#!/usr/bin/env python3 """ Group C Integration System ========================= Integrates all Group C components: - TA-ULS + Neuro-Symbolic Engine + Signal Processing - Enhanced cognitive 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 C components try: from tauls_transformer import TAULSLanguageModel, TAULSControlUnit, KFPLayer TAULS_AVAILABLE = True except ImportError: TAULS_AVAILABLE = False print("⚠️ TA-ULS transformer not available") try: from neuro_symbolic_engine import ( MirrorCastEngine, AdaptiveLinkPlanner, EntropyAnalyzer, DianneReflector, MatrixTransformer, JuliaSymbolEngine ) NEURO_SYMBOLIC_AVAILABLE = True except ImportError: NEURO_SYMBOLIC_AVAILABLE = False print("⚠️ Neuro-symbolic engine not available") try: from signal_processing import ( ModulationScheme, Modulators, ModConfig, FrameConfig, SecurityConfig ) SIGNAL_PROCESSING_AVAILABLE = True except ImportError: SIGNAL_PROCESSING_AVAILABLE = False print("⚠️ Signal processing not available") # Configure logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) @dataclass class GroupCConfig: """Configuration for Group C integration system.""" tauls_dim: int = 512 tauls_layers: int = 6 tauls_heads: int = 8 neuro_symbolic_enabled: bool = True signal_processing_enabled: bool = True enable_adaptive_planning: bool = True enable_entropy_analysis: bool = True enable_stability_monitoring: bool = True modulation_scheme: str = "qpsk" # qpsk, bpsk, ofdm, etc. @dataclass class GroupCResult: """Result from Group C processing.""" tauls_features: Dict[str, Any] = field(default_factory=dict) neuro_symbolic_features: Dict[str, Any] = field(default_factory=dict) signal_processing_features: Dict[str, Any] = field(default_factory=dict) stability_metrics: Dict[str, Any] = field(default_factory=dict) entropy_metrics: Dict[str, Any] = field(default_factory=dict) processing_time: float = 0.0 success: bool = False error_message: Optional[str] = None class GroupCIntegrationSystem: """ Integrated Group C system combining: - TA-ULS + Neuro-Symbolic Engine + Signal Processing - Enhanced cognitive processing pipeline """ def __init__(self, config: Optional[GroupCConfig] = None): self.config = config or GroupCConfig() self.initialized = False # Core components self.tauls_model = None self.neuro_symbolic_engine = None self.adaptive_planner = None self.signal_processor = None self.entropy_analyzer = None # Performance tracking self.stats = { "total_processing_requests": 0, "successful_processing": 0, "tauls_operations": 0, "neuro_symbolic_operations": 0, "signal_processing_operations": 0, "stability_events": 0, "average_processing_time": 0.0 } logger.info(f"🧠 Initializing Group C Integration System") logger.info(f" TA-ULS: {TAULS_AVAILABLE}") logger.info(f" Neuro-Symbolic: {NEURO_SYMBOLIC_AVAILABLE}") logger.info(f" Signal Processing: {SIGNAL_PROCESSING_AVAILABLE}") async def initialize(self) -> bool: """Initialize all Group C components.""" try: logger.info("🚀 Initializing Group C components...") # Initialize TA-ULS if TAULS_AVAILABLE: await self._initialize_tauls_components() # Initialize neuro-symbolic engine if NEURO_SYMBOLIC_AVAILABLE: await self._initialize_neuro_symbolic_components() # Initialize signal processing if SIGNAL_PROCESSING_AVAILABLE: await self._initialize_signal_processing_components() self.initialized = True logger.info("✅ Group C Integration System initialized successfully") return True except Exception as e: logger.error(f"❌ Group C initialization failed: {e}") return False async def _initialize_tauls_components(self): """Initialize TA-ULS transformer components.""" try: # Create TA-ULS language model self.tauls_model = TAULSLanguageModel( vocab_size=32000, d_model=self.config.tauls_dim, n_layers=self.config.tauls_layers, n_heads=self.config.tauls_heads, d_ff=self.config.tauls_dim * 4, max_seq_len=2048 ) logger.info("✅ TA-ULS components initialized") except