解释closed_tour = np.insert(closed_tour, np.where(closed_tour == u)[0][0]+1, min_j) closed_tour = np.insert(closed_tour, np.where(closed_tour == v)[0][0]+1, min_j) odd_vertices.remove(u) odd_vertices.remove(v)
时间: 2024-02-23 17:57:08 浏览: 17
这段代码是在一个求解最小权重完美匹配的算法中,用于更新奇数度数顶点的欧拉回路。具体来说,首先在欧拉回路中插入一个新顶点min_j,使得该顶点成为u的后继节点;然后在欧拉回路中插入一个新顶点min_j,使得该顶点成为v的后继节点;最后从奇数度数顶点列表中移除u和v,因为它们已经被匹配了。这样做的目的是将原图中的奇数度数顶点匹配成偶数度数顶点,以便后续的欧拉回路算法能够顺利进行。
相关问题
line 47, in tsp_path_planning closed_tour = np.array(list(vertex_cover) + [vertex_cover[0]]) TypeError: 'set' object is not subscriptable
这个错误是因为你尝试从一个集合对象中获取元素,但集合对象不支持像列表和元组那样的索引操作。解决方案是将该集合对象转换为列表对象,例如使用`list()`函数将`vertex_cover`转换为列表对象,如下所示:
```python
closed_tour = np.array(list(vertex_cover) + [list(vertex_cover)[0]])
```
这将把`vertex_cover`集合转换为列表,然后将其连接到第一个元素上,最后将结果转换为NumPy数组。这应该解决该错误。
num = [1] # 分子 den = [1, 0, 0] # 分母 sys = ctl.tf(num, den) def evaluate(X): n_particles = X.shape[0] # 获取粒子的数量 j = [0] * n_particles # 初始化误差数组 for i in range(n_particles): Kp, Ki, Kd = X[i] # 为每个粒子分别取值 pid = ctl.TransferFunction([Kd, Kp, Ki], [1, 0]) closed_loop = ctl.feedback(pid * sys, 1) t, y = ctl.step_response(closed_loop) error = 1.0 - y j[i] = np.sum(np.abs(error)) return np.array(j)将这段代码加入到上述代码中
你可以将给定的代码段添加到前面提供的代码中,以实现对粒子位置的循环计算和误差的累积求和。以下是添加了给定代码段的修改后的 `evaluate_fitness` 方法:
```python
import numpy as np
import matplotlib.pyplot as plt
import control as ctl
class Particle:
def __init__(self, dim):
self.position = np.random.uniform(-1, 1, dim)
self.velocity = np.zeros(dim)
self.best_position = self.position
self.best_fitness = float('inf')
class PSO:
def __init__(self, num_particles, dim, max_iter, c1, c2, w):
self.num_particles = num_particles
self.dim = dim
self.max_iter = max_iter
self.c1 = c1
self.c2 = c2
self.w = w
self.particles = [Particle(dim) for _ in range(num_particles)]
self.global_best_position = np.zeros(dim)
self.global_best_fitness = float('inf')
# 定义系统传递函数
num = [1] # 分子
den = [1, 0, 0] # 分母
self.sys = ctl.tf(num, den)
def optimize(self):
for _ in range(self.max_iter):
for particle in self.particles:
fitness = self.evaluate_fitness(particle.position)
if fitness < particle.best_fitness:
particle.best_position = particle.position
particle.best_fitness = fitness
if fitness < self.global_best_fitness:
self.global_best_position = particle.position
self.global_best_fitness = fitness
particle.velocity = (self.w * particle.velocity +
self.c1 * np.random.random() * (particle.best_position - particle.position) +
self.c2 * np.random.random() * (self.global_best_position - particle.position))
particle.position += particle.velocity
def evaluate_fitness(self, position):
n_particles = position.shape[0] # 获取粒子的数量
j = [0] * n_particles # 初始化误差数组
for i in range(n_particles):
Kp, Ki, Kd = position[i] # 为每个粒子分别取值
pid = ctl.TransferFunction([Kd, Kp, Ki], [1, 0])
closed_loop = ctl.feedback(pid * self.sys, 1)
t, y = ctl.step_response(closed_loop)
error = 1.0 - y
j[i] = np.sum(np.abs(error))
return np.array(j)
# 使用示例
num_particles = 10
dim = 3 # PID参数的维度
max_iter = 100
c1 = 2.0
c2 = 2.0
w = 0.7
pso = PSO(num_particles, dim, max_iter, c1, c2, w)
pso.optimize()
best_pid_params = pso.global_best_position
print("Best PID parameters:", best_pid_params)
```
这样,你的代码将计算每个粒子位置对应的 PID 控制器的误差累积,并返回一个包含所有粒子的误差数组。希望对你有所帮助!