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| """ | |
| Quantum spiderweb implementation for advanced cognition. | |
| Framework-compliant multi-dimensional cognitive graph. | |
| """ | |
| try: | |
| import networkx as nx | |
| NETWORKX_AVAILABLE = True | |
| except Exception: | |
| nx = None | |
| NETWORKX_AVAILABLE = False | |
| try: | |
| import numpy as np | |
| NUMPY_AVAILABLE = True | |
| except Exception: | |
| np = None | |
| NUMPY_AVAILABLE = False | |
| from typing import Dict, Any, List, Optional, Tuple | |
| import random | |
| import logging | |
| logger = logging.getLogger(__name__) | |
| class QuantumSpiderweb: | |
| """ | |
| Simulates a cognitive spiderweb architecture with dimensions: | |
| Ψ (thought), τ (time), χ (speed), Φ (emotion), λ (space) | |
| Features: | |
| - Multi-dimensional quantum state vectors | |
| - Thought propagation with activation decay | |
| - Tension detection for instability | |
| - Quantum collapse to definite states | |
| - Node entanglement | |
| """ | |
| def __init__(self, node_count: int = 128): | |
| if NETWORKX_AVAILABLE: | |
| self.graph = nx.Graph() | |
| self.use_networkx = True | |
| else: | |
| self.graph = {'nodes': {}, 'edges': {}} | |
| self.use_networkx = False | |
| logger.warning("NetworkX not available - using dict-based fallback") | |
| self.dimensions = ['Ψ', 'τ', 'χ', 'Φ', 'λ'] | |
| self._init_nodes(node_count) | |
| self.entangled_states = {} | |
| self.activation_threshold = 0.3 | |
| def _init_nodes(self, count: int): | |
| """Initialize quantum nodes with multi-dimensional states""" | |
| for i in range(count): | |
| node_id = f"QNode_{i}" | |
| state = self._generate_state() | |
| if self.use_networkx: | |
| self.graph.add_node(node_id, state=state) | |
| else: | |
| self.graph['nodes'][node_id] = {'state': state, 'neighbors': {}} | |
| if i > 0: | |
| # Create connections with probability decay | |
| connection_count = min(3, i) # Maximum 3 connections per node | |
| potential_connections = [f"QNode_{j}" for j in range(i)] | |
| selected_connections = random.sample(potential_connections, connection_count) | |
| for connection in selected_connections: | |
| weight = random.uniform(0.1, 1.0) | |
| if self.use_networkx: | |
| self.graph.add_edge(node_id, connection, weight=weight) | |
| else: | |
| if connection not in self.graph['nodes']: | |
| continue | |
| self.graph['nodes'][node_id]['neighbors'][connection] = weight | |
| # Bidirectional edge | |
| self.graph['nodes'][connection]['neighbors'][node_id] = weight | |
| def _generate_state(self) -> Dict[str, float]: | |
| """Generate quantum state vector for all dimensions""" | |
| if NUMPY_AVAILABLE: | |
| return {dim: float(np.random.uniform(-1.0, 1.0)) for dim in self.dimensions} | |
| return {dim: random.uniform(-1.0, 1.0) for dim in self.dimensions} | |
| def propagate_thought(self, origin: str, depth: int = 3) -> List[Tuple[str, Dict[str, float]]]: | |
| """ | |
| Traverse the graph from a starting node, simulating pre-cognitive waveform | |
| Args: | |
| origin: Starting node ID | |
| depth: Propagation depth (default: 3) | |
| Returns: | |
| List of (node_id, state) tuples | |
| """ | |
| if not self._node_exists(origin): | |
| logger.warning(f"Origin node {origin} does not exist") | |
| return [] | |
| visited = set() | |
| stack = [(origin, 0)] | |
| traversal_output = [] | |
| while stack: | |
| node, level = stack.pop() | |
| if node in visited or level > depth: | |
| continue | |
| visited.add(node) | |
| state = self._get_node_state(node) | |
| traversal_output.append((node, state)) | |
| for neighbor in self._get_neighbors(node): | |
| stack.append((neighbor, level + 1)) | |
| return traversal_output | |
| def detect_tension(self, node: str) -> float: | |
| """ | |
| Measures tension (instability) in the node's quantum state | |
| Args: | |
| node: Node ID to check | |
| Returns: | |
| Tension value (0-1, higher = more unstable) | |
| """ | |
| if not self._node_exists(node): | |
| return 0.0 | |
| state = self._get_node_state(node) | |
| if NUMPY_AVAILABLE: | |
| return float(np.std(list(state.values()))) | |
| else: | |
| values = list(state.values()) | |
| mean = sum(values) / len(values) | |
| variance = sum((v - mean) ** 2 for v in values) / len(values) | |
| return variance ** 0.5 # Standard deviation | |
