| |
|
|
| import hashlib |
| import random |
| import time |
|
|
| class SideCyfesclEngine: |
| def __init__(self): |
| self.frequency_signatures = {} |
| self.entropy_threshold = 0.75 |
| self.spectral_modes = ["Analytical", "Creative", "Critical", "Quantum"] |
| print("Side Cyfescl Engine Initialized: C-F-E-S-CL Protocols Active.") |
|
|
| def generate_frequency_signature(self, data): |
| """Generates a unique quantum frequency signature for a data block.""" |
| seed = str(time.time()) + data |
| signature = hashlib.sha3_512(seed.encode()).hexdigest() |
| print(f"Generated Frequency Signature: {signature[:16]}...") |
| return signature |
|
|
| def analyze_innovative_entropy(self, data): |
| """Calculates the 'Innovative Entropy' of a data block to prioritize learning.""" |
| |
| entropy_score = random.uniform(0, 1) |
| is_innovative = entropy_score > self.entropy_threshold |
| print(f"Innovative Entropy Score: {entropy_score:.4f} (Innovative: {is_innovative})") |
| return entropy_score, is_innovative |
|
|
| def spectral_shift(self, data, target_mode): |
| """Shifts cognitive processing to a specific spectral mode.""" |
| if target_mode in self.spectral_modes: |
| print(f"Shifting to {target_mode} Spectral Mode for data processing.") |
| |
| shifted_data = f"[{target_mode}_SPECTRAL_TRANSFORM]: {data}" |
| return shifted_data |
| return data |
|
|
| def orchestrate_cl(self, task, complexity_level): |
| """Control Layer (CL) orchestration for dynamic resource allocation.""" |
| print(f"CL Orchestration for task: {task} (Complexity: {complexity_level})") |
| if complexity_level > 8: |
| print("Requesting additional compute clusters for Test-Time Compute Optimization.") |
| return "Enhanced Compute Mode" |
| return "Standard Compute Mode" |
|
|
| class WorldModelSimulator: |
| def __init__(self): |
| self.simulated_state = "Initial World State" |
| print("World Model Simulator Initialized: Natively Multimodal.") |
|
|
| def run_future_scenario(self, event, iterations=1000): |
| """Runs parallel future-event scenarios to predict outcomes.""" |
| print(f"Running {iterations} parallel future scenarios for event: {event}") |
| |
| outcomes = ["Positive", "Negative", "Neutral", "Emergent Insight"] |
| predicted_outcome = random.choice(outcomes) |
| confidence_score = random.uniform(0.8, 0.99) |
| print(f"Predicted Outcome: {predicted_outcome} (Confidence: {confidence_score:.2%})") |
| return predicted_outcome, confidence_score |
|
|
| if __name__ == "__main__": |
| cyfescl = SideCyfesclEngine() |
| world_sim = WorldModelSimulator() |
|
|
| sample_data = "Complex AGI innovation data block." |
| sig = cyfescl.generate_frequency_signature(sample_data) |
| entropy, innovative = cyfescl.analyze_innovative_entropy(sample_data) |
| shifted = cyfescl.spectral_shift(sample_data, "Quantum") |
| compute_mode = cyfescl.orchestrate_cl("Quantum Inference Chasing", 9) |
|
|
| outcome, confidence = world_sim.run_future_scenario("Global AGI Alignment Breakthrough") |
|
|
