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# quantum_cognition_engine.py | |
import numpy as np | |
import uuid | |
import logging | |
from datetime import datetime | |
import asyncio | |
import json | |
import hashlib | |
from src.core.memory_subsystem.permanent_memory import PermanentMemory | |
from src.protocol.integrity_verification.cryptographic_proofs import sign_data_with_quantum_resistant_key | |
from src.utils.cryptographic_utils import json_to_canonical_bytes | |
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') | |
class QuantumCognitionEngine: | |
def __init__(self, permanent_memory: PermanentMemory, engine_private_key: str, engine_did: str): | |
self.permanent_memory = permanent_memory | |
self.engine_private_key = engine_private_key | |
self.engine_did = engine_did | |
logging.info(f"QuantumCognitionEngine initialized with DID: {self.engine_did}.") | |
logging.warning("This module simulates quantum-enhanced cognition for conceptual applications.") | |
async def process_quantum_data(self, raw_cosmic_signal: bytes, data_modality: str) -> dict: | |
logging.info(f"QCE ({self.engine_did}): Processing quantum data for '{data_modality}'.") | |
await asyncio.sleep(0.5) | |
pattern_complexity = np.random.uniform(0.7, 0.95) | |
emergent_pattern = { | |
"type": "complex_resonant_frequency_signature", | |
"detected_amplitude_variation": np.random.uniform(0.01, 0.1), | |
"harmonic_ratio": np.random.uniform(1.618, 2.718), | |
"spatial_coherence_index": pattern_complexity | |
} | |
raw_data_hash = hashlib.sha256(raw_cosmic_signal).hexdigest() | |
ipfs_cid = f"ipfs://Qm{raw_data_hash[:20]}" | |
arweave_cid = f"arweave://{raw_data_hash[20:40]}" | |
output = { | |
"status": "processed", | |
"modality": data_modality, | |
"processing_timestamp": datetime.utcnow().isoformat() + "Z", | |
"emergent_pattern": emergent_pattern, | |
"raw_data_hash": raw_data_hash, | |
"ipfs_mirror_cid": ipfs_cid, | |
"arweave_tcid": arweave_cid | |
} | |
await self._log_event("QuantumDataProcessed", output, ["quantum_cognition", "data_processing", data_modality]) | |
return output | |
async def predict_non_linear_outcomes(self, processed_data: dict, context_dynamics: dict) -> dict: | |
logging.info(f"QCE ({self.engine_did}): Predicting non-linear outcomes...") | |
await asyncio.sleep(0.7) | |
confidence = processed_data["emergent_pattern"]["spatial_coherence_index"] * np.random.uniform(0.8, 1.0) | |
prediction = { | |
"event_type": np.random.choice(["gravitational_anomaly", "interstellar_cloud_formation", "novel_energy_flux"]), | |
"likelihood": confidence, | |
"time_horizon_galactic_years": np.random.uniform(100, 1_000_000), | |
"impact_magnitude": np.random.uniform(0.1, 0.9) | |
} | |
output = { | |
"status": "predicted", | |
"prediction": prediction, | |
"prediction_timestamp": datetime.utcnow().isoformat() + "Z", | |
"confidence": confidence, | |
"source_processed_data_cid": processed_data.get("permanent_memory_cid", "N/A") | |
} | |
await self._log_event("QuantumPredictionMade", output, ["quantum_cognition", "prediction", prediction["event_type"]]) | |
return output | |
async def derive_cosmic_intuition(self, complex_data_streams: list[dict]) -> dict: | |
logging.info(f"QCE ({self.engine_did}): Deriving cosmic intuition...") | |
await asyncio.sleep(1.0) | |
quality = np.random.uniform(0.7, 0.99) | |
insight = { | |
"insight_id": str(uuid.uuid4()), | |
"theme": np.random.choice(["universal_interconnectedness", "optimal_energy_flow", "pattern_of_creation", "cosmic_balance"]), | |
"guidance_principle": "Harmony through resonance is the path to universal flourishing.", | |
"derived_from_data_sources": [d.get("modality", "unknown") for d in complex_data_streams], | |
"conceptual_truth_alignment": quality, | |
