sonw_quantum_emergence_analysis.py

class EmergenceAnalyzer:
    """
    Tools for analyzing emergence patterns in quantum consciousness evolution
    """
    def __init__(
        self,
        network: UnifiedEvolutionNetwork,
        constants: Optional[UnifiedConstants] = None
    ):
        self.network = network
        self.constants = constants or UnifiedConstants()
        
    def compute_emergence_metrics(
        self,
        states: Dict[str, torch.Tensor]
    ) -> Dict[str, float]:
        """Compute metrics for emergence patterns"""
        # Extract states
        quantum = states['quantum_states']['integrated_state']
        consciousness = states['consciousness_states']['integrated_consciousness']
        unified = states['unified_state']
        
        # Compute coherence
        quantum_coherence = torch.mean(torch.abs(quantum))
        consciousness_coherence = torch.mean(torch.abs(consciousness))
        unified_coherence = torch.mean(torch.abs(unified))
        
        # Compute entropy
        quantum_entropy = -torch.mean(
            F.softmax(quantum, dim=-1) *
            F.log_softmax(quantum, dim=-1)
        )
        consciousness_entropy = -torch.mean(
            F.softmax(consciousness, dim=-1) *
            F.log_softmax(consciousness, dim=-1)
        )
        
        # Compute complexity
        complexity = states['complexity']
        total_complexity = (
            complexity['quantum'] +
            complexity['consciousness']
        )
        
        return {
            'quantum_coherence': quantum_coherence.item(),
            'consciousness_coherence': consciousness_coherence.item(),
            'unified_coherence': unified_coherence.item(),
            'quantum_entropy': quantum_entropy.item(),
            'consciousness_entropy': consciousness_entropy.item(),
            'total_complexity': total_complexity
        }
        
    def analyze_evolution_trajectory(
        self,
        input_sequence: torch.Tensor,
        window_size: int = 10
    ) -> Dict[str, np.ndarray]:
        """Analyze evolution trajectory over time"""
        trajectories = {
            'quantum_coherence': [],
            'consciousness_coherence': [],
            'unified_coherence': [],
            'quantum_entropy': [],
            'consciousness_entropy': [],
            'total_complexity': []
        }
        
        # Process sequence in windows
        for i in range(0, len(input_sequence) - window_size + 1):
            window = input_sequence[i:i+window_size]
            
            with torch.no_grad():
                outputs = self.network(window, return_components=True)
                metrics = self.compute_emergence_metrics(outputs)
                
                for key, value in metrics.items():
                    trajectories[key].append(value)
                    
        return {
            key: np.array(values)
            for key, values in trajectories.items()
        }
        
    def detect_emergence_events(
        self,
        trajectories: Dict[str, np.ndarray],
        threshold: float = 0.1
    ) -> List[Dict[str, Union[int, str, float]]]:
        """Detect significant emergence events"""
        events = []
        
        for i in range(1, len(trajectories['total_complexity'])):
            # Compute changes
            complexity_change = (
                trajectories['total_complexity'][i] -
                trajectories['total_complexity'][i-1]
            )
            coherence_change = (
                trajectories['unified_coherence'][i] -
                trajectories['unified_coherence'][i-1]
            )
            
            # Detect significant changes
            if abs(complexity_change) > threshold:
                events.append({
                    'time': i,
                    'type': 'complexity_shift',
                    'magnitude': complexity_change,
                    'coherence_change': coherence_change
                })
                
        return events