forked from UNN/2026-rff_mp
46 lines
1.5 KiB
Python
46 lines
1.5 KiB
Python
import heapq
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from strategies.pathfinding_strategy import PathFindingStrategy
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class AStarStrategy(PathFindingStrategy):
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name = "A*"
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def heuristic(self, cell, exitCell):
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return abs(cell.x - exitCell.x) + abs(cell.y - exitCell.y)
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def findPath(self, maze, start, exitCell):
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self.visitedCount = 0
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if start is None or exitCell is None:
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return []
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open_set = []
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heapq.heappush(open_set, (0, 0, start.x, start.y, start))
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parent = {}
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g_score = {(start.x, start.y): 0}
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closed = set()
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while open_set:
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f_score, current_g, _, _, current = heapq.heappop(open_set)
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pos = (current.x, current.y)
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if pos in closed:
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continue
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closed.add(pos)
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self.visitedCount += 1
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if current.x == exitCell.x and current.y == exitCell.y:
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return self._restore_path(parent, start, exitCell)
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for neighbor in maze.getNeighbors(current):
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npos = (neighbor.x, neighbor.y)
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tentative_g = current_g + getattr(neighbor, "weight", 1)
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if tentative_g < g_score.get(npos, float("inf")):
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g_score[npos] = tentative_g
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parent[npos] = current
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new_f = tentative_g + self.heuristic(neighbor, exitCell)
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heapq.heappush(open_set, (new_f, tentative_g, neighbor.x, neighbor.y, neighbor))
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return []
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