Exception as e: logger.error(f"❌ TA-ULS initialization failed: {e}") raise async def _initialize_neuro_symbolic_components(self): """Initialize neuro-symbolic engine components.""" try: # Mirror cast engine self.neuro_symbolic_engine = MirrorCastEngine() # Adaptive link planner if self.config.enable_adaptive_planning: self.adaptive_planner = AdaptiveLinkPlanner() # Entropy analyzer if self.config.enable_entropy_analysis: self.entropy_analyzer = EntropyAnalyzer() logger.info("✅ Neuro-symbolic components initialized") except Exception as e: logger.error(f"❌ Neuro-symbolic initialization failed: {e}") raise async def _initialize_signal_processing_components(self): """Initialize signal processing components.""" try: # Modulators for signal processing self.signal_processor = Modulators() logger.info("✅ Signal processing components initialized") except Exception as e: logger.error(f"❌ Signal processing initialization failed: {e}") raise async def process_with_group_c( self, input_data: Any, context: Optional[Dict[str, Any]] = None ) -> GroupCResult: """ Process input data through all Group C components. Args: input_data: Input data to process context: Additional context information Returns: GroupCResult with all component outputs """ start_time = datetime.now() if not self.initialized: await self.initialize() if not self.initialized: return GroupCResult( success=False, error_message="Group C system not initialized", processing_time=0.0 ) try: logger.info("🔄 Processing through Group C components...") # Initialize result result = GroupCResult() # Process through TA-ULS if self.tauls_model: tauls_features = await self._process_tauls(input_data, context) result.tauls_features = tauls_features self.stats["tauls_operations"] += 1 # Extract stability metrics if "stability_metrics" in tauls_features: result.stability_metrics = tauls_features["stability_metrics"] if self._check_stability_event(tauls_features["stability_metrics"]): self.stats["stability_events"] += 1 # Process through neuro-symbolic engine if self.neuro_symbolic_engine: neuro_symbolic_features = await self._process_neuro_symbolic(input_data, context) result.neuro_symbolic_features = neuro_symbolic_features self.stats["neuro_symbolic_operations"] += 1 # Extract entropy metrics if "entropy_analysis" in neuro_symbolic_features: result.entropy_metrics = neuro_symbolic_features["entropy_analysis"] # Process through signal processing if self.signal_processor: signal_features = await self._process_signal(input_data, context) result.signal_processing_features = signal_features self.stats["signal_processing_operations"] += 1 # Adaptive planning if enabled if self.adaptive_planner and result.tauls_features and result.neuro_symbolic_features: adaptive_features = await self._perform_adaptive_planning(result, context) result.neuro_symbolic_features.update(adaptive_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 C processing completed in {processing_time:.3f}s") return result except Exception as e: logger.error(f"❌ Group C processing failed: {e}") processing_time = (datetime.now() - start_time).total_seconds() self._update_stats(processing_time, False) return GroupCResult( success=False, error_message=str(e), processing_time=processing_time ) async def _process_tauls(self, input_data: Any, context: Optional[Dict[str, Any]]) -> Dict[str, Any]: """Process input through TA-ULS transformer.""" try: # Convert input to tensor format if isinstance(input_data, str): # Simple tokenization for demo (in practice, use proper tokenizer) tokens = [ord(c) for c in input_data[:512]] # Limit to 512 tokens input_tensor = torch.tensor(tokens, dtype=torch.long).unsqueeze(0) elif isinstance(input_data, (list, tuple)): input_tensor = torch.tensor(input_data[:512], dtype=torch.long).unsqueeze(0) else: # Convert to numerical representation input_tensor = torch.tensor([float(input_data)], dtype=torch.long).unsqueeze(0) # Ensure