Python/graphs/depth_first_search.py
Christian Clauss cecf43d648
Pyupgrade to Python 3.9 (#4718)
* Pyupgrade to Python 3.9

* updating DIRECTORY.md

Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
2021-09-07 13:37:03 +02:00

48 lines
1.4 KiB
Python

"""Non recursive implementation of a DFS algorithm."""
from __future__ import annotations
def depth_first_search(graph: dict, start: str) -> set[str]:
"""Depth First Search on Graph
:param graph: directed graph in dictionary format
:param start: starting vertex as a string
:returns: the trace of the search
>>> input_G = { "A": ["B", "C", "D"], "B": ["A", "D", "E"],
... "C": ["A", "F"], "D": ["B", "D"], "E": ["B", "F"],
... "F": ["C", "E", "G"], "G": ["F"] }
>>> output_G = list({'A', 'B', 'C', 'D', 'E', 'F', 'G'})
>>> all(x in output_G for x in list(depth_first_search(input_G, "A")))
True
>>> all(x in output_G for x in list(depth_first_search(input_G, "G")))
True
"""
explored, stack = set(start), [start]
while stack:
v = stack.pop()
explored.add(v)
# Differences from BFS:
# 1) pop last element instead of first one
# 2) add adjacent elements to stack without exploring them
for adj in reversed(graph[v]):
if adj not in explored:
stack.append(adj)
return explored
G = {
"A": ["B", "C", "D"],
"B": ["A", "D", "E"],
"C": ["A", "F"],
"D": ["B", "D"],
"E": ["B", "F"],
"F": ["C", "E", "G"],
"G": ["F"],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
print(depth_first_search(G, "A"))