本文整理汇总了Python中priorityQueue.PriorityQueue.set方法的典型用法代码示例。如果您正苦于以下问题：Python PriorityQueue.set方法的具体用法？Python PriorityQueue.set怎么用？Python PriorityQueue.set使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类priorityQueue.PriorityQueue的用法示例。
在下文中一共展示了PriorityQueue.set方法的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: singleSourceShortestPath
# 需要导入模块: from priorityQueue import PriorityQueue [as 别名]
# 或者: from priorityQueue.PriorityQueue import set [as 别名]
def singleSourceShortestPath(self, srcID):
assert (srcID >= 0 and srcID < self.n)
# Implement Dijkstra's algorithm
# Input:
# self --> a reference to a MyGraph instance
# srcID: the id of the source vertex.
# Expected Output: (d,pi)
# d --> Map each vertex id v to the distance from srcID
# pi --> Map each reachable vertex id v (except for srcID) to a parent.
# Initialize the priority queue
pq = PriorityQueue(self.n) #create the priority queue
for i in range(0,self.n): # Iterate through all vertex ID
if ( i == srcID): # If ID is srcID
pq.set(i,0.0) # Distance of srcID should be zero
else: # ID is not srcID
pq.set(i, pq.Inf) # Distance should be infinity
d = {} # Initialize the map with distances to nodes
pi = {} # Initialize the map with parents of vertices
# COMPLETE the Dijkstra code here
return (d,pi)示例2: singleSourceShortestPath
# 需要导入模块: from priorityQueue import PriorityQueue [as 别名]
# 或者: from priorityQueue.PriorityQueue import set [as 别名]
def singleSourceShortestPath(self, srcID):
assert (srcID >= 0 and srcID < self.n) # ensure that srcID is between 0 and size of graph.
# Implement Dijkstra's algorithm
# Input:
# self --> a reference to a MyGraph instance
# srcID: the id of the source vertex.
# Expected Output: (d,pi)
# d --> Map each vertex id v to the distance from srcID
# pi --> Map each reachable vertex id v (except for srcID) to a parent.
# Initialize the priority queue
pq = PriorityQueue(self.n) #create the priority queue
for i in range(0,self.n): # Iterate through all vertex ID
if i == srcID: # If ID is srcID
pq.set(i, 0.0) # Distance of srcID should be zero
else: # ID is not srcID
pq.set(i, pq.Inf) # Distance should be infinity
d = {} # Initialize the map with distances to nodes
pi = {} # Initialize the map with parents of vertices
while not pq.isEmpty(): # loop until priority queue is empty.
(node, dist) = pq.extractMin() # extract smallest dist node.
d[node] = dist # update dictionary with shortest distance.
for (vertex, weight) in self.adjList[node]: # check the adjacency list of popped node.
newDist = dist + weight # calculate new distance.
if pq.hasKey(vertex): # if we haven't already popped the node in the adjacency list yet.
if newDist < pq.get(vertex): # check distance vs new calculated dist.
pq.set(vertex, newDist) # update priority queue.
pi[vertex] = node # update parent list.
return (d, pi)示例3: singleSourceShortestPath
# 需要导入模块: from priorityQueue import PriorityQueue [as 别名]
# 或者: from priorityQueue.PriorityQueue import set [as 别名]
def singleSourceShortestPath(self, srcID):
assert (srcID >= 0 and srcID < self.n)
# Implement Dijkstra's algorithm
# Input:
# self --> a reference to a MyGraph instance
# srcID: the id of the source vertex.
# Expected Output: (d,pi)
# d --> Map each vertex id v to the distance from srcID
# pi --> Map each reachable vertex id v (except for srcID) to a parent.
# Initialize the priority queue
pq = PriorityQueue(self.n) #create the priority queue
for i in range(0,self.n): # Iterate through all vertex ID
if ( i == srcID): # If ID is srcID
pq.set(i,0.0) # Distance of srcID should be zero
else: # ID is not srcID
pq.set(i, pq.Inf) # Distance should be infinity
d = {} # Initialize the map with distances to nodes
pi = {} # Initialize the map with parents of vertices
d[srcID] = 0 # first node to dist map since while won't catch it.
