visited={} Level={} #distance parent={} ofs_traversal_outp queue3DQueue () For node in adj_li visited[node]-
visited={} Level={} #distance parent={} ofs_traversal_outp queue3DQueue () For node in adj_li visited[node]-
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Solve the code using python language
![BFS can be implemented using Queue. Remember that stack is use to create a DFS. The steps for BFS
using Queue are:
1. Create empty queue and enqueue root node to it.
2. Do the following when queue is not empty
a. Dequeue a node from queue and print it
b. Enqueue left child of popped node to queue if not null
c. Enqueue right child of popped node to queue if not nul"
Implement the code shown in Figure
shown below: Show the outcome !
#bfs implementation
visited={}
12
13
level={} #distance dictionary
parent={}
bfs_traversal_output3[]
14
15
16
17
queue=Queue ()
18 v for node in adj_list.keys():
visited[node]=False
parent[node]=None
level[node]= -1 #inf
print(visited)
19
20
21
22
print(parent)
print(level)
#define source node
23
24
25
26
S="A"
visited[s]=True
level[s]=0
27
28
29
queue.put(s) #add source node to the queue
30 v while not queue.empty():
31
u=queue.get() #pop first element of queue
#add vertex 'u' to our bfs_traversal
bfs_traversal_output.append(u) #append it
for v in adj_list[u]:
if not visited[v]:
visited[v]=True
parent[v]=u
level[v]=level[u]+1
queue.put(v)
32
33
34 v
35 v
36
37
38
39
40
print(bfs_traversal_output)](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F3dfac456-657f-4b8c-b807-4b4489df3fc8%2Ff69ab6ed-453f-4eea-bbe3-82a5cc602602%2F2itq1xl_processed.jpeg&w=3840&q=75)
Transcribed Image Text:BFS can be implemented using Queue. Remember that stack is use to create a DFS. The steps for BFS
using Queue are:
1. Create empty queue and enqueue root node to it.
2. Do the following when queue is not empty
a. Dequeue a node from queue and print it
b. Enqueue left child of popped node to queue if not null
c. Enqueue right child of popped node to queue if not nul"
Implement the code shown in Figure
shown below: Show the outcome !
#bfs implementation
visited={}
12
13
level={} #distance dictionary
parent={}
bfs_traversal_output3[]
14
15
16
17
queue=Queue ()
18 v for node in adj_list.keys():
visited[node]=False
parent[node]=None
level[node]= -1 #inf
print(visited)
19
20
21
22
print(parent)
print(level)
#define source node
23
24
25
26
S="A"
visited[s]=True
level[s]=0
27
28
29
queue.put(s) #add source node to the queue
30 v while not queue.empty():
31
u=queue.get() #pop first element of queue
#add vertex 'u' to our bfs_traversal
bfs_traversal_output.append(u) #append it
for v in adj_list[u]:
if not visited[v]:
visited[v]=True
parent[v]=u
level[v]=level[u]+1
queue.put(v)
32
33
34 v
35 v
36
37
38
39
40
print(bfs_traversal_output)
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