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

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

/**
 * Deterministic eigen-pair post–processing.
 *
 * Many numerical eigensolvers return eigenvectors in arbitrary order and with
 * an arbitrary global ± sign per vector.  For downstream algorithms (e.g.
 * Spectral Clustering) we require a stable ordering and sign convention so
 * that identical input always produces identical embeddings.
 *
 * The convention implemented here matches scikit-learn:
 *   1. Eigen-pairs are sorted by ascending eigen-value.
 *   2. For every eigen-vector the component with the largest absolute value
 *      is made positive by optionally multiplying the vector by −1.
 *
 * The function operates purely on native JavaScript arrays to avoid pulling
 * TensorFlow.js into low-level utilities and reduce garbage generation.
 */
export interface EigenPairInput {
    eigenvalues: number[];
    eigenvectors: number[][];
}
export interface EigenPairOutput {
    /**
     * Eigen-values sorted in ascending order.  We expose them under two
     * property names to stay compatible with the acceptance criteria drafted
     * in task-12.3.1 (valuesSorted) *and* with existing internal call-sites
     * (eigenvalues).
     */
    eigenvalues: number[];
    /** Alias – kept for backwards-compatibility with task spec */
    valuesSorted: number[];
    /**
     * Column-wise eigen-vectors after sign correction.
     * Same dual naming scheme as for the eigen-values.
     */
    eigenvectors: number[][];
    /** Alias matching task spec wording */
    vectorsSorted: number[][];
}
/**
 * Applies deterministic ordering and sign convention to raw eigen-pairs.
 */
export declare function deterministic_eigenpair_processing(input: EigenPairInput): EigenPairOutput;
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