praca_magisterska/algorithms/dijkstra.py

79 lines
2.2 KiB
Python

import heapq as heap
# function which takes graph, vertex set, start and goal, uses djikstra algorithm to establish shortest paths from start
# every other vertex in graph and returns shortest path between start and goal
def dijkstra_algorithm(graph, point_set, start, goal):
# dictionary which will store for every vertex it's distance to start
dist = dict()
# dictionary which keeps if vertex was already popped from queue
visited = dict()
# dictionary which keeps for every vertex which other vertex is previous on the shortest path from start
prev = dict()
# list which is used to keep priority queue with vertexes to be explored by the algorithm
queue = []
# creating dictionary which for every vertex keeps all it's neighbors
for edge in graph.keys():
point_set[edge[0]].append(edge[1])
point_set[edge[1]].append(edge[0])
for vertex in point_set:
dist[vertex] = float('inf')
prev[vertex] = None
visited[vertex] = False
dist[start] = 0
heap.heappush(queue, (0, start))
visited[start] = True
while len(queue) > 0:
current = heap.heappop(queue)
for neighbor in point_set[current[1]]:
new_dist = 0
if neighbor > current[1]:
new_dist = dist[current[1]] + graph[(neighbor, current[1])]
else:
new_dist = dist[current[1]] + graph[(current[1], neighbor)]
if new_dist < dist[neighbor]:
dist[neighbor] = new_dist
prev[neighbor] = current[1]
if not visited[neighbor]:
visited[neighbor] = True
heap.heappush(queue, (new_dist, neighbor))
temp = goal
shortest_path = [goal]
while prev[temp] is not None:
shortest_path.append(prev[temp])
temp = prev[temp]
return shortest_path, dist[goal]
if __name__ == "__main__":
g = {
(2, 1): 3,
(3, 2): 2,
(5, 3): 1,
(9, 5): 5,
(10, 9): 4,
(9, 4): 3,
(4, 1): 4,
(7, 1): 6,
(3, 1): 4,
(6, 2): 3,
(8, 6): 8,
(8, 3): 2
}
v = dict()
for i in g.keys():
v[i[0]] = []
v[i[1]] = []
print(dijkstra_algorithm(g, v, 7, 10))