生成一段Python代码，模拟复杂网络中的SIS病毒传播模型。其中，节点有三类，对应的传染率分为0.1,0.2,0.3三个等级，对应的节点的恢复率分为0.1,0.2,0.3三个等级。

以下是生成的Python代码，用于模拟复杂网络中的SIS病毒传播模型：

import networkx as nx
import numpy as np

# Generate network graph
G = nx.erdos_renyi_graph(1000, 0.2)

# Assign node attributes
for node in G.nodes():
    if node % 3 == 0:
        G.nodes[node]['infectious_rate'] = 0.3
        G.nodes[node]['recovery_rate'] = 0.3
    elif node % 3 == 1:
        G.nodes[node]['infectious_rate'] = 0.2
        G.nodes[node]['recovery_rate'] = 0.2
    else:
        G.nodes[node]['infectious_rate'] = 0.1
        G.nodes[node]['recovery_rate'] = 0.1

# Initialize infected nodes
infected_nodes = set(np.random.choice(G.nodes(), size=50, replace=False))

# Simulate SIS virus spread
for t in range(100):
    # Infect susceptible nodes
    for node in G.nodes():
        if node not in infected_nodes:
            neighbors = list(G.adj[node])
            infectious_neighbors = [neighbor for neighbor in neighbors if neighbor in infected_nodes]
            infectious_rate = sum([G.nodes[infectious_neighbor]['infectious_rate'] for infectious_neighbor in infectious_neighbors])
            if np.random.rand() < infectious_rate:
                infected_nodes.add(node)
    # Recover infected nodes
    for node in infected_nodes.copy():
        recovery_rate = G.nodes[node]['recovery_rate']
        if np.random.rand() < recovery_rate:
            infected_nodes.remove(node)

# Calculate final infection rate
final_infection_rate = len(infected_nodes) / G.number_of_nodes()
print("Final infection rate:", final_infection_rate)

请注意，这只是一个示例代码，并不保证正确性和完整性。您可以根据需要对代码进行修改和调整。