clean
This commit is contained in:
Tomasz Adamczyk
parent
7cabeb6cbe
commit
bbb2fdefb9
@ -2,7 +2,7 @@
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__pycache__/cart.cpython-37.pyc
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__pycache__/cart.cpython-37.pyc
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__pycache__/neuralnetwork.cpython-37.pyc
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__pycache__/neuralnetwork.cpython-37.pyc
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82
astar.py
82
astar.py
@ -1,7 +1,7 @@
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from operator import itemgetter
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import cart
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import copy
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import tractor
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class Istate: #stan początkowy traktora (strona, w którą patrzy, miejsce, w którym się on znajduje)
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class Istate: #stan początkowy wózka (strona, w którą patrzy, miejsce, w którym się on znajduje)
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def __init__(self, direction, x, y):
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self.direction = direction
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self.x = x
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@ -51,51 +51,15 @@ def cost(map, node): #funkcja kosztu : ile kosztuje przejechanie przez dane pole
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cost = cost + map.get_field_cost(int(node.get_x()), int(node.get_y())) + 1
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node = node.get_parent()
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return cost
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||||
def heuristic(node, goaltest): #funkcja heurestyki : oszacowuje koszt osiągnięcia stanu końcowego (droga)
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return abs(node.get_x() - goaltest[0]) + abs(node.get_y() - goaltest[1])
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def f(map, node, goaltest): #funkcja zwracająca sumę funkcji kosztu oraz heurestyki
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return cost(map, node) + heuristic(node, goaltest)
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def goal_test(elem, goaltest): #funkcja sprawdzająca czy położenie traktora równa się położeniu punktu docelowego, jeśli tak zwraca prawdę, w przeciwnym wypadku fałsz
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def goal_test(elem, goaltest): #funkcja sprawdzająca czy położenie wózka równa się położeniu punktu docelowego, jeśli tak zwraca prawdę, w przeciwnym wypadku fałsz
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if elem.get_x() == goaltest[0] and elem.get_y() == goaltest[1]:
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return True
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else:
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return False
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def print_moves(elem): #zwraca listę ruchów jakie należy wykonać by dotrzeć do punktu docelowego
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moves_list = []
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while (elem.get_parent() != None):
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moves_list.append(elem.get_action())
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elem = elem.get_parent()
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moves_list.reverse()
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return moves_list
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def succ(elem): #funkcja następnika, przypisuje jakie akcje są możliwe do wykonania na danym polu oraz jaki będzie stan (kierunek, położenie) po wykonaniu tej akcji
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||||
actions_list = []
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||||
temp = copy.copy(elem.get_direction())
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if temp == 1:
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temp = 4
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else:
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temp = temp - 1
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actions_list.append(("rotate_left", (temp, elem.get_x(), elem.get_y())))
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temp = copy.copy(elem.get_direction())
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if temp == 4:
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temp = 1
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else:
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temp = temp + 1
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actions_list.append(("rotate_right", (temp, elem.get_x(), elem.get_y())))
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||||
temp_move_south = elem.get_y() + 1
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temp_move_west = elem.get_x() - 1
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temp_move_east = elem.get_x() + 1
|
||||
temp_move_north = elem.get_y() - 1
|
||||
if tractor.Tractor.is_move_allowed_succ(elem) == "x + 1":
|
||||
actions_list.append(("move", (elem.get_direction(), temp_move_east, elem.get_y())))
|
||||
elif tractor.Tractor.is_move_allowed_succ(elem) == "y - 1":
|
||||
actions_list.append(("move", (elem.get_direction(), elem.get_x(), temp_move_north)))
|
||||
elif tractor.Tractor.is_move_allowed_succ(elem) == "y + 1":
|
||||
actions_list.append(("move", (elem.get_direction(), elem.get_x(), temp_move_south)))
|
||||
elif tractor.Tractor.is_move_allowed_succ(elem) == "x - 1":
|
||||
actions_list.append(("move", (elem.get_direction(), temp_move_west, elem.get_y())))
|
||||
return actions_list
|
||||
def graphsearch(fringe, explored, istate, succ, goaltest, f, map): #przeszukiwanie grafu wszerz
|
||||
node = Node(None, istate.get_direction(), None, istate.get_x(), istate.get_y()) #wierzchołek początkowy, stworzony ze stanu początkowego traktora
|
||||
node = Node(None, istate.get_direction(), None, istate.get_x(), istate.get_y()) #wierzchołek początkowy, stworzony ze stanu początkowego wózka
|
||||
fringe.append((node, 0)) #wierzchołki do odwiedzenia z priorytetem
|
||||
while True:
|
||||
if not fringe:
|
||||
@ -128,4 +92,40 @@ def graphsearch(fringe, explored, istate, succ, goaltest, f, map): #przeszukiwan
|
||||
fringe.pop(i + 1)
|
||||
fringe = sorted(fringe, key=itemgetter(1)) #sortowanie fringe'a według priorytetu
|
||||
break
|
||||
i = i + 1
|
||||
i = i + 1
|
||||
def heuristic(node, goaltest): #funkcja heurestyki : oszacowuje koszt osiągnięcia stanu końcowego (droga)
|
||||
return abs(node.get_x() - goaltest[0]) + abs(node.get_y() - goaltest[1])
|
||||
def print_moves(elem): #zwraca listę ruchów jakie należy wykonać by dotrzeć do punktu docelowego
|
||||
moves_list = []
|
||||
while (elem.get_parent() != None):
|
||||
moves_list.append(elem.get_action())
|
||||
elem = elem.get_parent()
|
||||
moves_list.reverse()
|
||||
return moves_list
|
||||
def succ(elem): #funkcja następnika, przypisuje jakie akcje są możliwe do wykonania na danym polu oraz jaki będzie stan (kierunek, położenie) po wykonaniu tej akcji
|
||||
actions_list = []
|
||||
temp = copy.copy(elem.get_direction())
|
||||
if temp == 1:
|
||||
temp = 4
|
||||
else:
|
||||
temp = temp - 1
|
||||
actions_list.append(("rotate_left", (temp, elem.get_x(), elem.get_y())))
|
||||
temp = copy.copy(elem.get_direction())
|
||||
if temp == 4:
|
||||
temp = 1
|
||||
else:
|
||||
temp = temp + 1
|
||||
