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* Pyupgrade to Python 3.9 * updating DIRECTORY.md Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
186 lines
5.1 KiB
Python
186 lines
5.1 KiB
Python
"""
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https://en.wikipedia.org/wiki/Best-first_search#Greedy_BFS
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"""
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from __future__ import annotations
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Path = list[tuple[int, int]]
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grid = [
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[0, 0, 0, 0, 0, 0, 0],
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[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 1, 0, 0, 0, 0],
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[1, 0, 1, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 1, 0, 0],
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]
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delta = ([-1, 0], [0, -1], [1, 0], [0, 1]) # up, left, down, right
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class Node:
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"""
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>>> k = Node(0, 0, 4, 5, 0, None)
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>>> k.calculate_heuristic()
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9
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>>> n = Node(1, 4, 3, 4, 2, None)
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>>> n.calculate_heuristic()
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2
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>>> l = [k, n]
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>>> n == l[0]
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False
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>>> l.sort()
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>>> n == l[0]
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True
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"""
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def __init__(
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self,
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pos_x: int,
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pos_y: int,
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goal_x: int,
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goal_y: int,
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g_cost: float,
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parent: Node | None,
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):
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self.pos_x = pos_x
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self.pos_y = pos_y
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self.pos = (pos_y, pos_x)
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self.goal_x = goal_x
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self.goal_y = goal_y
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self.g_cost = g_cost
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self.parent = parent
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self.f_cost = self.calculate_heuristic()
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def calculate_heuristic(self) -> float:
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"""
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The heuristic here is the Manhattan Distance
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Could elaborate to offer more than one choice
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"""
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dy = abs(self.pos_x - self.goal_x)
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dx = abs(self.pos_y - self.goal_y)
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return dx + dy
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def __lt__(self, other) -> bool:
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return self.f_cost < other.f_cost
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class GreedyBestFirst:
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"""
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>>> gbf = GreedyBestFirst((0, 0), (len(grid) - 1, len(grid[0]) - 1))
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>>> [x.pos for x in gbf.get_successors(gbf.start)]
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[(1, 0), (0, 1)]
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>>> (gbf.start.pos_y + delta[3][0], gbf.start.pos_x + delta[3][1])
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(0, 1)
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>>> (gbf.start.pos_y + delta[2][0], gbf.start.pos_x + delta[2][1])
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(1, 0)
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>>> gbf.retrace_path(gbf.start)
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[(0, 0)]
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>>> gbf.search() # doctest: +NORMALIZE_WHITESPACE
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[(0, 0), (1, 0), (2, 0), (3, 0), (3, 1), (4, 1), (5, 1), (6, 1),
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(6, 2), (6, 3), (5, 3), (5, 4), (5, 5), (6, 5), (6, 6)]
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"""
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def __init__(self, start: tuple[int, int], goal: tuple[int, int]):
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self.start = Node(start[1], start[0], goal[1], goal[0], 0, None)
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self.target = Node(goal[1], goal[0], goal[1], goal[0], 99999, None)
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self.open_nodes = [self.start]
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self.closed_nodes: list[Node] = []
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self.reached = False
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def search(self) -> Path | None:
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"""
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Search for the path,
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if a path is not found, only the starting position is returned
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"""
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while self.open_nodes:
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# Open Nodes are sorted using __lt__
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self.open_nodes.sort()
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current_node = self.open_nodes.pop(0)
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if current_node.pos == self.target.pos:
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self.reached = True
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return self.retrace_path(current_node)
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self.closed_nodes.append(current_node)
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successors = self.get_successors(current_node)
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for child_node in successors:
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if child_node in self.closed_nodes:
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continue
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if child_node not in self.open_nodes:
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self.open_nodes.append(child_node)
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else:
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# retrieve the best current path
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better_node = self.open_nodes.pop(self.open_nodes.index(child_node))
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if child_node.g_cost < better_node.g_cost:
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self.open_nodes.append(child_node)
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else:
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self.open_nodes.append(better_node)
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if not self.reached:
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return [self.start.pos]
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return None
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def get_successors(self, parent: Node) -> list[Node]:
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"""
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Returns a list of successors (both in the grid and free spaces)
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"""
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successors = []
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for action in delta:
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pos_x = parent.pos_x + action[1]
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pos_y = parent.pos_y + action[0]
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if not (0 <= pos_x <= len(grid[0]) - 1 and 0 <= pos_y <= len(grid) - 1):
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continue
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if grid[pos_y][pos_x] != 0:
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continue
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successors.append(
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Node(
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pos_x,
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pos_y,
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self.target.pos_y,
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self.target.pos_x,
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parent.g_cost + 1,
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parent,
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)
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)
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return successors
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def retrace_path(self, node: Node | None) -> Path:
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"""
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Retrace the path from parents to parents until start node
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"""
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current_node = node
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path = []
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while current_node is not None:
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path.append((current_node.pos_y, current_node.pos_x))
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current_node = current_node.parent
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path.reverse()
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return path
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if __name__ == "__main__":
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init = (0, 0)
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goal = (len(grid) - 1, len(grid[0]) - 1)
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for elem in grid:
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print(elem)
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print("------")
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greedy_bf = GreedyBestFirst(init, goal)
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path = greedy_bf.search()
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if path:
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for pos_x, pos_y in path:
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grid[pos_x][pos_y] = 2
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for elem in grid:
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print(elem)
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