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061614880d
* [mypy] fix type annotations for graphs/a_star.py #4052 * updating DIRECTORY.md * Add from __future__ import anotations * rename delta by DIRECTIONS Co-authored-by: John Law <johnlaw.po@gmail.com> * Rename delta by DIRECTIONS in all code * Enclose script in __main__ code block * Refactor DIRECTIONS with comments for readibility * Delete heuristic example comment * Do not print, return all values * Fix multilines * fix black * Update a_star.py Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com> Co-authored-by: John Law <johnlaw.po@gmail.com>
109 lines
3.2 KiB
Python
109 lines
3.2 KiB
Python
from __future__ import annotations
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DIRECTIONS = [
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[-1, 0], # left
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[0, -1], # down
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[1, 0], # right
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[0, 1], # up
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]
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# function to search the path
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def search(
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grid: list[list[int]],
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init: list[int],
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goal: list[int],
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cost: int,
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heuristic: list[list[int]],
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) -> tuple[list[list[int]], list[list[int]]]:
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closed = [
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[0 for col in range(len(grid[0]))] for row in range(len(grid))
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] # the reference grid
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closed[init[0]][init[1]] = 1
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action = [
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[0 for col in range(len(grid[0]))] for row in range(len(grid))
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] # the action grid
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x = init[0]
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y = init[1]
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g = 0
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f = g + heuristic[x][y] # cost from starting cell to destination cell
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cell = [[f, g, x, y]]
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found = False # flag that is set when search is complete
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resign = False # flag set if we can't find expand
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while not found and not resign:
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if len(cell) == 0:
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raise ValueError("Algorithm is unable to find solution")
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else: # to choose the least costliest action so as to move closer to the goal
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cell.sort()
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cell.reverse()
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next = cell.pop()
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x = next[2]
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y = next[3]
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g = next[1]
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if x == goal[0] and y == goal[1]:
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found = True
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else:
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for i in range(len(DIRECTIONS)): # to try out different valid actions
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x2 = x + DIRECTIONS[i][0]
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y2 = y + DIRECTIONS[i][1]
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if x2 >= 0 and x2 < len(grid) and y2 >= 0 and y2 < len(grid[0]):
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if closed[x2][y2] == 0 and grid[x2][y2] == 0:
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g2 = g + cost
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f2 = g2 + heuristic[x2][y2]
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cell.append([f2, g2, x2, y2])
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closed[x2][y2] = 1
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action[x2][y2] = i
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invpath = []
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x = goal[0]
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y = goal[1]
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invpath.append([x, y]) # we get the reverse path from here
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while x != init[0] or y != init[1]:
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x2 = x - DIRECTIONS[action[x][y]][0]
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y2 = y - DIRECTIONS[action[x][y]][1]
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x = x2
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y = y2
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invpath.append([x, y])
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path = []
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for i in range(len(invpath)):
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path.append(invpath[len(invpath) - 1 - i])
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return path, action
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if __name__ == "__main__":
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grid = [
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[0, 1, 0, 0, 0, 0],
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[0, 1, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
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[0, 1, 0, 0, 0, 0],
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[0, 1, 0, 0, 1, 0],
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[0, 0, 0, 0, 1, 0],
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]
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init = [0, 0]
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# all coordinates are given in format [y,x]
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goal = [len(grid) - 1, len(grid[0]) - 1]
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cost = 1
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# the cost map which pushes the path closer to the goal
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heuristic = [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
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for i in range(len(grid)):
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for j in range(len(grid[0])):
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heuristic[i][j] = abs(i - goal[0]) + abs(j - goal[1])
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if grid[i][j] == 1:
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# added extra penalty in the heuristic map
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heuristic[i][j] = 99
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path, action = search(grid, init, goal, cost, heuristic)
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print("ACTION MAP")
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for i in range(len(action)):
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print(action[i])
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for i in range(len(path)):
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print(path[i])
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