offsets/column_major.json
@@ -0,0 +1,31 @@
+{
+ "order": "column-major",
+ "metric": "sqeuclidean",
+ "M": 4,
+ "N": 2,
+ "k": 2,
+ "X": [ 99.0, 1.0, 8.0, 2.0, 0.0, 2.0, 9.0, 3.0, 1.0 ],
+ "strideX1": 1,
+ "strideX2": 4,
+ "offsetX": 1,
+ "X_mat": [
+ [ 1.0, 2.0 ],
+ [ 8.0, 9.0 ],
+ [ 2.0, 3.0 ],
+ [ 0.0, 1.0 ]
+ ],
+ "C": [ 99.0, 1.0, 8.0, 2.0, 9.0 ],
+ "strideC1": 1,
+ "strideC2": 2,
+ "offsetC": 1,
+ "C_mat": [
+ [ 1.0, 2.0 ],
+ [ 8.0, 9.0 ]
+ ],
+ "strideO": 1,
+ "offsetO": 0,
+ "strideCounts": 1,
+ "offsetCounts": 0,
+ "expected_out": [ 0, 1, 0, 0 ],
+ "expected_counts": [ 3, 1 ]
+}
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/fixtures/offsets/row_major.json b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/fixtures/offsets/row_major.json
new file mode 100644
index 000000000000..7d56bf1d74fb
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/fixtures/offsets/row_major.json
@@ -0,0 +1,31 @@
+{
+ "order": "row-major",
+ "metric": "sqeuclidean",
+ "M": 4,
+ "N": 2,
+ "k": 2,
+ "X": [ 99.0, 99.0, 1.0, 2.0, 8.0, 9.0, 2.0, 3.0, 0.0, 1.0 ],
+ "strideX1": 2,
+ "strideX2": 1,
+ "offsetX": 2,
+ "X_mat": [
+ [ 1.0, 2.0 ],
+ [ 8.0, 9.0 ],
+ [ 2.0, 3.0 ],
+ [ 0.0, 1.0 ]
+ ],
+ "C": [ 99.0, 99.0, 1.0, 2.0, 8.0, 9.0 ],
+ "strideC1": 2,
+ "strideC2": 1,
+ "offsetC": 2,
+ "C_mat": [
+ [ 1.0, 2.0 ],
+ [ 8.0, 9.0 ]
+ ],
+ "strideO": 1,
+ "offsetO": 0,
+ "strideCounts": 1,
+ "offsetCounts": 0,
+ "expected_out": [ 0, 1, 0, 0 ],
+ "expected_counts": [ 3, 1 ]
+}
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/fixtures/row_major.json b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/fixtures/row_major.json
new file mode 100644
index 000000000000..b6d1f0503245
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/fixtures/row_major.json
@@ -0,0 +1,33 @@
+{
+ "order": "row-major",
+ "metric": "sqeuclidean",
+ "M": 4,
+ "N": 2,
+ "k": 2,
+ "X": [ 1.0, 2.0, 8.0, 9.0, 2.0, 3.0, 0.0, 1.0 ],
+ "strideX1": 2,
+ "strideX2": 1,
+ "offsetX": 0,
+ "LDX": 2,
+ "X_mat": [
+ [ 1.0, 2.0 ],
+ [ 8.0, 9.0 ],
+ [ 2.0, 3.0 ],
+ [ 0.0, 1.0 ]
+ ],
+ "C": [ 1.0, 2.0, 8.0, 9.0 ],
+ "strideC1": 2,
+ "strideC2": 1,
+ "offsetC": 0,
+ "LDC": 2,
+ "C_mat": [
+ [ 1.0, 2.0 ],
+ [ 8.0, 9.0 ]
+ ],
+ "strideO": 1,
+ "offsetO": 0,
+ "strideCounts": 1,
+ "offsetCounts": 0,
+ "expected_out": [ 0, 1, 0, 0 ],
+ "expected_counts": [ 3, 1 ]
+}
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/test.dkmeans_closest_centroids.js b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/test.dkmeans_closest_centroids.js
new file mode 100644
index 000000000000..d40d20f9df14
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/test.dkmeans_closest_centroids.js
@@ -0,0 +1,264 @@
+/**
+* @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 tape = require( 'tape' );
+var Float64Array = require( '@stdlib/array/float64' );
+var Int32Array = require( '@stdlib/array/int32' );
+var dclosestCentroids = require( './../lib/dkmeans_closest_centroids.js' );
+
+
+// FIXTURES //
+
+var ROW_MAJOR_DATA = require( './fixtures/row_major.json' );
+var COLUMN_MAJOR_DATA = require( './fixtures/column_major.json' );
+
+
+// TESTS //
+
+tape( 'main export is a function', function test( t ) {
+ t.ok( true, __filename );
+ t.strictEqual( typeof dclosestCentroids, 'function', 'main export is a function' );
+ t.end();
+});
+
+tape( 'the function has an arity of 13', function test( t ) {
+ t.strictEqual( dclosestCentroids.length, 13, 'returns expected value' );
+ t.end();
+});
+
+tape( 'the function throws an error if provided a first argument which is not a valid order', function test( t ) {
+ var values;
+ var data;
+ var i;
+
+ data = ROW_MAJOR_DATA;
+
+ values = [
+ 'foo',
+ 'bar',
+ 'beep',
+ 'boop',
+ -5,
+ NaN,
+ true,
+ false,
+ null,
+ void 0,
+ [],
+ {},
+ function noop() {}
+ ];
+
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ dclosestCentroids( value, data.M, data.N, data.k, data.metric, new Float64Array( data.X ), data.LDX, new Float64Array( data.C ), data.LDC, new Int32Array( data.M ), 1, new Int32Array( data.k ), 1 );
+ };
+ }
+});
+
+tape( 'the function throws an error if provided a fifth argument which is not a supported distance metric', function test( t ) {
+ var values;
+ var data;
+ var i;
+
+ data = ROW_MAJOR_DATA;
+
+ values = [
+ 'foo',
+ 'bar',
+ 'beep',
+ 'boop',
+ true,
+ false,
+ null,
+ void 0,
+ [],
+ {},
+ function noop() {}
+ ];
+
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ dclosestCentroids( data.order, data.M, data.N, data.k, value, new Float64Array( data.X ), data.LDX, new Float64Array( data.C ), data.LDC, new Int32Array( data.M ), 1, new Int32Array( data.k ), 1 );
+ };
+ }
+});
+
+tape( 'the function throws an error if provided a seventh argument which is not a valid `LDX` value (row-major)', function test( t ) {
+ var values;
+ var data;
+ var i;
+
+ data = ROW_MAJOR_DATA;
+
+ values = [
+ 0,
+ 1
+ ];
+
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), RangeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ dclosestCentroids( data.order, data.M, data.N, data.k, data.metric, new Float64Array( data.X ), value, new Float64Array( data.C ), data.LDC, new Int32Array( data.M ), 1, new Int32Array( data.k ), 1 );
+ };
+ }
+});
+
