def METHOD_NAME(input_file, city_file):
    """genenrate Adjacency Matrix from file 
    """
    print("generate adj_matrix data (take long time)...")
    # data
    df = pd.read_csv(
        input_file,
        sep='\t',
        names=['date', '迁出省份', '迁出城市', '迁入省份', '迁入城市', '人数'])
    # 只需要2020年的数据
    df['date'] = pd.to_datetime(df['date'], format="%Y%m%d")
    df = df.set_index('date')
    df = df['2020']
    city_df = pd.read_csv(city_file)
    # 剔除武汉
    city_df = city_df.drop(0)
    num = len(city_df)
    matrix = np.zeros([num, num])
    for i in city_df['city']:
        for j in city_df['city']:
            if (i == j):
                continue
            # 选出从i到j的每日人数
            cut = df[df['迁出城市'].str.contains(i)]
            cut = cut[cut['迁入城市'].str.contains(j)]
            # 求均值作为权重
            average = cut['人数'].mean()
            # 赋值给matrix
            i_index = int(city_df[city_df['city'] == i]['num']) - 1
            j_index = int(city_df[city_df['city'] == j]['num']) - 1
            matrix[i_index, j_index] = average
    np.savetxt("dataset/W_74.csv", matrix, delimiter=",")