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Copy pathmulti_heuristic_astar.py
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314 lines (267 loc) · 8.46 KB
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import heapq
import numpy as np
TPos = tuple[int, int]
class PriorityQueue:
def __init__(self):
self.elements = []
self.set = set()
def minkey(self):
if not self.empty():
return self.elements[0][0]
else:
return float("inf")
def empty(self):
return len(self.elements) == 0
def put(self, item, priority):
if item not in self.set:
heapq.heappush(self.elements, (priority, item))
self.set.add(item)
else:
# update
# print("update", item)
temp = []
(pri, x) = heapq.heappop(self.elements)
while x != item:
temp.append((pri, x))
(pri, x) = heapq.heappop(self.elements)
temp.append((priority, item))
for (pro, xxx) in temp:
heapq.heappush(self.elements, (pro, xxx))
def remove_element(self, item):
if item in self.set:
self.set.remove(item)
temp = []
(pro, x) = heapq.heappop(self.elements)
while x != item:
temp.append((pro, x))
(pro, x) = heapq.heappop(self.elements)
for (prito, yyy) in temp:
heapq.heappush(self.elements, (prito, yyy))
def top_show(self):
return self.elements[0][1]
def get(self):
(priority, item) = heapq.heappop(self.elements)
self.set.remove(item)
return (priority, item)
def consistent_heuristic(P: TPos, goal: TPos):
# euclidean distance
a = np.array(P)
b = np.array(goal)
return np.linalg.norm(a - b)
def heuristic_2(P: TPos, goal: TPos):
# integer division by time variable
return consistent_heuristic(P, goal) // t
def heuristic_1(P: TPos, goal: TPos):
# manhattan distance
return abs(P[0] - goal[0]) + abs(P[1] - goal[1])
def key(start: TPos, i: int, goal: TPos, g_function: dict[TPos, float]):
ans = g_function[start] + W1 * heuristics[i](start, goal)
return ans
def do_something(back_pointer, goal, start):
grid = np.chararray((n, n))
for i in range(n):
for j in range(n):
grid[i][j] = "*"
for i in range(n):
for j in range(n):
if (j, (n - 1) - i) in blocks:
grid[i][j] = "#"
grid[0][(n - 1)] = "-"
x = back_pointer[goal]
while x != start:
(x_c, y_c) = x
# print(x)
grid[(n - 1) - y_c][x_c] = "-"
x = back_pointer[x]
grid[(n - 1)][0] = "-"
for i in range(n):
for j in range(n):
if (i, j) == (0, n - 1):
print(grid[i][j], end=" ")
print("<-- End position", end=" ")
else:
print(grid[i][j], end=" ")
print()
print("^")
print("Start position")
print()
print("# is an obstacle")
print("- is the path taken by algorithm")
print("PATH TAKEN BY THE ALGORITHM IS:-")
x = back_pointer[goal]
while x != start:
print(x, end=" ")
x = back_pointer[x]
print(x)
quit()
def valid(p: TPos):
if p[0] < 0 or p[0] > n - 1:
return False
if p[1] < 0 or p[1] > n - 1:
return False
return True
def expand_state(
s,
j,
visited,
g_function,
close_list_anchor,
close_list_inad,
open_list,
back_pointer,
):
for itera in range(n_heuristic):
open_list[itera].remove_element(s)
# print("s", s)
# print("j", j)
(x, y) = s
left = (x - 1, y)
right = (x + 1, y)
up = (x, y + 1)
down = (x, y - 1)
for neighbours in [left, right, up, down]:
if neighbours not in blocks:
if valid(neighbours) and neighbours not in visited:
# print("neighbour", neighbours)
visited.add(neighbours)
back_pointer[neighbours] = -1
g_function[neighbours] = float("inf")
if valid(neighbours) and g_function[neighbours] > g_function[s] + 1:
g_function[neighbours] = g_function[s] + 1
back_pointer[neighbours] = s
if neighbours not in close_list_anchor:
open_list[0].put(neighbours, key(neighbours, 0, goal, g_function))
if neighbours not in close_list_inad:
for var in range(1, n_heuristic):
if key(neighbours, var, goal, g_function) <= W2 * key(
neighbours, 0, goal, g_function
):
open_list[j].put(
neighbours, key(neighbours, var, goal, g_function)
)
def make_common_ground():
some_list = []
for x in range(1, 5):
for y in range(1, 6):
some_list.append((x, y))
for x in range(15, 20):
some_list.append((x, 17))
for x in range(10, 19):
for y in range(1, 15):
some_list.append((x, y))
# L block
for x in range(1, 4):
for y in range(12, 19):
some_list.append((x, y))
for x in range(3, 13):
for y in range(16, 19):
some_list.append((x, y))
return some_list
heuristics = {0: consistent_heuristic, 1: heuristic_1, 2: heuristic_2}
blocks_blk = [
(0, 1),
(1, 1),
(2, 1),
(3, 1),
(4, 1),
(5, 1),
(6, 1),
(7, 1),
(8, 1),
(9, 1),
(10, 1),
(11, 1),
(12, 1),
(13, 1),
(14, 1),
(15, 1),
(16, 1),
(17, 1),
(18, 1),
(19, 1),
]
blocks_all = make_common_ground()
blocks = blocks_blk
# hyper parameters
W1 = 1
W2 = 1
n = 20
n_heuristic = 3 # one consistent and two other inconsistent
# start and end destination
start = (0, 0)
goal = (n - 1, n - 1)
t = 1
def multi_a_star(start: TPos, goal: TPos, n_heuristic: int):
g_function = {start: 0, goal: float("inf")}
back_pointer = {start: -1, goal: -1}
open_list = []
visited = set()
for i in range(n_heuristic):
open_list.append(PriorityQueue())
open_list[i].put(start, key(start, i, goal, g_function))
close_list_anchor: list[int] = []
close_list_inad: list[int] = []
while open_list[0].minkey() < float("inf"):
for i in range(1, n_heuristic):
# print(open_list[0].minkey(), open_list[i].minkey())
if open_list[i].minkey() <= W2 * open_list[0].minkey():
global t
t += 1
if g_function[goal] <= open_list[i].minkey():
if g_function[goal] < float("inf"):
do_something(back_pointer, goal, start)
else:
_, get_s = open_list[i].top_show()
visited.add(get_s)
expand_state(
get_s,
i,
visited,
g_function,
close_list_anchor,
close_list_inad,
open_list,
back_pointer,
)
close_list_inad.append(get_s)
else:
if g_function[goal] <= open_list[0].minkey():
if g_function[goal] < float("inf"):
do_something(back_pointer, goal, start)
else:
get_s = open_list[0].top_show()
visited.add(get_s)
expand_state(
get_s,
0,
visited,
g_function,
close_list_anchor,
close_list_inad,
open_list,
back_pointer,
)
close_list_anchor.append(get_s)
print("No path found to goal")
print()
for i in range(n - 1, -1, -1):
for j in range(n):
if (j, i) in blocks:
print("#", end=" ")
elif (j, i) in back_pointer:
if (j, i) == (n - 1, n - 1):
print("*", end=" ")
else:
print("-", end=" ")
else:
print("*", end=" ")
if (j, i) == (n - 1, n - 1):
print("<-- End position", end=" ")
print()
print("^")
print("Start position")
print()
print("# is an obstacle")
print("- is the path taken by algorithm")
if __name__ == "__main__":
multi_a_star(start, goal, n_heuristic)
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