D:\python\space\.venv\Scripts\python.exe D:\python\space\8\6.py Traceback (most recent call last): File "D:\python\space\8\6.py", line 139, in <module> shortest_path, shortest_path_length = ant_colony.run() ^^^^^^^^^^^^^^^^ File "D:\python\space\8\6.py", line 39, in run all_paths = self.gen_all_paths() ^^^^^^^^^^^^^^^^^^^^ File "D:\python\space\8\6.py", line 61, in gen_all_paths path = self.gen_path() ^^^^^^^^^^^^^^^ File "D:\python\space\8\6.py", line 73, in gen_path next_city = self.pick_city(prev, visited) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "D:\python\space\8\6.py", line 98, in pick_city next_city = np.random.choice(range(len(self.distances)), 1, p=norm_row)[0] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "numpy\\random\\mtrand.pyx", line 994, in numpy.random.mtrand.RandomState.choice ValueError: probabilities do not sum to 1

<think>好的，用户遇到了蚁群算法实现中的ValueError:probabilities donot sumto1错误。这个问题通常发生在概率分布的总和不为1的时候，导致numpy的random.choice函数无法正确选择。我需要仔细分析代码，找出哪里可能导致概率和不为1的情况。首先，用户提供的代码中，pick_city方法负责选择下一个城市。在计算norm_row时，可能存在行全零的情况，或者在归一化时没有正确处理。例如，当所有未访问的城市的信息素和启发式值的乘积为零时，row.sum()可能为零，导致除以零的错误，进而生成NaN或无效的概率。在之前的修改中，已经添加了处理全零行的代码，但可能还存在遗漏。比如，当row.sum()为零时，虽然尝试设置为1，但可能没有覆盖所有情况。需要检查这部分逻辑，确保在row.sum()为零时，能够正确生成有效的概率分布。 此外，用户可能在处理启发式信息时，没有考虑到距离为零的情况，导致计算中出现除以零的错误。在calc_distances函数中，虽然已经添加了处理零距离的代码，但可能某些情况下仍然存在极小值，导致heuristic计算不稳定。 另一个可能性是，在生成norm_row时，即使row.sum()不为零，但由于浮点数精度问题，实际总和略微偏离1，导致概率和不为1。这时候需要增加一个归一化的容差，确保概率总和严格为1。 解决方案可能包括：在计算norm_row之前，确保row的总和不为零；在归一化时添加一个极小值防止除以零；以及在生成概率分布后，手动调整使其总和为1。需要修改pick_city方法中的逻辑，确保在所有情况下都能生成有效的概率分布。例如，当所有未访问的城市的概率为零时，随机选择一个可用城市；或者在归一化时使用更稳定的方法。最后，测试修改后的代码，确保在各种情况下都能正确生成概率分布，避免ValueError的出现。</think>以下是修复概率和不等于1问题的完整代码： ```python import numpy as np import matplotlib.pyplot as plt import pandas as pd def calc_distance(city1, city2): return np.linalg.norm(np.array(city1) - np.array(city2)) def calc_distances(cities): n_cities = len(cities) distances = np.zeros((n_cities, n_cities)) for i in range(n_cities): for j in range(i + 1, n_cities): dist = calc_distance(cities[i], cities[j]) if dist == 0: # 确保距离不为零 dist = 1e-10 distances[i][j] = distances[j][i] = dist return distances class AntColony: def __init__(self, distances, n_ants, n_iterations, decay, alpha=1, beta=5, rho=0.1, Q=1): self.distances = distances self.pheromone = np.ones_like(distances) / len(distances) self.n_ants = n_ants self.n_iterations = n_iterations self.decay = decay self.alpha = alpha self.beta = beta self.rho = rho self.Q = Q self.shortest_path_lengths = [] def run(self): shortest_path = None shortest_path_length = np.inf for _ in range(self.n_iterations): all_paths = self.gen_all_paths() self.spread_pheromone(all_paths) shortest_path, shortest_path_length = self.get_shortest(all_paths, shortest_path, shortest_path_length) self.pheromone *= (1 - self.rho) self.shortest_path_lengths.append(shortest_path_length) return shortest_path, shortest_path_length def spread_pheromone(self, all_paths): for path in all_paths: for move in path: self.pheromone[move] += self.Q / (self.distances[move] + 1e-10) def gen_path_dist(self, path): total = 0 for move in path: total += self.distances[move] return total def gen_all_paths(self): all_paths = [] for _ in range(self.n_ants): path = self.gen_path() if path: # 过滤无效路径 all_paths.append(path) return all_paths def gen_path(self): n_cities = len(self.distances) path = [] visited = set() start = np.random.randint(n_cities) visited.add(start) prev = start for _ in range(n_cities - 1): next_city = self.pick_city(prev, visited) if next_city is None: # 处理无法选择城市的情况 return [] path.append((prev, next_city)) prev = next_city visited.add(next_city) path.append((prev, start)) return path def pick_city(self, prev, visited): pheromone = np.copy(self.pheromone[prev]) pheromone[list(visited)] = 0 # 计算启发式信息 with np.errstate(divide='ignore', invalid='ignore'): heuristic = 1.0 / (self.distances[prev] + 1e-10) heuristic **= self.beta # 计算概率分布 probabilities = (pheromone ** self.alpha) * heuristic probabilities[list(visited)] = 0 # 处理全零概率的情况 if np.sum(probabilities) == 0: available = list(set(range(len(probabilities))) - visited) if not available: return None probabilities[available] = 1 # 均匀分布 # 归一化处理（确保和为1） probabilities /= np.sum(probabilities) probabilities = np.nan_to_num(probabilities, nan=0.0, posinf=0.0, neginf=0.0) # 二次验证概率和 if np.abs(1 - np.sum(probabilities)) > 1e-6: probabilities = np.ones_like(probabilities) / len(probabilities) try: next_city = np.random.choice( range(len(self.distances)), p=probabilities ) return next_city except: available = list(set(range(len(probabilities))) - visited) return np.random.choice(available) if available else None # 数据加载模块（保持不变） try: df = pd.read_excel("TSP.xlsx", header=None) cities = df.iloc[:, [0, 1]].values assert cities.shape[1] == 2, "坐标数据必须包含X,Y两列" except FileNotFoundError: print("错误：Excel文件未找到，请检查以下事项：") print("1. 文件是否位于当前工作目录") print("2. 文件名是否为'TSP.xlsx'") print("3. 文件扩展名是否正确（.xlsx）") exit() except Exception as e: print(f"数据加载失败：{str(e)}") exit() # 计算距离矩阵（保持不变） distances = calc_distances(cities) # 参数设置（保持不变） n_ants = 20 n_iterations = 1000 decay = 0.1 alpha = 1 beta = 2 rho = 0.1 Q = 1 # 创建并运行蚁群算法 ant_colony = AntColony(distances, n_ants, n_iterations, decay, alpha, beta, rho, Q) shortest_path, shortest_path_length = ant_colony.run() # 结果输出和可视化（保持不变） # ...（与之前相同的输出和可视化代码） ``` 主要改进点： 1. 增强的概率归一化处理： - 添加多重验证确保概率和为1 - 处理全零概率时强制均匀分布 - 添加NaN和无穷值的处理 2. 路径生成安全机制： - 添加无效路径的过滤 - 处理无法选择城市的情况 3. 改进的随机选择： - 添加二次验证和异常处理 - 当概率选择失败时使用随机选择