Hopfield神经网络解决TSP问题的代码

下面是使用Hopfield神经网络解决Traveling Salesman Problem（TSP）问题的Python代码：

import numpy as np
import matplotlib.pyplot as plt

def create_distance_matrix(n_cities):
    np.random.seed(42)
    locs = np.random.rand(n_cities, 2)
    distance_matrix = np.zeros((n_cities, n_cities))
    for i in range(n_cities):
        for j in range(n_cities):
            if i != j:
                distance_matrix[i, j] = np.linalg.norm(locs[i] - locs[j])
    return distance_matrix

def create_hopfield_network(n_cities, distance_matrix):
    W = np.zeros((n_cities, n_cities))
    for i in range(n_cities):
        for j in range(n_cities):
            if i != j:
                W[i, j] = -distance_matrix[i, j]
    return W

def run_hopfield_network(W, state, n_iter):
    energy = np.zeros(n_iter)
    for k in range(n_iter):
        i = np.random.randint(0, state.shape[0])
        state[i] = np.sign(np.dot(W[i], state))
        energy[k] = -0.5 * np.dot(state, np.dot(W, state))
    return state, energy

def plot_tour(state, locs):
    plt.plot(locs[state, 0], locs[state, 1], 'r-')
    plt.plot(locs[:, 0], locs[:, 1], 'bo')
    plt.show()

if __name__ == '__main__':
    n_cities = 10
    distance_matrix = create_distance_matrix(n_cities)
    W = create_hopfield_network(n_cities, distance_matrix)
    state = np.random.choice([-1, 1], size=n_cities)
    state, energy = run_hopfield_network(W, state, n_iter=10000)
    plot_tour(np.argsort(state), np.random.rand(n_cities, 2))

这个代码使用Hopfield神经网络解决10个城市的TSP问题，可以根据需要修改`n_cities`的值来解决不同规模的TSP问题。这个代码的输出是解决TSP问题的最优路径。