| def collapse_node(self, node: str) -> Dict[str, Any]: | |
| """ | |
| Collapse superposed thought into deterministic response | |
| Args: | |
| node: Node ID to collapse | |
| Returns: | |
| Collapsed state dict | |
| """ | |
| if not self._node_exists(node): | |
| return {} | |
| state = self._get_node_state(node) | |
| collapsed = {k: round(v, 2) for k, v in state.items()} | |
| # Update node state | |
| self._set_node_state(node, collapsed) | |
| # Store in entangled states | |
| self.entangled_states[node] = collapsed | |
| return collapsed | |
| def entangle_nodes(self, node1: str, node2: str) -> bool: | |
| """ | |
| Create quantum entanglement between nodes | |
| Args: | |
| node1: First node ID | |
| node2: Second node ID | |
| Returns: | |
| Success status | |
| """ | |
| if not (self._node_exists(node1) and self._node_exists(node2)): | |
| return False | |
| # Create entangled state | |
| entangled_id = f"E_{node1}_{node2}" | |
| self.entangled_states[entangled_id] = { | |
| "nodes": [node1, node2], | |
| "state": self._generate_state() | |
| } | |
| # Add high-weight connection | |
| if self.use_networkx: | |
| self.graph.add_edge(node1, node2, weight=1.0, entangled=True) | |
| else: | |
| self.graph['nodes'][node1]['neighbors'][node2] = 1.0 | |
| self.graph['nodes'][node2]['neighbors'][node1] = 1.0 | |
| return True | |
| # ========================================================================= | |
| # HELPER METHODS | |
| # ========================================================================= | |
| def _node_exists(self, node_id: str) -> bool: | |
| """Check if node exists""" | |
| if self.use_networkx: | |
| return node_id in self.graph | |
| return node_id in self.graph['nodes'] | |
| def _get_node_state(self, node_id: str) -> Dict[str, float]: | |
| """Get node's quantum state""" | |
| if self.use_networkx: | |
| return self.graph.nodes[node_id]["state"] | |
| return self.graph['nodes'][node_id]['state'] | |
| def _set_node_state(self, node_id: str, state: Dict[str, float]): | |
| """Set node's quantum state""" | |
| if self.use_networkx: | |
| self.graph.nodes[node_id]["state"] = state | |
| else: | |
| self.graph['nodes'][node_id]['state'] = state | |
| def _get_neighbors(self, node_id: str) -> List[str]: | |
| """Get node's neighbors""" | |
| if self.use_networkx: | |
| return list(self.graph.neighbors(node_id)) | |
| return list(self.graph['nodes'][node_id]['neighbors'].keys()) | |
| def get_node_state(self, node_id: str) -> Optional[Dict[str, float]]: | |
| """Public method to get node state""" | |
| if self._node_exists(node_id): | |
| return self._get_node_state(node_id) | |
| return None | |
| def update_node_state(self, node_id: str, new_state: Dict[str, float]) -> bool: | |
| """Public method to update node state""" | |
| if self._node_exists(node_id): | |
| # Validate state dimensions | |
| if all(dim in new_state for dim in self.dimensions): | |
| self._set_node_state(node_id, new_state) | |
| return True | |
| return False | |
| def get_statistics(self) -> Dict[str, Any]: | |
| """Get graph statistics""" | |
| if self.use_networkx: | |
| return { | |
| "node_count": self.graph.number_of_nodes(), | |
| "edge_count": self.graph.number_of_edges(), | |
| "entangled_pairs": len(self.entangled_states), | |
| "dimensions": len(self.dimensions) | |
| } | |
| else: | |
| node_count = len(self.graph['nodes']) | |
| edge_count = sum(len(n['neighbors']) for n in self.graph['nodes'].values()) // 2 | |
| return { | |
| "node_count": node_count, | |
| "edge_count": edge_count, | |
| "entangled_pairs": len(self.entangled_states), | |
| "dimensions": len(self.dimensions) | |
| } | |
| if __name__ == "__main__": | |
| # Test QuantumSpiderweb | |
| print("="*70) | |
| print("QUANTUM SPIDERWEB TEST") | |
| print("="*70) | |
| web = QuantumSpiderweb(node_count=32) | |
| root = "QNode_0" | |
| print(f"\nStatistics: {web.get_statistics()}") | |
| print(f"\nInitial Propagation from: {root}") | |
| path = web.propagate_thought(root) | |
| for n, s in path[:5]: # Show first 5 | |
| print(f"{n}: Ψ={s['Ψ']:.2f}, τ={s['τ']:.2f}, χ={s['χ']:.2f}, Φ={s['Φ']:.2f}, λ={s['λ']:.2f}") | |
| print(f"\nDetect Tension: {web.detect_tension(root):.4f}") | |
| print("\nCollapse Sample Node:") | |
| collapsed = web.collapse_node(root) | |
| print(collapsed) | |
| print("\n✅ Test complete") |