proper dimensions if input_tensor.shape[1] > 512: input_tensor = input_tensor[:, :512] elif input_tensor.shape[1] < 512: # Pad with zeros padding = torch.zeros(1, 512 - input_tensor.shape[1], dtype=torch.long) input_tensor = torch.cat([input_tensor, padding], dim=1) # Process through TA-ULS model with torch.no_grad(): output = self.tauls_model(input_tensor) # Extract features logits = output.get('logits', torch.zeros(1, 512, 32000)) hidden_states = output.get('hidden_states', []) stability_metrics = output.get('stability_metrics', []) control_info = output.get('control_info', {}) # Calculate stability score stability_score = self._calculate_stability_score(stability_metrics) # Calculate coherence score coherence_score = self._calculate_coherence_score(hidden_states) return { "logits_shape": list(logits.shape), "hidden_states_count": len(hidden_states), "stability_metrics": { "stability_score": stability_score, "coherence_score": coherence_score, "fluctuation_intensity": control_info.get("fluctuation_intensity", 0.0), "kinetic_force": control_info.get("kinetic_force", 0.0) }, "tauls_output": { "model_dim": self.config.tauls_dim, "layers": self.config.tauls_layers, "heads": self.config.tauls_heads, "sequence_length": input_tensor.shape[1] } } except Exception as e: logger.error(f"❌ TA-ULS processing failed: {e}") return {"error": str(e)} async def _process_neuro_symbolic(self, input_data: Any, context: Optional[Dict[str, Any]]) -> Dict[str, Any]: """Process input through neuro-symbolic engine.""" try: # Use mirror cast engine for comprehensive analysis mirror_cast_result = self.neuro_symbolic_engine.cast(input_data) # Entropy analysis if available entropy_analysis = {} if self.entropy_analyzer: entropy_analysis = { "entropy_score": self.entropy_analyzer.measure(input_data), "information_density": self._calculate_information_density(input_data), "complexity_measure": self._calculate_complexity_measure(input_data) } # Extract key features neuro_symbolic_features = { "entropy_analysis": entropy_analysis, "reflection_insights": mirror_cast_result.get("reflection", {}), "matrix_projection": mirror_cast_result.get("matrix", {}), "symbolic_analysis": mirror_cast_result.get("symbolic", {}), "semantic_mapping": mirror_cast_result.get("semantic", {}), "fractal_analysis": mirror_cast_result.get("fractal", {}), "processing_time": mirror_cast_result.get("processing_time", 0.0), "timestamp": mirror_cast_result.get("timestamp", time.time()) } return neuro_symbolic_features except Exception as e: logger.error(f"❌ Neuro-symbolic processing failed: {e}") return {"error": str(e)} async def _process_signal(self, input_data: Any, context: Optional[Dict[str, Any]]) -> Dict[str, Any]: """Process input through signal processing system.""" try: # Convert input to signal format if isinstance(input_data, str): # Convert string to signal representation signal_data = np.frombuffer(input_data.encode('utf-8'), dtype=np.uint8) signal_data = signal_data.astype(np.float32) / 255.0 else: signal_data = np.array(input_data, dtype=np.float32) # Ensure proper signal length if len(signal_data) < 100: signal_data = np.pad(signal_data, (0, 100 - len(signal_data))) elif len(signal_data) > 1000: signal_data = signal_data[:1000] # Process through signal processor mod_config = ModConfig( sample_rate=48000, symbol_rate=1200, amplitude=0.7 ) # Choose modulation scheme modulation_scheme = ModulationScheme[self.config.modulation_scheme.upper()] # Modulate signal modulated_signal = self.signal_processor.modulate( signal_data, modulation_scheme, mod_config ) # Calculate signal metrics signal_power = np.mean(modulated_signal ** 2) signal_snr = self._calculate_signal_snr(modulated_signal) bandwidth_efficiency = self._calculate_bandwidth_efficiency(modulation_scheme) return { "modulation_scheme": self.config.modulation_scheme, "signal_length": len(modulated_signal), "signal_power": float(signal_power), "signal_snr": float(signal_snr), "bandwidth_efficiency": float(bandwidth_efficiency), "modulated_signal": modulated_signal[:100].tolist(), # First 100 samples "signal_processing_config": { "sample_rate": mod_config.sample_rate, "symbol_rate": mod_config.symbol_rate, "amplitude": mod_config.amplitude } } except Exception as e: logger.error(f"❌ Signal processing failed: {e}") return {"error": str(e)} async def _perform_adaptive_planning(self, result: GroupCResult, context: Optional[Dict[str, Any]]) -> Dict[str, Any]: """Perform adaptive planning based on TA-ULS and neuro-symbolic results.""" try: # Extract features for planning tauls_features = result.tauls_features neuro_symbolic_features = result.neuro_symbolic_features # Create planning context planning_context = { "stability_score": tauls_features.get("stability_metrics", {}).get("stability_score", 0.0), "coherence_score": tauls_features.get("stability_metrics", {}).get("coherence_score", 0.0), "entropy_score": neuro_symbolic_features.get("entropy_analysis", {}).get("entropy_score", 0.0), "complexity_measure": neuro_symbolic_features.get("entropy_analysis", {}).get("complexity_measure", 0.0) } # Perform adaptive planning adaptive_result = self.adaptive_planner.plan_adaptive(planning_context) return { "adaptive_planning": adaptive_result, "planning_context": planning_context, "recommendations": self._generate_recommendations(adaptive_result) } except Exception as e: logger.error(f"❌ Adaptive planning failed: {e}") return {"error": str(e)} def _calculate_stability_score(self, stability_metrics: List[Dict]) -> float: """Calculate overall stability score from TA-ULS metrics.""" if not stability_metrics: return 0.5 # Neutral score # Extract fluctuation intensity scores fluctuation_scores = [] for metric in stability_metrics: if "stability_info" in metric: fluctuation_intensity = metric["stability_info"] # Convert to stability score (lower fluctuation = higher stability) stability_score = max(0.0, 1.0 - fluctuation_intensity.mean().item()) fluctuation_scores.append(stability_score) return np.mean(fluctuation_scores) if fluctuation_scores else 0.5 def _calculate_coherence_score(self, hidden_states: List[torch.Tensor]) -> float: """Calculate coherence score from hidden states.""" if not hidden_states: return 0.5 # Neutral score # Calculate coherence between consecutive hidden states coherence_scores = [] for i in range(1, len(hidden_states)): state1 = hidden_states[i-1] state2 = hidden_states[i] # Calculate cosine similarity if state1.numel() > 0 and state2.numel() > 0: state1_flat = state1.flatten() state2_flat = state2.flatten() # Ensure same length min_len = min(len(state1_flat), len(state2_flat)) state1_flat = state1_flat[:min_len] state2_flat = state2_flat[:min_len] # Calculate cosine similarity dot_product = torch.dot(state1_flat, state2_flat) norm1 = torch.norm(state1_flat) norm2 = torch.norm(state2_flat) if norm1 > 0 and norm2 > 0: cosine_sim = dot_product / (norm1 * norm2) coherence_scores.append(cosine_sim.item()) return np.mean(coherence_scores) if coherence_scores else 0.5 def _calculate_information_density(self, data: Any) -> float: """Calculate information density of input data.""" data_str = str(data) if not data_str: return 0.0 # Calculate unique character ratio unique_chars = len(set(data_str)) total_chars = len(data_str) return unique_chars / total_chars if total_chars > 0 else 0.0 def _calculate_complexity_measure(self, data: Any) -> float: """Calculate complexity measure of input data.""" data_str = str(data) if not data_str: return 0.0 # Simple complexity measure based on structure complexity = 0.0 # Add complexity for special characters special_chars = sum(1 for c in data_str if not c.isalnum() and not c.isspace()) complexity += special_chars / len(data_str) * 0.3 # Add complexity for numbers numbers = sum(1 