算法图解-狄克斯特拉算法

 1 # the graph
 2 graph = {}
 3 graph["start"] = {}
 4 graph["start"]["a"] = 6
 5 graph["start"]["b"] = 2
 6 
 7 graph["a"] = {}
 8 graph["a"]["fin"] = 1
 9 
10 graph["b"] = {}
11 graph["b"]["a"] = 3
12 graph["b"]["fin"] = 5
13 
14 graph["fin"] = {}
15 
16 # the costs table
17 infinity = float("inf")
18 costs = {}
19 costs["a"] = 6
20 costs["b"] = 2
21 costs["fin"] = infinity
22 
23 # the parents table
24 parents = {}
25 parents["a"] = "start"
26 parents["b"] = "start"
27 parents["fin"] = None
28 
29 processed = []
30 
31 def find_lowest_cost_node(costs):
32     lowest_cost = float("inf")
33     lowest_cost_node = None
34     # Go through each node.
35     for node in costs:
36         cost = costs[node]
37         # If it‘s the lowest cost so far and hasn‘t been processed yet...
38         if cost < lowest_cost and node not in processed:
39             # ... set it as the new lowest-cost node.
40             lowest_cost = cost
41             lowest_cost_node = node
42     return lowest_cost_node
43 
44 # Find the lowest-cost node that you haven‘t processed yet.
45 node = find_lowest_cost_node(costs)
46 # If you‘ve processed all the nodes, this while loop is done.
47 while node is not None:
48     cost = costs[node]
49     # Go through all the neighbors of this node.
50     neighbors = graph[node]
51     for n in neighbors.keys():
52         new_cost = cost + neighbors[n]
53         # If it‘s cheaper to get to this neighbor by going through this node...
54         if costs[n] > new_cost:
55             # ... update the cost for this node.
56             costs[n] = new_cost
57             # This node becomes the new parent for this neighbor.
58             parents[n] = node
59     # Mark the node as processed.
60     processed.append(node)
61     # Find the next node to process, and loop.
62     node = find_lowest_cost_node(costs)
63 
64 print "Cost from the start to each node:"
65 print costs