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89 lines
2.3 KiB
Python
89 lines
2.3 KiB
Python
"""
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https://en.wikipedia.org/wiki/Breadth-first_search
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pseudo-code:
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breadth_first_search(graph G, start vertex s):
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// all nodes initially unexplored
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mark s as explored
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let Q = queue data structure, initialized with s
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while Q is non-empty:
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remove the first node of Q, call it v
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for each edge(v, w): // for w in graph[v]
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if w unexplored:
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mark w as explored
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add w to Q (at the end)
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"""
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from __future__ import annotations
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from collections import deque
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from queue import Queue
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from timeit import timeit
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G = {
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"A": ["B", "C"],
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"B": ["A", "D", "E"],
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"C": ["A", "F"],
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"D": ["B"],
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"E": ["B", "F"],
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"F": ["C", "E"],
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}
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def breadth_first_search(graph: dict, start: str) -> list[str]:
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"""
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Implementation of breadth first search using queue.Queue.
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>>> ''.join(breadth_first_search(G, 'A'))
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'ABCDEF'
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"""
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explored = {start}
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result = [start]
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queue: Queue = Queue()
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queue.put(start)
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while not queue.empty():
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v = queue.get()
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for w in graph[v]:
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if w not in explored:
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explored.add(w)
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result.append(w)
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queue.put(w)
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return result
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def breadth_first_search_with_deque(graph: dict, start: str) -> list[str]:
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"""
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Implementation of breadth first search using collection.queue.
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>>> ''.join(breadth_first_search_with_deque(G, 'A'))
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'ABCDEF'
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"""
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visited = {start}
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result = [start]
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queue = deque([start])
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while queue:
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v = queue.popleft()
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for child in graph[v]:
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if child not in visited:
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visited.add(child)
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result.append(child)
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queue.append(child)
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return result
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def benchmark_function(name: str) -> None:
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setup = f"from __main__ import G, {name}"
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number = 10000
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res = timeit(f"{name}(G, 'A')", setup=setup, number=number)
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print(f"{name:<35} finished {number} runs in {res:.5f} seconds")
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if __name__ == "__main__":
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import doctest
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doctest.testmod()
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benchmark_function("breadth_first_search")
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benchmark_function("breadth_first_search_with_deque")
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# breadth_first_search finished 10000 runs in 0.20999 seconds
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# breadth_first_search_with_deque finished 10000 runs in 0.01421 seconds
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