clustering-tfjs/dist/validation/calinski_harabasz.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 Calinski-Harabasz score (also known as Variance Ratio Criterion).
 *
 * The score is defined as the ratio of the between-cluster dispersion to the
 * within-cluster dispersion. Higher values indicate better-defined clusters.
 *
 * Formula: CH = (BSS / (k - 1)) / (WSS / (n - k))
 * where:
 * - BSS = between-cluster sum of squares
 * - WSS = within-cluster sum of squares
 * - k = number of clusters
 * - n = number of samples
 *
 * @param X - Data matrix of shape [n_samples, n_features]
 * @param labels - Cluster labels for each sample
 * @returns The Calinski-Harabasz score (higher is better)
 * @throws Error if k <= 1 or k >= n_samples
 */
export declare function calinskiHarabasz(X: DataMatrix, labels: LabelVector): number;
/**
 * Computes the Calinski-Harabasz score in a memory-efficient manner for large datasets.
 * This version processes clusters sequentially to minimize memory usage.
 *
 * @param X - Data matrix of shape [n_samples, n_features]
 * @param labels - Cluster labels for each sample
 * @returns The Calinski-Harabasz score
 */
export declare function calinskiHarabaszEfficient(X: DataMatrix, labels: LabelVector): number;
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