clustering-tfjs/dist/validation/davies_bouldin.d.ts

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

import { DataMatrix, LabelVector } from '../clustering/types';
/**
 * Computes the Davies-Bouldin score.
 *
 * The Davies-Bouldin index is defined as the average similarity measure
 * of each cluster with its most similar cluster. Lower values indicate
 * better clustering (clusters are more separated).
 *
 * Formula: DB = (1/k) * sum(max_{i≠j}(R_{ij}))
 * where R_{ij} = (s_i + s_j) / d_{ij}
 * - s_i = average distance from points in cluster i to its centroid
 * - d_{ij} = distance between centroids of clusters i and j
 *
 * @param X - Data matrix of shape [n_samples, n_features]
 * @param labels - Cluster labels for each sample
 * @returns The Davies-Bouldin score (lower is better)
 * @throws Error if k <= 1
 */
export declare function daviesBouldin(X: DataMatrix, labels: LabelVector): number;
/**
 * Computes the Davies-Bouldin score with optimized memory usage.
 * This version minimizes tensor allocations and disposals.
 *
 * @param X - Data matrix of shape [n_samples, n_features]
 * @param labels - Cluster labels for each sample
 * @returns The Davies-Bouldin score (lower is better)
 */
export declare function daviesBouldinEfficient(X: DataMatrix, labels: LabelVector): number;
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