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4d0a8f2355
* optimized recursive_bubble_sort
* Fixed doctest error due whitespace
* reduce loop times for optimization
* fixup! Format Python code with psf/black push
Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
115 lines
3.3 KiB
Python
115 lines
3.3 KiB
Python
"""Breadth-first search shortest path implementations.
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doctest:
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python -m doctest -v bfs_shortest_path.py
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Manual test:
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python bfs_shortest_path.py
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"""
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graph = {
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"A": ["B", "C", "E"],
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"B": ["A", "D", "E"],
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"C": ["A", "F", "G"],
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"D": ["B"],
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"E": ["A", "B", "D"],
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"F": ["C"],
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"G": ["C"],
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}
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def bfs_shortest_path(graph: dict, start, goal) -> str:
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"""Find shortest path between `start` and `goal` nodes.
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Args:
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graph (dict): node/list of neighboring nodes key/value pairs.
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start: start node.
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goal: target node.
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Returns:
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Shortest path between `start` and `goal` nodes as a string of nodes.
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'Not found' string if no path found.
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Example:
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>>> bfs_shortest_path(graph, "G", "D")
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['G', 'C', 'A', 'B', 'D']
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"""
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# keep track of explored nodes
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explored = []
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# keep track of all the paths to be checked
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queue = [[start]]
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# return path if start is goal
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if start == goal:
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return "That was easy! Start = goal"
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# keeps looping until all possible paths have been checked
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while queue:
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# pop the first path from the queue
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path = queue.pop(0)
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# get the last node from the path
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node = path[-1]
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if node not in explored:
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neighbours = graph[node]
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# go through all neighbour nodes, construct a new path and
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# push it into the queue
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for neighbour in neighbours:
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new_path = list(path)
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new_path.append(neighbour)
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queue.append(new_path)
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# return path if neighbour is goal
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if neighbour == goal:
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return new_path
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# mark node as explored
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explored.append(node)
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# in case there's no path between the 2 nodes
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return "So sorry, but a connecting path doesn't exist :("
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def bfs_shortest_path_distance(graph: dict, start, target) -> int:
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"""Find shortest path distance between `start` and `target` nodes.
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Args:
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graph: node/list of neighboring nodes key/value pairs.
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start: node to start search from.
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target: node to search for.
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Returns:
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Number of edges in shortest path between `start` and `target` nodes.
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-1 if no path exists.
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Example:
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>>> bfs_shortest_path_distance(graph, "G", "D")
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4
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>>> bfs_shortest_path_distance(graph, "A", "A")
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0
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>>> bfs_shortest_path_distance(graph, "A", "H")
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-1
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"""
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if not graph or start not in graph or target not in graph:
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return -1
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if start == target:
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return 0
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queue = [start]
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visited = [start]
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# Keep tab on distances from `start` node.
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dist = {start: 0, target: -1}
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while queue:
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node = queue.pop(0)
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if node == target:
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dist[target] = (
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dist[node] if dist[target] == -1 else min(dist[target], dist[node])
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)
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for adjacent in graph[node]:
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if adjacent not in visited:
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visited.append(adjacent)
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queue.append(adjacent)
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dist[adjacent] = dist[node] + 1
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return dist[target]
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if __name__ == "__main__":
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print(bfs_shortest_path(graph, "G", "D")) # returns ['G', 'C', 'A', 'B', 'D']
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print(bfs_shortest_path_distance(graph, "G", "D")) # returns 4
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