import { Engine, BodyId, Zodiac } from "./chart.js";
import { RankedMoment } from "./scan.js";
export declare const DEFAULT_BODIES: string[];
/**
 * Build a feature vector from explicit `(longitude, weight)` pairs: each pair
 * contributes a weighted unit-circle point `[w·cos(lon), w·sin(lon)]`. The
 * low-level primitive behind {@link chartFeatures}; most callers want that.
 *
 * @param weightedLons `[longitudeDeg, weight]` pairs, in the order they should
 *   appear in the vector.
 * @returns A flat vector, two entries per pair.
 */
export declare function featureVector(weightedLons: [number, number][]): number[];
/**
 * Cosine similarity of two feature vectors, in `[-1, 1]`. For vectors from
 * {@link chartFeatures} this is a weighted mean of `cos(Δlongitude)` per body:
 * `1` when the configurations coincide, falling off as bodies diverge.
 *
 * @param a First feature vector.
 * @param b Second feature vector (compared over the shorter length).
 * @returns Similarity in `[-1, 1]`; `0` if either vector is all zeros.
 */
export declare function cosineSimilarity(a: number[], b: number[]): number;
export interface FeatureOptions {
    bodies?: BodyId[];
    weights?: Record<string, number>;
    zodiac?: Zodiac;
}
/**
 * Encode the sky at an instant as a feature vector: each body's ecliptic
 * longitude becomes a weighted unit-circle point. The deterministic substrate
 * for matching and searching chart configurations — compare two with
 * {@link cosineSimilarity}, or rank a time range against one with
 * {@link searchConfigurations}.
 *
 * @param engine The engine used to evaluate positions.
 * @param jdUt Julian Day in UT.
 * @param opts `bodies` (ordered; defaults to the ten major bodies), per-body
 *   `weights`, and `zodiac` (tropical by default).
 * @returns A flat vector `[w·cos(lon), w·sin(lon), ...]`, two entries per body
 *   in `bodies` order.
 * @example
 * ```ts
 * const target = chartFeatures(engine, julianDay(2000, 1, 1));
 * const now = chartFeatures(engine, julianDay(2025, 6, 1));
 * cosineSimilarity(now, target); // 1 = identical configuration
 * ```
 */
export declare function chartFeatures(engine: Engine, jdUt: number, opts?: FeatureOptions): number[];
/**
 * Similarity between the sky at `jdUt` and a target feature vector — shorthand
 * for `cosineSimilarity(chartFeatures(engine, jdUt, opts), target)`. The scoring
 * function {@link searchConfigurations} maximizes.
 *
 * @param engine The engine used to evaluate positions.
 * @param jdUt Julian Day in UT.
 * @param target A target feature vector from {@link chartFeatures}.
 * @param opts {@link FeatureOptions} — must match those used to build `target`.
 * @returns Cosine similarity in `[-1, 1]`.
 */
export declare function configurationFit(engine: Engine, jdUt: number, target: number[], opts?: FeatureOptions): number;
export interface SearchConfigOptions extends FeatureOptions {
    start: number;
    end: number;
    step: number;
    limit?: number;
}
/**
 * Rank the instants in `[start, end]` by how closely the sky resembles a
 * `target` feature vector, best first — a realization search over the feature
 * space. Build `target` with {@link chartFeatures} (e.g. from a natal chart).
 *
 * @param engine The engine used to evaluate positions.
 * @param target A target feature vector from {@link chartFeatures}.
 * @param opts `start`/`end` (Julian Days, UT) and `step` (days) define the
 *   scan, `limit` caps the results, plus the {@link FeatureOptions} (`bodies`,
 *   `weights`, `zodiac`) — which must match those used to build `target`.
 * @returns Ranked `{ jd, score }` moments, highest similarity first.
 * @example
 * ```ts
 * const natal = chartFeatures(engine, julianDay(1990, 6, 10, 14, 30));
 * const matches = searchConfigurations(engine, natal, {
 *   start: julianDay(2025, 1, 1), end: julianDay(2026, 1, 1), step: 1, limit: 5,
 * });
 * matches[0].jd; // best-matching instant
 * ```
 */
export declare function searchConfigurations(engine: Engine, target: number[], opts: SearchConfigOptions): RankedMoment[];