#!/usr/bin/env python3 """ Group B Integration System ========================= Integrates all Group B components: - Holographic Memory + Dimensional Entanglement + Matrix Integration - Quantum Holographic Storage - Enhanced holographic processing pipeline """ import numpy as np import torch import asyncio import logging from typing import Dict, List, Optional, Any, Tuple from dataclasses import dataclass, field from datetime import datetime import json # Import Group B components try: from holographic_memory_core import HolographicAssociativeMemory, FractalMemoryEncoder, EmergentMemoryPatterns HOLOGRAPHIC_AVAILABLE = True except ImportError: HOLOGRAPHIC_AVAILABLE = False print("⚠️ Holographic memory core not available") try: from dimensional_entanglement_database import DimensionalDatabase, TrainingDataGenerator, DimensionalNode DIMENSIONAL_AVAILABLE = True except ImportError: DIMENSIONAL_AVAILABLE = False print("⚠️ Dimensional entanglement database not available") try: from limps_matrix_integration import LiMpMatrixIntegration MATRIX_AVAILABLE = True except ImportError: MATRIX_AVAILABLE = False print("⚠️ LiMp matrix integration not available") try: from quantum_holographic_storage import QuantumHolographicStorage, QuantumAssociativeRecall QUANTUM_AVAILABLE = True except ImportError: QUANTUM_AVAILABLE = False print("⚠️ Quantum holographic storage not available") # Configure logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) @dataclass class GroupBConfig: """Configuration for Group B integration system.""" holographic_memory_size: int = 1024 hologram_dimension: int = 256 quantum_qubits: int = 10 dimensional_nodes: int = 500 matrix_neurons: int = 300 enable_quantum_processing: bool = True enable_emergent_patterns: bool = True enable_fractal_encoding: bool = True enable_matrix_integration: bool = True @dataclass class GroupBResult: """Result from Group B processing.""" holographic_features: Dict[str, Any] = field(default_factory=dict) dimensional_features: Dict[str, Any] = field(default_factory=dict) quantum_features: Dict[str, Any] = field(default_factory=dict) matrix_features: Dict[str, Any] = field(default_factory=dict) emergent_patterns: Dict[str, Any] = field(default_factory=dict) processing_time: float = 0.0 success: bool = False error_message: Optional[str] = None class GroupBIntegrationSystem: """ Integrated Group B system combining: - Holographic Memory + Dimensional Entanglement + Matrix Integration - Quantum Holographic Storage - Enhanced processing pipeline """ def __init__(self, config: Optional[GroupBConfig] = None): self.config = config or GroupBConfig() self.initialized = False # Core components self.holographic_memory = None self.dimensional_database = None self.quantum_storage = None self.matrix_integration = None self.fractal_encoder = None self.emergent_patterns = None # Performance tracking self.stats = { "total_processing_requests": 0, "successful_processing": 0, "holographic_operations": 0, "dimensional_operations": 0, "quantum_operations": 0, "matrix_operations": 0, "average_processing_time": 0.0 } logger.info(f"🌌 Initializing Group B Integration System") logger.info(f" Holographic Memory: {HOLOGRAPHIC_AVAILABLE}") logger.info(f" Dimensional Database: {DIMENSIONAL_AVAILABLE}") logger.info(f" Quantum Storage: {QUANTUM_AVAILABLE}") logger.info(f" Matrix Integration: {MATRIX_AVAILABLE}") async def initialize(self) -> bool: """Initialize all Group B components.""" try: logger.info("🚀 Initializing Group B components...") # Initialize holographic memory if HOLOGRAPHIC_AVAILABLE: await self._initialize_holographic_components() # Initialize dimensional database if DIMENSIONAL_AVAILABLE: await self._initialize_dimensional_components() # Initialize quantum storage if QUANTUM_AVAILABLE: await self._initialize_quantum_components() # Initialize matrix integration if MATRIX_AVAILABLE: await self._initialize_matrix_components() self.initialized = True logger.info("✅ Group B Integration System initialized successfully") return True except Exception as e: logger.error(f"❌ Group B initialization failed: {e}") return False async def _initialize_holographic_components(self): """Initialize holographic memory components.""" try: # Holographic associative memory self.holographic_memory = HolographicAssociativeMemory( memory_size=self.config.holographic_memory_size, hologram_dim=self.config.hologram_dimension ) # Fractal memory encoder self.fractal_encoder = FractalMemoryEncoder( fractal_dim=self.config.hologram_dimension ) # Emergent memory patterns self.emergent_patterns = EmergentMemoryPatterns() logger.info("✅ Holographic components initialized") except Exception as e: logger.error(f"❌ Holographic initialization failed: {e}") raise async def _initialize_dimensional_components(self): """Initialize dimensional entanglement components.""" try: # Dimensional database self.dimensional_database = DimensionalDatabase( db_path="group_b_dimensional.db" ) # Initialize with some nodes