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import numpy as np |
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import networkx as nx |
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import random |
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from typing import Dict, Any |
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class QuantumSpiderweb: |
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""" |
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Simulates a cognitive spiderweb architecture with dimensions: |
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Ψ (thought), τ (time), χ (speed), Φ (emotion), λ (space) |
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""" |
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def __init__(self, node_count: int = 128): |
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self.graph = nx.Graph() |
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self.dimensions = ['Ψ', 'τ', 'χ', 'Φ', 'λ'] |
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self._init_nodes(node_count) |
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self.entangled_state = {} |
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def _init_nodes(self, count: int): |
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for i in range(count): |
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node_id = f"QNode_{i}" |
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state = self._generate_state() |
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self.graph.add_node(node_id, state=state) |
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if i > 0: |
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connection = f"QNode_{random.randint(0, i-1)}" |
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self.graph.add_edge(node_id, connection, weight=random.random()) |
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def _generate_state(self) -> Dict[str, float]: |
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return {dim: np.random.uniform(-1.0, 1.0) for dim in self.dimensions} |
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def propagate_thought(self, origin: str, depth: int = 3): |
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""" |
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Traverse the graph from a starting node, simulating pre-cognitive waveform |
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""" |
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visited = set() |
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stack = [(origin, 0)] |
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traversal_output = [] |
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while stack: |
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node, level = stack.pop() |
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if node in visited or level > depth: |
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continue |
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visited.add(node) |
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state = self.graph.nodes[node]['state'] |
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traversal_output.append((node, state)) |
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for neighbor in self.graph.neighbors(node): |
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stack.append((neighbor, level + 1)) |
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return traversal_output |
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def detect_tension(self, node: str) -> float: |
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""" |
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Measures tension (instability) in the node's quantum state |
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""" |
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state = self.graph.nodes[node]['state'] |
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return np.std(list(state.values())) |
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def collapse_node(self, node: str) -> Dict[str, Any]: |
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""" |
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Collapse superposed thought into deterministic response |
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""" |
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state = self.graph.nodes[node]['state'] |
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collapsed = {k: round(v, 2) for k, v in state.items()} |
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self.entangled_state[node] = collapsed |
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return collapsed |
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if __name__ == "__main__": |
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web = QuantumSpiderweb() |
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root = "QNode_0" |
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path = web.propagate_thought(root) |
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print("Initial Propagation from:", root) |
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for n, s in path: |
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print(f"{n}:", s) |
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print("\nCollapse Sample Node:") |
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print(web.collapse_node(root)) |
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