38 lines
1.1 KiB
Python
38 lines
1.1 KiB
Python
from heapq import heappush, heappop
|
|
import random
|
|
|
|
# Dijkstra's algorithm will traverse a directed graph with weighted edges. If
|
|
# the edges aren't weighted, we can pretend that each edges weighs 1. The
|
|
# algorithm will find the shortest path between points A and B.
|
|
|
|
def dijkstra(a, b, graph):
|
|
h = []
|
|
seen = set()
|
|
heappush(h, (0, a, [a], []))
|
|
while h:
|
|
km, x, path, steps = heappop(h)
|
|
|
|
if x == b:
|
|
for a, b, d in steps:
|
|
print("{} -> {} => {}".format(a, b, d))
|
|
return path, km
|
|
|
|
seen.add(x)
|
|
for c, dist in graph[x]:
|
|
if c not in seen:
|
|
heappush(h, (km + dist, c, path + [c], steps + [(x, c, dist)]))
|
|
return [], float('inf')
|
|
|
|
graph = {
|
|
1: [(3, 9), (2, 7), (6, 14)],
|
|
2: [(1, 7), (3, 10), (4, 15)],
|
|
3: [(1, 9), (6, 2), (4, 11), (2, 10)],
|
|
4: [(5, 6), (2, 15), (3, 11)],
|
|
5: [(4, 6), (6, 9)],
|
|
6: [(5, 9), (3, 2), (1, 14)],
|
|
}
|
|
|
|
beg = random.choice(list(graph.keys()))
|
|
end = random.choice(list(graph.keys()))
|
|
print("Searching for the shortest path from {} -> {}".format(beg, end))
|
|
print(dijkstra(beg, end, graph))
|