import * as tf from '../backend/adapter';
import type { SpectralClusteringParams } from './types';
interface OptimizationConfig {
    gamma: number;
    metric: 'calinski-harabasz' | 'davies-bouldin' | 'silhouette';
    attempts: number;
    use_validation: boolean;
}
interface OptimizationResult {
    labels: number[];
    config: OptimizationConfig;
    score?: number;
}
export declare function validation_based_optimization(embedding: tf.Tensor2D, n_clusters: number, metric: 'calinski-harabasz' | 'davies-bouldin' | 'silhouette', attempts: number, random_state?: number): Promise<OptimizationResult>;
/**
 * The embedding is computed once per gamma and reused across all
 * metric/attempt combinations to avoid redundant eigendecompositions.
 *
 * @throws {Error} If every gamma value produces a degenerate embedding so that
 *   no valid clustering is found.
 */
export declare function intensive_parameter_sweep(X: tf.Tensor2D, params: SpectralClusteringParams, compute_embedding_from_affinity: (affinity_matrix: tf.Tensor2D) => Promise<tf.Tensor2D>, compute_affinity_matrix: (X: tf.Tensor2D, params: SpectralClusteringParams) => tf.Tensor2D): Promise<OptimizationResult>;
export {};
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