load_data(): df = pd.read_csv('./TSP问题测试数据集/kroA200.tsp', sep=" ", skiprows=6, header=None) city = np.array(df[0][0:len(df) - 1])
时间: 2024-05-19 10:13:26 浏览: 9
这段代码的作用是从一个csv文件中读取TSP问题测试数据集,并将其中的城市信息存储到一个numpy数组中。具体来说,load_data()函数通过调用pandas库的read_csv()函数读取csv文件,从第6行开始读取,并忽略最后一行。然后,代码将读取到的城市信息存储到一个numpy数组中,并返回该数组。
相关问题
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:
```python
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。
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功能。要求求解过程中用2-opt邻域构造法,在内循环中用Metropolis准则判断是否接受解来实现等温过程。
好的,以下是使用模拟退火算法求解TSP问题的代码:
```python
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)
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])
# 生成初始解
init_solution = np.concatenate(([1], np.random.permutation(city[1:]), [1]))
# 计算初始解的总路程
def calculate_distance(solution):
distance = 0
for i in range(len(solution)-1):
distance += math.sqrt((city_x[solution[i]-1] - city_x[solution[i+1]-1])**2 + (city_y[solution[i]-1] - city_y[solution[i+1]-1])**2)
return distance
init_distance = calculate_distance(init_solution)
# 定义2-opt邻域操作
def swap(solution, i, j):
new_solution = copy.deepcopy(solution)
new_solution[i:j+1] = np.flip(solution[i:j+1])
return new_solution
# 定义Metropolis准则
def metropolis(delta, temperature):
if delta < 0:
return True
else:
p = math.exp(-delta/temperature)
return np.random.choice([True, False], p=[p, 1-p])
# 定义模拟退火函数
def simulated_annealing(init_solution, init_distance, max_iterations, max_temperature, min_temperature, cooling_rate):
current_solution = init_solution
current_distance = init_distance
best_solution = init_solution
best_distance = init_distance
temperature = max_temperature
iteration = 0
while temperature > min_temperature and iteration < max_iterations:
i, j = sorted(np.random.choice(range(1, len(city)), size=2, replace=False))
new_solution = swap(current_solution, i, j)
new_distance = calculate_distance(new_solution)
if metropolis(new_distance-current_distance, temperature):
current_solution, current_distance = new_solution, new_distance
if current_distance < best_distance:
best_solution, best_distance = current_solution, current_distance
temperature *= cooling_rate
iteration += 1
return best_solution, best_distance
# 运行模拟退火算法
max_iterations = 10000
max_temperature = 10000
min_temperature = 1
cooling_rate = 0.99
best_solution, best_distance = simulated_annealing(init_solution, init_distance, max_iterations, max_temperature, min_temperature, cooling_rate)
# 打印最优解和最优解的总路程
print("Best solution:", best_solution)
print("Best distance:", best_distance)
# 可视化最优解
best_city_x = city_x[best_solution-1]
best_city_y = city_y[best_solution-1]
plt.plot(best_city_x, best_city_y, 'o-')
plt.title("Simulated Annealing TSP Solution")
plt.show()
```
注意:由于你的路径可能与我的不同,需要根据实际情况修改读取数据的路径。
相关推荐
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)