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Tomasz Adamczyk 2021-05-31 18:16:14 +02:00
parent bbb2fdefb9
commit a43f35d179
15 changed files with 43 additions and 78 deletions
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70
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10
astar.py
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@ -51,14 +51,14 @@ def cost(map, node): #funkcja kosztu : ile kosztuje przejechanie przez dane pole
cost = cost + map.get_field_cost(int(node.get_x()), int(node.get_y())) + 1 cost = cost + map.get_field_cost(int(node.get_x()), int(node.get_y())) + 1
node = node.get_parent() node = node.get_parent()
return cost return cost
def f(map, node, goaltest): #funkcja zwracająca sumę funkcji kosztu oraz heurestyki def f(goaltest, map, node): #funkcja zwracająca sumę funkcji kosztu oraz heurestyki
return cost(map, node) + heuristic(node, goaltest) return cost(map, node) + heuristic(goaltest, node)
def goal_test(elem, goaltest): #funkcja sprawdzająca czy położenie wózka 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]: if elem.get_x() == goaltest[0] and elem.get_y() == goaltest[1]:
return True return True
else: else:
return False return False
def graphsearch(fringe, explored, istate, succ, goaltest, f, map): #przeszukiwanie grafu wszerz def graphsearch(explored, f, fringe, goaltest, istate, map, succ): #przeszukiwanie grafu wszerz
node = Node(None, istate.get_direction(), None, istate.get_x(), istate.get_y()) #wierzchołek początkowy, stworzony ze stanu początkowego wózka 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 fringe.append((node, 0)) #wierzchołki do odwiedzenia z priorytetem
while True: while True:
@ -79,7 +79,7 @@ def graphsearch(fringe, explored, istate, succ, goaltest, f, map): #przeszukiwan
for (x, y) in explored: for (x, y) in explored:
explored_tuple.append((x.get_direction(), x.get_x(), x.get_y())) explored_tuple.append((x.get_direction(), x.get_x(), x.get_y()))
x = Node(action, state[0], elem[0], state[1], state[2]) #stworzenie nowego wierzchołka, którego rodzicem jest elem x = Node(action, state[0], elem[0], state[1], state[2]) #stworzenie nowego wierzchołka, którego rodzicem jest elem
p = f(map, x, goaltest) #liczy priorytet p = f(goaltest, map, x) #liczy priorytet
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 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
fringe.append((x, p)) #dodanie wierzchołka na fringe fringe.append((x, p)) #dodanie wierzchołka na fringe
fringe = sorted(fringe, key=itemgetter(1)) #sortowanie fringe'a według priorytetu fringe = sorted(fringe, key=itemgetter(1)) #sortowanie fringe'a według priorytetu
@ -93,7 +93,7 @@ def graphsearch(fringe, explored, istate, succ, goaltest, f, map): #przeszukiwan
fringe = sorted(fringe, key=itemgetter(1)) #sortowanie fringe'a według priorytetu fringe = sorted(fringe, key=itemgetter(1)) #sortowanie fringe'a według priorytetu
break break
i = i + 1 i = i + 1
def heuristic(node, goaltest): #funkcja heurestyki : oszacowuje koszt osiągnięcia stanu końcowego (droga) def heuristic(goaltest, node): #funkcja heurestyki : oszacowuje koszt osiągnięcia stanu końcowego (droga)
return abs(node.get_x() - goaltest[0]) + abs(node.get_y() - goaltest[1]) 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 def print_moves(elem): #zwraca listę ruchów jakie należy wykonać by dotrzeć do punktu docelowego
moves_list = [] moves_list = []

4
cart.py
View File

@ -48,7 +48,7 @@ class Cart:
return self.y return self.y
def set_y(self, y): def set_y(self, y):
self.y = y self.y = y
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) def do_work(self, cart_rect, map1, station1): #jaką pracę wózek ma wykonać na danym polu, na którym aktualnie przebywa (zmienia stan logiczny danego pola)
loop = True loop = True
if self.get_all_amount_of_seeds() == 0: if self.get_all_amount_of_seeds() == 0:
loop = False loop = False
@ -125,7 +125,7 @@ class Cart:
field.get_soil().set_water_level(False) field.get_soil().set_water_level(False)
field.get_soil().set_state(False) field.get_soil().set_state(False)
self.set_collected_plants("wheat", self.get_collected_plants("wheat") + 1) self.set_collected_plants("wheat", self.get_collected_plants("wheat") + 1)
def handle_movement(self, move, cart_rect): #odpowiada za poruszanie się wózka po mapie def handle_movement(self, cart_rect, move): #odpowiada za poruszanie się wózka po mapie
if self.get_fuel() > 0: if self.get_fuel() > 0:
if move == "move": if move == "move":
self.move() self.move()

