Best First Search

 import heapq

graph_relation = {

    "S": ["A","B","C"],

    "A": ["D","E","G"],

    "B": ["G"],

    "C": ["G"],

    "D": [],

    "E": [],

    "G": [],

}


heuristic = {

    "S":8,

    "A":8,

    "B":2,

    "C":3,

    "D":-1,

    "E":-1,

    "G":0,

}

####

def best_first_search(graph,start,goal,heuristic):

    """

    graph:Dict having tree structure.

    start:Starting node of yhe tree.

    goal:Node to search.

    heuristic:List of heuristic cost.

    """


    graph=graph_relation

    heuristic=heuristic

    start='S'

    goal='G'

    que= [(heuristic[start],start)]

    came_from= {}

    came_from[start] = None

   

    while que:

        _,peak_node = heapq.heappop(que)

        type(_)

       

        if peak_node == goal:

            break

        for neighbor in graph[peak_node]:

            if neighbor not in came_from:

                heapq.heappush(que, (heuristic[neighbor],neighbor))

                came_from[neighbor] = peak_node

    return came_from

#####

start_node ='S'

goal_node = 'G'

came_from = best_first_search(graph_relation,start_node,goal_node,heuristic)

print(came_from)

#####

node = goal_node

path = [node]                              


while node != start_node:

    node = came_from[node]

    path.append(node)

path.reverse()

print("BFS path from" , start_node,"to",goal_node,":",path)


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