Chris Christiansen
9fcb9c354a
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48 lines
1.6 KiB
Python
48 lines
1.6 KiB
Python
import numpy as np
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from datetime import datetime
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from .emma_mdp import EmmaAction
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class MorphicMemory:
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"""
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Morphic Resonance pattern cache (in-memory).
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Resonansvekt: w = sim(s,s') * ln(1+n) * decay^d * R
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"""
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def __init__(self, decay_rate: float = 0.95):
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self.patterns: list[dict] = []
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self.decay = decay_rate
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def store(self, state_embedding: np.ndarray, action: EmmaAction, reward_val: float):
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for p in self.patterns:
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if _cosine(state_embedding, p["embedding"]) > 0.92:
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p["count"] += 1
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p["reward"] = 0.7 * p["reward"] + 0.3 * reward_val
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p["lastseen"] = datetime.now()
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return
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self.patterns.append({
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"embedding": state_embedding,
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"action": action,
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"reward": reward_val,
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"count": 1,
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"lastseen": datetime.now(),
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})
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def recall(self, state_embedding: np.ndarray, top_k: int = 3) -> list[dict]:
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scored = []
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now = datetime.now()
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for p in self.patterns:
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sim = _cosine(state_embedding, p["embedding"])
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age_days = (now - p["lastseen"]).days
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recency = self.decay ** age_days
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resonance = sim * np.log1p(p["count"]) * recency * p["reward"]
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scored.append((resonance, p))
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return [p for _, p in sorted(scored, reverse=True)[:top_k]]
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def __len__(self):
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return len(self.patterns)
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def _cosine(a: np.ndarray, b: np.ndarray) -> float:
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return float(np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b) + 1e-9))
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