1 # 广度优先搜索示例
 2 from collections import deque
 3 
 4 # 创建一个关系图（散列表）
 5 graph = {}
 6 # 单引号与双引号基本无使用上的区别，但是三引号可实现跨行输出
 7 graph["you"] = ["alice", 'bob', "claire"]
 8 graph["alice"] = ['peggy']
 9 graph["bob"] = ["anuj", 'peggy']
10 graph['claire'] = ["thom", 'jonny']
11 graph["peggy"] = []
12 graph["anuj"] = []
13 graph["thom"] = []
14 graph["jonny"] = []
15 
16 
17 search_queue = deque()
18 search_queue += graph["you"]
19 
20 # 判断是否为经销商
21 def person_is_seller(name):
22     return (name[-1] == 'o')
23         
24 def search(search_queue):
25     searched = []
26     while search_queue:
27        
28         person = search_queue.popleft()    #取出队列左边第一个元素
29          # 检查后不再检查
30         if person not in searched:
31             if person_is_seller(person):
32                 print(person + " is a mago seller !")
33                 return True
34             else:
35                 # 把TA的朋友加入搜索队列
36                 search_queue += graph[person]
37                 searched.append(person)
38     print("Can't find mago seller in your friends")
39     return False
40 search(search_queue)

 1 # Dijkstra算法
 2 
 3 # 寻找lowest_cost_node
 4 def find_lowest_cost_node(costs):
 5     lowest_cost = float("inf")
 6     lowest_cost_node = None
 7     for node in costs:
 8         cost = costs[node]
 9         if cost < lowest_cost and node not in processed:
10             lowest_cost = cost
11             lowest_cost_node = node
12     return lowest_cost_node
13 
14 
15 # Create a graph
16 graph = {}
17 graph['start'] = {}
18 graph['start']['a'] = 6
19 graph['start']['b'] = 2
20 graph['a'] = {}
21 graph['a']['fin'] = 1
22 graph['b'] = {}
23 graph['b']['fin'] = 5
24 graph['b']['a'] = 3
25 graph['fin'] = {}
26 
27 
28 # create a cost dict
29 infinity = float('inf')
30 costs = {}
31 costs['a'] = 6
32 costs['b'] = 2
33 costs['fin'] = infinity
34 
35 # creata a parent dict
36 parents = {}
37 parents['a'] = "start"
38 parents['b'] = "start"
39 parents['fin']  = None
40 
41 
42 # record processed nodes
43 processed = []
44 
45 
46 node = find_lowest_cost_node(costs)
47 while node is not None:
48     cost = costs[node]
49     neighbors = graph[node]
50     for n in neighbors.keys():
51         new_cost = cost + neighbors[n]
52         if costs[n] > new_cost:
53             costs[n] = new_cost
54             parents[n] = node
55     processed.append(node)
56     node = find_lowest_cost_node(costs)
57 
58 route = ['fin']
59 node = parents['fin']
60 route.append(node)
61 node = parents[node]
62 route.append(node)
63 node = parents[node]
64 route.append(node)
65 
66 
67 print(route)