6
graph.py
View File

@ -44,12 +44,12 @@ class Node: #wierzchołek grafu
return self.y return self.y
def set_y(self, y): def set_y(self, y):
self.y = y self.y = y
def goal_test(elem, goaltest): #funkcja sprawdzająca czy położenie wózka równa się położeniu punktu docelowego, jeśli tak zwraca prawdę, w przeciwnym wypadku fałsz def goal_test(goaltest, elem): #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]: if elem.get_x() == goaltest[0] and elem.get_y() == goaltest[1]:
return True return True
else: else:
return False return False
def graphsearch(fringe, explored, istate, succ, goaltest): #przeszukiwanie grafu wszerz def graphsearch(explored, fringe, goaltest, istate, succ): #przeszukiwanie grafu wszerz
node = Node(None, istate.get_direction(), None, istate.get_x(), istate.get_y()) #wierzchołek początkowy, stworzony ze stanu początkowego wózka 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 fringe.append(node) #wierzchołki do odwiedzenia
while True: while True:
@ -57,7 +57,7 @@ def graphsearch(fringe, explored, istate, succ, goaltest): #przeszukiwanie grafu
return False return False
elem = fringe.pop(0) #zdejmujemy wierzchołek z kolejki fringe i rozpatrujemy go elem = fringe.pop(0) #zdejmujemy wierzchołek z kolejki fringe i rozpatrujemy go
temp = copy.copy(elem) 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 if goal_test(goaltest, elem) 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) return print_moves(elem)
explored.append(elem) #dodajemy wierzchołek do listy wierzchołków odwiedzonych 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 for (action, state) in succ(temp): #iterujemy po wszystkich możliwych akcjach i stanach otrzymanych dla danego wierzchołka grafu

6
neuralnetwork.py
View File

@ -55,7 +55,7 @@ def create_neural_network():
images_path = glob.glob(pred_path+'/*.jpg') images_path = glob.glob(pred_path+'/*.jpg')
pred_dict = {} pred_dict = {}
for i in images_path: for i in images_path:
pred_dict[i[i.rfind('/') + 1:]] = prediction1(i, transformer1, model, classes) pred_dict[i[i.rfind('/') + 1:]] = prediction1(classes, i, model, transformer1)
print(pred_dict) print(pred_dict)
else: else:
model = ConvNet(num_classes=6).to(device) model = ConvNet(num_classes=6).to(device)
@ -93,11 +93,11 @@ def create_neural_network():
_, prediction = torch.max(outputs.data, 1) _, prediction = torch.max(outputs.data, 1)
test_accuracy += int(torch.sum(prediction == labels.data)) test_accuracy += int(torch.sum(prediction == labels.data))
test_accuracy = test_accuracy / test_count 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)) print('Epoch: ' + str(epoch + 1) + ' Train Loss: ' + str(train_loss) + ' Train Accuracy: ' + str(train_accuracy) + ' Test Accuracy: ' + str(test_accuracy))
if test_accuracy > best_accuracy: if test_accuracy > best_accuracy:
torch.save(model.state_dict(), 'resources/neural_network/checkpoint.model') torch.save(model.state_dict(), 'resources/neural_network/checkpoint.model')
best_accuracy = test_accuracy best_accuracy = test_accuracy
def prediction1(img_path, transformer, model, classes): def prediction1(classes, img_path, model, transformer):
image = Image.open(img_path) image = Image.open(img_path)
image_tensor = transformer(image).float() image_tensor = transformer(image).float()
image_tensor = image_tensor.unsqueeze_(0) image_tensor = image_tensor.unsqueeze_(0)