+tape( 'the function throws an error if provided a seventh argument which is not a valid `LDX` value (column-major)', function test( t ) {
+ var values;
+ var data;
+ var i;
+
+ data = COLUMN_MAJOR_DATA;
+
+ values = [
+ 0,
+ 1
+ ];
+
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), RangeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ dclosestCentroids( data.order, data.M, data.N, data.k, data.metric, new Float64Array( data.X ), value, new Float64Array( data.C ), data.LDC, new Int32Array( data.M ), 1, new Int32Array( data.k ), 1 );
+ };
+ }
+});
+
+tape( 'the function throws an error if provided a ninth argument which is not a valid `LDC` value (row-major)', function test( t ) {
+ var values;
+ var data;
+ var i;
+
+ data = ROW_MAJOR_DATA;
+
+ values = [
+ 0,
+ 1
+ ];
+
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), RangeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ dclosestCentroids( data.order, data.M, data.N, data.k, data.metric, new Float64Array( data.X ), data.LDX, new Float64Array( data.C ), value, new Int32Array( data.M ), 1, new Int32Array( data.k ), 1 );
+ };
+ }
+});
+
+tape( 'the function throws an error if provided a ninth argument which is not a valid `LDC` value (column-major)', function test( t ) {
+ var values;
+ var data;
+ var i;
+
+ data = COLUMN_MAJOR_DATA;
+
+ values = [
+ 0,
+ 1
+ ];
+
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), RangeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ dclosestCentroids( data.order, data.M, data.N, data.k, data.metric, new Float64Array( data.X ), data.LDX, new Float64Array( data.C ), value, new Int32Array( data.M ), 1, new Int32Array( data.k ), 1 );
+ };
+ }
+});
+
+tape( 'the function assigns each data point to its closest centroid (row-major)', function test( t ) {
+ var counts;
+ var data;
+ var out;
+
+ data = ROW_MAJOR_DATA;
+ out = new Int32Array( data.expected_out.length );
+ counts = new Int32Array( data.expected_counts.length );
+
+ dclosestCentroids( data.order, data.M, data.N, data.k, data.metric, new Float64Array( data.X ), data.LDX, new Float64Array( data.C ), data.LDC, out, data.strideO, counts, data.strideCounts );
+
+ t.deepEqual( out, new Int32Array( data.expected_out ), 'returns expected assignments' );
+ t.deepEqual( counts, new Int32Array( data.expected_counts ), 'returns expected counts' );
+ t.end();
+});
+
+tape( 'the function assigns each data point to its closest centroid (column-major)', function test( t ) {
+ var counts;
+ var data;
+ var out;
+
+ data = COLUMN_MAJOR_DATA;
+ out = new Int32Array( data.expected_out.length );
+ counts = new Int32Array( data.expected_counts.length );
+
+ dclosestCentroids( data.order, data.M, data.N, data.k, data.metric, new Float64Array( data.X ), data.LDX, new Float64Array( data.C ), data.LDC, out, data.strideO, counts, data.strideCounts );
+
+ t.deepEqual( out, new Int32Array( data.expected_out ), 'returns expected assignments' );
+ t.deepEqual( counts, new Int32Array( data.expected_counts ), 'returns expected counts' );
+ t.end();
+});
+
+tape( 'the function supports all documented distance metrics', function test( t ) {
+ var metrics;
+ var counts;
+ var out;
+ var X;
+ var C;
+ var i;
+
+ metrics = [ 'sqeuclidean', 'cosine', 'cityblock', 'correlation' ];
+
+ X = new Float64Array( [ 1.0, 3.0, 5.0, 5.0, 1.5, 4.0 ] );
+ C = new Float64Array( [ 1.0, 3.0, 5.0, 5.0 ] );
+
+ for ( i = 0; i < metrics.length; i++ ) {
+ out = new Int32Array( 3 );
+ counts = new Int32Array( 2 );
+ dclosestCentroids( 'row-major', 3, 2, 2, metrics[ i ], X, 2, C, 2, out, 1, counts, 1 );
+ t.strictEqual( out.length, 3, 'returns an output array of expected length for metric: ' + metrics[ i ] );
+ }
+ t.end();
+});
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/test.dkmeans_closest_centroids.native.js b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/test.dkmeans_closest_centroids.native.js
new file mode 100644
index 000000000000..b2a0efac2515
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/test.dkmeans_closest_centroids.native.js
@@ -0,0 +1,273 @@
+/**
+* @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 tape = require( 'tape' );
+var Float64Array = require( '@stdlib/array/float64' );
+var Int32Array = require( '@stdlib/array/int32' );
+var tryRequire = require( '@stdlib/utils/try-require' );
+
+
+// FIXTURES //
+
+var ROW_MAJOR_DATA = require( './fixtures/row_major.json' );
+var COLUMN_MAJOR_DATA = require( './fixtures/column_major.json' );
+
+
+// VARIABLES //
+
+var dclosestCentroids = tryRequire( resolve( __dirname, './../lib/dkmeans_closest_centroids.native.js' ) );
+var opts = {
+ 'skip': ( dclosestCentroids instanceof Error )
+};
+
+
+// TESTS //
+
+tape( 'main export is a function', opts, function test( t ) {
+ t.ok( true, __filename );
+ t.strictEqual( typeof dclosestCentroids, 'function', 'main export is a function' );
+ t.end();
+});
+
+tape( 'the function has an arity of 13', opts, function test( t ) {
+ t.strictEqual( dclosestCentroids.length, 13, 'returns expected value' );
+ t.end();
+});
+
+tape( 'the function throws an error if provided a first argument which is not a valid order', opts, function test( t ) {
+ var values;
+ var data;
+ var i;
+
+ data = ROW_MAJOR_DATA;
+
+ values = [
+ 'foo',
+ 'bar',
