| import type { SkynetCausalValenceLabel } from "../causal-valence/episode-ledger.js"; |
| import { |
| encodeSkynetRuntimeTrajectoryFeatures, |
| type SkynetRuntimeTrajectorySample, |
| } from "./trajectory-builder.js"; |
|
|
| export type SkynetRuntimeObserverModel = { |
| labels: SkynetCausalValenceLabel[]; |
| centroids: Record<SkynetCausalValenceLabel, number[]>; |
| counts: Record<SkynetCausalValenceLabel, number>; |
| }; |
|
|
| export type SkynetRuntimeObserverPrediction = { |
| label: SkynetCausalValenceLabel; |
| scores: Record<SkynetCausalValenceLabel, number>; |
| }; |
|
|
| const LABELS: SkynetCausalValenceLabel[] = ["progress", "relief", "stall", "frustration", "damage"]; |
|
|
| function zeroVector(length: number): number[] { |
| return Array.from({ length }, () => 0); |
| } |
|
|
| function cosineSimilarity(a: number[], b: number[]): number { |
| let dot = 0; |
| let normA = 0; |
| let normB = 0; |
| for (let index = 0; index < a.length; index += 1) { |
| const av = a[index] ?? 0; |
| const bv = b[index] ?? 0; |
| dot += av * bv; |
| normA += av * av; |
| normB += bv * bv; |
| } |
| if (normA === 0 || normB === 0) { |
| return 0; |
| } |
| return dot / (Math.sqrt(normA) * Math.sqrt(normB)); |
| } |
|
|
| export function trainSkynetRuntimeObserverModel( |
| samples: SkynetRuntimeTrajectorySample[], |
| ): SkynetRuntimeObserverModel | null { |
| if (samples.length === 0) { |
| return null; |
| } |
| const vectorLength = encodeSkynetRuntimeTrajectoryFeatures(samples[0]).length; |
| const sums = LABELS.reduce( |
| (acc, label) => { |
| acc[label] = zeroVector(vectorLength); |
| return acc; |
| }, |
| {} as Record<SkynetCausalValenceLabel, number[]>, |
| ); |
| const counts = LABELS.reduce( |
| (acc, label) => { |
| acc[label] = 0; |
| return acc; |
| }, |
| {} as Record<SkynetCausalValenceLabel, number>, |
| ); |
|
|
| for (const sample of samples) { |
| const vector = encodeSkynetRuntimeTrajectoryFeatures(sample); |
| counts[sample.targetLabel] += 1; |
| for (let index = 0; index < vector.length; index += 1) { |
| sums[sample.targetLabel][index] += vector[index] ?? 0; |
| } |
| } |
|
|
| const centroids = LABELS.reduce( |
| (acc, label) => { |
| const count = counts[label]; |
| acc[label] = count > 0 ? sums[label].map((value) => value / count) : zeroVector(vectorLength); |
| return acc; |
| }, |
| {} as Record<SkynetCausalValenceLabel, number[]>, |
| ); |
|
|
| return { |
| labels: LABELS.filter((label) => counts[label] > 0), |
| centroids, |
| counts, |
| }; |
| } |
|
|
| export function predictSkynetRuntimeObserverLabel( |
| model: SkynetRuntimeObserverModel, |
| sample: SkynetRuntimeTrajectorySample, |
| ): SkynetRuntimeObserverPrediction { |
| const vector = encodeSkynetRuntimeTrajectoryFeatures(sample); |
| const scores = model.labels.reduce( |
| (acc, label) => { |
| acc[label] = cosineSimilarity(vector, model.centroids[label]); |
| return acc; |
| }, |
| {} as Record<SkynetCausalValenceLabel, number>, |
| ); |
| const label = |
| model.labels |
| .slice() |
| .sort( |
| (left, right) => |
| (scores[right] ?? Number.NEGATIVE_INFINITY) - (scores[left] ?? Number.NEGATIVE_INFINITY), |
| ) |
| .at(0) ?? "stall"; |
| return { label, scores }; |
| } |
|
|
