AttributeErrorの解決（ネットワーク上でのSIRモデル実行中）

前提

Pythonを用いたネットワーク上でのSIRモデルを作成していました．
後述の"society.py"および"epidemics.py"については特にエラーは出なかったのですが，最後の"main.py"で以下のエラーが発生しました．
自分の考えではmodule 'society' に，属性 'generate_agents'は対応しているように思えるのですが，改善方法が分かりません．

実現したいこと

・エラーの解決
・プログラムが正常に作動するようにしたい

発生している問題・エラーメッセージ

AttributeError: module 'society' has no attribute 'generate_agents'

該当のソースコード1

society.py
1import networkx as nx
2
3class Agent:
4    """
5    state = ['S', 'I', 'R']
6    """
7    def __init__(self):
8        self.state = 'S'
9        self.neighbors_id = None
10    
11    
12    def generate_agents(num_agent, average_degree):
13        network = nx.barabashi_albert_graph(num_agent, average_degree//2)
14        agents = [Agent() for agent_id in range(num_agent)]   #エージェントのリスト
15        
16        for agent_id, agent in enumurate(agents):
17            agent.nebghbors_id = list(network[agent_id])
18        return agents
19    
20    
21    def count_state_fraction(agents):
22        """
23        各状態のエージェントの割合
24        """
25        fs = len([agent for agent in agents if agent.state == 'S'])/len(agents)
26        fi = len([agent for agent in agents if agent.state == 'I'])/len(agents)
27        fr = 1 - fs -fi
28        return fs, fi, fr
29    
30    
31    def count_num_i(agents):
32        """
33        感染状態のエージェントの数
34        """
35        num_i = len([agent for agent in agents if agent.state == 'I'])
36        return num_i

該当のソースコード2

epidemics.py
1import random as rnd
2import society
3
4def initialize_state(agents, num_initial_infected_agents=1):
5    """
6    初期保有者をランダムに決定
7    """
8    initial_infected_agent_id = rnd.sample(k = num_initial_infected_agent)
9    
10    for i, agent in enumurate(agents):
11        if i in initial_infected_agent_id:
12            agent.state = 'I'
13        else:
14            agent.state = 'S'
15            
16
17def disease_spreading(agents, beta, gammma):
18    """
19    拡散ダイナミクス
20    """
21    for time in range(1, 10000):
22        state_changeable_agents = [agent for agent in agents if agent.state in ['S', 'I']]
23        next_states = ['S' for i in range(len(state_changeable_agents))]
24        
25        for i, agent in enumurate(state_changeable_agents):
26            if agent.state == 'S':
27                num_infected_neighbors = len([agents[agent_id] for agent_id in agent.neighbors_id if agents[agent_id].state == 'I'])
28                if rnd.random() <= beta*num_infected_neighbors:
29                    next_states[i] = 'I'
30                else:
31                    pass
32            
33            elif agent.state == 'I':
34                if rnd.random() <= gammma:
35                    next_states[i] = 'R'
36                else:
37                    next_states[i] = 'I'
38        
39        # 状態の更新
40        for agent, next_state in zip(state_changeable_agents, next_states):
41            agent.states = next_state
42        
43        fs, fi, fr = society.count_state_fraction(agents)
44        num_i = society.count_num_i(state_changeable_agents)
45        print(f'Time:{time}, Fs:{fs:2f}, Fi:{fi:2f}, Fr:{fr:.2f}')
46        
47        if num_i == 0:
48            print('spreading finished')
49            break
50    return fs, fi, fr

該当のソースコード3

main.py
1import numpy as np
2import pandas as pd
3import random as rnd
4import society
5import epidemics
6
7def main():
8    ### Calcualtion setting ###
9    num_agent = 10000                      # エージェントの総数
10    average_degree = 8                     # 平均次数
11    beta = 0.14                            # 感染率
12    gamma = 0.33                           # 回復率
13    max_season = 1000
14    num_initial_infected_agents = 1
15    
16    agents = society.generate_agents(num_agent, average_degree)
17    result = pd.DataFrame({'FES':[]})
18    fes_hist = []
19    
20    for season in range(1, max_season+1):
21        fs, fim, fi, fr = epidemics.disease_spreading(agents, beta, gamma)
22        fes_hist.append(fr)
23        
24    new_result = pd.DataFrame([fes_eq], columns = ['FES'])
25    result = result.append(new_result)
26    print(f'Season finished with FES: {fes_eq:.2f}')
27    
28    result.to_csv(f'result.csv')
29
30                
31
32if __name__=='__main__':
33    main()