cities = np.random.rand(n_cities, 2) * 100什么意思
时间: 2024-05-28 16:14:17 浏览: 17
这行代码定义了一个包含n_cities个城市的列表(或者说是矩阵),每个城市的坐标是一个长度为2的二元组,其中x和y坐标的值都是在0到100之间的随机数。np.random.rand函数生成的是指定形状的随机数组,每个元素都是在0到1之间均匀分布的随机数。通过将这些随机数乘以100,就可以将它们缩放到0到100的范围内。因此,cities就是一个包含n_cities个随机坐标的城市列表。
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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:
```python
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。
粒子群优化算法最优路径Python代码
粒子群优化算法(Particle Swarm Optimization, PSO)是一种基于群体智能的优化算法,常用于解决连续优化问题。其基本思想是将一群随机的“粒子”放在搜索空间中,通过不断调整每个粒子的位置和速度来寻找最优解。以下是一个使用Python实现的PSO最优路径代码示例:
```
import numpy as np
import matplotlib.pyplot as plt
def dist(x1, y1, x2, y2):
return np.sqrt((x1-x2)**2 + (y1-y2)**2)
def calc_path_distance(path, x, y):
distance = 0
for i in range(len(path)-1):
distance += dist(x[path[i]], y[path[i]], x[path[i+1]], y[path[i+1]])
return distance
class Particle:
def __init__(self, x, y, path):
self.path = path
self.distance = calc_path_distance(self.path, x, y)
self.velocity = np.zeros_like(self.path)
class PSO:
def __init__(self, x, y, num_particles, max_iter):
self.x = x
self.y = y
self.num_particles = num_particles
self.max_iter = max_iter
self.particles = [Particle(x, y, np.random.permutation(len(x))) for i in range(num_particles)]
self.global_best = min(self.particles, key=lambda x: x.distance)
def update_velocity(self, particle):
w = 0.8
c1 = 1.4
c2 = 1.4
r1 = np.random.rand(len(particle.path))
r2 = np.random.rand(len(particle.path))
particle.velocity = w*particle.velocity + c1*r1*(particle.path - particle.path) + c2*r2*(self.global_best.path - particle.path)
def update_position(self, particle):
particle.path += particle.velocity
particle.distance = calc_path_distance(particle.path, self.x, self.y)
def run(self):
for i in range(self.max_iter):
for particle in self.particles:
self.update_velocity(particle)
self.update_position(particle)
if particle.distance < particle.best_distance:
particle.best_distance = particle.distance
particle.best_path = particle.path
if particle.distance < self.global_best.distance:
self.global_best = particle
if __name__ == '__main__':
# Generate random cities
n_cities = 20
x = np.random.rand(n_cities)
y = np.random.rand(n_cities)
# Initialize PSO and run algorithm
pso = PSO(x, y, num_particles=50, max_iter=1000)
pso.run()
# Plot the best path found by PSO
best_path = pso.global_best.path
plt.plot(x[best_path], y[best_path], 'o-')
plt.show()
```
此代码通过粒子群优化算法求解20个随机城市的最短路径。您可以根据自己的需要进行修改和调整。如果您有任何问题,请随时问我。