Will this Dijkstra shortest path algorithm work in the bge
Dijkstra shortest path algorithm in python it is for 2017.
import sys
class Vertex:
def __init__(self, node):
self.id = node
self.adjacent = {}
# Set distance to infinity for all nodes
self.distance = sys.maxint
# Mark all nodes unvisited
self.visited = False
# Predecessor
self.previous = None
def add_neighbor(self, neighbor, weight=0):
self.adjacent[neighbor] = weight
def get_connections(self):
return self.adjacent.keys()
def get_id(self):
return self.id
def get_weight(self, neighbor):
return self.adjacent[neighbor]
def set_distance(self, dist):
self.distance = dist
def get_distance(self):
return self.distance
def set_previous(self, prev):
self.previous = prev
def set_visited(self):
self.visited = True
def __str__(self):
return str(self.id) + ' adjacent: ' + str([x.id for x in self.adjacent])
class Graph:
def __init__(self):
self.vert_dict = {}
self.num_vertices = 0
def __iter__(self):
return iter(self.vert_dict.values())
def add_vertex(self, node):
self.num_vertices = self.num_vertices + 1
new_vertex = Vertex(node)
self.vert_dict[node] = new_vertex
return new_vertex
def get_vertex(self, n):
if n in self.vert_dict:
return self.vert_dict[n]
else:
return None
def add_edge(self, frm, to, cost = 0):
if frm not in self.vert_dict:
self.add_vertex(frm)
if to not in self.vert_dict:
self.add_vertex(to)
self.vert_dict[frm].add_neighbor(self.vert_dict[to], cost)
self.vert_dict[to].add_neighbor(self.vert_dict[frm], cost)
def get_vertices(self):
return self.vert_dict.keys()
def set_previous(self, current):
self.previous = current
def get_previous(self, current):
return self.previous
def shortest(v, path):
''' make shortest path from v.previous'''
if v.previous:
path.append(v.previous.get_id())
shortest(v.previous, path)
return
import heapq
def dijkstra(aGraph, start):
print '''Dijkstra's shortest path'''
# Set the distance for the start node to zero
start.set_distance(0)
# Put tuple pair into the priority queue
unvisited_queue = [(v.get_distance(),v) for v in aGraph]
heapq.heapify(unvisited_queue)
while len(unvisited_queue):
# Pops a vertex with the smallest distance
uv = heapq.heappop(unvisited_queue)
current = uv[1]
current.set_visited()
#for next in v.adjacent:
for next in current.adjacent:
# if visited, skip
if next.visited:
continue
new_dist = current.get_distance() + current.get_weight(next)
if new_dist < next.get_distance():
next.set_distance(new_dist)
next.set_previous(current)
print 'updated : current = %s next = %s new_dist = %s' \
%(current.get_id(), next.get_id(), next.get_distance())
else:
print 'not updated : current = %s next = %s new_dist = %s' \
%(current.get_id(), next.get_id(), next.get_distance())
# Rebuild heap
# 1. Pop every item
while len(unvisited_queue):
heapq.heappop(unvisited_queue)
# 2. Put all vertices not visited into the queue
unvisited_queue = [(v.get_distance(),v) for v in aGraph if not v.visited]
heapq.heapify(unvisited_queue)
if __name__ == '__main__':
g = Graph()
g.add_vertex('a')
g.add_vertex('b')
g.add_vertex('c')
g.add_vertex('d')
g.add_vertex('e')
g.add_vertex('f')
g.add_edge('a', 'b', 7)
g.add_edge('a', 'c', 9)
g.add_edge('a', 'f', 14)
g.add_edge('b', 'c', 10)
g.add_edge('b', 'd', 15)
g.add_edge('c', 'd', 11)
g.add_edge('c', 'f', 2)
g.add_edge('d', 'e', 6)
g.add_edge('e', 'f', 9)
print 'Graph data:'
for v in g:
for w in v.get_connections():
vid = v.get_id()
wid = w.get_id()
print '( %s , %s, %3d)' % ( vid, wid, v.get_weight(w))
dijkstra(g, g.get_vertex('a'))
target = g.get_vertex('e')
path = [target.get_id()]
shortest(target, path)
print 'The shortest path : %s' %(path[::-1])
