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* Create TestShiva * Delete TestShiva * Implementation of the Dijkstra-Algorithm in a binary grid * Update double_ended_queue.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update least_common_multiple.py * Update sol1.py * Update pyproject.toml * Update pyproject.toml * https://github.com/astral-sh/ruff-pre-commit v0.0.274 --------- Co-authored-by: ShivaDahal99 <130563462+ShivaDahal99@users.noreply.github.com> Co-authored-by: jlhuhn <134317018+jlhuhn@users.noreply.github.com> Co-authored-by: Christian Clauss <cclauss@me.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
90 lines
2.8 KiB
Python
90 lines
2.8 KiB
Python
"""
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This script implements the Dijkstra algorithm on a binary grid.
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The grid consists of 0s and 1s, where 1 represents
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a walkable node and 0 represents an obstacle.
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The algorithm finds the shortest path from a start node to a destination node.
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Diagonal movement can be allowed or disallowed.
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"""
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from heapq import heappop, heappush
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import numpy as np
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def dijkstra(
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grid: np.ndarray,
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source: tuple[int, int],
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destination: tuple[int, int],
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allow_diagonal: bool,
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) -> tuple[float | int, list[tuple[int, int]]]:
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"""
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Implements Dijkstra's algorithm on a binary grid.
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Args:
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grid (np.ndarray): A 2D numpy array representing the grid.
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1 represents a walkable node and 0 represents an obstacle.
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source (Tuple[int, int]): A tuple representing the start node.
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destination (Tuple[int, int]): A tuple representing the
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destination node.
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allow_diagonal (bool): A boolean determining whether
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diagonal movements are allowed.
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Returns:
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Tuple[Union[float, int], List[Tuple[int, int]]]:
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The shortest distance from the start node to the destination node
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and the shortest path as a list of nodes.
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>>> dijkstra(np.array([[1, 1, 1], [0, 1, 0], [0, 1, 1]]), (0, 0), (2, 2), False)
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(4.0, [(0, 0), (0, 1), (1, 1), (2, 1), (2, 2)])
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>>> dijkstra(np.array([[1, 1, 1], [0, 1, 0], [0, 1, 1]]), (0, 0), (2, 2), True)
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(2.0, [(0, 0), (1, 1), (2, 2)])
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>>> dijkstra(np.array([[1, 1, 1], [0, 0, 1], [0, 1, 1]]), (0, 0), (2, 2), False)
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(4.0, [(0, 0), (0, 1), (0, 2), (1, 2), (2, 2)])
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"""
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rows, cols = grid.shape
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dx = [-1, 1, 0, 0]
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dy = [0, 0, -1, 1]
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if allow_diagonal:
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dx += [-1, -1, 1, 1]
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dy += [-1, 1, -1, 1]
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queue, visited = [(0, source)], set()
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matrix = np.full((rows, cols), np.inf)
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matrix[source] = 0
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predecessors = np.empty((rows, cols), dtype=object)
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predecessors[source] = None
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while queue:
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(dist, (x, y)) = heappop(queue)
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if (x, y) in visited:
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continue
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visited.add((x, y))
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if (x, y) == destination:
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path = []
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while (x, y) != source:
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path.append((x, y))
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x, y = predecessors[x, y]
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path.append(source) # add the source manually
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path.reverse()
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return matrix[destination], path
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for i in range(len(dx)):
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nx, ny = x + dx[i], y + dy[i]
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if 0 <= nx < rows and 0 <= ny < cols:
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next_node = grid[nx][ny]
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if next_node == 1 and matrix[nx, ny] > dist + 1:
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heappush(queue, (dist + 1, (nx, ny)))
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matrix[nx, ny] = dist + 1
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predecessors[nx, ny] = (x, y)
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return np.inf, []
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if __name__ == "__main__":
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import doctest
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doctest.testmod()
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