python 实现A*算法

function A*(start, goal)
    // The set of nodes already evaluated
    closedSet := {}

    // The set of currently discovered nodes that are not evaluated yet.
    // Initially, only the start node is known.
    openSet := {start}

    // For each node, which node it can most efficiently be reached from.
    // If a node can be reached from many nodes, cameFrom will eventually contain the
    // most efficient previous step.
    cameFrom := an empty map

    // For each node, the cost of getting from the start node to that node.
    gScore := map with default value of Infinity

    // The cost of going from start to start is zero.
    gScore[start] := 0

    // For each node, the total cost of getting from the start node to the goal
    // by passing by that node. That value is partly known, partly heuristic.
    fScore := map with default value of Infinity

    // For the first node, that value is completely heuristic.
    fScore[start] := heuristic_cost_estimate(start, goal)

    while openSet is not empty
        current := the node in openSet having the lowest fScore[] value
        if current = goal
            return reconstruct_path(cameFrom, current)

        openSet.Remove(current)
        closedSet.Add(current)

        for each neighbor of current
            if neighbor in closedSet
                continue		// Ignore the neighbor which is already evaluated.

            if neighbor not in openSet	// Discover a new node
                openSet.Add(neighbor)
            
            // The distance from start to a neighbor
            //the "dist_between" function may vary as per the solution requirements.
            tentative_gScore := gScore[current] + dist_between(current, neighbor)
            if tentative_gScore >= gScore[neighbor]
                continue		// This is not a better path.

            // This path is the best until now. Record it!
            cameFrom[neighbor] := current
            gScore[neighbor] := tentative_gScore
            fScore[neighbor] := gScore[neighbor] + heuristic_cost_estimate(neighbor, goal) 

    return failure

function reconstruct_path(cameFrom, current)
    total_path := {current}
    while current in cameFrom.Keys:
        current := cameFrom[current]
        total_path.append(current)
    return total_path

import math
def shortest_path(M,start,goal):
    sx=M.intersections[start][0]
    sy=M.intersections[start][1]
    gx=M.intersections[goal][0]
    gy=M.intersections[goal][1] 
    h=math.sqrt((sx-gx)*(sx-gx)+(sy-gy)*(sy-gy))
    closedSet=set()
    openSet=set()
    openSet.add(start)
    gScore={}
    gScore[start]=0
    fScore={}
    fScore[start]=h
    cameFrom={}
    sumg=0
    NEW=0
    BOOL=False
    while len(openSet)!=0: 
        MAX=1000
        for new in openSet:
            print("new",new)
            if fScore[new]<MAX:
                MAX=fScore[new]
                #print("MAX=",MAX)
                NEW=new
        current=NEW
        print("current=",current)
        if current==goal:
            return reconstruct_path(cameFrom,current)
        openSet.remove(current)
        closedSet.add(current)
        #dafult=M.roads(current)
        for neighbor in M.roads[current]:
            BOOL=False
            print("key=",neighbor)
            a={neighbor}
            if len(a&closedSet)>0:
                continue
            print("key is not in closeSet")
            if len(a&openSet)==0:
                openSet.add(neighbor)   
            else:
                BOOL=True
            x=  M.intersections[current][0]
            y=  M.intersections[current][1]
            x1=M.intersections[neighbor][0]
            y1=M.intersections[neighbor][1]
            g=math.sqrt((x-x1)*(x-x1)+(y-y1)*(y-y1))
            h=math.sqrt((x1-gx)*(x1-gx)+(y1-gy)*(y1-gy))  
            
            new_gScore=gScore[current]+g
            if BOOL==True:
                if new_gScore>=gScore[neighbor]:
                    continue
            print("new_gScore",new_gScore) 
            cameFrom[neighbor]=current
            gScore[neighbor]=new_gScore          
            fScore[neighbor] = new_gScore+h
            print("fScore",neighbor,"is",new_gScore+h)
            print("fScore=",new_gScore+h)
            
        print("__________++--------------++_________")
           
            
                
                
            
def reconstruct_path(cameFrom,current):
    print("已到达lllll")
    total_path=[]
    total_path.append(current)
    for key,value in cameFrom.items():
        print("key",key,":","value",value)
        
    while current in cameFrom.keys():
       
        current=cameFrom[current]
        total_path.append(current)
    total_path=list(reversed(total_path))    
    return total_path