while not pq.isEmpty():
min = pq.extractMin()
# Look at neighbors
for neighbor in self.adjList[min[0]]: # where neighbor has not yet been removed from pq?
alt = min[1] + neighbor[1]
if pq.hasKey(neighbor[0]) and alt < pq.get(neighbor[0]): # previously stored needs to be replayed
# Update distances and parents everywhere
pq.set(neighbor[0], alt)
d[neighbor[0]] = alt
pi[neighbor[0]] = min[0]
return (d,pi)示例4: singleSourceShortestPath
# 需要导入模块: from priorityQueue import PriorityQueue [as 别名]
# 或者: from priorityQueue.PriorityQueue import set [as 别名]
def singleSourceShortestPath(self, srcID):
assert (srcID >= 0 and srcID < self.n)
# Implement Dijkstra's algorithm
# Input:
# self --> a reference to a MyGraph instance
# srcID: the id of the source vertex.
# Expected Output: (d,pi)
# d --> Map each vertex id v to the distance from srcID
# pi --> Map each reachable vertex id v (except for srcID) to a parent.
# Initialize the priority queue
pq = PriorityQueue(self.n) #create the priority queue
cur=self
for i in range(0,self.n): # Iterate through all vertex ID
if ( i == srcID): # If ID is srcID
pq.set(i,0.0) # Distance of srcID should be zero
else: # ID is not srcID
pq.set(i, pq.Inf) # Distance should be infinity
d = {} # Initialize the map with distances to nodes
pi = {} # Initialize the map with parents of vertices
minKey, minDist=pq.extractMin()
d[minKey]=minDist #set orignal source distance to 0
pi[minKey]=minKey #set sources to orginal source
while(pq.isEmpty()==False):# COMPLETE the Dijkstra code here
lst=self.adjList[minKey]#grap array of lists of format (vertices,weight) for node minKey
for n in lst:#loop through said list
##grap the next node attached to this node somehow
dist=n[1]+minDist#grab distance from list
node=n[0] #grab node from list
if(pq.hasKey(node)):#check if that nodes min dist has been found
if(pq.get(node)>dist):#check if path to the node from minKey is less then current path
pq.set(node,dist)#set the nodes distance in the queue
d[node]=dist#set the nodes distance in the return array
pi[node]=minKey#sets the nodes parents
minKey, minDist=pq.extractMin()#extract next node dont need to extract last node as all the paths will be filled would probably work better in a do while loop
return (d,pi)示例5: singleSourceShortestPath
# 需要导入模块: from priorityQueue import PriorityQueue [as 别名]
# 或者: from priorityQueue.PriorityQueue import set [as 别名]
def singleSourceShortestPath(self, srcID):
assert (srcID >= 0 and srcID < self.n)
# Implement Dijkstra's algorithm
# Input:
# self --> a reference to a MyGraph instance
# srcID: the id of the source vertex.
# Expected Output: (d,pi)
# d --> Map each vertex id v to the distance from srcID
# pi --> Map each reachable vertex id v (except for srcID) to a parent.
# Initialize the priority queue
pq = PriorityQueue(self.n) #create the priority queue
for i in range(0,self.n): # Iterate through all vertex ID
if ( i == srcID): # If ID is srcID
pq.set(i,0.0) # Distance of srcID should be zero
else: # ID is not srcID
pq.set(i, pq.Inf) # Distance should be infinity
d = {} # Initialize the map with distances to nodes
pi = {} # Initialize the map with parents of vertices
parent = None
while not pq.isEmpty():
minNode = pq.extractMin()
if parent is None:
d[minNode[0]] = minNode[1]
pi[minNode[0]] = minNode[0]
elif minNode[1] != pq.Inf:
d[minNode[0]] = float(round(decimal.Decimal(minNode[1]), 2))
else:
d[minNode[0]] = pq.Inf
lst = self.adjList[minNode[0]]
for i in range(len(lst)):
if pq.hasKey(lst[i][0]) and pq.get(lst[i][0]) > lst[i][1] + d[minNode[0]]:
pi[lst[i][0]] = minNode[0]
pq.set(lst[i][0], lst[i][1] + d[minNode[0]])
parent = minNode[0]
return (d,pi)