Create lab1.py

7th sem (Machine Learning)/lab1.py

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+def aStarAlgo(start_node, stop_node):
+open_set = set(start_node)
+closed_set = set()
+g = {} #store distance from starting node
+parents = {}# parents contains an adjacency map of all nodes
+#ditance of starting node from itself is zero
+g[start_node] = 0
+#start_node is root node i.e it has no parent nodes
+#so start_node is set to its own parent node
+parents[start_node] = start_node
+while len(open_set) > 0:
+n = None
+#node with lowest f() is found
+for v in open_set:
+if n == None or g[v] + heuristic(v) < g[n] + heuristic(n):
+n = v
+if n == stop_node or Graph_nodes[n] == None:
+pass
+else:
+for (m, weight) in get_neighbors(n):
+#nodes 'm' not in first and last set are added to first
+#n is set its parent
+if m not in open_set and m not in closed_set:
+open_set.add(m)
+parents[m] = n
+g[m] = g[n] + weight
+#for each node m,compare its distance from start i.e g(m) to the
+#from start through n node
+else:
+if g[m] > g[n] + weight:
+#update g(m)
+
+g[m] = g[n] + weight
+#change parent of m to n
+parents[m] = n
+#if m in closed set,remove and add to open
+if m in closed_set:
+closed_set.remove(m)
+open_set.add(m)
+if n == None:
+print('Path does not exist!')
+return None
+# if the current node is the stop_node
+# then we begin reconstructin the path from it to the start_node
+if n == stop_node:
+path = []
+while parents[n] != n:
+path.append(n)
+n = parents[n]
+path.append(start_node)
+path.reverse()
+print('Path found: {}'.format(path))
+return path
+# remove n from the open_list, and add it to closed_list
+# because all of his neighbors were inspected
+open_set.remove(n)
+closed_set.add(n)
+print('Path does not exist!')
+return None
+#define fuction to return neighbor and its distance
+#from the passed node
+def get_neighbors(v):
+if v in Graph_nodes:
+return Graph_nodes[v]
+else:
+return None
+
+#for simplicity we ll consider heuristic distances given
+#and this function returns heuristic distance for all nodes
+def heuristic(n):
+H_dist = {
+'A': 10,
+'B': 8,
+
+'C': 5,
+'D': 7,
+'E': 3,
+'F': 6,
+'G': 5,
+'H': 3,
+'I': 1,
+'J': 0
+}
+
+return H_dist[n]
+#Describe your graph here
+Graph_nodes = {
+'A': [('B', 6), ('F', 3)],
+'B': [('C', 3), ('D', 2)],
+'C': [('D', 1), ('E', 5)],
+'D': [('C', 1), ('E', 8)],
+'E': [('I', 5), ('J', 5)],
+'F': [('G', 1),('H', 7)] ,
+'G': [('I', 3)],
+'H': [('I', 2)],
+'I': [('E', 5), ('J', 3)],
+}
+aStarAlgo('A', 'J')