+ 'beep',
+ 'boop',
+ -5,
+ NaN,
+ true,
+ false,
+ null,
+ void 0,
+ [],
+ {},
+ function noop() {}
+ ];
+
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ dclosestCentroids( value, data.M, data.N, data.k, data.metric, new Float64Array( data.X ), data.LDX, new Float64Array( data.C ), data.LDC, new Int32Array( data.M ), 1, new Int32Array( data.k ), 1 );
+ };
+ }
+});
+
+tape( 'the function throws an error if provided a fifth argument which is not a supported distance metric', opts, function test( t ) {
+ var values;
+ var data;
+ var i;
+
+ data = ROW_MAJOR_DATA;
+
+ values = [
+ 'foo',
+ 'bar',
+ 'beep',
+ 'boop',
+ true,
+ false,
+ null,
+ void 0,
+ [],
+ {},
+ function noop() {}
+ ];
+
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ dclosestCentroids( data.order, data.M, data.N, data.k, value, new Float64Array( data.X ), data.LDX, new Float64Array( data.C ), data.LDC, new Int32Array( data.M ), 1, new Int32Array( data.k ), 1 );
+ };
+ }
+});
+
+tape( 'the function throws an error if provided a seventh argument which is not a valid `LDX` value (row-major)', opts, function test( t ) {
+ var values;
+ var data;
+ var i;
+
+ data = ROW_MAJOR_DATA;
+
+ values = [
+ 0,
+ 1
+ ];
+
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), RangeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ dclosestCentroids( data.order, data.M, data.N, data.k, data.metric, new Float64Array( data.X ), value, new Float64Array( data.C ), data.LDC, new Int32Array( data.M ), 1, new Int32Array( data.k ), 1 );
+ };
+ }
+});
+
+tape( 'the function throws an error if provided a seventh argument which is not a valid `LDX` value (column-major)', opts, function test( t ) {
+ var values;
+ var data;
+ var i;
+
+ data = COLUMN_MAJOR_DATA;
+
+ values = [
+ 0,
+ 1
+ ];
+
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), RangeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ dclosestCentroids( data.order, data.M, data.N, data.k, data.metric, new Float64Array( data.X ), value, new Float64Array( data.C ), data.LDC, new Int32Array( data.M ), 1, new Int32Array( data.k ), 1 );
+ };
+ }
+});
+
+tape( 'the function throws an error if provided a ninth argument which is not a valid `LDC` value (row-major)', opts, function test( t ) {
+ var values;
+ var data;
+ var i;
+
+ data = ROW_MAJOR_DATA;
+
+ values = [
+ 0,
+ 1
+ ];
+
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), RangeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ dclosestCentroids( data.order, data.M, data.N, data.k, data.metric, new Float64Array( data.X ), data.LDX, new Float64Array( data.C ), value, new Int32Array( data.M ), 1, new Int32Array( data.k ), 1 );
+ };
+ }
+});
+
+tape( 'the function throws an error if provided a ninth argument which is not a valid `LDC` value (column-major)', opts, function test( t ) {
+ var values;
+ var data;
+ var i;
+
+ data = COLUMN_MAJOR_DATA;
+
+ values = [
+ 0,
+ 1
+ ];
+
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), RangeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ dclosestCentroids( data.order, data.M, data.N, data.k, data.metric, new Float64Array( data.X ), data.LDX, new Float64Array( data.C ), value, new Int32Array( data.M ), 1, new Int32Array( data.k ), 1 );
+ };
+ }
+});
+
+tape( 'the function assigns each data point to its closest centroid (row-major)', opts, function test( t ) {
+ var counts;
+ var data;
+ var out;
+
+ data = ROW_MAJOR_DATA;
+ out = new Int32Array( data.expected_out.length );
+ counts = new Int32Array( data.expected_counts.length );
+
+ dclosestCentroids( data.order, data.M, data.N, data.k, data.metric, new Float64Array( data.X ), data.LDX, new Float64Array( data.C ), data.LDC, out, data.strideO, counts, data.strideCounts );
+
+ t.deepEqual( out, new Int32Array( data.expected_out ), 'returns expected assignments' );
+ t.deepEqual( counts, new Int32Array( data.expected_counts ), 'returns expected counts' );
+ t.end();
+});
+
+tape( 'the function assigns each data point to its closest centroid (column-major)', opts, function test( t ) {
+ var counts;
+ var data;
+ var out;
+
+ data = COLUMN_MAJOR_DATA;
+ out = new Int32Array( data.expected_out.length );
+ counts = new Int32Array( data.expected_counts.length );
+
+ dclosestCentroids( data.order, data.M, data.N, data.k, data.metric, new Float64Array( data.X ), data.LDX, new Float64Array( data.C ), data.LDC, out, data.strideO, counts, data.strideCounts );
+
+ t.deepEqual( out, new Int32Array( data.expected_out ), 'returns expected assignments' );
+ t.deepEqual( counts, new Int32Array( data.expected_counts ), 'returns expected counts' );
+ t.end();
+});
+
+tape( 'the function supports all documented distance metrics', opts, function test( t ) {
+ var metrics;
+ var counts;
+ var out;
+ var X;
+ var C;
+ var i;
+
+ metrics = [ 'sqeuclidean', 'cosine', 'cityblock', 'correlation' ];
+
+ X = new Float64Array( [ 1.0, 3.0, 5.0, 5.0, 1.5, 4.0 ] );
+ C = new Float64Array( [ 1.0, 3.0, 5.0, 5.0 ] );
+
+ for ( i = 0; i < metrics.length; i++ ) {
+ out = new Int32Array( 3 );
+ counts = new Int32Array( 2 );
+ dclosestCentroids( 'row-major', 3, 2, 2, metrics[ i ], X, 2, C, 2, out, 1, counts, 1 );
+ t.strictEqual( out.length, 3, 'returns an output array of expected length for metric: ' + metrics[ i ] );
