/**
 * K-means clustering — Lloyd's algorithm with greedy k-means++ seeding.
 *
 * Faithful port of scikit-learn's `sklearn.cluster.KMeans` (algorithm="lloyd"):
 *   - greedy k-means++ initialization with 2 + floor(log k) local trials
 *     (Arthur & Vassilvitskii 2007; sklearn `_kmeans_plusplus`, _kmeans.py l.180)
 *   - Lloyd expectation-maximization with strict-label and center-shift
 *     tolerance convergence (sklearn `_kmeans_single_lloyd`, _kmeans.py l.630)
 *   - `nInit` independent restarts, keeping the lowest-inertia result
 *   - dataset-scaled tolerance `mean(var(X, axis=0)) * tol` (sklearn `_tolerance`)
 *
 * Determinism is total: the run is driven by a seeded mulberry32 PRNG. With a
 * fixed `seed` the labels, centers and inertia are reproducible bit-for-bit;
 * there is deliberately NO `Date.now()`/`Math.random()` fallback.
 *
 * Validated against committed reference fixtures
 * (three separable blobs, k=3, generated by sklearn.cluster.KMeans).
 *
 * @param {Array<ArrayLike<number>>} X2d
 *   Observations, shape (nSamples, nFeatures). Each row is a plain array or a
 *   typed array; all rows must share the same length.
 * @param {number} k - number of clusters (1 ≤ k ≤ nSamples).
 * @param {Object} [options]
 * @param {number} [options.nInit=10] - number of k-means++ restarts (≥ 1).
 * @param {number} [options.maxIter=300] - max Lloyd iterations per restart (≥ 1).
 * @param {number} [options.seed=0] - PRNG seed for reproducible seeding.
 * @param {number} [options.tol=1e-4] - relative center-shift tolerance
 *   (scaled by the mean feature variance, matching sklearn).
 * @returns {{ labels: Int32Array, centers: number[][], inertia: number }}
 *   `labels[i]` is the cluster index of observation i, `centers[c]` is the
 *   centroid of cluster c, and `inertia` is the summed squared distance of
 *   every observation to its assigned centroid.
 */
export function kmeans(X2d: Array<ArrayLike<number>>, k: number, { nInit, maxIter, seed, tol }?: {
    nInit?: number;
    maxIter?: number;
    seed?: number;
    tol?: number;
}): {
    labels: Int32Array;
    centers: number[][];
    inertia: number;
};