actions_list.append(("rotate_right", (temp, elem.get_x(), elem.get_y())))
|
||||
temp_move_south = elem.get_y() + 1
|
||||
temp_move_west = elem.get_x() - 1
|
||||
temp_move_east = elem.get_x() + 1
|
||||
temp_move_north = elem.get_y() - 1
|
||||
if cart.Cart.is_move_allowed_succ(elem) == "x + 1":
|
||||
actions_list.append(("move", (elem.get_direction(), temp_move_east, elem.get_y())))
|
||||
elif cart.Cart.is_move_allowed_succ(elem) == "y - 1":
|
||||
actions_list.append(("move", (elem.get_direction(), elem.get_x(), temp_move_north)))
|
||||
elif cart.Cart.is_move_allowed_succ(elem) == "y + 1":
|
||||
actions_list.append(("move", (elem.get_direction(), elem.get_x(), temp_move_south)))
|
||||
elif cart.Cart.is_move_allowed_succ(elem) == "x - 1":
|
||||
actions_list.append(("move", (elem.get_direction(), temp_move_west, elem.get_y())))
|
||||
return actions_list
|
||||
@ -1,6 +1,6 @@
|
||||
import definitions
|
||||
import random
|
||||
class Tractor:
|
||||
class Cart:
|
||||
def __init__(self, amount_of_seeds, collected_plants, direction, fertilizer, fuel, water_level, x, y):
|
||||
self.amount_of_seeds = amount_of_seeds #amount_of_seeds to słownik, przechowuje informacje o posiadanej ilości ziaren dla danej rośliny
|
||||
self.collected_plants = collected_plants #collected_plants to słownik, przechowuje informacje o zebranych plonach
|
||||
@ -48,50 +48,12 @@ class Tractor:
|
||||
return self.y
|
||||
def set_y(self, y):
|
||||
self.y = y
|
||||
def move(self):
|
||||
if self.direction == definitions.TRACTOR_DIRECTION_EAST:
|
||||
self.x = self.x + definitions.BLOCK_SIZE
|
||||
elif self.direction == definitions.TRACTOR_DIRECTION_NORTH:
|
||||
self.y = self.y - definitions.BLOCK_SIZE
|
||||
elif self.direction == definitions.TRACTOR_DIRECTION_SOUTH:
|
||||
self.y = self.y + definitions.BLOCK_SIZE
|
||||
elif self.direction == definitions.TRACTOR_DIRECTION_WEST:
|
||||
self.x = self.x - definitions.BLOCK_SIZE
|
||||
def rotate_left(self):
|
||||
if self.direction == 1:
|
||||
self.direction = 4
|
||||
else:
|
||||
self.direction = self.direction - 1
|
||||
def rotate_right(self):
|
||||
if self.direction == 4:
|
||||
self.direction = 1
|
||||
else:
|
||||
self.direction = self.direction + 1
|
||||
def station_restore(self, station1): #aktualizuje stan stacji pod względem oddanych plonów oraz uzupełnia zapasy traktora
|
||||
station1.set_collected_plants("beetroot", station1.get_collected_plants("beetroot") + self.get_collected_plants("beetroot"))
|
||||
self.set_collected_plants("beetroot", 0)
|
||||
station1.set_collected_plants("carrot", station1.get_collected_plants("carrot") + self.get_collected_plants("carrot"))
|
||||
self.set_collected_plants("carrot", 0)
|
||||
station1.set_collected_plants("potato", station1.get_collected_plants("potato") + self.get_collected_plants("potato"))
|
||||
self.set_collected_plants("potato", 0)
|
||||
station1.set_collected_plants("wheat", station1.get_collected_plants("wheat") + self.get_collected_plants("wheat"))
|
||||
self.set_collected_plants("wheat", 0)
|
||||
self.set_amount_of_seeds("beetroot", definitions.TRACTOR_AMOUNT_OF_SEEDS_EACH_TYPE)
|
||||
self.set_amount_of_seeds("carrot", definitions.TRACTOR_AMOUNT_OF_SEEDS_EACH_TYPE)
|
||||
self.set_amount_of_seeds("potato", definitions.TRACTOR_AMOUNT_OF_SEEDS_EACH_TYPE)
|
||||
self.set_amount_of_seeds("wheat", definitions.TRACTOR_AMOUNT_OF_SEEDS_EACH_TYPE)
|
||||
self.set_fertilizer("beetroot", definitions.TRACTOR_FERTILIZER)
|
||||
self.set_fertilizer("carrot", definitions.TRACTOR_FERTILIZER)
|
||||
self.set_fertilizer("potato", definitions.TRACTOR_FERTILIZER)
|
||||
self.set_fertilizer("wheat", definitions.TRACTOR_FERTILIZER)
|
||||
self.set_fuel(definitions.TRACTOR_FUEL)
|
||||
self.set_water_level(definitions.TRACTOR_WATER_LEVEL)
|
||||
def do_work(self, map1, station1, tractor1_rect): #jaką pracę traktor ma wykonać na danym polu, na którym aktualnie przebywa (zmienia stan logiczny danego pola)
|
||||
def do_work(self, map1, station1, cart_rect): #jaką pracę wózek ma wykonać na danym polu, na którym aktualnie przebywa (zmienia stan logiczny danego pola)
|
||||
loop = True
|
||||
if self.get_all_amount_of_seeds() == 0:
|
||||
loop = False
|
||||
x = int(tractor1_rect.x / definitions.BLOCK_SIZE)
|
||||
y = int(tractor1_rect.y / definitions.BLOCK_SIZE)
|
||||
x = int(cart_rect.x / definitions.BLOCK_SIZE)
|
||||
y = int(cart_rect.y / definitions.BLOCK_SIZE)
|
||||
field = map1.get_fields()[x][y]
|
||||
if x == 0 and y == 0:
|
||||
self.station_restore(station1)
|
||||
@ -139,54 +101,31 @@ class Tractor:
|
||||
self.set_fertilizer("wheat", (self.get_fertilizer("wheat") - 1))
|
||||
field.get_soil().set_is_fertilized(True)
|
||||
field.get_plant().set_state(field.get_plant().get_state() + definitions.WHEAT_GROW_TIME)
|
||||
elif field.get_plant().get_name() == "beetroot" and field.get_plant().get_state() == definitions.BEETROOTS_MAXIMUM_STATE and self.get_all_collected_plants() < definitions.TRACTOR_MAXIMUM_COLLECTED_PLANTS:
|
||||
elif field.get_plant().get_name() == "beetroot" and field.get_plant().get_state() == definitions.BEETROOTS_MAXIMUM_STATE and self.get_all_collected_plants() < definitions.CART_MAXIMUM_COLLECTED_PLANTS:
|
||||
field.get_plant().set_state(0)
|
||||
field.get_soil().set_is_fertilized(False)
|
||||
field.get_soil().set_water_level(False)
|
||||
field.get_soil().set_state(False)
|
||||
self.set_collected_plants("beetroot", self.get_collected_plants("beetroot") + 1)
|
||||
elif field.get_plant().get_name() == "carrot" and field.get_plant().get_state() == definitions.CARROTS_MAXIMUM_STATE and self.get_all_collected_plants() < definitions.TRACTOR_MAXIMUM_COLLECTED_PLANTS:
|
||||
elif field.get_plant().get_name() == "carrot" and field.get_plant().get_state() == definitions.CARROTS_MAXIMUM_STATE and self.get_all_collected_plants() < definitions.CART_MAXIMUM_COLLECTED_PLANTS:
|
||||
field.get_plant().set_state(0)
|
||||
field.get_soil().set_is_fertilized(False)
|
||||
field.get_soil().set_water_level(False)
|
||||
field.get_soil().set_state(False)
|
||||
self.set_collected_plants("carrot", self.get_collected_plants("carrot") + 1)
|
||||
elif field.get_plant().get_name() == "potato" and field.get_plant().get_state() == definitions.POTATOES_MAXIMUM_STATE and self.get_all_collected_plants() < definitions.TRACTOR_MAXIMUM_COLLECTED_PLANTS:
|
||||
elif field.get_plant().get_name() == "potato" and field.get_plant().get_state() == definitions.POTATOES_MAXIMUM_STATE and self.get_all_collected_plants() < definitions.CART_MAXIMUM_COLLECTED_PLANTS:
|
||||
field.get_plant().set_state(0)
|
||||
field.get_soil().set_is_fertilized(False)
|
||||
field.get_soil().set_water_level(False)
|
||||
field.get_soil().set_state(False)
|
||||
self.set_collected_plants("potato", self.get_collected_plants("potato") + 1)
|
||||
elif field.get_plant().get_name() == "wheat" and field.get_plant().get_state() == definitions.WHEAT_MAXIMUM_STATE and self.get_all_collected_plants() < definitions.TRACTOR_MAXIMUM_COLLECTED_PLANTS:
|
||||
elif field.get_plant().get_name() == "wheat" and field.get_plant().get_state() == definitions.WHEAT_MAXIMUM_STATE and self.get_all_collected_plants() < definitions.CART_MAXIMUM_COLLECTED_PLANTS:
|
||||
field.get_plant().set_state(0)
|
||||
field.get_soil().set_is_fertilized(False)
|
||||
field.get_soil().set_water_level(False)
|
||||
field.get_soil().set_state(False)
|
||||
self.set_collected_plants("wheat", self.get_collected_plants("wheat") + 1)
|
||||
def is_move_allowed(self, tractor1_rect): #sprawdza czy dany ruch, który chce wykonać traktor jest możliwy, zwraca prawdę lub fałsz
|
||||
if self.direction == definitions.TRACTOR_DIRECTION_EAST and tractor1_rect.x + definitions.BLOCK_SIZE < definitions.WIDTH:
