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def path_cost(graph, path, weights=None): | |
pathcost = 0 | |
for i in range(len(path)): | |
if i > 0: | |
edge=graph.es.find(_source=path[i-1], _target=path[i]) | |
if weights != None: | |
pathcost += edge[weights] | |
else: | |
#just count the number of edges | |
pathcost += 1 | |
return pathcost | |
def yen_igraph(graph, source, target, num_k, weights): | |
import queue | |
#Shortest path from the source to the target | |
A = [graph.get_shortest_paths(source, to=target, weights=weights, output="vpath")[0]] | |
A_costs = [path_cost(graph, A[0], weights)] | |
#Initialize the heap to store the potential kth shortest path | |
B = queue.PriorityQueue() | |
for k in range(1, num_k): | |
#The spur node ranges from the first node to the next to last node in the shortest path | |
for i in range(len(A[k-1])-1): | |
#Spur node is retrieved from the previous k-shortest path, k − 1 | |
spurNode = A[k-1][i] | |
#The sequence of nodes from the source to the spur node of the previous k-shortest path | |
rootPath = A[k-1][:i] | |
#We store the removed edges | |
removed_edges = [] | |
for path in A: | |
if len(path) - 1 > i and rootPath == path[:i]: | |
#Remove the links that are part of the previous shortest paths which share the same root path | |
edge = graph.es.select(_source=path[i], _target=path[i+1]) | |
if len(edge) == 0: | |
continue #edge already deleted | |
edge = edge[0] | |
removed_edges.append((path[i], path[i+1], edge.attributes())) | |
edge.delete() | |
#Calculate the spur path from the spur node to the sink | |
spurPath = graph.get_shortest_paths(spurNode, to=target, weights=weights, output="vpath")[0] | |
if len(spurPath) > 0: | |
#Entire path is made up of the root path and spur path | |
totalPath = rootPath + spurPath | |
totalPathCost = path_cost(graph, totalPath, weights) | |
#Add the potential k-shortest path to the heap | |
B.put((totalPathCost, totalPath)) | |
#Add back the edges that were removed from the graph | |
for removed_edge in removed_edges: | |
node_start, node_end, cost = removed_edge | |
graph.add_edge(node_start, node_end) | |
edge = graph.es.select(_source=node_start, _target=node_end)[0] | |
edge.update_attributes(cost) | |
#Sort the potential k-shortest paths by cost | |
#B is already sorted | |
#Add the lowest cost path becomes the k-shortest path. | |
while True: | |
cost_, path_ = B.get() | |
if path_ not in A: | |
#We found a new path to add | |
A.append(path_) | |
A_costs.append(cost_) | |
break | |
return A, A_costs |
If you got the answer let me know too Please....I am stack :(
Just use the string you used to define the weights... In my case I have used 'weight' when creating the graph.
If you got the answer let me know too Please....I am stack :(
Just use the string you used to define the weights... In my case I have used 'weight' when creating the graph.
I am using adjacency matrix...and i need the program to find the weight bcz it won't work if i put the weight manually
hi, could you please give me an example about how to define a igraph between use the code you provided? thanks
Do lines 47-52 need to be after lines 54-59? Otherwise, path_cost() generates errors because some edges are removed from the graph.
hi, could you please give me an example about how to define a igraph between use the code you provided? thanks
hey, have you got how to define igraph..??
I'm also stuck here
igraph is just a Python lib: https://igraph.org/python/
I can't answer other questions as it's been ages since I coded this, sorry
Do lines 47-52 need to be after lines 54-59? Otherwise, path_cost() generates errors because some edges are removed from the graph.
Yes, I believe this is true (at least this fixes my bug when num_k
is large)
If you got the answer let me know too Please....I am stack :(