请以import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns import copy import math import random import time from multiprocessing import Pool as ThreadPool #读取数据 path1='att48.tsp' path2='eil76.tsp' path3='pcb442.tsp' path4='rd100.tsp' path5='tsp225.tsp' def readcity(path): df = pd.read_csv("C:\\文件\\现代优化算法\\TSP训练数据集\\"+path, sep=" ", skiprows=6, header=None) return df #选择文件 df = readcity(path4) df.head() city = np.array(range(1,len(df[0][0:len(df)-1])+1)) city_x = np.array(df[1][0:len(df)-1]) city_y = np.array(df[2][0:len(df)-1])为开头补充完成一个模拟退火求解tsp问题的代码，我发给你的代码是用于获取输入数据并将其转化成坐标的

下面是一个简单的模拟退火求解TSP问题的代码，它使用了上面提到的数据读取和坐标转化代码：

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import copy
import math
import random
import time
from multiprocessing import Pool as ThreadPool

# 读取数据
path1='att48.tsp'
path2='eil76.tsp'
path3='pcb442.tsp'
path4='rd100.tsp'
path5='tsp225.tsp'

def readcity(path):
    df = pd.read_csv("C:\\文件\\现代优化算法\\TSP训练数据集\\"+path, sep=" ", skiprows=6, header=None)
    return df

# 选择文件
df = readcity(path4)
df.head()

# 获取城市坐标
city = np.array(range(1,len(df[0][0:len(df)-1])+1))
city_x = np.array(df[1][0:len(df)-1])
city_y = np.array(df[2][0:len(df)-1])
city_pos = np.stack((city_x, city_y), axis=1)

# 计算两点之间的距离
def distance(city1, city2):
    return math.sqrt((city1[0]-city2[0])**2 + (city1[1]-city2[1])**2)

# 计算路径长度
def path_length(path):
    length = 0
    for i in range(len(path)-1):
        length += distance(city_pos[path[i]-1], city_pos[path[i+1]-1])
    length += distance(city_pos[path[-1]-1], city_pos[path[0]-1])
    return length

# 生成随机解
def generate_random_solution():
    return np.random.permutation(city)

# 退火算法求解TSP问题
def simulated_annealing():
    # 设置参数
    initial_temperature = 100
    cooling_rate = 0.99
    temperature = initial_temperature
    current_path = generate_random_solution()
    current_length = path_length(current_path)
    best_path = current_path
    best_length = current_length

    # 迭代求解
    while temperature > 1:
        # 生成新解
        new_path = copy.deepcopy(current_path)
        i = random.randint(0, len(city)-1)
        j = random.randint(0, len(city)-1)
        new_path[i], new_path[j] = new_path[j], new_path[i]
        new_length = path_length(new_path)

        # 计算接受概率
        delta = new_length - current_length
        if delta < 0 or random.random() < math.exp(-delta/temperature):
            current_path = new_path
            current_length = new_length

        # 更新最优解
        if current_length < best_length:
            best_path = current_path
            best_length = current_length

        # 降温
        temperature *= cooling_rate

    return best_path, best_length

# 运行算法
start_time = time.time()
best_path, best_length = simulated_annealing()
end_time = time.time()

# 打印结果
print("最优路径：", best_path)
print("最优路径长度：", best_length)
print("运行时间：", end_time - start_time)

# 绘制路径
plt.figure(figsize=(8, 8))
plt.plot(city_pos[best_path-1, 0], city_pos[best_path-1, 1], 'o-')
plt.title("Simulated Annealing TSP Solution (length={})".format(round(best_length)))
plt.show()

这段代码实现了一个简单的模拟退火算法求解TSP问题。它首先读取数据，然后计算出每个城市的坐标，并且定义了计算两个城市之间距离和计算路径长度的函数。接着，它使用随机解作为初始解，并且迭代地生成新解并计算接受概率，直到降温至最低温度。最后，它输出最优路径和路径长度，并且绘制路径。