clustering-tfjs/dist/utils/eigen_improved.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';
/**
 * Improved Jacobi eigendecomposition for symmetric matrices.
 *
 * Enhancements over the basic Jacobi method:
 * 1. Cyclic Jacobi - systematically sweep through all pairs
 * 2. Threshold scaling - adapt threshold as we converge
 * 3. Better handling of small pivots
 * 4. Post-processing to ensure non-negative eigenvalues for PSD matrices
 */
export declare function improved_jacobi_eigen(matrix: tf.Tensor2D, { maxIterations, tolerance, isPSD, }?: {
    maxIterations?: number;
    tolerance?: number;
    isPSD?: boolean;
}): {
    eigenvalues: number[];
    eigenvectors: number[][];
};
/**
 * Specialized version for normalized Laplacians.
 * Takes advantage of the known properties:
 * - Symmetric
 * - Positive semi-definite
 * - Eigenvalues in [0, 2]
 * - Smallest eigenvalue(s) ≈ 0 for connected components
 */
export declare function laplacian_eigen_decomposition(laplacian: tf.Tensor2D, k: number): tf.Tensor2D;
//# sourceMappingURL=eigen_improved.d.ts.map