-
Notifications
You must be signed in to change notification settings - Fork 2
/
utils.py
153 lines (117 loc) · 3.87 KB
/
utils.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
from dataclasses import dataclass
import heapq
from typing import Callable, Iterator, List, Optional
from consts import ACTIONS, DOWN, LEFT, RIGHT, UP
class PriorityQueue:
def __init__(self):
self.heap = []
self.count = 0
def empty(self):
return len(self.heap) == 0
def push(self, item, priority):
entry = (priority, self.count, item)
heapq.heappush(self.heap, entry)
self.count += 1
def pop(self):
(_, _, item) = heapq.heappop(self.heap)
return item
def __len__(self):
return len(self.heap)
class Point:
def __init__(self, x: int, y: int) -> None:
self._x = x
self._y = y
@property
def x(self) -> int:
return self._x
@property
def y(self) -> int:
return self._y
def update(self, new: "Point") -> None:
self._x = new.x
self._y = new.y
def __hash__(self) -> int:
return hash((self._x, self._y))
def __eq__(self, other) -> bool:
return self.x == other.x and self.y == other.y
def __neq__(self, other) -> bool:
return not self == other
def __repr__(self) -> str:
return f"Point(x={self.x}, y={self.y})"
class ShortestPathState:
def __init__(
self,
pos: Point,
cost: int,
prev: Optional["ShortestPathState"] = None,
action: Optional[str] = None,
):
self.pos = pos
self.cost = cost
self.prev = prev
self.action = action
def __eq__(self, other):
return self.pos == other.pos
def __hash__(self) -> int:
return hash(self.pos)
class ShortestPathSearchProblem:
def __init__(self, start: Point, goal: Point, visited: List[List[bool]]):
self.start = start
self.goal = goal
self.visited = visited
def get_start_state(self) -> ShortestPathState:
return ShortestPathState(self.start, 0)
def is_goal_state(self, state: ShortestPathState) -> bool:
return state.pos == self.goal
def get_successors(self, state: ShortestPathState) -> Iterator[ShortestPathState]:
m = len(self.visited)
n = len(self.visited[0])
for dir in (LEFT, DOWN, RIGHT, UP):
x, y = dir
children = Point(state.pos.x + x, state.pos.y + y)
cond = (
children.x >= 0
and children.x < n
and children.y >= 0
and children.y < m
and (self.visited[children.y][children.x] or
Point(children.x, children.y) == self.goal
)
)
if cond:
yield ShortestPathState(children, state.cost + 1, state, ACTIONS[dir])
def null_heuristic(
problem: ShortestPathSearchProblem, state: ShortestPathSearchProblem
) -> int:
return 0
# Manhattan heuristic
def manhattan_heuristic(
problem: ShortestPathSearchProblem, state: ShortestPathState
) -> int:
goal_pos = problem.goal
curr_pos = state.pos
return abs(goal_pos.x - curr_pos.x) + abs(goal_pos.y - curr_pos.y)
def a_star_route(
problem: ShortestPathSearchProblem,
heuristic: Callable[
[ShortestPathSearchProblem, ShortestPathState], int
] = null_heuristic,
) -> List[str]:
pq = PriorityQueue()
start_state = problem.get_start_state()
pq.push(start_state, start_state.cost + heuristic(problem, start_state))
visited = set()
solution = []
while not pq.empty():
state = pq.pop()
if problem.is_goal_state(state):
while state:
if state.action:
solution.insert(0, state.action)
state = state.prev
break
visited.add(state)
for child in problem.get_successors(state):
if child not in visited:
pq.push(child, child.cost + heuristic(problem, child))
return solution