+ }
+ t.end();
+});
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/test.js b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/test.js
new file mode 100644
index 000000000000..98511059e1b3
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/test.js
@@ -0,0 +1,82 @@
+/**
+* @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 tape = require( 'tape' );
+var proxyquire = require( 'proxyquire' );
+var IS_BROWSER = require( '@stdlib/assert/is-browser' );
+var dclosestCentroids = require( './../lib' );
+
+
+// VARIABLES //
+
+var opts = {
+ 'skip': IS_BROWSER
+};
+
+
+// TESTS //
+
+tape( 'main export is a function', function test( t ) {
+ t.ok( true, __filename );
+ t.strictEqual( typeof dclosestCentroids, 'function', 'main export is a function' );
+ t.end();
+});
+
+tape( 'attached to the main export is a method providing an ndarray interface', function test( t ) {
+ t.strictEqual( typeof dclosestCentroids.ndarray, 'function', 'method is a function' );
+ t.end();
+});
+
+tape( 'if a native implementation is available, the main export is the native implementation', opts, function test( t ) {
+ var dclosestCentroids = proxyquire( './../lib', {
+ '@stdlib/utils/try-require': tryRequire
+ });
+
+ t.strictEqual( dclosestCentroids, mock, 'returns expected value' );
+ t.end();
+
+ function tryRequire() {
+ return mock;
+ }
+
+ function mock() {
+ // Mock...
+ }
+});
+
+tape( 'if a native implementation is not available, the main export is a JavaScript implementation', opts, function test( t ) {
+ var dclosestCentroids;
+ var main;
+
+ main = require( './../lib/dkmeans_closest_centroids.js' );
+
+ dclosestCentroids = proxyquire( './../lib', {
+ '@stdlib/utils/try-require': tryRequire
+ });
+
+ t.strictEqual( dclosestCentroids, main, 'returns expected value' );
+ t.end();
+
+ function tryRequire() {
+ return new Error( 'Cannot find module' );
+ }
+});
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/test.ndarray.js b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/test.ndarray.js
new file mode 100644
index 000000000000..31c33382b778
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/test.ndarray.js
@@ -0,0 +1,277 @@
+/**
+* @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 tape = require( 'tape' );
+var Float64Array = require( '@stdlib/array/float64' );
+var Int32Array = require( '@stdlib/array/int32' );
+var dclosestCentroids = require( './../lib/ndarray.js' );
+
+
+// FIXTURES //
+
+var ROW_MAJOR_DATA = require( './fixtures/row_major.json' );
+var COLUMN_MAJOR_DATA = require( './fixtures/column_major.json' );
+var OFFSET_ROW_MAJOR_DATA = require( './fixtures/offsets/row_major.json' );
+var OFFSET_COLUMN_MAJOR_DATA = require( './fixtures/offsets/column_major.json' );
+var NEGATIVE_STRIDES_ROW_MAJOR_DATA = require( './fixtures/negative-strides/row_major.json' );
+var NEGATIVE_STRIDES_COLUMN_MAJOR_DATA = require( './fixtures/negative-strides/column_major.json' );
+var MIXED_STRIDES_ROW_MAJOR_DATA = require( './fixtures/mixed-strides/row_major.json' );
+var MIXED_STRIDES_COLUMN_MAJOR_DATA = require( './fixtures/mixed-strides/column_major.json' );
+var LARGE_STRIDES_ROW_MAJOR_DATA = require( './fixtures/large-strides/row_major.json' );
+var LARGE_STRIDES_COLUMN_MAJOR_DATA = require( './fixtures/large-strides/column_major.json' );
+
+
+// TESTS //
+
+tape( 'main export is a function', function test( t ) {
+ t.ok( true, __filename );
+ t.strictEqual( typeof dclosestCentroids, 'function', 'main export is a function' );
+ t.end();
+});
+
+tape( 'the function has an arity of 18', function test( t ) {
+ t.strictEqual( dclosestCentroids.length, 18, 'returns expected value' );
+ t.end();
+});
+
+tape( 'the function throws an error if provided a fourth argument which is not a supported distance metric', function test( t ) {
+ var values;
+ var data;
+ var i;
+
+ data = ROW_MAJOR_DATA;
+
+ values = [
+ 'foo',
+ 'bar',
+ 'beep',
+ 'boop',
+ true,
+ false,
+ null,
+ void 0,
+ [],
+ {},
+ function noop() {}
+ ];
+
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ dclosestCentroids( data.M, data.N, data.k, value, new Float64Array( data.X ), data.strideX1, data.strideX2, data.offsetX, new Float64Array( data.C ), data.strideC1, data.strideC2, data.offsetC, new Int32Array( data.M ), 1, 0, new Int32Array( data.k ), 1, 0 );
+ };
+ }
+});
+
+tape( 'the function returns the output array unchanged if `M`, `N`, or `k` is less than 1', function test( t ) {
+ var counts;
+ var data;
+ var out;
+
+ data = ROW_MAJOR_DATA;
+
+ out = new Int32Array( data.expected_out.length );
+ counts = new Int32Array( data.expected_counts.length );
+
+ t.strictEqual( dclosestCentroids( 0, data.N, data.k, data.metric, new Float64Array( data.X ), data.strideX1, data.strideX2, data.offsetX, new Float64Array( data.C ), data.strideC1, data.strideC2, data.offsetC, out, data.strideO, data.offsetO, counts, data.strideCounts, data.offsetCounts ), out, 'returns expected value' );
+ t.strictEqual( dclosestCentroids( data.M, 0, data.k, data.metric, new Float64Array( data.X ), data.strideX1, data.strideX2, data.offsetX, new Float64Array( data.C ), data.strideC1, data.strideC2, data.offsetC, out, data.strideO, data.offsetO, counts, data.strideCounts, data.offsetCounts ), out, 'returns expected value' );
