Please do not give solution in image formate thanku. Write a Python code for the following scenario : 1: Use Breadth First Search and Recursive Best First Search and implement the algorithms in Python. The program should be able to dynamically take in start and goal nodes from the user at run time. Compare to interpret the results in terms of the algorithm working, performance & shortest path if obtained relevant to the given problem 2: Print the minimum connections that keep the whole network up and running. For each incremental depth limit, the corresponding node generating sequence should be legibly printed in the result Consider Heuristic Value as : The edge cost is an approximation towards the transmission cost between any pair of nodes. For heuristic design, consider all the possible paths between any arbitrary node n to the goal node. The average of the total transmission cost across all these paths is the heuristic value h(n) Consider the following undirected graph definition : class Graph: def __init__(self): self.graph = defaultdict(list) def get_neighbors(self, node): return self.graph.get(node, []) def add_edge(self, u, v, weight=1): self.graph[u].append((v, weight)) self.graph[v].append((u, weight)) # Assuming an undirected graph Consider Undirected Weighted Graph ….
Please do not give solution in image formate thanku.
Write a Python code for the following scenario :
1: Use Breadth First Search and Recursive Best First Search and implement the algorithms in Python. The program should be able to dynamically take in start and goal nodes from the user at run time. Compare to interpret the results in terms of the algorithm working, performance & shortest path if obtained relevant to the given problem
2: Print the minimum connections that keep the whole network up and running. For each incremental depth limit, the corresponding node generating sequence should be legibly printed in the result
Consider Heuristic Value as :
The edge cost is an approximation towards the transmission cost between any pair of nodes. For heuristic design, consider all the possible paths between any arbitrary node n to the goal node. The average of the total transmission cost across all these paths is the heuristic value h(n)
Consider the following undirected graph definition :
class Graph:
def __init__(self):
self.graph = defaultdict(list)
def get_neighbors(self, node):
return self.graph.get(node, [])
def add_edge(self, u, v, weight=1):
self.graph[u].append((v, weight))
self.graph[v].append((u, weight)) # Assuming an undirected graph
Consider Undirected Weighted Graph ….
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