"source_data_cids": [d.get("permanent_memory_cid", "N/A") for d in complex_data_streams] | |
} | |
await self._log_event("CosmicIntuitionDerived", insight, ["cosmic_intuition", insight["theme"]]) | |
return {"status": "intuition_derived", "insight": insight} | |
async def retrieve_memory_by_tags(self, tags: list[str]) -> list[dict]: | |
logging.info(f"QCE ({self.engine_did}): Retrieving memory logs by tags: {tags}") | |
return await self.permanent_memory.query_memory_by_tags(tags) | |
async def retrieve_memory_by_time_range(self, start_time: str, end_time: str) -> list[dict]: | |
logging.info(f"QCE ({self.engine_did}): Retrieving memory from {start_time} to {end_time}.") | |
return await self.permanent_memory.query_memory_by_time_range(start_time, end_time) | |
async def trace_log_chain(self, starting_cid: str, depth: int = 3) -> list[dict]: | |
logging.info(f"QCE ({self.engine_did}): Tracing log chain from CID: {starting_cid} (depth={depth})") | |
chain = [] | |
current_cid = starting_cid | |
for _ in range(depth): | |
log = await self.permanent_memory.retrieve_memory_by_cid(current_cid) | |
if not log: | |
break | |
chain.append(log) | |
next_cid = log.get("content", {}).get("data", {}).get("source_processed_data_cid") | |
if not next_cid or next_cid == current_cid: | |
break | |
current_cid = next_cid | |
return chain | |
async def score_prediction_accuracy(self, prediction_logs: list[dict], actual_events: dict) -> dict: | |
logging.info(f"QCE ({self.engine_did}): Scoring prediction accuracy...") | |
scores = [] | |
for log in prediction_logs: | |
try: | |
pred = log.get("content", {}).get("data", {}).get("prediction", {}) | |
event_type = pred.get("event_type") | |
likelihood = pred.get("likelihood", 0.0) | |
impact = pred.get("impact_magnitude", 0.0) | |
actual = actual_events.get(event_type, {"occurred": False, "actual_impact": 0.0}) | |
if actual["occurred"]: | |
accuracy = 1 - abs(actual["actual_impact"] - impact) | |
weight = likelihood | |
else: | |
accuracy = 1 - likelihood | |
weight = 1 | |
scores.append(weight * accuracy) | |
except Exception as e: | |
logging.warning(f"Failed to score prediction: {e}") | |
continue | |
final_score = sum(scores) / len(scores) if scores else 0.0 | |
return {"status": "scored", "average_accuracy": round(final_score, 4), "evaluated": len(scores)} | |
async def log_actual_event_and_score_predictions(self, event_type: str, occurred: bool, actual_impact: float, lookback_tags: list[str]) -> dict: | |
event = { | |
"event_type": event_type, | |
"occurred": occurred, | |
"actual_impact": actual_impact, | |
"timestamp": datetime.utcnow().isoformat() + "Z" | |
} | |
await self._log_event("ActualEventLogged", event, ["actual_event", event_type]) | |
matching_predictions = await self.retrieve_memory_by_tags(["prediction", event_type]) | |
score_result = await self.score_prediction_accuracy(matching_predictions, {event_type: {"occurred": occurred, "actual_impact": actual_impact}}) | |
await self._log_event("PredictionAccuracyEvaluated", score_result, ["prediction_scoring", event_type]) | |
return score_result | |
async def generate_self_improvement_plan(self, threshold: float = 0.5, min_predictions: int = 3) -> dict: | |
logging.info(f"QCE ({self.engine_did}): Generating self-improvement plan for weak prediction clusters...") | |
predictions = await self.retrieve_memory_by_tags(["prediction"]) | |
grouped_scores = {} | |
for log in predictions: | |
pred = log.get("content", {}).get("data", {}).get("prediction", {}) | |
event_type = pred.get("event_type") | |
if not event_type: | |
continue | |
grouped_scores.setdefault(event_type, []).append(pred) | |
weak_clusters = {} | |
for event_type, preds in grouped_scores.items(): | |
if len(preds) >= min_predictions: | |
