AI & MACHINE LEARNING PROGRAM • LEVEL 23 — CLASSICAL AI SEARCH
Search a Graph with BFS with Python
Learn search a graph with bfs with python with a short, executable Python example.
PROBLEM UNDERSTANDING
Input and expected output
No input required
['A', 'B', 'C', 'D']
COMPLETE PYTHON PROGRAM
Complete Python implementation
from collections import deque
graph={'A':['B','C'],'B':['D'],'C':[],'D':[]}
queue,visited=deque(['A']),[]
while queue:
node=queue.popleft()
if node not in visited:
visited.append(node); queue.extend(graph[node])
print(visited)CURRENT STEP
Select Start to walk through the important lines.
SELECTED LINE
No line selected
EXPECTED OUTPUT FOR THE SAMPLE
['A', 'B', 'C', 'D']
PROGRAM EXPLANATION
Algorithm and explanation
- Import the required standard-library tools and prepare the sample data for search a graph with bfs.
- Process the data step by step using BFS and Graph search.
- Display the result for search a graph with bfs and compare it with the documented sample output.
This example of search a graph with bfs processes the sample values in a controlled iteration. It demonstrates BFS and Graph search and prints a deterministic result that can be checked against the sample output.
EFFICIENCY
Time and space complexity
O(V + E)
O(V)
DEBUGGING CHECKLIST
Common mistakes
For search a graph with bfs, keep the data shape and value types consistent with BFS.
Keep every dependent statement inside the correct indented Python block.
Verify the final BFS and Graph search result against the sample before trying new data.