Sven (AAMOS AI)
115 lines
3.6 KiB
TypeScript
115 lines
3.6 KiB
TypeScript
/**
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* Antagandeprofiler för matlagning (Fas 3 §6.2).
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* Rensa, deterministiska funktioner för att uppdatera per-hushålls-per-ingrediens-profiler
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* utifrån faktiska svar efter en cooking session.
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*/
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export interface ProfileUpdateInput {
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householdId: string;
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canonicalIngredientId: string;
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plannedPortions: number;
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actualPortionsEaten: number;
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leftoverEstimatePortions: number;
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sessionId: string;
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date: string;
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}
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export interface ExistingProfile {
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averageEatenPortions: number | null;
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averageLeftoverPortions: number | null;
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observationCount: number;
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lastSessionAnswers?: Array<{
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sessionId: string;
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eaten: number;
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leftovers: number;
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date: string;
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}> | null;
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}
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/**
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* Beräkna nya profilvärden med exponentiellt glidande medelvärde.
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* alpha = 1 / (observationCount + 1) ger jämn viktning över tid.
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*/
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export function updateCookingAssumptionProfile(
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input: ProfileUpdateInput,
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existing: ExistingProfile,
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): {
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averageEatenPortions: number;
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averageLeftoverPortions: number;
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observationCount: number;
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lastSessionAnswers: Array<{ sessionId: string; eaten: number; leftovers: number; date: string }>;
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} {
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const eaten = clamp(input.actualPortionsEaten, 0, input.plannedPortions);
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const leftovers = clamp(input.leftoverEstimatePortions, 0, input.plannedPortions - eaten);
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const prevEaten = existing.averageEatenPortions ?? eaten;
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const prevLeftovers = existing.averageLeftoverPortions ?? leftovers;
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const prevCount = existing.observationCount ?? 0;
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const alpha = 1 / (prevCount + 1);
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const averageEatenPortions = round2(prevEaten + alpha * (eaten - prevEaten));
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const averageLeftoverPortions = round2(prevLeftovers + alpha * (leftovers - prevLeftovers));
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const observationCount = prevCount + 1;
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const history = existing.lastSessionAnswers ?? [];
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const lastSessionAnswers = [
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{ sessionId: input.sessionId, eaten, leftovers, date: input.date },
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...history,
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].slice(0, 10);
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return { averageEatenPortions, averageLeftoverPortions, observationCount, lastSessionAnswers };
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}
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/**
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* Återställ en antagandeprofil genom att ta bort ett specifikt sessions-id
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* från historiken och räkna om medelvärdena deterministiskt från resterande
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* observationer. Ren funktion – alla beroenden är explicita argument.
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*/
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export function rollbackCookingAssumptionProfile(
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sessionId: string,
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existing: ExistingProfile,
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): {
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averageEatenPortions: number | null;
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averageLeftoverPortions: number | null;
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observationCount: number;
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lastSessionAnswers: Array<{ sessionId: string; eaten: number; leftovers: number; date: string }>;
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} {
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const history = (existing.lastSessionAnswers ?? []).filter((a) => a.sessionId !== sessionId);
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if (history.length === 0) {
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return {
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averageEatenPortions: null,
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averageLeftoverPortions: null,
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observationCount: 0,
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lastSessionAnswers: [],
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};
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}
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// Räkna om EMA från scratch i kronologisk ordning (äldst först).
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const chronological = [...history].reverse();
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let avgEaten = chronological[0]!.eaten;
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let avgLeftovers = chronological[0]!.leftovers;
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for (let i = 1; i < chronological.length; i++) {
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const alpha = 1 / (i + 1);
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const a = chronological[i]!;
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avgEaten = round2(avgEaten + alpha * (a.eaten - avgEaten));
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avgLeftovers = round2(avgLeftovers + alpha * (a.leftovers - avgLeftovers));
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}
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return {
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averageEatenPortions: avgEaten,
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averageLeftoverPortions: avgLeftovers,
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observationCount: history.length,
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lastSessionAnswers: history,
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};
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}
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function clamp(n: number, min: number, max: number): number {
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return Math.max(min, Math.min(max, n));
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}
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function round2(n: number): number {
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return Math.round(n * 100) / 100;
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}
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