| import sys | |
| from pathlib import Path | |
| sys.path.insert(0, str(Path(__file__).resolve().parents[1])) | |
| import argparse, json, numpy as np | |
| from lamm.state import AssociativeMemory | |
| def run(seed=314159, d=48, lam=.001, steps=200): | |
| rng=np.random.default_rng(seed) | |
| m=AssociativeMemory(d,lam) | |
| e=[] | |
| for _ in range(steps): | |
| m.update(rng.normal(size=d)*.01,rng.normal(size=d)*.01) | |
| e.append(m.energy()) | |
| return { | |
| "lambda":lam, | |
| "mean_energy":float(np.mean(e)), | |
| "std_energy":float(np.std(e)), | |
| "min_energy":float(np.min(e)), | |
| "max_energy":float(np.max(e)), | |
| "final_energy":float(e[-1]) | |
| } | |
| for lam in [.0001,.001,.01,.1]: | |
| print(json.dumps(run(lam=lam))) | |