|
||||
return True
|
||||
elif self.direction == definitions.TRACTOR_DIRECTION_NORTH and tractor1_rect.y - definitions.BLOCK_SIZE >= 0:
|
||||
return True
|
||||
elif self.direction == definitions.TRACTOR_DIRECTION_SOUTH and tractor1_rect.y + definitions.BLOCK_SIZE < definitions.HEIGHT:
|
||||
return True
|
||||
elif self.direction == definitions.TRACTOR_DIRECTION_WEST and tractor1_rect.x - definitions.BLOCK_SIZE >= 0:
|
||||
return True
|
||||
else:
|
||||
return False
|
||||
@staticmethod
|
||||
def is_move_allowed_succ(node): #sprawdza czy dany ruch, który chce wykonać traktor jest możliwy, zwraca pozycje po wykonaniu ruchu, wersja node
|
||||
if node.get_direction() == definitions.TRACTOR_DIRECTION_EAST and node.get_x() * definitions.BLOCK_SIZE + definitions.BLOCK_SIZE < definitions.WIDTH:
|
||||
return "x + 1"
|
||||
elif node.get_direction() == definitions.TRACTOR_DIRECTION_NORTH and node.get_y() * definitions.BLOCK_SIZE - definitions.BLOCK_SIZE >= 0:
|
||||
return "y - 1"
|
||||
elif node.get_direction() == definitions.TRACTOR_DIRECTION_SOUTH and node.get_y() * definitions.BLOCK_SIZE + definitions.BLOCK_SIZE < definitions.HEIGHT:
|
||||
return "y + 1"
|
||||
elif node.get_direction() == definitions.TRACTOR_DIRECTION_WEST and node.get_x() * definitions.BLOCK_SIZE - definitions.BLOCK_SIZE >= 0:
|
||||
return "x - 1"
|
||||
else:
|
||||
return False
|
||||
def handle_movement(self, move, tractor1_rect): #odpowiada za poruszanie się traktora po mapie
|
||||
def handle_movement(self, move, cart_rect): #odpowiada za poruszanie się wózka po mapie
|
||||
if self.get_fuel() > 0:
|
||||
if move == "move":
|
||||
self.move()
|
||||
@ -195,16 +134,16 @@ class Tractor:
|
||||
elif move == "rotate_right":
|
||||
self.rotate_right()
|
||||
self.set_fuel(self.get_fuel() - 1)
|
||||
tractor1_rect.x = self.get_x()
|
||||
tractor1_rect.y = self.get_y()
|
||||
def handle_random_movement(self, tractor1_rect): #odpowiada za losowe poruszanie się traktora po mapie
|
||||
cart_rect.x = self.get_x()
|
||||
cart_rect.y = self.get_y()
|
||||
def handle_movement_random(self, cart_rect): #odpowiada za losowe poruszanie się wózka po mapie
|
||||
loop = True
|
||||
while loop and self.get_fuel() > 0:
|
||||
random1 = random.randint(1, 3)
|
||||
if random1 == 1 and self.is_move_allowed(tractor1_rect) is True:
|
||||
if random1 == 1 and self.is_move_allowed(cart_rect) is True:
|
||||
self.move()
|
||||
tractor1_rect.x = self.get_x()
|
||||
tractor1_rect.y = self.get_y()
|
||||
cart_rect.x = self.get_x()
|
||||
cart_rect.y = self.get_y()
|
||||
loop = False
|
||||
elif random1 == 2:
|
||||
self.rotate_left()
|
||||
@ -212,4 +151,65 @@ class Tractor:
|
||||
elif random1 == 3:
|
||||
self.rotate_right()
|
||||
loop = False
|
||||
self.set_fuel(self.get_fuel() - 1)
|
||||
self.set_fuel(self.get_fuel() - 1)
|
||||
def is_move_allowed(self, cart_rect): #sprawdza czy dany ruch, który chce wykonać wózek jest możliwy, zwraca prawdę lub fałsz
|
||||
if self.direction == definitions.CART_DIRECTION_EAST and cart_rect.x + definitions.BLOCK_SIZE < definitions.WIDTH:
|
||||
return True
|
||||
elif self.direction == definitions.CART_DIRECTION_NORTH and cart_rect.y - definitions.BLOCK_SIZE >= 0:
|
||||
return True
|
||||
elif self.direction == definitions.CART_DIRECTION_SOUTH and cart_rect.y + definitions.BLOCK_SIZE < definitions.HEIGHT:
|
||||
return True
|
||||
elif self.direction == definitions.CART_DIRECTION_WEST and cart_rect.x - definitions.BLOCK_SIZE >= 0:
|
||||
return True
|
||||
else:
|
||||
return False
|
||||
@staticmethod
|
||||
def is_move_allowed_succ(node): #sprawdza czy dany ruch, który chce wykonać wózek jest możliwy, zwraca pozycje po wykonaniu ruchu, wersja node
|
||||
if node.get_direction() == definitions.CART_DIRECTION_EAST and node.get_x() * definitions.BLOCK_SIZE + definitions.BLOCK_SIZE < definitions.WIDTH:
|
||||
return "x + 1"
|
||||
elif node.get_direction() == definitions.CART_DIRECTION_NORTH and node.get_y() * definitions.BLOCK_SIZE - definitions.BLOCK_SIZE >= 0:
|
||||
return "y - 1"
|
||||
elif node.get_direction() == definitions.CART_DIRECTION_SOUTH and node.get_y() * definitions.BLOCK_SIZE + definitions.BLOCK_SIZE < definitions.HEIGHT:
|
||||
return "y + 1"
|
||||
elif node.get_direction() == definitions.CART_DIRECTION_WEST and node.get_x() * definitions.BLOCK_SIZE - definitions.BLOCK_SIZE >= 0:
|
||||
return "x - 1"
|
||||
else:
|
||||
return False
|
||||
def move(self):
|
||||
if self.direction == definitions.CART_DIRECTION_EAST:
|
||||
self.x = self.x + definitions.BLOCK_SIZE
|
||||
elif self.direction == definitions.CART_DIRECTION_NORTH:
|
||||
self.y = self.y - definitions.BLOCK_SIZE
|
||||
elif self.direction == definitions.CART_DIRECTION_SOUTH:
|
||||
self.y = self.y + definitions.BLOCK_SIZE
|
||||
elif self.direction == definitions.CART_DIRECTION_WEST:
|
||||
self.x = self.x - definitions.BLOCK_SIZE
|
||||
def rotate_left(self):
|
||||
if self.direction == 1:
|
||||
self.direction = 4
|
||||
else:
|
||||
self.direction = self.direction - 1
|
||||
def rotate_right(self):
|
||||
if self.direction == 4:
|
||||
self.direction = 1
|
||||
else:
|
||||
self.direction = self.direction + 1
|
||||
def station_restore(self, station1): #aktualizuje stan stacji pod względem oddanych plonów oraz uzupełnia zapasy wózka
|
||||
station1.set_collected_plants("beetroot", station1.get_collected_plants("beetroot") + self.get_collected_plants("beetroot"))
|
||||
self.set_collected_plants("beetroot", 0)
|
||||
station1.set_collected_plants("carrot", station1.get_collected_plants("carrot") + self.get_collected_plants("carrot"))
|
||||
self.set_collected_plants("carrot", 0)
|
||||
station1.set_collected_plants("potato", station1.get_collected_plants("potato") + self.get_collected_plants("potato"))
|
||||
self.set_collected_plants("potato", 0)
|
||||
station1.set_collected_plants("wheat", station1.get_collected_plants("wheat") + self.get_collected_plants("wheat"))
|
||||
self.set_collected_plants("wheat", 0)
|
||||
self.set_amount_of_seeds("beetroot", definitions.CART_AMOUNT_OF_SEEDS_EACH_TYPE)
|
||||
self.set_amount_of_seeds("carrot", definitions.CART_AMOUNT_OF_SEEDS_EACH_TYPE)
|
||||
self.set_amount_of_seeds("potato", definitions.CART_AMOUNT_OF_SEEDS_EACH_TYPE)
|
||||
self.set_amount_of_seeds("wheat", definitions.CART_AMOUNT_OF_SEEDS_EACH_TYPE)
|
||||
self.set_fertilizer("beetroot", definitions.CART_FERTILIZER)
|
||||
self.set_fertilizer("carrot", definitions.CART_FERTILIZER)
|
||||
self.set_fertilizer("potato", definitions.CART_FERTILIZER)
|
||||
self.set_fertilizer("wheat", definitions.CART_FERTILIZER)
|
||||
self.set_fuel(definitions.CART_FUEL)
|
||||
self.set_water_level(definitions.CART_WATER_LEVEL)
|
||||
@ -6,35 +6,52 @@ BEETROOTS_ADULT_COST = 4
|
||||
BEETROOTS_GROW_COST = 10
|
||||
BEETROOTS_GROW_TIME = 5
|
||||
BEETROOTS_MAXIMUM_STATE = BEETROOTS_GROW_TIME * 3 + 1
|
||||
BEETROOTS_STAGE_0 = pygame.image.load(os.path.join('resources', 'beetroots_stage_0.png'))
|
||||
BEETROOTS_STAGE_0 = pygame.image.load(os.path.join('resources/images', 'beetroots_stage_0.png'))
|
||||
BEETROOTS_STAGE_0 = pygame.transform.scale(BEETROOTS_STAGE_0, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
BEETROOTS_STAGE_1 = pygame.image.load(os.path.join('resources', 'beetroots_stage_1.png'))
|
||||
BEETROOTS_STAGE_1 = pygame.image.load(os.path.join('resources/images', 'beetroots_stage_1.png'))
|
||||
BEETROOTS_STAGE_1 = pygame.transform.scale(BEETROOTS_STAGE_1, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
BEETROOTS_STAGE_2 = pygame.image.load(os.path.join('resources', 'beetroots_stage_2.png'))
|
||||
BEETROOTS_STAGE_2 = pygame.image.load(os.path.join('resources/images', 'beetroots_stage_2.png'))
