image_recognition #5
@ -1,8 +1,9 @@
|
||||
class Node:
|
||||
def __init__(self, state, parent='', action=''):
|
||||
def __init__(self, state, parent='', action='', distance=0):
|
||||
self.state = state
|
||||
self.parent = parent
|
||||
self.action = action
|
||||
self.distance = distance
|
||||
|
||||
class Search:
|
||||
def __init__(self, cell_size, cell_number):
|
||||
@ -30,11 +31,27 @@ class Search:
|
||||
possible = [['left', x, y, 'DOWN'], ['right', x, y, 'UP']]
|
||||
if x != 0: possible.append(['move', x - self.cell_size, y, 'LEFT'])
|
||||
return possible
|
||||
|
||||
def cost(self, node, stones, goal, flowers):
|
||||
# cost = node.distance
|
||||
cost = 0
|
||||
# cost += 10 if stones[node.state[0], node.state[1]] == 1 else 1
|
||||
cost += 1000000 if (node.state[0], node.state[1]) in stones else 1
|
||||
cost += 30 if (node.state[0], node.state[1]) in flowers else 1
|
||||
|
||||
if node.parent:
|
||||
node = node.parent
|
||||
cost += node.distance # should return only elem.action in prod
|
||||
return cost
|
||||
|
||||
def heuristic(self, node, goal):
|
||||
return abs(node.state[0] - goal[0]) + abs(node.state[1] - goal[1])
|
||||
|
||||
#bandaid to know about stones
|
||||
def astarsearch(self, istate, goaltest, stones):
|
||||
def astarsearch(self, istate, goaltest, stones, flowers):
|
||||
|
||||
#to be expanded
|
||||
def cost(x, y):
|
||||
def cost_old(x, y):
|
||||
if (x, y) in stones:
|
||||
return 10
|
||||
else:
|
||||
@ -45,7 +62,12 @@ class Search:
|
||||
y = istate[1]
|
||||
angle = istate[2]
|
||||
|
||||
fringe = [(Node([x, y, angle]), cost(x, y))] # queue (moves/states to check)
|
||||
# fringe = [(Node([x, y, angle]), cost_old(x, y))] # queue (moves/states to check)
|
||||
fringe = [(Node([x, y, angle]))] # queue (moves/states to check)
|
||||
fringe[0].distance = self.cost(fringe[0], stones, goaltest, flowers)
|
||||
fringe.append((Node([x, y, angle]), self.cost(fringe[0], stones, goaltest, flowers)))
|
||||
fringe.pop(0)
|
||||
|
||||
explored = []
|
||||
|
||||
while True:
|
||||
@ -71,15 +93,18 @@ class Search:
|
||||
explored.append(elem.state)
|
||||
|
||||
for (action, state_x, state_y, state_angle) in self.succ(elem.state):
|
||||
x = Node([state_x, state_y, state_angle], elem, action)
|
||||
x = Node([state_x, state_y, state_angle], elem, action)
|
||||
x.parent = elem
|
||||
|
||||
priority = cost(state_x, state_y)
|
||||
fringe_states = [node.state for (node, p) in fringe]
|
||||
priority = self.cost(elem, stones, goaltest, flowers) + self.heuristic(elem, goaltest)
|
||||
elem.distance = priority
|
||||
# priority = cost_old(x, y) + self.heuristic(elem, goaltest)
|
||||
fringe_states = [node.state for (node, p) in fringe]
|
||||
|
||||
if x.state not in fringe_states and x.state not in explored:
|
||||
fringe.append((x, priority))
|
||||
elif x.state in fringe_states:
|
||||
for i in range(len(fringe)):
|
||||
if fringe[i][0].state == x.state:
|
||||
if fringe[i][1] > priority:
|
||||
fringe[i] = (x, priority)
|
||||
if x.state not in fringe_states and x.state not in explored:
|
||||
fringe.append((x, priority))
|
||||
elif x.state in fringe_states:
|
||||
for i in range(len(fringe)):
|
||||
if fringe[i][0].state == x.state:
|
||||
if fringe[i][1] > priority:
|
||||
fringe[i] = (x, priority)
|
||||
|
2
main.py
2
main.py
@ -102,7 +102,7 @@ class Game:
|
||||
angles = {0: 'UP', 90: 'RIGHT', 270: 'LEFT', 180: 'DOWN'}
|
||||
#bandaid to know about stones
|
||||
tractor_next_moves = astar_search_object.astarsearch(
|
||||
[self.tractor.x, self.tractor.y, angles[self.tractor.angle]], [random_x, random_y], self.blocks.stones)
|
||||
[self.tractor.x, self.tractor.y, angles[self.tractor.angle]], [random_x, random_y], self.blocks.stones, self.flower_body)
|
||||
else:
|
||||
self.tractor.move(tractor_next_moves.pop(0)[0], self.cell_size, self.cell_number)
|
||||
elif event.type == QUIT:
|
||||
|
Loading…
Reference in New Issue
Block a user