+ t.strictEqual( dclosestCentroids( data.M, data.N, 0, data.metric, new Float64Array( data.X ), data.strideX1, data.strideX2, data.offsetX, new Float64Array( data.C ), data.strideC1, data.strideC2, data.offsetC, out, data.strideO, data.offsetO, counts, data.strideCounts, data.offsetCounts ), out, 'returns expected value' );
+
+ t.deepEqual( out, new Int32Array( data.expected_out.length ), 'leaves output unchanged' );
+ t.deepEqual( counts, new Int32Array( data.expected_counts.length ), 'leaves counts unchanged' );
+
+ t.end();
+});
+
+tape( 'the function assigns each data point to its closest centroid (row-major)', function test( t ) {
+ var counts;
+ var data;
+ var out;
+
+ data = ROW_MAJOR_DATA;
+
+ out = new Int32Array( data.expected_out.length );
+ counts = new Int32Array( data.expected_counts.length );
+
+ dclosestCentroids( data.M, data.N, data.k, data.metric, new Float64Array( data.X ), data.strideX1, data.strideX2, data.offsetX, new Float64Array( data.C ), data.strideC1, data.strideC2, data.offsetC, out, data.strideO, data.offsetO, counts, data.strideCounts, data.offsetCounts );
+
+ t.deepEqual( out, new Int32Array( data.expected_out ), 'returns expected assignments' );
+ t.deepEqual( counts, new Int32Array( data.expected_counts ), 'returns expected counts' );
+ t.end();
+});
+
+tape( 'the function assigns each data point to its closest centroid (column-major)', function test( t ) {
+ var counts;
+ var data;
+ var out;
+
+ data = COLUMN_MAJOR_DATA;
+
+ out = new Int32Array( data.expected_out.length );
+ counts = new Int32Array( data.expected_counts.length );
+
+ dclosestCentroids( data.M, data.N, data.k, data.metric, new Float64Array( data.X ), data.strideX1, data.strideX2, data.offsetX, new Float64Array( data.C ), data.strideC1, data.strideC2, data.offsetC, out, data.strideO, data.offsetO, counts, data.strideCounts, data.offsetCounts );
+
+ t.deepEqual( out, new Int32Array( data.expected_out ), 'returns expected assignments' );
+ t.deepEqual( counts, new Int32Array( data.expected_counts ), 'returns expected counts' );
+ t.end();
+});
+
+tape( 'the function assigns each data point to its closest centroid (row-major, offsets)', function test( t ) {
+ var counts;
+ var data;
+ var out;
+
+ data = OFFSET_ROW_MAJOR_DATA;
+
+ out = new Int32Array( data.expected_out.length );
+ counts = new Int32Array( data.expected_counts.length );
+
+ dclosestCentroids( data.M, data.N, data.k, data.metric, new Float64Array( data.X ), data.strideX1, data.strideX2, data.offsetX, new Float64Array( data.C ), data.strideC1, data.strideC2, data.offsetC, out, data.strideO, data.offsetO, counts, data.strideCounts, data.offsetCounts );
+
+ t.deepEqual( out, new Int32Array( data.expected_out ), 'returns expected assignments' );
+ t.deepEqual( counts, new Int32Array( data.expected_counts ), 'returns expected counts' );
+ t.end();
+});
+
+tape( 'the function assigns each data point to its closest centroid (column-major, offsets)', function test( t ) {
+ var counts;
+ var data;
+ var out;
+
+ data = OFFSET_COLUMN_MAJOR_DATA;
+
+ out = new Int32Array( data.expected_out.length );
+ counts = new Int32Array( data.expected_counts.length );
+
+ dclosestCentroids( data.M, data.N, data.k, data.metric, new Float64Array( data.X ), data.strideX1, data.strideX2, data.offsetX, new Float64Array( data.C ), data.strideC1, data.strideC2, data.offsetC, out, data.strideO, data.offsetO, counts, data.strideCounts, data.offsetCounts );
+
+ t.deepEqual( out, new Int32Array( data.expected_out ), 'returns expected assignments' );
+ t.deepEqual( counts, new Int32Array( data.expected_counts ), 'returns expected counts' );
+ t.end();
+});
+
+tape( 'the function assigns each data point to its closest centroid (row-major, negative strides)', function test( t ) {
+ var counts;
+ var data;
+ var out;
+
+ data = NEGATIVE_STRIDES_ROW_MAJOR_DATA;
+
+ out = new Int32Array( data.expected_out.length );
+ counts = new Int32Array( data.expected_counts.length );
+
+ dclosestCentroids( data.M, data.N, data.k, data.metric, new Float64Array( data.X ), data.strideX1, data.strideX2, data.offsetX, new Float64Array( data.C ), data.strideC1, data.strideC2, data.offsetC, out, data.strideO, data.offsetO, counts, data.strideCounts, data.offsetCounts );
+
+ t.deepEqual( out, new Int32Array( data.expected_out ), 'returns expected assignments' );
+ t.deepEqual( counts, new Int32Array( data.expected_counts ), 'returns expected counts' );
+ t.end();
+});
+
+tape( 'the function assigns each data point to its closest centroid (column-major, negative strides)', function test( t ) {
+ var counts;
+ var data;
+ var out;
+
+ data = NEGATIVE_STRIDES_COLUMN_MAJOR_DATA;
+
+ out = new Int32Array( data.expected_out.length );
+ counts = new Int32Array( data.expected_counts.length );
+
+ dclosestCentroids( data.M, data.N, data.k, data.metric, new Float64Array( data.X ), data.strideX1, data.strideX2, data.offsetX, new Float64Array( data.C ), data.strideC1, data.strideC2, data.offsetC, out, data.strideO, data.offsetO, counts, data.strideCounts, data.offsetCounts );
+
+ t.deepEqual( out, new Int32Array( data.expected_out ), 'returns expected assignments' );
+ t.deepEqual( counts, new Int32Array( data.expected_counts ), 'returns expected counts' );
