clustering-tfjs/dist/utils/rng/mt19937.d.ts

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

/**
 * Minimal TypeScript implementation of the original MT19937 32-bit variant
 * used by NumPy's legacy `RandomState` (and therefore by scikit-learn).
 *
 * This port only exposes the functionality required by the k-means++ seeding
 * routine:
 *   • Generation of 32-bit unsigned integers (\[0, 2**32))
 *   • High-precision uniform floats in the half-open interval \[0, 1)
 *
 * The algorithm closely follows the reference implementation described in
 * Matsumoto & Nishimura (1998) and the public domain C code.
 */
export declare class MT19937 {
    private static readonly N;
    private static readonly M;
    private static readonly MATRIX_A;
    private static readonly UPPER_MASK;
    private static readonly LOWER_MASK;
    /** State vector – 624 32-bit unsigned ints. */
    private mt;
    /** Current index within the state vector. */
    private index;
    constructor(seed: number);
    /** Returns next 32-bit unsigned int in \[0, 2**32). */
    nextUint32(): number;
    /**
     * Returns a 53-bit precision float in the interval \[0, 1) identical to
     * NumPy's `random_sample` implementation.
     */
    nextFloat(): number;
    /** Uniform integer in \[0, max). Mirrors NumPy's rejection sampling to
     *  eliminate modulo bias so that sequences match exactly.
     */
    nextInt(max: number): number;
    private init;
    private twist;
}
//# sourceMappingURL=mt19937.d.ts.map