kosmetyczne zmiany
This commit is contained in:
Tomasz Adamczyk
parent
bbb2fdefb9
commit
a43f35d179
@ -2,49 +2,13 @@
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10
astar.py
10
astar.py
@ -51,14 +51,14 @@ 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 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 f(goaltest, map, node): #funkcja zwracająca sumę funkcji kosztu oraz heurestyki
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return cost(map, node) + heuristic(goaltest, node)
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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 graphsearch(fringe, explored, istate, succ, goaltest, f, map): #przeszukiwanie grafu wszerz
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def graphsearch(explored, f, fringe, goaltest, istate, map, succ): #przeszukiwanie grafu wszerz
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node = Node(None, istate.get_direction(), None, istate.get_x(), istate.get_y()) #wierzchołek początkowy, stworzony ze stanu początkowego wózka
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fringe.append((node, 0)) #wierzchołki do odwiedzenia z priorytetem
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while True:
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@ -79,7 +79,7 @@ def graphsearch(fringe, explored, istate, succ, goaltest, f, map): #przeszukiwan
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for (x, y) in explored:
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explored_tuple.append((x.get_direction(), x.get_x(), x.get_y()))
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x = Node(action, state[0], elem[0], state[1], state[2]) #stworzenie nowego wierzchołka, którego rodzicem jest elem
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p = f(map, x, goaltest) #liczy priorytet
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p = f(goaltest, map, x) #liczy priorytet
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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
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fringe.append((x, p)) #dodanie wierzchołka na fringe
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fringe = sorted(fringe, key=itemgetter(1)) #sortowanie fringe'a według priorytetu
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@ -93,7 +93,7 @@ def graphsearch(fringe, explored, istate, succ, goaltest, f, map): #przeszukiwan
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fringe = sorted(fringe, key=itemgetter(1)) #sortowanie fringe'a według priorytetu
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break
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i = i + 1
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def heuristic(node, goaltest): #funkcja heurestyki : oszacowuje koszt osiągnięcia stanu końcowego (droga)
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def heuristic(goaltest, node): #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 print_moves(elem): #zwraca listę ruchów jakie należy wykonać by dotrzeć do punktu docelowego
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moves_list = []
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4
cart.py
4
cart.py
@ -48,7 +48,7 @@ class Cart:
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return self.y
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def set_y(self, y):
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self.y = y
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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)
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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)
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loop = True
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if self.get_all_amount_of_seeds() == 0:
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loop = False
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@ -125,7 +125,7 @@ class Cart:
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field.get_soil().set_water_level(False)
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field.get_soil().set_state(False)
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self.set_collected_plants("wheat", self.get_collected_plants("wheat") + 1)
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def handle_movement(self, move, cart_rect): #odpowiada za poruszanie się wózka po mapie
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def handle_movement(self, cart_rect, move): #odpowiada za poruszanie się wózka po mapie
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if self.get_fuel() > 0:
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if move == "move":
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self.move()
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6
graph.py
6
graph.py
@ -44,12 +44,12 @@ class Node: #wierzchołek grafu
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return self.y
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def set_y(self, y):
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self.y = y
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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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def goal_test(goaltest, elem): #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]:
|
||||
return True
|
||||
else:
|
||||
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
|
||||
fringe.append(node) #wierzchołki do odwiedzenia
|
||||
while True:
|
||||
@ -57,7 +57,7 @@ def graphsearch(fringe, explored, istate, succ, goaltest): #przeszukiwanie grafu
|
||||
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
|
||||
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)
|
||||
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
|
||||
|
||||
@ -55,7 +55,7 @@ def create_neural_network():
|
||||
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)
|
||||
pred_dict[i[i.rfind('/') + 1:]] = prediction1(classes, i, model, transformer1)
|
||||
print(pred_dict)
|
||||
else:
|
||||
model = ConvNet(num_classes=6).to(device)
|
||||
@ -93,11 +93,11 @@ def create_neural_network():
|
||||
_, 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))
|
||||
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:
|
||||
torch.save(model.state_dict(), 'resources/neural_network/checkpoint.model')
|
||||
best_accuracy = test_accuracy
|
||||
def prediction1(img_path, transformer, model, classes):
|
||||
def prediction1(classes, img_path, model, transformer):
|
||||
image = Image.open(img_path)
|
||||
image_tensor = transformer(image).float()
|
||||
image_tensor = image_tensor.unsqueeze_(0)
|
||||
|
||||
13
plant.py
13
plant.py
@ -12,12 +12,12 @@ class Plant:
|
||||
def set_state(self, state):
|
||||
self.state = state
|
||||
@staticmethod
|
||||
def get_mature_plant(map1, cart): #pobiera współrzędne jakiejś dojrzałej rośliny
|
||||
def get_mature_plant(map): #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]
|
||||
field = map.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
|
||||
@ -36,11 +36,10 @@ class Plant:
|
||||
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
|
||||
def grow_plants(map): #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]
|
||||
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:
|
||||
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:
|
||||
@ -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:
|
||||
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
|
||||
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 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:
|
||||
return True
|
||||
elif field.get_plant().get_name() == "carrot" and field.get_plant().get_state() == definitions.CARROTS_MAXIMUM_STATE:
|
||||
|
||||
10
py.py
10
py.py
@ -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
|
||||
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_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
|
||||
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
|
||||
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:
|
||||
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)
|
||||
cart1.do_work(cart1_rect, map1, station1) #wykonaj pracę na danym polu
|
||||
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
|
||||
pygame.quit()
|
||||
if __name__ == "__main__":
|
||||
|
||||
@ -6,7 +6,7 @@ import pickle
|
||||
import pydotplus
|
||||
from sklearn import tree
|
||||
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
|
||||
return decision
|
||||
def treelearn(): #zwraca utworzone drzewo decyzyjne
|
||||
|
||||
Loading…
Reference in New Issue
Block a user