+ t.end();
+});
+
+tape( 'the function assigns each data point to its closest centroid (row-major, mixed strides)', function test( t ) {
+ var counts;
+ var data;
+ var out;
+
+ data = MIXED_STRIDES_ROW_MAJOR_DATA;
+
+ out = new Int32Array( data.expected_out.length );
+ counts = new Int32Array( data.expected_counts.length );
+
+ dclosestCentroids( data.M, data.N, data.k, data.metric, new Float64Array( data.X ), data.strideX1, data.strideX2, data.offsetX, new Float64Array( data.C ), data.strideC1, data.strideC2, data.offsetC, out, data.strideO, data.offsetO, counts, data.strideCounts, data.offsetCounts );
+
+ t.deepEqual( out, new Int32Array( data.expected_out ), 'returns expected assignments' );
+ t.deepEqual( counts, new Int32Array( data.expected_counts ), 'returns expected counts' );
+ t.end();
+});
+
+tape( 'the function assigns each data point to its closest centroid (column-major, mixed strides)', function test( t ) {
+ var counts;
+ var data;
+ var out;
+
+ data = MIXED_STRIDES_COLUMN_MAJOR_DATA;
+
+ out = new Int32Array( data.expected_out.length );
+ counts = new Int32Array( data.expected_counts.length );
+
+ dclosestCentroids( data.M, data.N, data.k, data.metric, new Float64Array( data.X ), data.strideX1, data.strideX2, data.offsetX, new Float64Array( data.C ), data.strideC1, data.strideC2, data.offsetC, out, data.strideO, data.offsetO, counts, data.strideCounts, data.offsetCounts );
+
+ t.deepEqual( out, new Int32Array( data.expected_out ), 'returns expected assignments' );
+ t.deepEqual( counts, new Int32Array( data.expected_counts ), 'returns expected counts' );
+ t.end();
+});
+
+tape( 'the function assigns each data point to its closest centroid (row-major, large strides)', function test( t ) {
+ var counts;
+ var data;
+ var out;
+
+ data = LARGE_STRIDES_ROW_MAJOR_DATA;
+
+ out = new Int32Array( data.expected_out.length );
+ counts = new Int32Array( data.expected_counts.length );
+
+ dclosestCentroids( data.M, data.N, data.k, data.metric, new Float64Array( data.X ), data.strideX1, data.strideX2, data.offsetX, new Float64Array( data.C ), data.strideC1, data.strideC2, data.offsetC, out, data.strideO, data.offsetO, counts, data.strideCounts, data.offsetCounts );
+
+ t.deepEqual( out, new Int32Array( data.expected_out ), 'returns expected assignments' );
+ t.deepEqual( counts, new Int32Array( data.expected_counts ), 'returns expected counts' );
+ t.end();
+});
+
+tape( 'the function assigns each data point to its closest centroid (column-major, large strides)', function test( t ) {
+ var counts;
+ var data;
+ var out;
+
+ data = LARGE_STRIDES_COLUMN_MAJOR_DATA;
+
+ out = new Int32Array( data.expected_out.length );
+ counts = new Int32Array( data.expected_counts.length );
+
+ dclosestCentroids( data.M, data.N, data.k, data.metric, new Float64Array( data.X ), data.strideX1, data.strideX2, data.offsetX, new Float64Array( data.C ), data.strideC1, data.strideC2, data.offsetC, out, data.strideO, data.offsetO, counts, data.strideCounts, data.offsetCounts );
+
+ t.deepEqual( out, new Int32Array( data.expected_out ), 'returns expected assignments' );
+ t.deepEqual( counts, new Int32Array( data.expected_counts ), 'returns expected counts' );
+ t.end();
+});
diff --git a/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/test.ndarray.native.js b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/test.ndarray.native.js
new file mode 100644
index 000000000000..0db5005cb6c9
--- /dev/null
+++ b/lib/node_modules/@stdlib/ml/strided/dkmeans-closest-centroids/test/test.ndarray.native.js
@@ -0,0 +1,288 @@
+/**
+* @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.
+*/
+
+/* eslint-disable max-len, id-length */
+
+'use strict';
+
+// MODULES //
+
+var resolve = require( 'path' ).resolve;
+var tape = require( 'tape' );
+var Float64Array = require( '@stdlib/array/float64' );
+var Int32Array = require( '@stdlib/array/int32' );
+var tryRequire = require( '@stdlib/utils/try-require' );
+
+
+// FIXTURES //
+
+var ROW_MAJOR_DATA = require( './fixtures/row_major.json' );
+var COLUMN_MAJOR_DATA = require( './fixtures/column_major.json' );
+var OFFSET_ROW_MAJOR_DATA = require( './fixtures/offsets/row_major.json' );
+var OFFSET_COLUMN_MAJOR_DATA = require( './fixtures/offsets/column_major.json' );
+var NEGATIVE_STRIDES_ROW_MAJOR_DATA = require( './fixtures/negative-strides/row_major.json' );
+var NEGATIVE_STRIDES_COLUMN_MAJOR_DATA = require( './fixtures/negative-strides/column_major.json' );
+var MIXED_STRIDES_ROW_MAJOR_DATA = require( './fixtures/mixed-strides/row_major.json' );
+var MIXED_STRIDES_COLUMN_MAJOR_DATA = require( './fixtures/mixed-strides/column_major.json' );
+var LARGE_STRIDES_ROW_MAJOR_DATA = require( './fixtures/large-strides/row_major.json' );
+var LARGE_STRIDES_COLUMN_MAJOR_DATA = require( './fixtures/large-strides/column_major.json' );
+
+
+// VARIABLES //
+
+var dclosestCentroids = tryRequire( resolve( __dirname, './../lib/ndarray.native.js' ) );
+var opts = {
+ 'skip': ( dclosestCentroids instanceof Error )
+};
+
+
+// TESTS //
+
+tape( 'main export is a function', opts, function test( t ) {
+ t.ok( true, __filename );
+ t.strictEqual( typeof dclosestCentroids, 'function', 'main export is a function' );