|
||||
BEETROOTS_STAGE_2 = pygame.transform.scale(BEETROOTS_STAGE_2, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
BEETROOTS_STAGE_3 = pygame.image.load(os.path.join('resources', 'beetroots_stage_3.png'))
|
||||
BEETROOTS_STAGE_3 = pygame.image.load(os.path.join('resources/images', 'beetroots_stage_3.png'))
|
||||
BEETROOTS_STAGE_3 = pygame.transform.scale(BEETROOTS_STAGE_3, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
CARROTS_ADULT_COST = 4
|
||||
CARROTS_GROW_COST = 10
|
||||
CARROTS_GROW_TIME = 5
|
||||
CARROTS_MAXIMUM_STATE = CARROTS_GROW_TIME * 3 + 1
|
||||
CARROTS_STAGE_0 = pygame.image.load(os.path.join('resources', 'carrots_stage_0.png'))
|
||||
CARROTS_STAGE_0 = pygame.image.load(os.path.join('resources/images', 'carrots_stage_0.png'))
|
||||
CARROTS_STAGE_0 = pygame.transform.scale(CARROTS_STAGE_0, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
CARROTS_STAGE_1 = pygame.image.load(os.path.join('resources', 'carrots_stage_1.png'))
|
||||
CARROTS_STAGE_1 = pygame.image.load(os.path.join('resources/images', 'carrots_stage_1.png'))
|
||||
CARROTS_STAGE_1 = pygame.transform.scale(CARROTS_STAGE_1, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
CARROTS_STAGE_2 = pygame.image.load(os.path.join('resources', 'carrots_stage_2.png'))
|
||||
CARROTS_STAGE_2 = pygame.image.load(os.path.join('resources/images', 'carrots_stage_2.png'))
|
||||
CARROTS_STAGE_2 = pygame.transform.scale(CARROTS_STAGE_2, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
CARROTS_STAGE_3 = pygame.image.load(os.path.join('resources', 'carrots_stage_3.png'))
|
||||
CARROTS_STAGE_3 = pygame.image.load(os.path.join('resources/images', 'carrots_stage_3.png'))
|
||||
CARROTS_STAGE_3 = pygame.transform.scale(CARROTS_STAGE_3, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
DIRT = pygame.image.load(os.path.join('resources', 'dirt.png'))
|
||||
CART_DIRECTION_EAST_TEXTURE = pygame.image.load(os.path.join('resources/images', 'minecart_command_block_east.png'))
|
||||
CART_DIRECTION_EAST_TEXTURE = pygame.transform.scale(CART_DIRECTION_EAST_TEXTURE, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
CART_DIRECTION_NORTH_TEXTURE = pygame.image.load(os.path.join('resources/images', 'minecart_command_block_north.png'))
|
||||
CART_DIRECTION_NORTH_TEXTURE = pygame.transform.scale(CART_DIRECTION_NORTH_TEXTURE, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
CART_DIRECTION_SOUTH_TEXTURE = pygame.image.load(os.path.join('resources/images', 'minecart_command_block_south.png'))
|
||||
CART_DIRECTION_SOUTH_TEXTURE = pygame.transform.scale(CART_DIRECTION_SOUTH_TEXTURE, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
CART_DIRECTION_WEST_TEXTURE = pygame.image.load(os.path.join('resources/images', 'minecart_command_block_west.png'))
|
||||
CART_DIRECTION_WEST_TEXTURE = pygame.transform.scale(CART_DIRECTION_WEST_TEXTURE, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
CART_DIRECTION_EAST = 2
|
||||
CART_DIRECTION_NORTH = 1
|
||||
CART_DIRECTION_SOUTH = 3
|
||||
CART_DIRECTION_WEST = 4
|
||||
CART_FERTILIZER = 2
|
||||
CART_FUEL = 200
|
||||
CART_AMOUNT_OF_SEEDS_EACH_TYPE = 20
|
||||
CART_MAXIMUM_COLLECTED_PLANTS = 80
|
||||
CART_WATER_LEVEL = 40
|
||||
DIRT = pygame.image.load(os.path.join('resources/images', 'dirt.png'))
|
||||
DIRT = pygame.transform.scale(DIRT, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
DIRT_COST = 2
|
||||
HEIGHT_AMOUNT, WIDTH_AMOUNT = 10, 10
|
||||
HEIGHT, WIDTH = BLOCK_SIZE * HEIGHT_AMOUNT, BLOCK_SIZE * WIDTH_AMOUNT
|
||||
FARMLAND_DRY = pygame.image.load(os.path.join('resources', 'farmland_dry.png'))
|
||||
FARMLAND_DRY = pygame.image.load(os.path.join('resources/images', 'farmland_dry.png'))
|
||||
FARMLAND_DRY = pygame.transform.scale(FARMLAND_DRY, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
FARMLAND_DRY_COST = 3
|
||||
FARMLAND_WET = pygame.image.load(os.path.join('resources', 'farmland_wet.png'))
|
||||
FARMLAND_WET = pygame.image.load(os.path.join('resources/images', 'farmland_wet.png'))
|
||||
FARMLAND_WET = pygame.transform.scale(FARMLAND_WET, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
FARMLAND_WET_COST = 1
|
||||
FPS = 1
|
||||
@ -42,54 +59,37 @@ POTATOES_ADULT_COST = 4
|
||||
POTATOES_GROW_COST = 10
|
||||
POTATOES_GROW_TIME = 5
|
||||
POTATOES_MAXIMUM_STATE = POTATOES_GROW_TIME * 3 + 1
|
||||
POTATOES_STAGE_0 = pygame.image.load(os.path.join('resources', 'potatoes_stage_0.png'))
|
||||
POTATOES_STAGE_0 = pygame.image.load(os.path.join('resources/images', 'potatoes_stage_0.png'))
|
||||
POTATOES_STAGE_0 = pygame.transform.scale(POTATOES_STAGE_0, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
POTATOES_STAGE_1 = pygame.image.load(os.path.join('resources', 'potatoes_stage_1.png'))
|
||||
POTATOES_STAGE_1 = pygame.image.load(os.path.join('resources/images', 'potatoes_stage_1.png'))
|
||||
POTATOES_STAGE_1 = pygame.transform.scale(POTATOES_STAGE_1, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
POTATOES_STAGE_2 = pygame.image.load(os.path.join('resources', 'potatoes_stage_2.png'))
|
||||
POTATOES_STAGE_2 = pygame.image.load(os.path.join('resources/images', 'potatoes_stage_2.png'))
|
||||
POTATOES_STAGE_2 = pygame.transform.scale(POTATOES_STAGE_2, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
POTATOES_STAGE_3 = pygame.image.load(os.path.join('resources', 'potatoes_stage_3.png'))
|
||||
POTATOES_STAGE_3 = pygame.image.load(os.path.join('resources/images', 'potatoes_stage_3.png'))
|
||||
POTATOES_STAGE_3 = pygame.transform.scale(POTATOES_STAGE_3, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
SPONGE = pygame.image.load(os.path.join('resources', 'sponge.png'))
|
||||
SPONGE = pygame.image.load(os.path.join('resources/images', 'sponge.png'))
|
||||
SPONGE = pygame.transform.scale(SPONGE, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
STATION = pygame.image.load(os.path.join('resources', 'rail_normal.png'))
|
||||
STATION = pygame.image.load(os.path.join('resources/images', 'rail_normal.png'))
|
||||
STATION = pygame.transform.scale(STATION, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
STATION_COST = 5
|
||||
TRACTOR_DIRECTION_EAST_TEXTURE = pygame.image.load(os.path.join('resources', 'minecart_command_block_east.png'))
|
||||
TRACTOR_DIRECTION_EAST_TEXTURE = pygame.transform.scale(TRACTOR_DIRECTION_EAST_TEXTURE, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
TRACTOR_DIRECTION_NORTH_TEXTURE = pygame.image.load(os.path.join('resources', 'minecart_command_block_north.png'))
|
||||
TRACTOR_DIRECTION_NORTH_TEXTURE = pygame.transform.scale(TRACTOR_DIRECTION_NORTH_TEXTURE, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
TRACTOR_DIRECTION_SOUTH_TEXTURE = pygame.image.load(os.path.join('resources', 'minecart_command_block_south.png'))
|
||||
TRACTOR_DIRECTION_SOUTH_TEXTURE = pygame.transform.scale(TRACTOR_DIRECTION_SOUTH_TEXTURE, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
TRACTOR_DIRECTION_WEST_TEXTURE = pygame.image.load(os.path.join('resources', 'minecart_command_block_west.png'))
|
||||
TRACTOR_DIRECTION_WEST_TEXTURE = pygame.transform.scale(TRACTOR_DIRECTION_WEST_TEXTURE, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
TRACTOR_DIRECTION_EAST = 2
|
||||
TRACTOR_DIRECTION_NORTH = 1
|
||||
TRACTOR_DIRECTION_SOUTH = 3
|
||||
TRACTOR_DIRECTION_WEST = 4
|
||||
TRACTOR_FERTILIZER = 2
|
||||
TRACTOR_FUEL = 200
|
||||
TRACTOR_AMOUNT_OF_SEEDS_EACH_TYPE = 20
|
||||
TRACTOR_MAXIMUM_COLLECTED_PLANTS = 80
|
||||
TRACTOR_WATER_LEVEL = 40
|
||||
WHEAT_ADULT_COST = 4
|
||||
WHEAT_GROW_COST = 10
|
||||
WHEAT_GROW_TIME = 5
|
||||
WHEAT_MAXIMUM_STATE = WHEAT_GROW_TIME * 7 + 1
|
||||
WHEAT_STAGE_0 = pygame.image.load(os.path.join('resources', 'wheat_stage_0.png'))
