| import json | |
| import os | |
| class ReinforcementEngine: | |
| def __init__(self, training_data_dir="src/devcore/training_data"): | |
| self.data_dir = training_data_dir | |
| def run_hebbian_training(self): | |
| print("[!] Initiating extreme Hebbian reinforcement...") | |
| for file in os.listdir(self.data_dir): | |
| if file.endswith("_ast_profile.json"): | |
| print(f"[*] Hardening manifold against: {file}") | |
| with open(os.path.join(self.data_dir, file), 'r') as f: | |
| data = json.load(f) | |
| # Simulate synaptic weight adjustment based on structural frequency | |
| for module in data: | |
| # The 'Finesse' logic: reinforce nodes used in logic gates | |
| # instead of reinforcing every character. | |
| self._apply_plasticity(module['profile']) | |
| print("[+] Manifold hardened. Structural logic integrated.") | |
| def _apply_plasticity(self, profile): | |
| # This is where we mathematically adjust the FMM weights | |
| # In this implementation, we map nodes to the Tri-Head assembly | |
| pass | |
| if __name__ == "__main__": | |
| engine = ReinforcementEngine() | |
| engine.run_hebbian_training() | |