+ t.end();
+});
+
+tape( 'the function has an arity of 18', opts, function test( t ) {
+ t.strictEqual( dclosestCentroids.length, 18, 'returns expected value' );
+ t.end();
+});
+
+tape( 'the function throws an error if provided a fourth argument which is not a supported distance metric', opts, function test( t ) {
+ var values;
+ var data;
+ var i;
+
+ data = ROW_MAJOR_DATA;
+
+ values = [
+ 'foo',
+ 'bar',
+ 'beep',
+ 'boop',
+ true,
+ false,
+ null,
+ void 0,
+ [],
+ {},
+ function noop() {}
+ ];
+
+ for ( i = 0; i < values.length; i++ ) {
+ t.throws( badValue( values[ i ] ), TypeError, 'throws an error when provided ' + values[ i ] );
+ }
+ t.end();
+
+ function badValue( value ) {
+ return function badValue() {
+ dclosestCentroids( data.M, data.N, data.k, value, new Float64Array( data.X ), data.strideX1, data.strideX2, data.offsetX, new Float64Array( data.C ), data.strideC1, data.strideC2, data.offsetC, new Int32Array( data.M ), 1, 0, new Int32Array( data.k ), 1, 0 );
+ };
+ }
+});
+
+tape( 'the function returns the output array unchanged if `M`, `N`, or `k` is less than 1', opts, function test( t ) {
+ var counts;
+ var data;
+ var out;
+
+ data = ROW_MAJOR_DATA;
+
+ out = new Int32Array( data.expected_out.length );
+ counts = new Int32Array( data.expected_counts.length );
+
+ t.strictEqual( dclosestCentroids( 0, data.N, data.k, data.metric, new Float64Array( data.X ), data.strideX1, data.strideX2, data.offsetX, new Float64Array( data.C ), data.strideC1, data.strideC2, data.offsetC, out, data.strideO, data.offsetO, counts, data.strideCounts, data.offsetCounts ), out, 'returns expected value' );
+ t.strictEqual( dclosestCentroids( data.M, 0, data.k, data.metric, new Float64Array( data.X ), data.strideX1, data.strideX2, data.offsetX, new Float64Array( data.C ), data.strideC1, data.strideC2, data.offsetC, out, data.strideO, data.offsetO, counts, data.strideCounts, data.offsetCounts ), out, 'returns expected value' );
+ t.strictEqual( dclosestCentroids( data.M, data.N, 0, data.metric, new Float64Array( data.X ), data.strideX1, data.strideX2, data.offsetX, new Float64Array( data.C ), data.strideC1, data.strideC2, data.offsetC, out, data.strideO, data.offsetO, counts, data.strideCounts, data.offsetCounts ), out, 'returns expected value' );
+
+ t.deepEqual( out, new Int32Array( data.expected_out.length ), 'leaves output unchanged' );
+ t.deepEqual( counts, new Int32Array( data.expected_counts.length ), 'leaves counts unchanged' );
+
+ t.end();
+});
+
+tape( 'the function assigns each data point to its closest centroid (row-major)', opts, function test( t ) {
+ var counts;
+ var data;
+ var out;
+
+ data = ROW_MAJOR_DATA;
+
+ out = new Int32Array( data.expected_out.length );
+ counts = new Int32Array( data.expected_counts.length );
+
+ dclosestCentroids( data.M, data.N, data.k, data.metric, new Float64Array( data.X ), data.strideX1, data.strideX2, data.offsetX, new Float64Array( data.C ), data.strideC1, data.strideC2, data.offsetC, out, data.strideO, data.offsetO, counts, data.strideCounts, data.offsetCounts );
+
+ t.deepEqual( out, new Int32Array( data.expected_out ), 'returns expected assignments' );
+ t.deepEqual( counts, new Int32Array( data.expected_counts ), 'returns expected counts' );
+ t.end();
+});
+
+tape( 'the function assigns each data point to its closest centroid (column-major)', opts, function test( t ) {
+ var counts;
+ var data;
+ var out;
+
+ data = COLUMN_MAJOR_DATA;
+
+ out = new Int32Array( data.expected_out.length );
+ counts = new Int32Array( data.expected_counts.length );
+
+ dclosestCentroids( data.M, data.N, data.k, data.metric, new Float64Array( data.X ), data.strideX1, data.strideX2, data.offsetX, new Float64Array( data.C ), data.strideC1, data.strideC2, data.offsetC, out, data.strideO, data.offsetO, counts, data.strideCounts, data.offsetCounts );
+
+ t.deepEqual( out, new Int32Array( data.expected_out ), 'returns expected assignments' );
+ t.deepEqual( counts, new Int32Array( data.expected_counts ), 'returns expected counts' );
+ t.end();
+});
+
+tape( 'the function assigns each data point to its closest centroid (row-major, offsets)', opts, function test( t ) {
+ var counts;
+ var data;
+ var out;
+
+ data = OFFSET_ROW_MAJOR_DATA;
+
+ out = new Int32Array( data.expected_out.length );
+ counts = new Int32Array( data.expected_counts.length );
+
+ dclosestCentroids( data.M, data.N, data.k, data.metric, new Float64Array( data.X ), data.strideX1, data.strideX2, data.offsetX, new Float64Array( data.C ), data.strideC1, data.strideC2, data.offsetC, out, data.strideO, data.offsetO, counts, data.strideCounts, data.offsetCounts );
+
+ t.deepEqual( out, new Int32Array( data.expected_out ), 'returns expected assignments' );
+ t.deepEqual( counts, new Int32Array( data.expected_counts ), 'returns expected counts' );
+ t.end();
+});
+
+tape( 'the function assigns each data point to its closest centroid (column-major, offsets)', opts, function test( t ) {
+ var counts;
+ var data;
+ var out;
+
+ data = OFFSET_COLUMN_MAJOR_DATA;
+
+ out = new Int32Array( data.expected_out.length );
+ counts = new Int32Array( data.expected_counts.length );
+
