To study and implement Graph search algorithms in Python Provide the implementation of DFS and BFS algorithms in Python In this lab, you are going to implement searching algorithms in Python. There are two popular searching algorithms i.e. Depth First Search and Breadth First Search. DFS(G,v) ( v is the vertex where the search starts ) Stack S := {}; ( start with an empty stack ) for each vertex u, set visited[u] := false; push S, v; while (S is not empty) do u := pop S; if (not visited[u]) then visited[u] := true; for each unvisited neighbour w of u push S, w; end if end while END DFS() _____________________________________________________________________________ BFS(Graph, root): create empty set S create empty queue Q root.parent = NIL add root to S Q.enqueue(root) while Q is not empty: current = Q.dequeue() if current is the goal: return current for each node n that is adjacent to current: if n is not in S: add n to S n.parent = current Q.enqueue(n)
To study and implement Graph search
Provide the implementation of DFS and BFS algorithms in Python
In this lab, you are going to implement searching algorithms in Python. There are two popular
searching algorithms i.e. Depth First Search and Breadth First Search.
DFS(G,v) ( v is the vertex where the search starts )
Stack S := {}; ( start with an empty stack )
for each vertex u, set visited[u] := false;
push S, v;
while (S is not empty) do
u := pop S;
if (not visited[u]) then
visited[u] := true;
for each unvisited neighbour w of u
push S, w;
end if
end while
END DFS()
_____________________________________________________________________________
BFS(Graph, root):
create empty set S
create empty queue Q
root.parent = NIL
add root to S
Q.enqueue(root)
while Q is not empty:
current = Q.dequeue()
if current is the goal:
return current
for each node n that is adjacent to current:
if n is not in S:
add n to S
n.parent = current
Q.enqueue(n)
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