import { type OutcomeEvent } from './solution-outcomes.js';
export type FitnessState = 'draft' | 'active' | 'champion' | 'underperform';
export interface FitnessRecord {
    solution: string;
    injected: number;
    accepted: number;
    corrected: number;
    errored: number;
    unknown: number;
    /** Laplace-smoothed acceptance ratio × log(1+injected). */
    fitness: number;
    state: FitnessState;
    /** ms since last injection event. Infinity if never injected. */
    last_injected_ago_ms: number;
}
export interface FitnessOptions {
    /**
     * Minimum injections required before a solution is evaluated against the
     * underperform threshold. Below this, state stays at `draft`.
     */
    minEvalInjections?: number;
    /**
     * Injections required to qualify as champion (in addition to fitness cut).
     */
    minChampionInjections?: number;
    /**
     * Champion cut: fitness must exceed this fraction of the max fitness in
     * the current population. Default 0.7 → top 30% by ratio of max.
     */
    championFraction?: number;
    /**
     * Underperform cut: fitness must fall below this fraction of the median.
     */
    underperformFraction?: number;
    /** Pre-loaded events (for tests). Defaults to `readAllOutcomes()`. */
    events?: OutcomeEvent[];
}
/**
 * Compute fitness scores for every solution with at least one recorded
 * outcome event.
 *
 * Formula: `fitness = (accept + 1) / (accept + correct + error + 1) × log(1 + injected)`
 *   - `accept` = positive (silence = consent)
 *   - `correct` = negative (explicit user correction within window)
 *   - `error` = weak negative (tool failed while solution was pending)
 *   - `unknown` = ignored (session ended mid-pending; we can't tell)
 *
 * Epsilon smoothing (+1) means a cold solution with 1 injection and 1
 * accept produces `2/2 × log(2) ≈ 0.69`, not a meaningless `1.0 × 0` or
 * `∞`. Log confidence penalizes small-sample champions.
 */
export declare function computeFitness(opts?: FitnessOptions): FitnessRecord[];