Machine Learning A* Algorithm | Read Now

MACHINE LEARNING VTU LAB

Implement A* Search algorithm

A* Algorithm program Code

class Graph:
    def __init__(self,adjac_lis):
        self.adjac_lis = adjac_lis
    def get_neighbours(self,v):
        return self.adjac_lis[v]
    def h(self,n):
        H={'A':1,'B':1, 'C':1,'D':1}
        return H[n]
    def a_star_algorithm(self,start,stop):
        open_lst = set([start])
        closed_lst = set([])
        dist ={}
        dist[start] = 0
        prenode ={}
        prenode[start] =start
        while len(open_lst)>0:
            n = None
            for v in open_lst:
                if n==None or dist[v]+self.h(v)<dist[n]+self.h(n):
                    n=v;
            if n==None:
                print("path doesnot exist")
                return None
            if n==stop:
                 reconst_path=[]
                 while prenode[n]!=n:
                     reconst_path.append(n)
                     n = prenode[n]
                 reconst_path.append(start)
                 reconst_path.reverse()
                 print("path found:{}".format(reconst_path))
                 return reconst_path
            for (m,weight) in self.get_neighbours(n):
                if m not in open_lst and m not in closed_lst:
                    open_lst.add(m)
                    prenode[m] = n
                    dist[m] = dist[n]+weight
                else:
                    if dist[m]>dist[n]+weight:
                        dist[m] = dist[n]+weight
                        prenode[m]=n
                        if m in closed_lst:
                            closed_lst.remove(m)
                            open_lst.add(m)
            open_lst.remove(n)
            closed_lst.add(n)
        print("Path doesnot exist")
        return None
adjac_lis ={'A':[('B',1),('C',3),('D',7)],'B':[('D',5)],'C':[('D',12)]}
graph1=Graph(adjac_lis)
graph1.a_star_algorithm('A', 'D')

Output

path found:['A', 'B', 'D']

['A', 'B', 'D']