/**
 * router-calibrator.ts — Post-hoc isotonic calibration for the KRR router
 * (ADR-149 iter 22).
 *
 * The bundled KRR systematically under-predicts at the low end (gap 0.45 at
 * 0.0–0.1) and over-predicts at the high end (gap 0.15 at 0.9–1.0). Both
 * deviations are monotone, so a piecewise-constant non-decreasing function
 * fit via Pool-Adjacent-Violators (PAV) can correct them without retraining
 * the KRR.
 *
 * USAGE
 *
 *   const cal = IsotonicCalibrator.fit([[0.07, 0.52], [0.55, 0.60], …]);
 *   const corrected = cal.transform(rawKrrScore);
 *
 *   // Persist & reload:
 *   writeFileSync(path, JSON.stringify(cal.toJSON()));
 *   const loaded = IsotonicCalibrator.fromJSON(JSON.parse(readFileSync(path, 'utf8')));
 *
 * Pure TS — no native deps. ~25 lines of fitting logic, O(n log n) sort +
 * O(n) PAV pass.
 *
 * @module router-calibrator
 */
export interface CalibratorBucket {
    /** Inclusive lower bound of predicted-values pooled into this bucket. */
    predMin: number;
    /** Inclusive upper bound. */
    predMax: number;
    /** Calibrated output value (mean of observed values in the pool). */
    calibrated: number;
    /** Number of (pred,obs) pairs pooled. */
    count: number;
}
export interface CalibratorJSON {
    v: 1;
    buckets: CalibratorBucket[];
}
export declare class IsotonicCalibrator {
    private buckets;
    private constructor();
    /**
     * Fit an isotonic regression to (predicted, observed) pairs via PAV.
     *
     * Result is a non-decreasing piecewise-constant function over the
     * empirical predicted-value range, equivalent in spirit to sklearn's
     * IsotonicRegression(increasing=True, out_of_bounds='clip').
     */
    static fit(pairs: Array<[number, number]>): IsotonicCalibrator;
    /**
     * Map a raw predicted value to its calibrated value. Uses piecewise-linear
     * interpolation between adjacent bucket midpoints, clamped at the
     * empirical edges (extrapolation outside the training range returns the
     * nearest edge bucket's calibrated value).
     */
    transform(x: number): number;
    /** Pure-JSON serialization — calibrator JSON is small (typically <2kB). */
    toJSON(): CalibratorJSON;
    static fromJSON(j: CalibratorJSON): IsotonicCalibrator;
    /** Diagnostic — number of distinct calibration points after PAV. */
    get bucketCount(): number;
    /** Diagnostic — return a copy of the bucket array (read-only view). */
    inspect(): CalibratorBucket[];
}
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