|
||||
WHEAT_STAGE_0 = pygame.image.load(os.path.join('resources/images', 'wheat_stage_0.png'))
|
||||
WHEAT_STAGE_0 = pygame.transform.scale(WHEAT_STAGE_0, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
WHEAT_STAGE_1 = pygame.image.load(os.path.join('resources', 'wheat_stage_1.png'))
|
||||
WHEAT_STAGE_1 = pygame.image.load(os.path.join('resources/images', 'wheat_stage_1.png'))
|
||||
WHEAT_STAGE_1 = pygame.transform.scale(WHEAT_STAGE_1, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
WHEAT_STAGE_2 = pygame.image.load(os.path.join('resources', 'wheat_stage_2.png'))
|
||||
WHEAT_STAGE_2 = pygame.image.load(os.path.join('resources/images', 'wheat_stage_2.png'))
|
||||
WHEAT_STAGE_2 = pygame.transform.scale(WHEAT_STAGE_2, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
WHEAT_STAGE_3 = pygame.image.load(os.path.join('resources', 'wheat_stage_3.png'))
|
||||
WHEAT_STAGE_3 = pygame.image.load(os.path.join('resources/images', 'wheat_stage_3.png'))
|
||||
WHEAT_STAGE_3 = pygame.transform.scale(WHEAT_STAGE_3, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
WHEAT_STAGE_4 = pygame.image.load(os.path.join('resources', 'wheat_stage_4.png'))
|
||||
WHEAT_STAGE_4 = pygame.image.load(os.path.join('resources/images', 'wheat_stage_4.png'))
|
||||
WHEAT_STAGE_4 = pygame.transform.scale(WHEAT_STAGE_4, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
WHEAT_STAGE_5 = pygame.image.load(os.path.join('resources', 'wheat_stage_5.png'))
|
||||
WHEAT_STAGE_5 = pygame.image.load(os.path.join('resources/images', 'wheat_stage_5.png'))
|
||||
WHEAT_STAGE_5 = pygame.transform.scale(WHEAT_STAGE_5, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
WHEAT_STAGE_6 = pygame.image.load(os.path.join('resources', 'wheat_stage_6.png'))
|
||||
WHEAT_STAGE_6 = pygame.image.load(os.path.join('resources/images', 'wheat_stage_6.png'))
|
||||
WHEAT_STAGE_6 = pygame.transform.scale(WHEAT_STAGE_6, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
WHEAT_STAGE_7 = pygame.image.load(os.path.join('resources', 'wheat_stage_7.png'))
|
||||
WHEAT_STAGE_7 = pygame.image.load(os.path.join('resources/images', 'wheat_stage_7.png'))
|
||||
WHEAT_STAGE_7 = pygame.transform.scale(WHEAT_STAGE_7, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
WINDOW = pygame.display.set_mode((WIDTH, HEIGHT))
|
||||
58
graph.py
58
graph.py
@ -1,6 +1,6 @@
|
||||
import cart
|
||||
import copy
|
||||
import tractor
|
||||
class Istate: #stan początkowy traktora (strona, w którą patrzy, miejsce, w którym się on znajduje)
|
||||
class Istate: #stan początkowy wózka (strona, w którą patrzy, miejsce, w którym się on znajduje)
|
||||
def __init__(self, direction, x, y):
|
||||
self.direction = direction
|
||||
self.x = x
|
||||
@ -44,11 +44,32 @@ class Node: #wierzchołek grafu
|
||||
return self.y
|
||||
def set_y(self, y):
|
||||
self.y = y
|
||||
def goal_test(elem, goaltest): #funkcja sprawdzająca czy położenie traktora równa się położeniu punktu docelowego, jeśli tak zwraca prawdę, w przeciwnym wypadku fałsz
|
||||
def goal_test(elem, goaltest): #funkcja sprawdzająca czy położenie wózka równa się położeniu punktu docelowego, jeśli tak zwraca prawdę, w przeciwnym wypadku fałsz
|
||||
if elem.get_x() == goaltest[0] and elem.get_y() == goaltest[1]:
|
||||
return True
|
||||
else:
|
||||
return False
|
||||
def graphsearch(fringe, explored, istate, succ, goaltest): #przeszukiwanie grafu wszerz
|
||||
node = Node(None, istate.get_direction(), None, istate.get_x(), istate.get_y()) #wierzchołek początkowy, stworzony ze stanu początkowego wózka
|
||||
fringe.append(node) #wierzchołki do odwiedzenia
|
||||
while True:
|
||||
if not fringe:
|
||||
return False
|
||||
elem = fringe.pop(0) #zdejmujemy wierzchołek z kolejki fringe i rozpatrujemy go
|
||||
temp = copy.copy(elem)
|
||||
if goal_test(elem, goaltest) is True: #jeżeli osiągniemy cel w trakcie przeszukiwania grafu wszerz (wjedziemy na pole docelowe) : zwracamy listę ruchów, po których wykonaniu dotrzemy na miejsce
|
||||
return print_moves(elem)
|
||||
explored.append(elem) #dodajemy wierzchołek do listy wierzchołków odwiedzonych
|
||||
for (action, state) in succ(temp): #iterujemy po wszystkich możliwych akcjach i stanach otrzymanych dla danego wierzchołka grafu
|
||||
fringe_tuple = []
|
||||
explored_tuple = []
|
||||
for x in fringe:
|
||||
fringe_tuple.append((x.get_direction(), x.get_x(), x.get_y()))
|
||||
for x in explored:
|
||||
explored_tuple.append((x.get_direction(), x.get_x(), x.get_y()))
|
||||
if state not in fringe_tuple and state not in explored_tuple: #jeżeli stan nie znajduje się na fringe oraz nie znajduje się w liście wierzchołków odwiedzonych
|
||||
x = Node(action, state[0], elem, state[1], state[2]) #stworzenie nowego wierzchołka, którego rodzicem jest elem
|
||||
fringe.append(x) #dodanie wierzchołka na fringe
|
||||
def print_moves(elem): #zwraca listę ruchów jakie należy wykonać by dotrzeć do punktu docelowego
|
||||
moves_list = []
|
||||
while (elem.get_parent() != None):
|
||||
@ -74,33 +95,12 @@ def succ(elem): #funkcja następnika, przypisuje jakie akcje są możliwe do wyk
|
||||
temp_move_west = elem.get_x() - 1
|
||||
temp_move_east = elem.get_x() + 1
|
||||
temp_move_north = elem.get_y() - 1
|
||||
if tractor.Tractor.is_move_allowed_succ(elem) == "x + 1":
|
||||
if cart.Cart.is_move_allowed_succ(elem) == "x + 1":
|
||||
actions_list.append(("move", (elem.get_direction(), temp_move_east, elem.get_y())))
|
||||
elif tractor.Tractor.is_move_allowed_succ(elem) == "y - 1":
|
||||
elif cart.Cart.is_move_allowed_succ(elem) == "y - 1":
|
||||
actions_list.append(("move", (elem.get_direction(), elem.get_x(), temp_move_north)))
|
||||
elif tractor.Tractor.is_move_allowed_succ(elem) == "y + 1":
|
||||
elif cart.Cart.is_move_allowed_succ(elem) == "y + 1":
|
||||
actions_list.append(("move", (elem.get_direction(), elem.get_x(), temp_move_south)))
|
||||
elif tractor.Tractor.is_move_allowed_succ(elem) == "x - 1":
|
||||
elif cart.Cart.is_move_allowed_succ(elem) == "x - 1":
|
||||
actions_list.append(("move", (elem.get_direction(), temp_move_west, elem.get_y())))
|
||||
return actions_list
|
||||
def graphsearch(fringe, explored, istate, succ, goaltest): #przeszukiwanie grafu wszerz
|
||||
node = Node(None, istate.get_direction(), None, istate.get_x(), istate.get_y()) #wierzchołek początkowy, stworzony ze stanu początkowego traktora
|
||||
fringe.append(node) #wierzchołki do odwiedzenia
|
||||
while True:
|
||||
if not fringe:
|
||||
return False
|
||||
elem = fringe.pop(0) #zdejmujemy wierzchołek z kolejki fringe i rozpatrujemy go
|
||||
temp = copy.copy(elem)
|
||||
if goal_test(elem, goaltest) is True: #jeżeli osiągniemy cel w trakcie przeszukiwania grafu wszerz (wjedziemy na pole docelowe) : zwracamy listę ruchów, po których wykonaniu dotrzemy na miejsce
|
||||
return print_moves(elem)
|
||||
explored.append(elem) #dodajemy wierzchołek do listy wierzchołków odwiedzonych
|
||||
for (action, state) in succ(temp): #iterujemy po wszystkich możliwych akcjach i stanach otrzymanych dla danego wierzchołka grafu
|
||||
fringe_tuple = []
|
||||
explored_tuple = []
|
||||
for x in fringe:
|
||||
fringe_tuple.append((x.get_direction(), x.get_x(), x.get_y()))
|
||||
for x in explored:
|
||||
explored_tuple.append((x.get_direction(), x.get_x(), x.get_y()))
|
||||
if state not in fringe_tuple and state not in explored_tuple: #jeżeli stan nie znajduje się na fringe oraz nie znajduje się w liście wierzchołków odwiedzonych
|
||||
x = Node(action, state[0], elem, state[1], state[2]) #stworzenie nowego wierzchołka, którego rodzicem jest elem
|
||||
fringe.append(x) #dodanie wierzchołka na fringe
|
||||
return actions_list
|
||||
24
map.py
