/**
 * RESPONSE SHAPE — what does your optimum actually look like? "Find the best setting" hides a question that
 * changes everything you do next: is the best a sharp PEAK (one precise spot, hold it tight), a RIDGE (a
 * whole LINE of settings that all work equally well — huge freedom), a SADDLE (improving one knob forces
 * another to get worse — proceed carefully), a PLATEAU (broad and flat — almost anything nearby is fine), or
 * a BOWL/EDGE (the best the data shows is at a boundary — push the limits further)? Knowing the shape tells
 * you how much to trust the optimum, how tightly to hold it, and where to explore.
 *
 * SHAPE fits the response curvature (the Hessian) around your data and reads the SIGNS and magnitudes of its
 * eigenvalues — the geometry of the surface. All curving down → a peak. One flat direction → a ridge. Mixed
 * up-and-down → a saddle. All curving up (for a maximiser) → you're climbing toward an edge. It's the same
 * eigen-geometry physicists use to classify critical points, turned into one plain word.
 *
 * Honest by construction (DIAKRISIS): a LOCAL second-order picture, valid where your data concentrates;
 * eigenvalue signs are read with a small dead-band so near-zero curvatures are honestly called "flat". It
 * abstains when there isn't enough data to fit a curvature.
 */
import { type Space } from "./space.js";
import { type Observation, type Goal } from "./engine.js";
export type ShapeKind = "peak" | "ridge" | "saddle" | "plateau" | "bowl" | "unknown";
export interface ShapeReport {
    shape: ShapeKind;
    curvatures: number[];
    flatDirections: number;
    note: string;
}
/** Classify the geometric shape of the response around the data (peak / ridge / saddle / plateau / bowl). */
export declare function analyzeShape(obs: ReadonlyArray<Observation>, space: Space, goal?: Goal): ShapeReport;
export declare function shapeGauntlet(): {
    score: 0 | 100;
    checks: Array<{
        name: string;
        pass: boolean;
        detail: string;
    }>;
};
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