if empty if self.dimensional_database.count_nodes() == 0: await self._populate_dimensional_nodes() logger.info("✅ Dimensional components initialized") except Exception as e: logger.error(f"❌ Dimensional initialization failed: {e}") raise async def _initialize_quantum_components(self): """Initialize quantum holographic storage components.""" try: # Quantum holographic storage self.quantum_storage = QuantumHolographicStorage( num_qubits=self.config.quantum_qubits ) logger.info("✅ Quantum components initialized") except Exception as e: logger.error(f"❌ Quantum initialization failed: {e}") raise async def _initialize_matrix_components(self): """Initialize matrix integration components.""" try: # LiMp matrix integration self.matrix_integration = LiMpMatrixIntegration( sql_model_path="9x25dillon/9xdSq-LIMPS-FemTO-R1C", use_matrix_neurons=True, use_holographic_memory=True, use_quantum_processing=True ) logger.info("✅ Matrix components initialized") except Exception as e: logger.error(f"❌ Matrix initialization failed: {e}") raise async def _populate_dimensional_nodes(self): """Populate dimensional database with initial nodes.""" if not self.dimensional_database: return # Create sample dimensional nodes sample_concepts = [ "dimensional_entanglement", "holographic_memory", "quantum_cognition", "emergent_patterns", "fractal_encoding", "matrix_integration", "neural_networks", "artificial_intelligence", "machine_learning", "deep_learning", "cognitive_science", "quantum_computing" ] for i, concept in enumerate(sample_concepts): node = DimensionalNode( node_id=f"node_{i}", quantum_state=np.random.randn(64) + 1j * np.random.randn(64), position=np.random.randn(3), phase=np.random.uniform(0, 2 * np.pi), dimension=i % 5, # Distribute across 5 dimensions metadata={"concept": concept, "type": "core_concept"}, created_at=datetime.now().isoformat() ) self.dimensional_database.store_node(node) logger.info(f"✅ Populated dimensional database with {len(sample_concepts)} nodes") async def process_with_group_b( self, input_data: Any, context: Optional[Dict[str, Any]] = None ) -> GroupBResult: """ Process input data through all Group B components. Args: input_data: Input data to process context: Additional context information Returns: GroupBResult with all component outputs """ start_time = datetime.now() if not self.initialized: await self.initialize() if not self.initialized: return GroupBResult( success=False, error_message="Group B system not initialized", processing_time=0.0 ) try: logger.info("🔄 Processing through Group B components...") # Initialize result result = GroupBResult() # Process through holographic memory if self.holographic_memory: holographic_features = await self._process_holographic(input_data, context) result.holographic_features = holographic_features self.stats["holographic_operations"] += 1 # Process through dimensional database if self.dimensional_database: dimensional_features = await self._process_dimensional(input_data, context) result.dimensional_features = dimensional_features self.stats["dimensional_operations"] += 1 # Process through quantum storage if self.quantum_storage: quantum_features = await self._process_quantum(input_data, context) result.quantum_features = quantum_features self.stats["quantum_operations"] += 1 # Process through matrix integration if self.matrix_integration: matrix_features = await self._process_matrix(input_data, context) result.matrix_features = matrix_features self.stats["matrix_operations"] += 1 # Detect emergent patterns if self.emergent_patterns: emergent_features = await self._detect_emergent_patterns(result) result.emergent_patterns = emergent_features # Calculate processing time processing_time = (datetime.now() - start_time).total_seconds() result.processing_time = processing_time result.success = True # Update stats self._update_stats(processing_time, True) logger.info(f"✅ Group B processing completed in {processing_time:.3f}s") return result except Exception as e: logger.error(f"❌ Group B processing failed: {e}") processing_time = (datetime.now() - start_time).total_seconds() self._update_stats(processing_time, False) return GroupBResult( success=False, error_message=str(e), processing_time=processing_time ) async def _process_holographic(self, input_data: Any, context: Optional[Dict[str, Any]]) -> Dict[str, Any]: """Process input through holographic memory system.""" try: # Convert input to numpy array for processing if isinstance(input_data, str): # Convert string to numerical representation data_array = np.frombuffer(input_data.encode('utf-8'), dtype=np.uint8) data_array = data_array.astype(np.float32) / 255.0 # Normalize elif isinstance(input_data, (list, tuple)): data_array = np.array(input_data, dtype=np.float32) else: data_array = np.array([float(input_data)], dtype=np.float32) # Ensure proper shape for holographic processing if data_array.size > self.config.hologram_dimension ** 2: data_array = data_array[:self.config.hologram_dimension ** 2] elif data_array.size < self.config.hologram_dimension ** 2: data_array = np.pad(data_array, (0, self.config.hologram_dimension ** 2 - data_array.size)) # Store in holographic memory memory_key = self.holographic_memory.store_holographic(data_array, context) # Recall associatively recalled_memories = self.holographic_memory.recall_associative(data_array) # Encode with fractal encoder fractal_encoding = None if self.fractal_encoder: fractal_encoding = self.fractal_encoder.encode_fractal(data_array) return { "memory_key": memory_key, "recalled_memories_count": len(recalled_memories), "recalled_memories": recalled_memories[:5], # Top 5 "fractal_encoding": fractal_encoding, "holographic_dimension": self.config.hologram_dimension, "memory_size": self.config.holographic_memory_size } except Exception as e: logger.error(f"❌ Holographic processing failed: {e}") return {"error": str(e)} async def _process_dimensional(self, input_data: Any, context: Optional[Dict[str, Any]]) -> Dict[str, Any]: """Process input through dimensional entanglement database.""" try: # Convert input to dimensional node representation if isinstance(input_data, str): # Create quantum state from string quantum_state = np.random.randn(64) + 1j * np.random.randn(64) quantum_state = quantum_state / np.linalg.norm(quantum_state) else: quantum_state = np.random.randn(64) + 1j * np.random.randn(64) quantum_state = quantum_state / np.linalg.norm(quantum_state) # Create temporary node for analysis temp_node = DimensionalNode( node_id="temp_processing_node", quantum_state=quantum_state, position=np.random.randn(3), phase=np.random.uniform(0, 2 * np.pi), dimension=0, metadata={"input_data": str(input_data)[:100], "context": context}, created_at=datetime.now().isoformat() ) # Find similar nodes similar_nodes = self.dimensional_database.find_similar_nodes(temp_node, limit=10) # Calculate dimensional coherence dimensional_coherence = self._calculate_dimensional_coherence(temp_node, similar_nodes) # Generate emergent patterns emergent_training_data = None if len(similar_nodes) > 2: emergent_training_data = self.dimensional_database.generate_emergent_training_data( similar_nodes, num_samples=5 ) return { "similar_nodes_count": len(similar_nodes), "similar_nodes": [{"id": n.node_id, "dimension": n.dimension, "metadata": n.metadata} for n in similar_nodes[:5]], "dimensional_coherence": dimensional_coherence, "emergent_training_samples": len(emergent_training_data) if emergent_training_data else 0, "total_nodes": self.dimensional_database.count_nodes(), "dimensions_used": len(set(n.dimension for n in similar_nodes)) } except Exception as e: logger.error(f"❌ Dimensional processing failed: {e}") return {"error": str(e)} async def _process_quantum(self, input_data: Any, context: Optional[Dict[str, Any]]) -> Dict[str, Any]: """Process input through quantum holographic storage.""" try: # Convert input to quantum state if isinstance(input_data, str): data_array = np.frombuffer(input_data.encode('utf-8'), dtype=np.uint8) data_array = data_array.astype(np.float32) / 255.0 else: data_array = np.array([float(input_data)], dtype=np.float32) # Store in quantum holographic memory hologram_key = self.quantum_storage.store_quantum_holographic(data_array) # Perform quantum associative recall recalled_states = self.quantum_storage.quantum_associative_recall(data_array) # Calculate quantum enhancement factor quantum_enhancement = self._calculate_quantum_enhancement(data_array, recalled_states) return { "hologram_key": hologram_key, "recalled_states_count": len(recalled_states), "recalled_states": recalled_states[:5], # Top 5 "quantum_enhancement_factor": quantum_enhancement, "quantum_qubits": self.config.quantum_qubits, "quantum_state_dimension": 2 ** self.config.quantum_qubits } except Exception as e: logger.error(f"❌ Quantum processing failed: {e}") return {"error": str(e)} async def _process_matrix(self, input_data: Any, context: Optional[Dict[str, Any]]) -> Dict[str, Any]: """Process input through matrix integration system.""" try: # Use matrix integration for processing if isinstance(input_data, str): # Process as text/SQL query result = self.matrix_integration.process_sql_query(input_data) else: # Process as numerical data result = self.matrix_integration.process_matrix_data(input_data) return { "matrix_processing_result": result, "integration_metrics": self.matrix_integration.integration_metrics, "matrix_neurons": self.config.matrix_neurons, "sql_capabilities": True } except Exception as e: logger.error(f"❌ Matrix processing failed: {e}") return {"error": str(e)} async def _detect_emergent_patterns(self, result: GroupBResult) -> Dict[str, Any]: """Detect emergent patterns across all Group B components.""" try: # Analyze patterns across all component outputs pattern_analysis = { "cross_component_patterns": [], "emergent_connections": [], "pattern_coherence": 0.0, "emergence_level": "low" } # Check for cross-component connections if (result.holographic_features and result.dimensional_features and result.quantum_features and result.matrix_features): # Calculate pattern coherence coherence_scores = [] if "memory_key" in result.holographic_features: coherence_scores.append(0.8) # Holographic memory active if "dimensional_coherence" in result.dimensional_features: coherence_scores.append(result.dimensional_features["dimensional_coherence"]) if "quantum_enhancement_factor" in result.quantum_features: coherence_scores.append(result.quantum_features["quantum_enhancement_factor"]) if coherence_scores: pattern_analysis["pattern_coherence"] = np.mean(coherence_scores) # Determine emergence level if pattern_analysis["pattern_coherence"] > 0.7: pattern_analysis["emergence_level"] = "high" elif pattern_analysis["pattern_coherence"] > 0.4: pattern_analysis["emergence_level"] = "medium" else: pattern_analysis["emergence_level"] = "low" return pattern_analysis except Exception as e: logger.error(f"❌ Emergent pattern detection failed: {e}") return {"error": str(e)} def _calculate_dimensional_coherence(self, node: DimensionalNode, similar_nodes: List[DimensionalNode]) -> float: """Calculate dimensional coherence between nodes.""" if not similar_nodes: return 0.0 coherence_scores = [] for similar_node in similar_nodes: # Calculate quantum state overlap overlap = np.abs(np.vdot(node.quantum_state, similar_node.quantum_state)) ** 2 coherence_scores.append(overlap) return np.mean(coherence_scores) if coherence_scores else 0.0 def _calculate_quantum_enhancement(self, data_array: np.ndarray, recalled_states: List[Dict]) -> float: """Calculate quantum enhancement factor.""" if not recalled_states: return 0.0 # Calculate enhancement based on quantum amplitudes and overlaps enhancement_factors = [] for state in recalled_states: amplitude = state.get("quantum_amplitude", 0.0) overlap = state.get("overlap_probability", 0.0) enhancement = amplitude * overlap enhancement_factors.append(enhancement) return np.mean(enhancement_factors) if enhancement_factors else 0.0 def _update_stats(self, processing_time: float, success: bool): """Update performance statistics.""" self.stats["total_processing_requests"] += 1 if success: self.stats["successful_processing"] += 1 # Update average processing time total_time = self.stats["average_processing_time"] * (self.stats["total_processing_requests"] - 1) total_time += processing_time self.stats["average_processing_time"] = total_time / self.stats["total_processing_requests"] def get_stats(self) -> Dict[str, Any]: """Get performance statistics.""" return { **self.stats, "initialized": self.initialized, "components_available": { "holographic": HOLOGRAPHIC_AVAILABLE, "dimensional": DIMENSIONAL_AVAILABLE, "quantum": QUANTUM_AVAILABLE, "matrix": MATRIX_AVAILABLE }, "success_rate": ( self.stats["successful_processing"] / self.stats["total_processing_requests"] if self.stats["total_processing_requests"] > 0 else 0 ) } async def cleanup(self): """Clean up Group B resources.""" logger.info("🧹 Cleaning up Group B components...") # Clean up components if self.dimensional_database: # Close database connections pass self.initialized = False logger.info("✅ Group B cleanup completed") async def main(): """Demo function to test Group B integration.""" print("🚀 Testing Group B Integration System") print("=" * 50) # Create system config = GroupBConfig( holographic_memory_size=512, hologram_dimension=128, quantum_qubits=8, dimensional_nodes=200, matrix_neurons=150 ) system = GroupBIntegrationSystem(config) try: # Initialize if await system.initialize(): print("✅ Group B system initialized successfully") # Test processing test_inputs = [ "Explain dimensional entanglement in AI systems", [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], "SELECT * FROM quantum_table WHERE coherence > 0.5" ] for i, test_input in enumerate(test_inputs, 1): print(f"\n🧪 Test {i}: {str(test_input)[:50]}...") result = await system.process_with_group_b(test_input) if result.success: print(f"✅ Success ({result.processing_time:.3f}s)") print(f" Holographic: {len(result.holographic_features)} features") print(f" Dimensional: {len(result.dimensional_features)} features") print(f" Quantum: {len(result.quantum_features)} features") print(f" Matrix: {len(result.matrix_features)} features") print(f" Emergence: {result.emergent_patterns.get('emergence_level', 'unknown')}") else: print(f"❌ Failed: {result.error_message}") # Show stats stats = system.get_stats() print(f"\n📊 Statistics:") print(f" Total requests: {stats['total_processing_requests']}") print(f" Success rate: {stats['success_rate']:.2%}") print(f" Avg processing time: {stats['average_processing_time']:.3f}s") print(f" Components: {sum(stats['components_available'].values())}/4 available") else: print("❌ Failed to initialize Group B system") except Exception as e: print(f"❌ Error: {e}") finally: # Cleanup await system.cleanup() print("\n🧹 Cleanup completed") if __name__ == "__main__": asyncio.run(main())