for c in data_str if c.isdigit()) complexity += numbers / len(data_str) * 0.2 # Add complexity for mixed case has_upper = any(c.isupper() for c in data_str) has_lower = any(c.islower() for c in data_str) complexity += 0.1 if has_upper and has_lower else 0.0 return min(1.0, complexity) def _calculate_signal_snr(self, signal: np.ndarray) -> float: """Calculate signal-to-noise ratio.""" signal_power = np.mean(signal ** 2) noise_power = np.var(signal - np.mean(signal)) if noise_power > 0: snr = 10 * np.log10(signal_power / noise_power) return max(0.0, snr) # Ensure non-negative return 0.0 def _calculate_bandwidth_efficiency(self, modulation_scheme: ModulationScheme) -> float: """Calculate bandwidth efficiency for modulation scheme.""" efficiency_map = { ModulationScheme.BFSK: 0.5, ModulationScheme.BPSK: 1.0, ModulationScheme.QPSK: 2.0, ModulationScheme.QAM16: 4.0, ModulationScheme.OFDM: 3.5, ModulationScheme.DSSS_BPSK: 0.8 } return efficiency_map.get(modulation_scheme, 1.0) def _check_stability_event(self, stability_metrics: Dict[str, Any]) -> bool: """Check if a stability event occurred.""" stability_score = stability_metrics.get("stability_score", 0.5) return stability_score < 0.3 # Low stability threshold def _generate_recommendations(self, adaptive_result: Dict[str, Any]) -> List[str]: """Generate recommendations based on adaptive planning result.""" recommendations = [] # Add stability recommendations if "stability_improvement" in adaptive_result: recommendations.append("Consider stability enhancement techniques") # Add performance recommendations if "performance_optimization" in adaptive_result: recommendations.append("Apply performance optimization strategies") # Add modulation recommendations if "modulation_adjustment" in adaptive_result: recommendations.append("Adjust modulation scheme for better efficiency") return recommendations 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": { "tauls": TAULS_AVAILABLE, "neuro_symbolic": NEURO_SYMBOLIC_AVAILABLE, "signal_processing": SIGNAL_PROCESSING_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 C resources.""" logger.info("🧹 Cleaning up Group C components...") # Clean up TA-ULS model if self.tauls_model: del self.tauls_model self.initialized = False logger.info("✅ Group C cleanup completed") async def main(): """Demo function to test Group C integration.""" print("🚀 Testing Group C Integration System") print("=" * 50) # Create system config = GroupCConfig( tauls_dim=256, tauls_layers=4, tauls_heads=8, modulation_scheme="qpsk" ) system = GroupCIntegrationSystem(config) try: # Initialize if await system.initialize(): print("✅ Group C system initialized successfully") # Test processing test_inputs = [ "Explain the concept of dimensional entanglement in AI systems.", "How does quantum cognition enhance machine learning?", [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] ] for i, test_input in enumerate(test_inputs, 1): print(f"\n🧪 Test {i}: {str(test_input)[:50]}...") result = await system.process_with_group_c(test_input) if result.success: print(f"✅ Success ({result.processing_time:.3f}s)") print(f" TA-ULS: {len(result.tauls_features)} features") print(f" Neuro-Symbolic: {len(result.neuro_symbolic_features)} features") print(f" Signal Processing: {len(result.signal_processing_features)} features") print(f" Stability Score: {result.stability_metrics.get('stability_score', 0.0):.3f}") print(f" Entropy Score: {result.entropy_metrics.get('entropy_score', 0.0):.3f}") 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" Stability events: {stats['stability_events']}") print(f" Components: {sum(stats['components_available'].values())}/3 available") else: print("❌ Failed to initialize Group C system") except Exception as e: print(f"❌ Error: {e}") finally: # Cleanup await system.cleanup() print("\n🧹 Cleanup completed") if __name__ == "__main__": asyncio.run(main())