+ dclosestCentroids( data.M, data.N, data.k, data.metric, new Float64Array( data.X ), data.strideX1, data.strideX2, data.offsetX, new Float64Array( data.C ), data.strideC1, data.strideC2, data.offsetC, out, data.strideO, data.offsetO, counts, data.strideCounts, data.offsetCounts );
+
+ t.deepEqual( out, new Int32Array( data.expected_out ), 'returns expected assignments' );
+ t.deepEqual( counts, new Int32Array( data.expected_counts ), 'returns expected counts' );
+ t.end();
+});
+
+tape( 'the function assigns each data point to its closest centroid (row-major, negative strides)', opts, function test( t ) {
+ var counts;
+ var data;
+ var out;
+
+ data = NEGATIVE_STRIDES_ROW_MAJOR_DATA;
+
+ out = new Int32Array( data.expected_out.length );
+ counts = new Int32Array( data.expected_counts.length );
+
+ dclosestCentroids( data.M, data.N, data.k, data.metric, new Float64Array( data.X ), data.strideX1, data.strideX2, data.offsetX, new Float64Array( data.C ), data.strideC1, data.strideC2, data.offsetC, out, data.strideO, data.offsetO, counts, data.strideCounts, data.offsetCounts );
+
+ t.deepEqual( out, new Int32Array( data.expected_out ), 'returns expected assignments' );
+ t.deepEqual( counts, new Int32Array( data.expected_counts ), 'returns expected counts' );
+ t.end();
+});
+
+tape( 'the function assigns each data point to its closest centroid (column-major, negative strides)', opts, function test( t ) {
+ var counts;
+ var data;
+ var out;
+
+ data = NEGATIVE_STRIDES_COLUMN_MAJOR_DATA;
+
+ out = new Int32Array( data.expected_out.length );
+ counts = new Int32Array( data.expected_counts.length );
+
+ dclosestCentroids( data.M, data.N, data.k, data.metric, new Float64Array( data.X ), data.strideX1, data.strideX2, data.offsetX, new Float64Array( data.C ), data.strideC1, data.strideC2, data.offsetC, out, data.strideO, data.offsetO, counts, data.strideCounts, data.offsetCounts );
+
+ t.deepEqual( out, new Int32Array( data.expected_out ), 'returns expected assignments' );
+ t.deepEqual( counts, new Int32Array( data.expected_counts ), 'returns expected counts' );
+ t.end();
+});
+
+tape( 'the function assigns each data point to its closest centroid (row-major, mixed strides)', opts, function test( t ) {
+ var counts;
+ var data;
+ var out;
+
+ data = MIXED_STRIDES_ROW_MAJOR_DATA;
+
+ out = new Int32Array( data.expected_out.length );
+ counts = new Int32Array( data.expected_counts.length );
+
+ dclosestCentroids( data.M, data.N, data.k, data.metric, new Float64Array( data.X ), data.strideX1, data.strideX2, data.offsetX, new Float64Array( data.C ), data.strideC1, data.strideC2, data.offsetC, out, data.strideO, data.offsetO, counts, data.strideCounts, data.offsetCounts );
+
+ t.deepEqual( out, new Int32Array( data.expected_out ), 'returns expected assignments' );
+ t.deepEqual( counts, new Int32Array( data.expected_counts ), 'returns expected counts' );
+ t.end();
+});
+
+tape( 'the function assigns each data point to its closest centroid (column-major, mixed strides)', opts, function test( t ) {
+ var counts;
+ var data;
+ var out;
+
+ data = MIXED_STRIDES_COLUMN_MAJOR_DATA;
+
+ out = new Int32Array( data.expected_out.length );
+ counts = new Int32Array( data.expected_counts.length );
+
+ dclosestCentroids( data.M, data.N, data.k, data.metric, new Float64Array( data.X ), data.strideX1, data.strideX2, data.offsetX, new Float64Array( data.C ), data.strideC1, data.strideC2, data.offsetC, out, data.strideO, data.offsetO, counts, data.strideCounts, data.offsetCounts );
+
+ t.deepEqual( out, new Int32Array( data.expected_out ), 'returns expected assignments' );
+ t.deepEqual( counts, new Int32Array( data.expected_counts ), 'returns expected counts' );
+ t.end();
+});
+
+tape( 'the function assigns each data point to its closest centroid (row-major, large strides)', opts, function test( t ) {
+ var counts;
+ var data;
+ var out;
+
+ data = LARGE_STRIDES_ROW_MAJOR_DATA;
+
+ out = new Int32Array( data.expected_out.length );
+ counts = new Int32Array( data.expected_counts.length );
+
+ dclosestCentroids( data.M, data.N, data.k, data.metric, new Float64Array( data.X ), data.strideX1, data.strideX2, data.offsetX, new Float64Array( data.C ), data.strideC1, data.strideC2, data.offsetC, out, data.strideO, data.offsetO, counts, data.strideCounts, data.offsetCounts );
+
+ t.deepEqual( out, new Int32Array( data.expected_out ), 'returns expected assignments' );
+ t.deepEqual( counts, new Int32Array( data.expected_counts ), 'returns expected counts' );
+ t.end();
+});
+
+tape( 'the function assigns each data point to its closest centroid (column-major, large strides)', opts, function test( t ) {
+ var counts;
+ var data;
+ var out;
+
+ data = LARGE_STRIDES_COLUMN_MAJOR_DATA;
+
+ out = new Int32Array( data.expected_out.length );
+ counts = new Int32Array( data.expected_counts.length );
+
+ dclosestCentroids( data.M, data.N, data.k, data.metric, new Float64Array( data.X ), data.strideX1, data.strideX2, data.offsetX, new Float64Array( data.C ), data.strideC1, data.strideC2, data.offsetC, out, data.strideO, data.offsetO, counts, data.strideCounts, data.offsetCounts );
+
+ t.deepEqual( out, new Int32Array( data.expected_out ), 'returns expected assignments' );
+ t.deepEqual( counts, new Int32Array( data.expected_counts ), 'returns expected counts' );
+ t.end();
+});