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