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53 lines
1.5 KiB
53 lines
1.5 KiB
11 months ago
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import numpy as np
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def dfs_tsp(graph, start, current, path, visited, cost, min_cost, min_path):
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if len(path) == len(graph) and graph[current][start] != np.inf:
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path.append(start)
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cost += graph[current][start]
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if cost < min_cost[0]:
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min_cost[0] = cost
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min_path[0] = path.copy()
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path.pop()
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cost -= graph[current][start]
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return
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for next_node in range(len(graph)):
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if graph[current][next_node] != np.inf and not visited[next_node]:
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visited[next_node] = True
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path.append(next_node)
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cost += graph[current][next_node]
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dfs_tsp(graph, start, next_node, path, visited, cost, min_cost, min_path)
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visited[next_node] = False
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path.pop()
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cost -= graph[current][next_node]
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def tsp_dfs(graph):
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n = len(graph)
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min_cost = [float('inf')]
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min_path = [[]]
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for start_node in range(n):
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visited = [False] * n
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path = [start_node]
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cost = 0
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visited[start_node] = True
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dfs_tsp(graph, start_node, start_node, path, visited, cost, min_cost, min_path)
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return min_cost[0], min_path[0]
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# 示例使用
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distances = np.array([
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[np.inf, 50, 99, np.inf, 64, np.inf],
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[50, np.inf, 12, 45, 74, 13],
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[99, 12, np.inf, 25, np.inf, 61],
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[np.inf, 45, 25, np.inf, 45, 47],
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[64, 74, np.inf, 45, np.inf, np.inf],
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[np.inf, 13, 61, 47, np.inf, np.inf]
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])
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min_cost, min_path = tsp_dfs(distances)
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print("Minimum cost:", min_cost)
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print("Minimum path:", min_path)
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