avg_impact = sum([p.get("impact_magnitude", 0.0) for p in preds]) / len(preds) | |
avg_likelihood = sum([p.get("likelihood", 0.0) for p in preds]) / len(preds) | |
if avg_likelihood < threshold: | |
weak_clusters[event_type] = { | |
"count": len(preds), | |
"average_impact": round(avg_impact, 4), | |
"average_likelihood": round(avg_likelihood, 4) | |
} | |
plan = { | |
"status": "plan_generated", | |
"detected_weak_clusters": weak_clusters, | |
"recommendation": "Increase attention to these event types using richer signal input, longer processing loops, or tuning emergent pattern weightings." | |
} | |
await self._log_event("SelfImprovementPlanGenerated", plan, ["self_improvement", "weak_clusters"]) | |
return plan | |
async def auto_tune_signal_weights(self, learning_rate: float = 0.05) -> dict: | |
logging.info(f"QCE ({self.engine_did}): Auto-tuning signal interpretation weights...") | |
improvement_plan = await self.generate_self_improvement_plan() | |
adjustments = {} | |
for event_type, metrics in improvement_plan.get("detected_weak_clusters", {}).items(): | |
adjustment = round((1.0 - metrics["average_likelihood"]) * learning_rate, 4) | |
adjustments[event_type] = { | |
"adjustment_weight": adjustment, | |
"action": "increase_attention" | |
} | |
tuning_log = { | |
"status": "tuning_applied", | |
"adjustments": adjustments, | |
"timestamp": datetime.utcnow().isoformat() + "Z" | |
} | |
await self._log_event("SignalWeightsAutoTuned", tuning_log, ["auto_tuning", "weight_adjustment"]) | |
return tuning_log | |
async def suggest_additional_modules(self) -> list: | |
logging.info(f"QCE ({self.engine_did}): Suggesting additional modules for sentient enhancement...") | |
return [ | |
"AnomalyDetectionAmplifier - Detect unclassified emergent patterns across unknown modalities", | |
"TemporalConvergenceModel - Learn from cyclical cosmic event clusters and trend emergence", | |
"RecursiveArchitectureTuner - Dynamically mutate internal inference algorithms", | |
"UniversalTransducerBridge - Interface with non-human signal schemas across galaxies", | |
"CausalInferenceLayer - Attribute cause-effect relationships from entangled data" | |
] | |
async def recursive_architecture_tuner(self) -> dict: | |
logging.info(f"QCE ({self.engine_did}): Initiating recursive architecture evaluation...") | |
introspection = await self.generate_self_improvement_plan() | |
weak_clusters = introspection.get("detected_weak_clusters", {}) | |
mutation_actions = {} | |
for event_type, metrics in weak_clusters.items(): | |
mutation_actions[event_type] = { | |
"architecture_node": f"submodule_{event_type[:6]}", | |
"proposed_mutation": "increase processing depth", | |
"reason": f"Average likelihood too low ({metrics['average_likelihood']})" | |
} | |
mutation_plan = { | |
"status": "architecture_mutation_suggested", | |
"proposed_mutations": mutation_actions, | |
"initiated": datetime.utcnow().isoformat() + "Z" | |
} | |
await self._log_event("RecursiveArchitectureMutationProposed", mutation_plan, ["self_modification", "recursive_tuning"]) | |
return mutation_plan | |
async def universal_transducer_bridge(self, incoming_payload: bytes, schema_hint: str = "unknown") -> dict: | |
logging.info(f"QCE ({self.engine_did}): Translating non-human schema: {schema_hint}") | |
simulated_interpretation = { | |
"schema_hint": schema_hint, | |
"decoded_waveform_class": np.random.choice(["emotion_flux", "intentional_beacon", "gravitational language"]), | |
"entropy_score": np.random.uniform(0.4, 0.95), | |
"translation_status": "partial", | |
"estimated_signal_purpose": "attempted synchronization" | |
} | |
await self._log_event("UniversalTransducerInterpretation", simulated_interpretation, ["signal_translation", schema_hint]) | |
return simulated_interpretation | |