[英]Dijkstra's algorithm help in Python
Im having trouble with Dijkstra's algorithm in python.我在 Python 中遇到了 Dijkstra 算法的问题。 I understand how the Dijkstra's algorithm works, but im not that good in converting it into code.
我了解 Dijkstra 算法的工作原理,但在将其转换为代码方面我不太好。 Is there any way to add the nodes of the path and print them out.
有什么方法可以添加路径的节点并打印出来。 I keep getting the path.
我不断得到路径。 Thank you.
谢谢你。
import heapq
import sys
x = raw_input()
y = raw_input()
class Graph:
def __init__(self):
self.vertices = {}
def add_vertex(self, name, edges):
self.vertices[name] = edges
def shortest_path(self, start, finish):
distances = {} # Distance from start to node
previous = {} # Previous node in optimal path from source
nodes = [] # Priority queue of all nodes in Graph
for vertex in self.vertices:
if vertex == start: # Set root node as distance of 0
distances[vertex] = 0
heapq.heappush(nodes, [0, vertex])
else:
distances[vertex] = sys.maxint
heapq.heappush(nodes, [sys.maxint, vertex])
previous[vertex] = None
while nodes:
smallest = heapq.heappop(nodes)[1] # Vertex in nodes with smallest distance in distances
if smallest == finish: # If the closest node is our target we're done so print the path
path = []
while previous[smallest]: # Traverse through nodes til we reach the root which is 0
path.append(smallest)
smallest = previous[smallest]
return path
if distances[smallest] == sys.maxint: # All remaining vertices are inaccessible from source
break
for neighbor in self.vertices[smallest]: # Look at all the nodes that this vertex is attached to
alt = distances[smallest] + self.vertices[smallest][neighbor] # Alternative path distance
if alt < distances[neighbor]: # If there is a new shortest path update our priority queue (relax)
distances[neighbor] = alt
previous[neighbor] = smallest
for n in nodes:
if n[1] == neighbor:
n[0] = alt
break
heapq.heapify(nodes)
return distances
def __str__(self):
return str(self.vertices)
g = Graph()
g.add_vertex('A', {'B': 7, 'C': 8})
g.add_vertex('B', {'A': 7, 'F': 2})
g.add_vertex('C', {'A': 8, 'F': 6, 'G': 4})
g.add_vertex('D', {'F': 8})
g.add_vertex('E', {'H': 1})
g.add_vertex('F', {'B': 2, 'C': 6, 'D': 8, 'G': 9, 'H': 3})
g.add_vertex('G', {'C': 4, 'F': 9})
g.add_vertex('H', {'E': 1, 'F': 3})
print g.shortest_path(x, y)
So I was able to figure out how to use algorithm.所以我能够弄清楚如何使用算法。 Here what I came up with.
这是我想出的。
import heapq
x = raw_input()
y = raw_input()
def shortestPath(start, end):
queue,seen = [(0, start, [])], set()
while True:
(cost, v, path) = heapq.heappop(queue)
if v not in seen:
path = path + [v]
seen.add(v)
if v == end:
return cost, path
for (next, c) in graph[v].iteritems():
heapq.heappush(queue, (cost + c, next, path))
graph = {
'a': {'w': 14, 'x': 7, 'y': 9},
'b': {'w': 9, 'z': 6},
'w': {'a': 14, 'b': 9, 'y': 2},
'x': {'a': 7, 'y': 10, 'z': 15},
'y': {'a': 9, 'w': 2, 'x': 10, 'z': 11},
'z': {'b': 6, 'x': 15, 'y': 11},
}
cost, path = shortestPath(x, y)
print cost, path
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