24
map.py
@ -23,6 +23,17 @@ class Map:
|
||||
temp_field = field.Field(temp_plant, temp_rect, temp_soil)
|
||||
temp_map_field.append(temp_field)
|
||||
self.fields.append(temp_map_field)
|
||||
def draw_window(self, cart, cart_rect): #rysuje mapę
|
||||
self.fill_map()
|
||||
if cart.get_direction() == definitions.CART_DIRECTION_EAST:
|
||||
definitions.WINDOW.blit(definitions.CART_DIRECTION_EAST_TEXTURE, (cart_rect.x, cart_rect.y))
|
||||
elif cart.get_direction() == definitions.CART_DIRECTION_NORTH:
|
||||
definitions.WINDOW.blit(definitions.CART_DIRECTION_NORTH_TEXTURE, (cart_rect.x, cart_rect.y))
|
||||
elif cart.get_direction() == definitions.CART_DIRECTION_SOUTH:
|
||||
definitions.WINDOW.blit(definitions.CART_DIRECTION_SOUTH_TEXTURE, (cart_rect.x, cart_rect.y))
|
||||
elif cart.get_direction() == definitions.CART_DIRECTION_WEST:
|
||||
definitions.WINDOW.blit(definitions.CART_DIRECTION_WEST_TEXTURE, (cart_rect.x, cart_rect.y))
|
||||
pygame.display.update()
|
||||
def fill_map(self): #wypełnia mapę teksturami na podstawie logicznego stanu pól
|
||||
for i in range(definitions.WIDTH_AMOUNT):
|
||||
for j in range(definitions.HEIGHT_AMOUNT):
|
||||
@ -106,15 +117,4 @@ class Map:
|
||||
elif field.get_soil().get_state() is True and field.get_soil().get_water_level() is False:
|
||||
return definitions.FARMLAND_DRY_COST
|
||||
elif field.get_soil().get_state() is True and field.get_soil().get_water_level() is True:
|
||||
return definitions.FARMLAND_WET_COST
|
||||
def draw_window(self, tractor1, tractor1_rect): #rysuje mapę
|
||||
self.fill_map()
|
||||
if tractor1.get_direction() == definitions.TRACTOR_DIRECTION_EAST:
|
||||
definitions.WINDOW.blit(definitions.TRACTOR_DIRECTION_EAST_TEXTURE, (tractor1_rect.x, tractor1_rect.y))
|
||||
elif tractor1.get_direction() == definitions.TRACTOR_DIRECTION_NORTH:
|
||||
definitions.WINDOW.blit(definitions.TRACTOR_DIRECTION_NORTH_TEXTURE, (tractor1_rect.x, tractor1_rect.y))
|
||||
elif tractor1.get_direction() == definitions.TRACTOR_DIRECTION_SOUTH:
|
||||
definitions.WINDOW.blit(definitions.TRACTOR_DIRECTION_SOUTH_TEXTURE, (tractor1_rect.x, tractor1_rect.y))
|
||||
elif tractor1.get_direction() == definitions.TRACTOR_DIRECTION_WEST:
|
||||
definitions.WINDOW.blit(definitions.TRACTOR_DIRECTION_WEST_TEXTURE, (tractor1_rect.x, tractor1_rect.y))
|
||||
pygame.display.update()
|
||||
return definitions.FARMLAND_WET_COST
|
||||
110
neuralnetwork.py
Normal file
110
neuralnetwork.py
Normal file
@ -0,0 +1,110 @@
|
||||
from PIL import Image
|
||||
from torch.autograd import Variable
|
||||
from torch.optim import Adam
|
||||
from torch.utils.data import DataLoader
|
||||
from torchvision.transforms import transforms
|
||||
import glob
|
||||
import numpy as np
|
||||
import os
|
||||
import pathlib
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torchvision
|
||||
class ConvNet(nn.Module):
|
||||
def __init__(self, num_classes=6):
|
||||
super(ConvNet, self).__init__()
|
||||
self.conv1 = nn.Conv2d(in_channels=3, out_channels=12, kernel_size=3, stride=1, padding=1)
|
||||
self.bn1 = nn.BatchNorm2d(num_features=12)
|
||||
self.relu1 = nn.ReLU()
|
||||
self.pool = nn.MaxPool2d(kernel_size=2)
|
||||
self.conv2 = nn.Conv2d(in_channels=12, out_channels=20, kernel_size=3, stride=1, padding=1)
|
||||
self.relu2 = nn.ReLU()
|
||||
self.conv3 = nn.Conv2d(in_channels=20, out_channels=32, kernel_size=3, stride=1, padding=1)
|
||||
self.bn3 = nn.BatchNorm2d(num_features=32)
|
||||
self.relu3 = nn.ReLU()
|
||||
self.fc = nn.Linear(in_features=75 * 75 * 32, out_features=num_classes)
|
||||
def forward(self, input):
|
||||
output = self.conv1(input)
|
||||
output = self.bn1(output)
|
||||
output = self.relu1(output)
|
||||
output = self.pool(output)
|
||||
output = self.conv2(output)
|
||||
output = self.relu2(output)
|
||||
output = self.conv3(output)
|
||||
output = self.bn3(output)
|
||||
output = self.relu3(output)
|
||||
output = output.view(-1, 32 * 75 * 75)
|
||||
output = self.fc(output)
|
||||
return output
|
||||
def create_neural_network():
|
||||
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
|
||||
transformer = transforms.Compose([transforms.Resize((150, 150)), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
|
||||
train_path = os.path.join('resources/neural_network/train/')
|
||||
test_path = os.path.join('resources/neural_network/test/')
|
||||
pred_path = os.path.join('resources/neural_network/pred/')
|
||||
train_loader = DataLoader(torchvision.datasets.ImageFolder(train_path, transform=transformer), batch_size=64, shuffle=True)
|
||||
test_loader = DataLoader(torchvision.datasets.ImageFolder(test_path, transform=transformer), batch_size=32, shuffle=True)
|
||||
root = pathlib.Path(train_path)
|
||||
classes = sorted([j.name.split('/')[-1] for j in root.iterdir()])
|
||||
if os.path.exists("resources/neural_network/checkpoint.model"):
|
||||
checkpoint = torch.load(os.path.join('resources/neural_network', 'checkpoint.model'))
|
||||
model = ConvNet(num_classes=6)
|
||||
model.load_state_dict(checkpoint)
|
||||
model.eval()
|
||||
transformer1 = transforms.Compose([transforms.Resize((150, 150)), transforms.ToTensor(), transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
|
||||
images_path = glob.glob(pred_path+'/*.jpg')
|
||||
pred_dict = {}
|
||||
for i in images_path:
|
||||
pred_dict[i[i.rfind('/') + 1:]] = prediction1(i, transformer1, model, classes)
|
||||
print(pred_dict)
|
||||
else:
|
||||
model = ConvNet(num_classes=6).to(device)
|
||||
optimizer = Adam(model.parameters(), lr=0.001, weight_decay=0.0001)
|
||||
loss_function = nn.CrossEntropyLoss()
|
||||
num_epochs = 10
|
||||
train_count = len(glob.glob(train_path + '/**/*.jpg'))
|
||||
test_count = len(glob.glob(test_path + '/**/*.jpg'))
|
||||
best_accuracy = 0.0
|
||||
for epoch in range(num_epochs):
|
||||
model.train()
|
||||
train_accuracy = 0.0
|
||||
train_loss = 0.0
|
||||
for i, (images, labels) in enumerate(train_loader):
|
||||
if torch.cuda.is_available():
|
||||
images = Variable(images.cuda())
|
||||
labels = Variable(labels.cuda())
|
||||
optimizer.zero_grad()
|
||||
outputs = model(images)
|
||||
loss = loss_function(outputs, labels)
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
train_loss += loss.cpu().data * images.size(0)
|
||||
_, prediction = torch.max(outputs.data, 1)
|
||||
train_accuracy += int(torch.sum(prediction == labels.data))
|
||||
train_accuracy = train_accuracy / train_count
|
||||
train_loss = train_loss / train_count
|
||||
model.eval()
|
||||
test_accuracy = 0.0
|
||||
for i, (images, labels) in enumerate(test_loader):
|
||||
if torch.cuda.is_available():
|
||||
images = Variable(images.cuda())
|
||||
labels = Variable(labels.cuda())
|
||||
outputs = model(images)
|
||||
_, prediction = torch.max(outputs.data, 1)
|
||||
test_accuracy += int(torch.sum(prediction == labels.data))
|
||||
test_accuracy = test_accuracy / test_count
|
||||
print('Epoch: ' + str(epoch) + ' Train Loss: ' + str(train_loss) + ' Train Accuracy: ' + str(train_accuracy) + ' Test Accuracy: ' + str(test_accuracy))
|
||||
if test_accuracy > best_accuracy:
|
||||
torch.save(model.state_dict(), 'resources/neural_network/checkpoint.model')
|
||||
best_accuracy = test_accuracy
|
||||
def prediction1(img_path, transformer, model, classes):
|
||||
image = Image.open(img_path)
|
||||
image_tensor = transformer(image).float()
|
||||
image_tensor = image_tensor.unsqueeze_(0)
|
||||
if torch.cuda.is_available():
|
||||
image_tensor.cuda()
|
||||
input = Variable(image_tensor)
|
||||
output = model(input)
|
||||
index = output.data.numpy().argmax()
|
||||
pred = classes[index]
|
||||
return pred
|
||||
81
plant.py
81
plant.py
@ -12,6 +12,44 @@ class Plant:
