def search(self):
pq = pque.IndexedPriorityQLow(self.costToThisNode,
len(self.graph.nodes))
pq.insert(self.source)
while not pq.empty():
nextClosesNode = pq.pop()
self.shortestPathTree[nextClosesNode] = self.searchFrontier[
nextClosesNode]
#if nextClosesNode == self.target:
# return
if self.isSatisfied(nextClosesNode):
self.target = nextClosesNode
return
for edge in self.graph.edges[self.graph.nodes[nextClosesNode]]:
newCost = self.costToThisNode[nextClosesNode] + edge.cost
if self.searchFrontier[edge.to.id] == None:
self.costToThisNode[edge.to.id] = newCost
pq.insert(edge.to.id)
self.searchFrontier[edge.to.id] = edge
elif newCost < self.costToThisNode[
edge.to.id] and self.shortestPathTree[
edge.to.id] == None:
self.costToThisNode[edge.to.id] = newCost
pq.changePriority(edge.to.id)
self.searchFrontier[edge.to.id] = edge
print "pq empty"
self.target = self.source
def search(self):
pq = pque.IndexedPriorityQLow(self.fCosts, len(self.graph.nodes))
pq.insert(self.source)
while not pq.empty():
nextClosesNode = pq.pop()
self.shortestPathTree[nextClosesNode] = self.searchFrontier[
nextClosesNode]
if nextClosesNode == self.target:
return
for edge in self.graph.edges[self.graph.nodes[nextClosesNode]]:
hcost = self.calculateHeuristicCost(self.target, edge.to.id)
gcost = self.gCosts[nextClosesNode] + edge.cost
if self.searchFrontier[edge.to.id] == None:
self.fCosts[edge.to.id] = gcost + hcost
self.gCosts[edge.to.id] = gcost
pq.insert(edge.to.id)
self.searchFrontier[edge.to.id] = edge
elif gcost < self.gCosts[edge.to.id] and self.shortestPathTree[
edge.to.id] == None:
self.fCosts[edge.to.id] = gcost + hcost
self.gCosts[edge.to.id] = gcost
pq.changePriority(edge.to.id)
self.searchFrontier[edge.to.id] = edge
