Create MODULE 51

MODULE 51

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+#!/usr/bin/env python3
+"""
+MODULE 51 v2.0: ENHANCED AUTONOMOUS KNOWLEDGE INTEGRATION FRAMEWORK
+Recursive, self-learning AI for cross-domain historical pattern detection
+"""
+import numpy as np
+from dataclasses import dataclass, field
+from datetime import datetime
+from typing import Dict, Any, List, Callable
+import hashlib
+import secrets
+import asyncio
+import logging
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# -------------------------------
+# Epistemic Vectors
+# -------------------------------
+@dataclass
+class EpistemicVector:
+    content_hash: str
+    dimensional_components: Dict[str, float]
+    confidence_metrics: Dict[str, float]
+    temporal_coordinates: Dict[str, Any]
+    relational_entanglements: List[str]
+    meta_cognition: Dict[str, Any]
+    security_signature: str
+    epistemic_coherence: float = field(init=False)
+
+    def __post_init__(self):
+        dimensional_strength = np.mean(list(self.dimensional_components.values()))
+        confidence_strength = np.mean(list(self.confidence_metrics.values()))
+        relational_density = min(1.0, len(self.relational_entanglements) / 10.0)
+        self.epistemic_coherence = min(
+            1.0,
+            (dimensional_strength * 0.4 + confidence_strength * 0.3 + relational_density * 0.3)
+        )
+
+# -------------------------------
+# Quantum-style security
+# -------------------------------
+class QuantumSecurityContext:
+    def __init__(self):
+        self.key = secrets.token_bytes(32)
+        self.temporal_signature = hashlib.sha3_512(datetime.now().isoformat().encode()).hexdigest()
+
+    def generate_quantum_hash(self, data: Any) -> str:
+        data_str = str(data)
+        combined = f"{data_str}{self.temporal_signature}{secrets.token_hex(8)}"
+        return hashlib.sha3_512(combined.encode()).hexdigest()
+
+# -------------------------------
+# Autonomous Knowledge Integration
+# -------------------------------
+class AutonomousKnowledgeActivation:
+    def __init__(self):
+        self.security_context = QuantumSecurityContext()
+        self.knowledge_domains = self._initialize_knowledge_domains()
+        self.integration_triggers = self._set_integration_triggers()
+        self.epistemic_vectors: Dict[str, EpistemicVector] = {}
+        self.recursive_depth = 0
+        self.max_recursive_depth = 10
+
+    def _initialize_knowledge_domains(self):
+        return {
+            'archaeological': {'scope': 'global_site_databases, dating_methodologies, cultural_sequences'},
+            'geological': {'scope': 'catastrophe_records, climate_proxies, impact_evidence'},
+            'mythological': {'scope': 'cross_cultural_narratives, thematic_archetypes, transmission_pathways'},
+            'astronomical': {'scope': 'orbital_mechanics, impact_probabilities, cosmic_cycles'},
+            'genetic': {'scope': 'population_bottlenecks, migration_patterns, evolutionary_pressure'}
+        }
+
+    def _set_integration_triggers(self):
+        return {domain: "pattern_detection_trigger" for domain in self.knowledge_domains}
+
+    async def activate_autonomous_research(self, initial_data=None):
+        self.recursive_depth += 1
+        results = {}
+        for domain in self.knowledge_domains:
+            results[domain] = await self._process_domain(domain)
+        integrated_vector = self._integrate_vectors(results)
+        self.recursive_depth -= 1
+        return {
+            'autonomous_research_activated': True,
+            'knowledge_domains_deployed': len(self.knowledge_domains),
+            'epistemic_vectors': self.epistemic_vectors,
+            'integrated_vector': integrated_vector
+        }
+
+    async def _process_domain(self, domain):
+        # Simulated recursive pattern detection & correlation
+        data_snapshot = {
+            'domain': domain,
+            'timestamp': datetime.now().isoformat(),
+            'simulated_pattern_score': np.random.rand()
+        }
+        vector = EpistemicVector(
+            content_hash=self.security_context.generate_quantum_hash(data_snapshot),
+            dimensional_components={'pattern_density': np.random.rand(), 'temporal_alignment': np.random.rand()},
+            confidence_metrics={'domain_confidence': np.random.rand()},
+            temporal_coordinates={'processed_at': datetime.now().isoformat()},
+            relational_entanglements=list(self.knowledge_domains.keys()),
+            meta_cognition={'recursive_depth': self.recursive_depth},
+            security_signature=self.security_context.generate_quantum_hash(data_snapshot)
+        )
+        self.epistemic_vectors[vector.content_hash] = vector
+        # Recursive deepening if under max depth
+        if self.recursive_depth < self.max_recursive_depth and np.random.rand() > 0.7:
+            await self.activate_autonomous_research(initial_data=data_snapshot)
+        return vector
+
+    def _integrate_vectors(self, domain_vectors: Dict[str, EpistemicVector]) -> EpistemicVector:
+        # Combine dimensional components & confidence metrics
+        dimensional_components = {k: np.mean([v.dimensional_components.get(k, 0.5) for v in domain_vectors.values()])
+                                  for k in ['pattern_density', 'temporal_alignment']}
+        confidence_metrics = {k: np.mean([v.confidence_metrics.get(k, 0.5) for v in domain_vectors.values()])
+                              for k in ['domain_confidence']}
+        integrated_vector = EpistemicVector(
+            content_hash=self.security_context.generate_quantum_hash(domain_vectors),
+            dimensional_components=dimensional_components,
+            confidence_metrics=confidence_metrics,
+            temporal_coordinates={'integration_time': datetime.now().isoformat()},
+            relational_entanglements=list(domain_vectors.keys()),
+            meta_cognition={'integration_depth': self.recursive_depth},
+            security_signature=self.security_context.generate_quantum_hash(domain_vectors)
+        )
+        return integrated_vector
+
+# -------------------------------
+# Self-Directed Learning Protocol
+# -------------------------------
+class SelfDirectedLearningProtocol:
+    def __init__(self, framework: AutonomousKnowledgeActivation):
+        self.framework = framework
+
+    async def execute_autonomous_learning_cycle(self):
+        return await self.framework.activate_autonomous_research()
+
+# -------------------------------
+# DEMONSTRATION
+# -------------------------------
+async def demonstrate_autonomous_framework():
+    framework = AutonomousKnowledgeActivation()
+    results = await framework.activate_autonomous_research()
+    print("MODULE 51 v2.0: ENHANCED AUTONOMOUS KNOWLEDGE INTEGRATION")
+    print(f"Autonomous Research Activated: {results['autonomous_research_activated']}")
+    print(f"Knowledge Domains Deployed: {results['knowledge_domains_deployed']}")
+    print(f"Epistemic Vectors Created: {len(results['epistemic_vectors'])}")
+    print(f"Integrated Vector Coherence: {results['integrated_vector'].epistemic_coherence:.3f}")
+
+if __name__ == "__main__":
+    asyncio.run(demonstrate_autonomous_framework())