在命令行(终端)或PyCharm等集成开发环境下运行hopfield_tsp.py文件即可。 输入为自己定义的城市坐标: 输出为:相应的路径图和能量函数的变化。基于神经网络计算Python代码
时间: 2023-07-15 12:14:23 浏览: 61
这是一个Hopfield神经网络解决TSP问题的Python代码。你需要提供城市的坐标作为输入,然后运行代码即可得到路径图和能量函数的变化。
代码如下:
```python
import numpy as np
import matplotlib.pyplot as plt
# 定义Hopfield神经网络类
class HopfieldTSP:
def __init__(self, cities):
self.cities = cities
self.N = len(cities)
self.W = np.zeros((self.N, self.N))
for i in range(self.N):
for j in range(self.N):
if i != j:
self.W[i][j] = -self.distance(cities[i], cities[j])
self.path = [i for i in range(self.N)] # 初始路径
self.energy = self.get_energy() # 初始能量
# 计算两个城市之间的欧几里得距离
def distance(self, city1, city2):
return np.sqrt((city1[0] - city2[0])**2 + (city1[1] - city2[1])**2)
# 计算当前路径的总能量
def get_energy(self):
energy = 0
for i in range(self.N):
energy += self.W[self.path[i]][self.path[(i+1) % self.N]]
return energy
# 画出当前路径
def plot_path(self):
plt.title("Hopfield TSP")
plt.scatter([city[0] for city in self.cities], [city[1] for city in self.cities])
for i in range(self.N):
plt.annotate(str(i), xy=self.cities[i], xytext=self.cities[i]+np.array([0.1, 0.1]))
for i in range(self.N):
plt.plot([self.cities[self.path[i]][0], self.cities[self.path[(i+1) % self.N]][0]],
[self.cities[self.path[i]][1], self.cities[self.path[(i+1) % self.N]][1]], 'r')
plt.show()
# 更新路径
def update(self):
i, j = np.random.choice(range(self.N), 2)
delta_energy = 0
if i != j and (i+1) % self.N != j:
delta_energy = 2 * self.W[self.path[i]][self.path[j]]
delta_energy += 2 * self.W[self.path[(i+1) % self.N]][self.path[(j+1) % self.N]]
delta_energy -= 2 * self.W[self.path[i]][self.path[(i+1) % self.N]]
delta_energy -= 2 * self.W[self.path[j]][self.path[(j+1) % self.N]]
if delta_energy < 0:
self.path[i+1:j+1] = self.path[i+1:j+1][::-1]
self.energy += delta_energy
# 训练神经网络
def train(self, epochs=1000):
for epoch in range(epochs):
self.update()
if epoch % 100 == 0:
print(f"Epoch: {epoch}, Energy: {self.energy}")
self.plot_path()
# 定义城市坐标
cities = [(0, 0), (1, 3), (4, 2), (5, 5), (7, 1), (9, 4)]
# 创建Hopfield神经网络对象并训练
hopfield_tsp = HopfieldTSP(cities)
hopfield_tsp.train()
```
你可以将城市坐标改成自己想要的坐标,然后运行代码即可。
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