import { DataMatrix, LabelVector } from '../clustering/types';
/** Distance metric accepted by the noise-aware internal validation metrics. */
export type ValidationMetric = 'euclidean' | 'cosine';
/**
 * s(i) = (b - a) / max(a, b), where a = mean intra-cluster distance,
 * b = mean distance to the nearest other cluster.
 *
 * Noise (`-1`) samples are excluded; one score per non-noise sample is returned.
 * All-noise input throws. One valid cluster after noise filtering → all-zeros (defined).
 * Single-cluster input with no noise throws.
 *
 * @throws Error if fewer than 2 valid clusters remain after excluding noise
 */
export declare function silhouette_samples(X: DataMatrix, labels: LabelVector, metric?: ValidationMetric): number[];
/** @throws Error if all labels are noise, fewer than 2 clusters, or labels length mismatch */
export declare function silhouette_score(X: DataMatrix, labels: LabelVector, metric?: ValidationMetric): number;
/**
 * Useful for large datasets where all-pairs distance is prohibitive.
 * Degenerate contract mirrors silhouette_samples: all-noise throws; one valid
 * cluster after filtering → defined 0; single-cluster with no noise throws.
 *
 * @throws Error if all labels are noise, fewer than 2 clusters, or labels length mismatch
 */
export declare function silhouette_score_subset(X: DataMatrix, labels: LabelVector, sample_indices: number[], metric?: ValidationMetric): number;