无法获得Dijkstra算法的最终路径

Question

我通过重新创建路径寻找算法来自我教学AI（所以不，它不是任务或任何东西），更具体地说是Dijkstra，并设法让一切开始工作（算法扩展完全像Dijkstra和can确定最短路径的权重）。然而，我正在努力提出回溯部分，它采用最短的路径并返回顶点

我经常阅读并研究了很多关于Dijkstra表并且在概念上完全理解它（我可以正确地解决Dijkstra表中的许多问题）但是我无法翻译它返回最短顶点的回溯部分路径。

有人介意给我一些指导吗？

代码：

from heapq import heappop, heappush, heappushpop
import collections
import copy

def min_val(destVal, marked, target_edge):
    if(destVal > marked + target_edge):
        destVal = marked + target_edge
        return destVal
    else:
        return destVal

def weighted(graph, start, end):

true_graph = copy.deepcopy(graph)

current_min_distance = 0
trail = []
trail_value =[0]
to_expand = collections.deque()
to_expand_list = []
to_expand_list_value = []
new_node = start
trail.append(start)
destVal_node = []
destVal_value = []
full_destVal = {}
parents = {}
print "Starting point: ", graph[start]
lowest_distance = 0;

while True:
    mindistance = float("inf")
    table_check = []
    table_check_values = []
    print "____________________________________"


    to_remove = []
    starting_node = new_node

    minigraph = graph[new_node]
    print "Starting point: ", new_node
    print "Graph of current node: ", graph[new_node]


    print "To expand beginning: ", to_expand
    table_check = full_destVal.keys()
    table_check_values = full_destVal.values()

    for node in to_expand:


        if node not in minigraph:
            minigraph[node] = full_to_expand.get(node)

    full_trail = dict(zip(trail, trail_value))        
    full_to_expand = dict(zip(to_expand_list, to_expand_list_value))


    if new_node == end:
        print ""
        print ""
        print "**********************************Soulution Found**********************************"
        print ""
        print "Trail: ", trail
        print "Parents: ", parents
        print "Length of shortest path: ", lowest_distance

        solution = [end]
        n = end

        while n in parents and parents[n]:
            solution.append(parents[n])
            n = parents[n]

        return solution[::-1]

    #Goes through each node in the minigraph
    for node in minigraph:
        full_to_expand = dict(zip(to_expand_list, to_expand_list_value))

        if node not in to_expand and node not in trail:
            to_expand.append(node)
            to_expand_list.append(node)
            print minigraph
            to_expand_list_value.append(minigraph[node])



        if node not in trail and to_expand:
            parents[node] = new_node
            mindistance = float("inf")
            if node in full_destVal:
                mindistance = full_destVal[node]
            else:
                minidistance = float("inf")
            heappush(q, minigraph[node])
            full_to_expand = dict(zip(to_expand_list, to_expand_list_value))
            to_compare = min_val(mindistance, int(full_trail.get(starting_node)), int(minigraph[node]))
            table_check.append(node)
            table_check_values.append(to_compare)
            full_table_check = dict(zip(table_check, table_check_values))
            to_expand_list_value[to_expand_list.index(node)] = to_compare
            mindistance = min(table_check_values)
            new_node = table_check[table_check_values.index(min(table_check_values))]         
            if new_node not in trail:
                trail_value.append(min(table_check_values))
                trail.append(new_node)
    full_trail = dict(zip(trail, trail_value))        
    full_destVal = dict(zip(table_check, table_check_values))
    lowest_distance = mindistance

    print "Chosen node:", mindistance
    print "Node to visit: ", new_node
    print "Complete trail of nodes visited ",full_trail
    print "Full to_expand_list: ", full_to_expand
    print "Current minigraph",minigraph


    full_destVal.pop(new_node, None)
    table_check = full_destVal.keys()
    table_check_values = full_destVal.values()

    full_to_expand.pop(new_node, None)
    to_expand_list = full_to_expand.keys()
    to_expand_list_value = full_to_expand.values()



#Sample weighted graphs

graph = {'A': {'B':4, 'C':3, 'D': 6},
     'B': {'A':3, 'D':8},
     'C': {'D':7, 'E':5},
     'D': {'E':1},
     'E':{}}

grid = {'A': {'B':16, 'C':9, 'D': 35},
     'B': {'A':16, 'D':12, 'E':25},
     'C': {'A':9, 'D':15, 'F':22},
     'D': {'A':35,'B':12,'C':15,'E':14,'F':17, 'G':19},
     'E':{'B':25, 'D':14, 'G':8,},
    'F': {'C':22,'D':17,'G':14},
    'G': {'D':19,'E':8,'F':14},
    }

jim = {
'B': {'A': 5, 'D': 1, 'G': 2},
'A': {'B': 5, 'D': 3, 'E': 12, 'F' :5},
'D': {'B': 1, 'G': 1, 'E': 1, 'A': 3},
'G': {'B': 2, 'D': 1, 'C': 2},
'C': {'G': 2, 'E': 1, 'F': 16},
'E': {'A': 12, 'D': 1, 'C': 1, 'F': 2},
'F': {'A': 5, 'E': 2, 'C': 16}}



#print weighted(graph, 'A','E')

print weighted(grid, 'A','G')

#print weighted(jim, 'A','G')