clustering-tfjs/dist/clustering/kmeans.d.ts

High-performance TypeScript clustering algorithms (K-Means, Spectral, Agglomerative) with TensorFlow.js acceleration and scikit-learn compatibility

import type { BaseClustering, DataMatrix, LabelVector, KMeansParams } from './types';
import * as tf from '../tf-adapter';
/**
 * Extremely lightweight – yet reasonably efficient – K-Means implementation
 * intended solely as an internal helper for SpectralClustering.
 *
 * The class purposefully **does not** try to match the full scikit-learn API
 * but merely exposes the minimal surface required by downstream tasks.
 */
export declare class KMeans implements BaseClustering<KMeansParams> {
    readonly params: KMeansParams;
    /** Lazily populated labels after calling {@link fit}. */
    labels_: LabelVector | null;
    /** Final cluster centroids (shape: nClusters × nFeatures). */
    centroids_: tf.Tensor2D | null;
    /** Final value of the inertia criterion (sum of squared distances). */
    inertia_: number | null;
    private static readonly DEFAULT_MAX_ITER;
    private static readonly DEFAULT_TOL;
    private static readonly DEFAULT_N_INIT;
    constructor(params: KMeansParams);
    /** Provides deterministic or non-deterministic random stream aligned with NumPy. */
    private static makeRandomStream;
    private static validateParams;
    fit(X: DataMatrix): Promise<void>;
    fitPredict(X: DataMatrix): Promise<LabelVector>;
}
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