clustering-tfjs/dist/utils/constant_eigenvector.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';
/**
 * Creates the constant eigenvector for connected graphs in spectral clustering.
 *
 * For a connected graph, the smallest eigenvalue of the normalized Laplacian is 0,
 * and its corresponding eigenvector should be constant. However, numerical computation
 * can introduce small variations. sklearn replaces this with the theoretical constant
 * eigenvector to improve clustering stability.
 *
 * sklearn uses a simple constant vector 1/sqrt(n) for all entries, not the
 * degree-weighted version. This is then scaled by the spectral embedding normalization.
 *
 * @param affinity The affinity matrix
 * @returns The constant eigenvector as a column vector (n x 1)
 */
export declare function createConstantEigenvector(affinity: tf.Tensor2D): tf.Tensor2D;
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