13
plant.py
View File

@ -12,12 +12,12 @@ class Plant:
def set_state(self, state): def set_state(self, state):
self.state = state self.state = state
@staticmethod @staticmethod
def get_mature_plant(map1, cart): #pobiera współrzędne jakiejś dojrzałej rośliny def get_mature_plant(map): #pobiera współrzędne jakiejś dojrzałej rośliny
x = -1 x = -1
y = -1 y = -1
for i in range(definitions.WIDTH_AMOUNT): for i in range(definitions.WIDTH_AMOUNT):
for j in range(definitions.HEIGHT_AMOUNT): for j in range(definitions.HEIGHT_AMOUNT):
field = map1.get_fields()[i][j] field = map.get_fields()[i][j]
if field.get_plant().get_name() == "beetroot" and field.get_plant().get_state() == definitions.BEETROOTS_MAXIMUM_STATE: if field.get_plant().get_name() == "beetroot" and field.get_plant().get_state() == definitions.BEETROOTS_MAXIMUM_STATE:
x = i x = i
y = j y = j
@ -36,11 +36,10 @@ class Plant:
else: else:
return x, y return x, y
@staticmethod @staticmethod
def grow_plants( def grow_plants(map): #metoda statyczna, która zwiększa pole state (etap rozwoju rośliny) dla danej rośliny na danym polu o 1
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 i in range(definitions.WIDTH_AMOUNT):
for j in range(definitions.HEIGHT_AMOUNT): for j in range(definitions.HEIGHT_AMOUNT):
field = map1.get_fields()[i][j] field = map.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: 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) 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: elif field.get_plant().get_name() == "carrot" and field.get_plant().get_state() > 0 and field.get_plant().get_state() < definitions.CARROTS_MAXIMUM_STATE:
@ -50,10 +49,10 @@ class Plant:
elif field.get_plant().get_name() == "wheat" and field.get_plant().get_state() > 0 and field.get_plant().get_state() < definitions.WHEAT_MAXIMUM_STATE: 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) field.get_plant().set_state(field.get_plant().get_state() + 1)
@staticmethod @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 def if_any_mature_plant(map): #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 i in range(definitions.WIDTH_AMOUNT):
for j in range(definitions.HEIGHT_AMOUNT): for j in range(definitions.HEIGHT_AMOUNT):
field = map1.get_fields()[i][j] field = map.get_fields()[i][j]
if field.get_plant().get_name() == "beetroot" and field.get_plant().get_state() == definitions.BEETROOTS_MAXIMUM_STATE: if field.get_plant().get_name() == "beetroot" and field.get_plant().get_state() == definitions.BEETROOTS_MAXIMUM_STATE:
return True return True
elif field.get_plant().get_name() == "carrot" and field.get_plant().get_state() == definitions.CARROTS_MAXIMUM_STATE: elif field.get_plant().get_name() == "carrot" and field.get_plant().get_state() == definitions.CARROTS_MAXIMUM_STATE:

10
py.py
View File

@ -34,15 +34,15 @@ def main():
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 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(cart1.get_direction(), cart1.get_x() / definitions.BLOCK_SIZE, cart1.get_y() / definitions.BLOCK_SIZE) #stan początkowy wózka (jego orientacja oraz jego aktualne współrzędne) 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 if decision == [0]: #jeżeli decyzja jest 0 (brak powrotu do stacji) to uprawiaj pole
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 move_list = (astar.graphsearch([], astar.f, [], plant.Plant.get_mature_plant(map1), istate, map1, graph.succ)) #lista z ruchami, które należy po kolei wykonać, astar
else: #jeżeli decyzja jest 1 (powrót do stacji) to wróć do stacji uzupełnić zapasy 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 move_list = (graph.graphsearch([], [], (0, 0), istate, graph.succ)) #lista z ruchami, które należy po kolei wykonać, graphsearch
elif move_list: #jeżeli move_list nie jest pusta elif move_list: #jeżeli move_list nie jest pusta
cart1.handle_movement(move_list.pop(0), cart1_rect) #wykonaj kolejny ruch oraz zdejmij ten ruch z początku listy cart1.handle_movement(cart1_rect, move_list.pop(0)) #wykonaj kolejny ruch oraz zdejmij ten ruch z początku listy
else: else:
cart1.handle_movement_random(cart1_rect) #wykonuj losowe ruchy cart1.handle_movement_random(cart1_rect) #wykonuj losowe ruchy
cart1.do_work(map1, station1, cart1_rect) #wykonaj pracę na danym polu cart1.do_work(cart1_rect, map1, station1) #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) decision = treelearn.make_decision(cart1.get_all_amount_of_seeds(), cart1.get_all_collected_plants(), cart1.get_all_fertilizer(), cart1.get_fuel(), tree, cart1.get_water_level()) #podejmij decyzję czy wracać do stacji (0 : NIE, 1 : TAK)
plant.Plant.grow_plants(map1) #zwiększ poziom dojrzałości roślin plant.Plant.grow_plants(map1) #zwiększ poziom dojrzałości roślin
pygame.quit() pygame.quit()
if __name__ == "__main__": if __name__ == "__main__":

2
treelearn.py
View File

@ -6,7 +6,7 @@ import pickle
import pydotplus import pydotplus
from sklearn import tree from sklearn import tree
from sklearn.tree import DecisionTreeClassifier from sklearn.tree import DecisionTreeClassifier
def make_decision(tree, amount_of_seeds, collected_plants, fertilizer, fuel, water_level): #zwraca decyzję o powrocie do stacji (0 : NIE, 1 : TAK) def make_decision(amount_of_seeds, collected_plants, fertilizer, fuel, tree, water_level): #zwraca decyzję o powrocie do stacji (0 : NIE, 1 : TAK)
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 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
return decision return decision
def treelearn(): #zwraca utworzone drzewo decyzyjne def treelearn(): #zwraca utworzone drzewo decyzyjne