|
||||
def set_state(self, state):
|
||||
self.state = state
|
||||
@staticmethod
|
||||
def get_mature_plant(map1, cart): #pobiera współrzędne jakiejś dojrzałej rośliny
|
||||
x = -1
|
||||
y = -1
|
||||
for i in range(definitions.WIDTH_AMOUNT):
|
||||
for j in range(definitions.HEIGHT_AMOUNT):
|
||||
field = map1.get_fields()[i][j]
|
||||
if field.get_plant().get_name() == "beetroot" and field.get_plant().get_state() == definitions.BEETROOTS_MAXIMUM_STATE:
|
||||
x = i
|
||||
y = j
|
||||
elif field.get_plant().get_name() == "carrot" and field.get_plant().get_state() == definitions.CARROTS_MAXIMUM_STATE:
|
||||
x = i
|
||||
y = j
|
||||
elif field.get_plant().get_name() == "potato" and field.get_plant().get_state() == definitions.POTATOES_MAXIMUM_STATE:
|
||||
x = i
|
||||
y = j
|
||||
|
||||
elif field.get_plant().get_name() == "wheat" and field.get_plant().get_state() == definitions.WHEAT_MAXIMUM_STATE:
|
||||
x = i
|
||||
y = j
|
||||
if x == -1 and y == -1:
|
||||
return False
|
||||
else:
|
||||
return x, y
|
||||
@staticmethod
|
||||
def grow_plants(
|
||||
map1): #metoda statyczna, która zwiększa pole state (etap rozwoju rośliny) dla danej rośliny na danym polu o 1
|
||||
for i in range(definitions.WIDTH_AMOUNT):
|
||||
for j in range(definitions.HEIGHT_AMOUNT):
|
||||
field = map1.get_fields()[i][j]
|
||||
if field.get_plant().get_name() == "beetroot" and field.get_plant().get_state() > 0 and field.get_plant().get_state() < definitions.BEETROOTS_MAXIMUM_STATE:
|
||||
field.get_plant().set_state(field.get_plant().get_state() + 1)
|
||||
elif field.get_plant().get_name() == "carrot" and field.get_plant().get_state() > 0 and field.get_plant().get_state() < definitions.CARROTS_MAXIMUM_STATE:
|
||||
field.get_plant().set_state(field.get_plant().get_state() + 1)
|
||||
elif field.get_plant().get_name() == "potato" and field.get_plant().get_state() > 0 and field.get_plant().get_state() < definitions.POTATOES_MAXIMUM_STATE:
|
||||
field.get_plant().set_state(field.get_plant().get_state() + 1)
|
||||
elif field.get_plant().get_name() == "wheat" and field.get_plant().get_state() > 0 and field.get_plant().get_state() < definitions.WHEAT_MAXIMUM_STATE:
|
||||
field.get_plant().set_state(field.get_plant().get_state() + 1)
|
||||
@staticmethod
|
||||
def if_any_mature_plant(map1): #sprawdza czy na polu występuje choć jedna dojrzała roślina, jeśli tak zwraca prawdę, w przeciwnym razie zwraca fałsz
|
||||
for i in range(definitions.WIDTH_AMOUNT):
|
||||
for j in range(definitions.HEIGHT_AMOUNT):
|
||||
@ -24,45 +62,4 @@ class Plant:
|
||||
return True
|
||||
elif field.get_plant().get_name() == "wheat" and field.get_plant().get_state() == definitions.WHEAT_MAXIMUM_STATE:
|
||||
return True
|
||||
return False
|
||||
@staticmethod
|
||||
def get_closest_mature_plant(map1, tractor1): #TO DO, pobiera współrzędne najbliższej dojrzałej rośliny od miejsca, w którym znajduje się traktor
|
||||
x = -1
|
||||
y = -1
|
||||
min = definitions.WIDTH_AMOUNT + definitions.HEIGHT_AMOUNT + 1
|
||||
for i in range(definitions.WIDTH_AMOUNT):
|
||||
for j in range(definitions.HEIGHT_AMOUNT):
|
||||
field = map1.get_fields()[i][j]
|
||||
if field.get_plant().get_name() == "beetroot" and field.get_plant().get_state() == definitions.BEETROOTS_MAXIMUM_STATE: #and abs(tractor1.get_x() - i) + abs(tractor1.get_y() - j) < min:
|
||||
x = i
|
||||
y = j
|
||||
min = abs(tractor1.get_x() - i) + abs(tractor1.get_y() - j)
|
||||
elif field.get_plant().get_name() == "carrot" and field.get_plant().get_state() == definitions.CARROTS_MAXIMUM_STATE: #and abs(tractor1.get_x() - i) + abs(tractor1.get_y() - j) < min:
|
||||
x = i
|
||||
y = j
|
||||
min = abs(tractor1.get_x() - i) + abs(tractor1.get_y() - j)
|
||||
elif field.get_plant().get_name() == "potato" and field.get_plant().get_state() == definitions.POTATOES_MAXIMUM_STATE: #and abs(tractor1.get_x() - i) + abs(tractor1.get_y() - j) < min:
|
||||
x = i
|
||||
y = j
|
||||
min = abs(tractor1.get_x() - i) + abs(tractor1.get_y() - j)
|
||||
elif field.get_plant().get_name() == "wheat" and field.get_plant().get_state() == definitions.WHEAT_MAXIMUM_STATE: #and abs(tractor1.get_x() - i) + abs(tractor1.get_y() - j) < min:
|
||||
x = i
|
||||
y = j
|
||||
min = abs(tractor1.get_x() - i) + abs(tractor1.get_y() - j)
|
||||
if x == -1 and y == -1:
|
||||
return False
|
||||
else:
|
||||
return x, y
|
||||
@staticmethod
|
||||
def grow_plants(map1): #metoda statyczna, która zwiększa pole state (etap rozwoju rośliny) dla danej rośliny na danym polu o 1
|
||||
for i in range(definitions.WIDTH_AMOUNT):
|
||||
for j in range(definitions.HEIGHT_AMOUNT):
|
||||
field = map1.get_fields()[i][j]
|
||||
if field.get_plant().get_name() == "beetroot" and field.get_plant().get_state() > 0 and field.get_plant().get_state() < definitions.BEETROOTS_MAXIMUM_STATE:
|
||||
field.get_plant().set_state(field.get_plant().get_state() + 1)
|
||||
elif field.get_plant().get_name() == "carrot" and field.get_plant().get_state() > 0 and field.get_plant().get_state() < definitions.CARROTS_MAXIMUM_STATE:
|
||||
field.get_plant().set_state(field.get_plant().get_state() + 1)
|
||||
elif field.get_plant().get_name() == "potato" and field.get_plant().get_state() > 0 and field.get_plant().get_state() < definitions.POTATOES_MAXIMUM_STATE:
|
||||
field.get_plant().set_state(field.get_plant().get_state() + 1)
|
||||
elif field.get_plant().get_name() == "wheat" and field.get_plant().get_state() > 0 and field.get_plant().get_state() < definitions.WHEAT_MAXIMUM_STATE:
|
||||
field.get_plant().set_state(field.get_plant().get_state() + 1)
|
||||
return False
|
||||
29
py.py
29
py.py
@ -1,47 +1,48 @@
|
||||
import astar
|
||||
import cart
|
||||
import definitions
|
||||
import graph
|
||||
import map
|
||||
import neuralnetwork
|
||||
import plant
|
||||
import pygame
|
||||
import station
|
||||
import treelearn
|
||||
import tractor
|
||||
pygame.display.set_caption("Smart Tractor")
|
||||
pygame.display.set_caption("Smart Cart")
|
||||
def main():
|
||||
#tworzenie podstawowych obiektów
|
||||
map1 = map.Map([])
|
||||
map1.create_base_map()
|
||||
move_list = []
|
||||
amount_of_seeds_dict = {"beetroot": definitions.TRACTOR_AMOUNT_OF_SEEDS_EACH_TYPE, "carrot": definitions.TRACTOR_AMOUNT_OF_SEEDS_EACH_TYPE, "potato": definitions.TRACTOR_AMOUNT_OF_SEEDS_EACH_TYPE, "wheat": definitions.TRACTOR_AMOUNT_OF_SEEDS_EACH_TYPE}
|
||||
amount_of_seeds_dict = {"beetroot": definitions.CART_AMOUNT_OF_SEEDS_EACH_TYPE, "carrot": definitions.CART_AMOUNT_OF_SEEDS_EACH_TYPE, "potato": definitions.CART_AMOUNT_OF_SEEDS_EACH_TYPE, "wheat": definitions.CART_AMOUNT_OF_SEEDS_EACH_TYPE}
|
||||
collected_plants_dict = {"beetroot": 0, "carrot": 0, "potato": 0, "wheat": 0}
|
||||
fertilizer_dict = {"beetroot": definitions.TRACTOR_FERTILIZER, "carrot": definitions.TRACTOR_FERTILIZER, "potato": definitions.TRACTOR_FERTILIZER, "wheat": definitions.TRACTOR_FERTILIZER}
|
||||
fertilizer_dict = {"beetroot": definitions.CART_FERTILIZER, "carrot": definitions.CART_FERTILIZER, "potato": definitions.CART_FERTILIZER, "wheat": definitions.CART_FERTILIZER}
|
||||
station1 = station.Station(collected_plants_dict)
|
||||
tractor1 = tractor.Tractor(amount_of_seeds_dict, collected_plants_dict, definitions.TRACTOR_DIRECTION_NORTH, fertilizer_dict, definitions.TRACTOR_FUEL, definitions.TRACTOR_WATER_LEVEL, 0, 0)
|
||||
tractor1_rect = pygame.Rect(tractor1.get_x(), tractor1.get_y(), definitions.BLOCK_SIZE, definitions.BLOCK_SIZE)
|
||||
cart1 = cart.Cart(amount_of_seeds_dict, collected_plants_dict, definitions.CART_DIRECTION_NORTH, fertilizer_dict, definitions.CART_FUEL, definitions.CART_WATER_LEVEL, 0, 0)
|
||||
