clustering-tfjs/dist/utils/connected_components.d.ts

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

import * as tf from '../tf-adapter';
/**
 * Detects the number of connected components in a graph based on its affinity matrix.
 *
 * A graph is considered fully connected if there's only 1 component.
 * Multiple components are detected by counting near-zero eigenvalues of the Laplacian.
 *
 * @param affinity - Affinity/adjacency matrix (n x n)
 * @param tolerance - Tolerance for detecting zero eigenvalues (default: 1e-2)
 * @returns Object containing:
 *   - numComponents: Number of connected components
 *   - isFullyConnected: Whether the graph has only 1 component
 *   - componentLabels: Array indicating which component each node belongs to
 */
export declare function detectConnectedComponents(affinity: tf.Tensor2D, tolerance?: number): {
    numComponents: number;
    isFullyConnected: boolean;
    componentLabels: Int32Array;
};
/**
 * Issues a warning if the graph is not fully connected, similar to sklearn.
 *
 * @param affinity - Affinity matrix to check
 * @param tolerance - Tolerance for detecting zero eigenvalues
 * @returns true if graph is fully connected, false otherwise
 */
export declare function checkGraphConnectivity(affinity: tf.Tensor2D, tolerance?: number): boolean;
//# sourceMappingURL=connected_components.d.ts.map