| """ |
| Contradiction Handler — when new knowledge contradicts existing. |
| """ |
| from dataclasses import dataclass |
|
|
|
|
| @dataclass |
| class ExperienceWeight: |
| parent_cell_id: str |
| value: float = 0.0 |
| n_updates: int = 0 |
| created_step: int = 0 |
| last_fp: str = "" |
|
|
|
|
| class ContradictionHandler: |
| def __init__(self, similarity_threshold=0.75, verbose=False): |
| self.similarity_threshold = similarity_threshold |
| self.verbose = verbose |
| self.experiences = {} |
| self.n_contradictions = 0 |
|
|
| def detect(self, new_fp, existing_fps): |
| try: |
| from cognitive_log import hamming_similarity |
| except ImportError: |
| return None |
| best = (None, 0.0) |
| for fp in existing_fps: |
| sim = hamming_similarity(new_fp, fp) |
| if sim > best[1]: |
| best = (fp, sim) |
| if best[1] >= self.similarity_threshold: |
| self.n_contradictions += 1 |
| return best[0] |
| return None |
|
|
| def attach_experience(self, parent_cell, new_fp, step): |
| cid = parent_cell.cell_id |
| if cid in self.experiences: |
| exp = self.experiences[cid] |
| exp.n_updates += 1 |
| exp.last_fp = new_fp |
| return exp |
| exp = ExperienceWeight(parent_cell_id=cid, value=0.0, |
| n_updates=1, created_step=step, last_fp=new_fp) |
| self.experiences[cid] = exp |
| return exp |
|
|
| def stats(self): |
| return {"n_contradictions": self.n_contradictions, |
| "n_experiences": len(self.experiences)} |
|
|