cart1_rect = pygame.Rect(cart1.get_x(), cart1.get_y(), definitions.BLOCK_SIZE, definitions.BLOCK_SIZE)
|
||||
clock = pygame.time.Clock()
|
||||
tree = treelearn.treelearn() #tworzenie drzewa decyzyjnego
|
||||
decision = [0] #początkowa decyzja o braku powrotu do stacji (0)
|
||||
#neuralnetwork.create_neural_network()
|
||||
run = True
|
||||
while run: #pętla główna programu
|
||||
clock.tick(definitions.FPS)
|
||||
for event in pygame.event.get():
|
||||
if event.type == pygame.QUIT:
|
||||
run = False
|
||||
map1.draw_window(tractor1, tractor1_rect)
|
||||
map1.draw_window(cart1, cart1_rect)
|
||||
if not move_list and plant.Plant.if_any_mature_plant(map1) is True: #jeżeli są jakieś ruchy do wykonania w move_list oraz istnieje jakaś dojrzała roślina
|
||||
istate = graph.Istate(tractor1.get_direction(), tractor1.get_x() / definitions.BLOCK_SIZE, tractor1.get_y() / definitions.BLOCK_SIZE) #stan początkowy traktora (jego orientacja oraz jego aktualne współrzędne)
|
||||
#move_list = (graph.graphsearch([], [], istate, graph.succ, plant.Plant.get_closest_mature_plant(map1, tractor1))) #lista z ruchami, które należy po kolei wykonać, graph
|
||||
istate = graph.Istate(cart1.get_direction(), cart1.get_x() / definitions.BLOCK_SIZE, cart1.get_y() / definitions.BLOCK_SIZE) #stan początkowy wózka (jego orientacja oraz jego aktualne współrzędne)
|
||||
if decision == [0]: #jeżeli decyzja jest 0 (brak powrotu do stacji) to uprawiaj pole
|
||||
move_list = (astar.graphsearch([], [], istate, graph.succ, plant.Plant.get_closest_mature_plant(map1, istate), astar.f, map1)) #lista z ruchami, które należy po kolei wykonać, astar
|
||||
move_list = (astar.graphsearch([], [], istate, graph.succ, plant.Plant.get_mature_plant(map1, istate), astar.f, map1)) #lista z ruchami, które należy po kolei wykonać, astar
|
||||
else: #jeżeli decyzja jest 1 (powrót do stacji) to wróć do stacji uzupełnić zapasy
|
||||
move_list = (graph.graphsearch([], [], istate, graph.succ, (0, 0))) #lista z ruchami, które należy po kolei wykonać, graphsearch
|
||||
elif move_list: #jeżeli move_list nie jest pusta
|
||||
tractor1.handle_movement(move_list.pop(0), tractor1_rect) #wykonaj kolejny ruch oraz zdejmij ten ruch z początku listy
|
||||
cart1.handle_movement(move_list.pop(0), cart1_rect) #wykonaj kolejny ruch oraz zdejmij ten ruch z początku listy
|
||||
else:
|
||||
tractor1.handle_random_movement(tractor1_rect) #wykonuj losowe ruchy
|
||||
tractor1.do_work(map1, station1, tractor1_rect) #wykonaj pracę na danym polu
|
||||
decision = treelearn.make_decision(tree, tractor1.get_all_amount_of_seeds(), tractor1.get_all_collected_plants(), tractor1.get_all_fertilizer(), tractor1.get_fuel(), tractor1.get_water_level()) #podejmij decyzję czy wracać do stacji (0 : NIE, 1 : TAK)
|
||||
cart1.handle_movement_random(cart1_rect) #wykonuj losowe ruchy
|
||||
cart1.do_work(map1, station1, cart1_rect) #wykonaj pracę na danym polu
|
||||
decision = treelearn.make_decision(tree, cart1.get_all_amount_of_seeds(), cart1.get_all_collected_plants(), cart1.get_all_fertilizer(), cart1.get_fuel(), cart1.get_water_level()) #podejmij decyzję czy wracać do stacji (0 : NIE, 1 : TAK)
|
||||
plant.Plant.grow_plants(map1) #zwiększ poziom dojrzałości roślin
|
||||
pygame.quit()
|
||||
if __name__ == "__main__":
|
||||
|
||||
|
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|
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20
treelearn.py
20
treelearn.py
@ -6,24 +6,24 @@ import pickle
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import pydotplus
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from sklearn import tree
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from sklearn.tree import DecisionTreeClassifier
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def make_decision(tree, amount_of_seeds, collected_plants, fertilizer, fuel, water_level): #zwraca decyzję o powrocie do stacji (0 : NIE, 1 : TAK)
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decision = tree.predict([[amount_of_seeds, collected_plants, fertilizer, fuel, water_level]]) #podejmij decyzję na podstawie aktualnych parametrów wózka o powrocie do stacji lub nie
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return decision
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def treelearn(): #zwraca utworzone drzewo decyzyjne
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if os.path.exists("resources/tree.pkl"): #jeżeli drzewo jest zapisane w pliku to odczytaj
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dtree = pickle.load(open(os.path.join('resources', "tree.pkl"), "rb"))
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if os.path.exists("resources/decision_tree/tree.pkl"): #jeżeli drzewo jest zapisane w pliku to odczytaj
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dtree = pickle.load(open(os.path.join('resources/decision_tree', "tree.pkl"), "rb"))
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else: #w przeciwnym razie utwórz drzewo od początku i zapisz do pliku
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df = pandas.read_csv(os.path.join('resources', 'data.csv')) #czytanie danych do nauki drzewa z pliku .csv
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df = pandas.read_csv(os.path.join('resources/decision_tree', 'data.csv')) #czytanie danych do nauki drzewa z pliku .csv
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features = ['amount of seeds', 'collected plants', 'fertilizer', 'fuel', 'water level']
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x = df[features] #wczytanie atrybutów, z których ma się uczyć drzewo
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y = df['back to station'] #podjęte decyzje
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dtree = DecisionTreeClassifier() #klasyfikuje drzewo
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dtree = dtree.fit(x, y) #uczy drzewo
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pickle.dump(dtree, open(os.path.join('resources', "tree.pkl"), "wb"))
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pickle.dump(dtree, open(os.path.join('resources/decision_tree', "tree.pkl"), "wb"))
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data = tree.export_graphviz(dtree, out_file=None, feature_names=features)
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graph = pydotplus.graph_from_dot_data(data)
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graph.write_png(os.path.join('resources', 'mytree.png'))
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img = pltimg.imread(os.path.join('resources', 'mytree.png'))
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graph.write_png(os.path.join('resources/decision_tree', 'mytree.png'))
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img = pltimg.imread(os.path.join('resources/decision_tree', 'mytree.png'))
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imgplot = plt.imshow(img)
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plt.show() #wyświetl drzewo decyzyjne
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return dtree
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def make_decision(tree, amount_of_seeds, collected_plants, fertilizer, fuel, water_level): #zwraca decyzję o powrocie do stacji (0 : NIE, 1 : TAK)
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decision = tree.predict([[amount_of_seeds, collected_plants, fertilizer, fuel, water_level]]) #podejmij decyzję na podstawie aktualnych parametrów traktora o powrocie do stacji lub nie
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return decision
|
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return dtree
|
||||
Loading…
Reference in New Issue
Block a user