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Commit ca445f5

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prajwalc22pre-commit-ci[bot]MaximSmolskiy
authored
Add bidirectional search algorithm implementation (#12649)
* Add bidirectional search algorithm implementation * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix style and linting issues in bidirectional search * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add doctest for main function * Add doctest for main function * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fixed deprications * fixed deprications * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * removed unused import * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update bidirectional_search.py * Update bidirectional_search.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update bidirectional_search.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Maxim Smolskiy <mithridatus@mail.ru>
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‎graphs/bidirectional_search.py

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"""
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Bidirectional Search Algorithm.
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This algorithm searches from both the source and target nodes simultaneously,
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meeting somewhere in the middle. This approach can significantly reduce the
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search space compared to a traditional one-directional search.
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Time Complexity: O(b^(d/2)) where b is the branching factor and d is the depth
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Space Complexity: O(b^(d/2))
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https://en.wikipedia.org/wiki/Bidirectional_search
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"""
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from collections import deque
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def expand_search(
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graph: dict[int, list[int]],
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queue: deque[int],
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parents: dict[int, int | None],
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opposite_direction_parents: dict[int, int | None],
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) -> int | None:
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if not queue:
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return None
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current = queue.popleft()
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for neighbor in graph[current]:
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if neighbor in parents:
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continue
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parents[neighbor] = current
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queue.append(neighbor)
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# Check if this creates an intersection
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if neighbor in opposite_direction_parents:
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return neighbor
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return None
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def construct_path(current: int | None, parents: dict[int, int | None]) -> list[int]:
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path: list[int] = []
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while current is not None:
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path.append(current)
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current = parents[current]
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return path
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def bidirectional_search(
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graph: dict[int, list[int]], start: int, goal: int
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) -> list[int] | None:
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"""
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Perform bidirectional search on a graph to find the shortest path.
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Args:
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graph: A dictionary where keys are nodes and values are lists of adjacent nodes
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start: The starting node
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goal: The target node
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Returns:
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A list representing the path from start to goal, or None if no path exists
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Examples:
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>>> graph = {
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... 0: [1, 2],
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... 1: [0, 3, 4],
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... 2: [0, 5, 6],
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... 3: [1, 7],
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... 4: [1, 8],
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... 5: [2, 9],
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... 6: [2, 10],
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... 7: [3, 11],
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... 8: [4, 11],
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... 9: [5, 11],
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... 10: [6, 11],
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... 11: [7, 8, 9, 10],
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... }
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>>> bidirectional_search(graph=graph, start=0, goal=11)
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[0, 1, 3, 7, 11]
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>>> bidirectional_search(graph=graph, start=5, goal=5)
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[5]
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>>> disconnected_graph = {
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... 0: [1, 2],
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... 1: [0],
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... 2: [0],
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... 3: [4],
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... 4: [3],
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... }
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>>> bidirectional_search(graph=disconnected_graph, start=0, goal=3) is None
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True
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"""
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if start == goal:
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return [start]
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# Check if start and goal are in the graph
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if start not in graph or goal not in graph:
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return None
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# Initialize forward and backward search dictionaries
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# Each maps a node to its parent in the search
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forward_parents: dict[int, int | None] = {start: None}
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backward_parents: dict[int, int | None] = {goal: None}
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# Initialize forward and backward search queues
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forward_queue = deque([start])
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backward_queue = deque([goal])
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# Intersection node (where the two searches meet)
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intersection = None
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# Continue until both queues are empty or an intersection is found
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while forward_queue and backward_queue and intersection is None:
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# Expand forward search
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intersection = expand_search(
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graph=graph,
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queue=forward_queue,
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parents=forward_parents,
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opposite_direction_parents=backward_parents,
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)
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# If no intersection found, expand backward search
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if intersection is not None:
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break
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intersection = expand_search(
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graph=graph,
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queue=backward_queue,
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parents=backward_parents,
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opposite_direction_parents=forward_parents,
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)
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# If no intersection found, there's no path
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if intersection is None:
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return None
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# Construct path from start to intersection
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forward_path: list[int] = construct_path(
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current=intersection, parents=forward_parents
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)
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forward_path.reverse()
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# Construct path from intersection to goal
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backward_path: list[int] = construct_path(
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current=backward_parents[intersection], parents=backward_parents
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)
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# Return the complete path
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return forward_path + backward_path
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def main() -> None:
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"""
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Run example of bidirectional search algorithm.
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Examples:
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>>> main() # doctest: +NORMALIZE_WHITESPACE
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Path from 0 to 11: [0, 1, 3, 7, 11]
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Path from 5 to 5: [5]
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Path from 0 to 3: None
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"""
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# Example graph represented as an adjacency list
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example_graph = {
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0: [1, 2],
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1: [0, 3, 4],
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2: [0, 5, 6],
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3: [1, 7],
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4: [1, 8],
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5: [2, 9],
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6: [2, 10],
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7: [3, 11],
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8: [4, 11],
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9: [5, 11],
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10: [6, 11],
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11: [7, 8, 9, 10],
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}
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# Test case 1: Path exists
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start, goal = 0, 11
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path = bidirectional_search(graph=example_graph, start=start, goal=goal)
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print(f"Path from {start} to {goal}: {path}")
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# Test case 2: Start and goal are the same
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start, goal = 5, 5
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path = bidirectional_search(graph=example_graph, start=start, goal=goal)
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print(f"Path from {start} to {goal}: {path}")
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# Test case 3: No path exists (disconnected graph)
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disconnected_graph = {
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0: [1, 2],
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1: [0],
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2: [0],
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3: [4],
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4: [3],
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}
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start, goal = 0, 3
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path = bidirectional_search(graph=disconnected_graph, start=start, goal=goal)
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print(f"Path from {start} to {goal}: {path}")
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if __name__ == "__main__":
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main()

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