Compare commits
No commits in common. "master" and "neural_network_two_crops" have entirely different histories.
master
...
neural_net
25
App.py
25
App.py
@ -10,7 +10,6 @@ import Ui
|
||||
import BFS
|
||||
import AStar
|
||||
import neuralnetwork
|
||||
import json
|
||||
|
||||
|
||||
bfs1_flag=False
|
||||
@ -21,9 +20,8 @@ Astar2 = False
|
||||
if bfs3_flag or Astar or Astar2:
|
||||
Pole.stoneFlag = True
|
||||
TreeFlag=False
|
||||
nnFlag=False
|
||||
nnFlag=True
|
||||
newModel=False
|
||||
finalFlag = True
|
||||
|
||||
pygame.init()
|
||||
show_console=True
|
||||
@ -45,15 +43,7 @@ def init_demo(): #Demo purpose
|
||||
old_info=""
|
||||
traktor.draw_tractor()
|
||||
time.sleep(2)
|
||||
if not finalFlag:
|
||||
pole.randomize_colors(nnFlag)
|
||||
else:
|
||||
population = 120
|
||||
iterat = 2500
|
||||
roulette = True
|
||||
with open(f'pole_pop{population}_iter{iterat}_{roulette}.json', 'r') as file:
|
||||
garden_data = json.load(file)
|
||||
pole.setPlantsByList(garden_data)
|
||||
pole.randomize_colors(nnFlag)
|
||||
traktor.draw_tractor()
|
||||
start_flag=True
|
||||
while True:
|
||||
@ -85,7 +75,7 @@ def init_demo(): #Demo purpose
|
||||
print_to_console("Traktor porusza sie obliczona sciezka BFS")
|
||||
traktor.move_by_root(bfsRoot2, pole, [traktor.irrigateSlot])
|
||||
if(bfs3_flag):
|
||||
bfsRoot3 = BFS.BFS3({'x': 0, 'y': 0, 'direction': "E"},goalTreasure)
|
||||
bfsRoot3 = BFS.BFS3({'x': 0, 'y': 0, 'direction': "E"})
|
||||
#displayControler: NUM_X: 20, NUM_Y: 12 (skarb) CHANGE THIS IN DCON BY HAND!!!!!!!!
|
||||
bfsRoot3.reverse()
|
||||
print_to_console("Traktor porusza sie obliczona sciezka BFS")
|
||||
@ -138,16 +128,11 @@ def init_demo(): #Demo purpose
|
||||
print_to_console("sieć nuronowa nauczona")
|
||||
print('model został wygenerowany')
|
||||
else:
|
||||
model = neuralnetwork.loadModel('model_500_hidden.pth')
|
||||
model = neuralnetwork.loadModel('model.pth')
|
||||
print_to_console("model został załądowny")
|
||||
testset = neuralnetwork.getDataset(False)
|
||||
print(neuralnetwork.accuracy(model, testset))
|
||||
traktor.snake_move_predict_plant(pole, model, headers=['Coords','Real plant','Predicted plant','Result','Fertilizer'], actions=[traktor.fertilize_slot])
|
||||
if(finalFlag):
|
||||
pass
|
||||
model = neuralnetwork.loadModel('model_500_hidden.pth')
|
||||
Tractor.drzewo.treeLearn()
|
||||
traktor.snake_move_predict_plant(pole, model, headers=['Coords','Real plant','Predicted plant','Result','Water decision'], actions=[traktor.irigate_slot_NN])
|
||||
traktor.snake_move_predict_plant(pole, model)
|
||||
start_flag=False
|
||||
# demo_move()
|
||||
old_info=get_info(old_info)
|
||||
|
||||
8
BFS.py
8
BFS.py
@ -133,12 +133,8 @@ def check3(tab, state):
|
||||
return True
|
||||
|
||||
|
||||
def BFS3(istate,GT):
|
||||
randomGT=False
|
||||
if(randomGT==True):
|
||||
goalTreassuere = (random.randint(0,NUM_X-1), random.randint(0,NUM_Y-1))
|
||||
else:
|
||||
goalTreassuere=GT
|
||||
def BFS3(istate):
|
||||
goalTreassuere = (random.randint(0,NUM_X-1), random.randint(0,NUM_Y-1))
|
||||
print(goalTreassuere)
|
||||
fringe = []
|
||||
explored = []
|
||||
|
||||
@ -8,7 +8,7 @@ class Drzewo:
|
||||
self.tree=self.treeLearn()
|
||||
|
||||
def treeLearn(self):
|
||||
csvdata=pandas.read_csv('Data/dataTree2.csv')
|
||||
csvdata=pandas.read_csv('Data/dataTree.csv')
|
||||
#csvdata = pandas.read_csv('Data/dataTree2.csv')
|
||||
x=csvdata[atributes]
|
||||
decision=csvdata['action']
|
||||
@ -18,7 +18,7 @@ class Drzewo:
|
||||
def plotTree(self):
|
||||
plt.figure(figsize=(20,30))
|
||||
skltree.plot_tree(self.tree,filled=True,feature_names=atributes)
|
||||
plt.title("Drzewo decyzyjne wytrenowane na przygotowanych danych: ")
|
||||
plt.title("Drzewo decyzyjne wytrenowane na przygotowanych danych")
|
||||
plt.savefig('tree.png')
|
||||
#plt.show()
|
||||
def makeDecision(self,values):
|
||||
|
||||
@ -1,139 +0,0 @@
|
||||
import json
|
||||
import random
|
||||
|
||||
from displayControler import NUM_Y, NUM_X
|
||||
|
||||
iterat = 2500
|
||||
population = 120
|
||||
roulette = True
|
||||
|
||||
plants = ['corn', 'potato', 'tomato', 'carrot']
|
||||
initial_yields = {'corn': 38, 'potato': 40, 'tomato': 43, 'carrot': 45}
|
||||
yield_reduction = {
|
||||
'corn': {'corn': -4.5, 'potato': -3, 'tomato': -7, 'carrot': -7},
|
||||
'potato': {'corn': -7, 'potato': -5, 'tomato': -10, 'carrot': -6},
|
||||
'tomato': {'corn': -4, 'potato': -5, 'tomato': -7, 'carrot': -7},
|
||||
'carrot': {'corn': -11, 'potato': -5, 'tomato': -4, 'carrot': -7}
|
||||
}
|
||||
yield_reduction2 = {
|
||||
'corn': {'corn': None, 'potato': -4, 'tomato': -2, 'carrot': -4},
|
||||
'potato': {'corn': None, 'potato': -5, 'tomato': -5, 'carrot': -2},
|
||||
'tomato': {'corn': -5, 'potato': -3, 'tomato': -7, 'carrot': None},
|
||||
'carrot': {'corn': -3, 'potato': -6, 'tomato': -4, 'carrot': -9}
|
||||
}
|
||||
yield_multiplier = {'corn': 1.25, 'potato': 1.17, 'tomato': 1.22, 'carrot': 1.13}
|
||||
yield_multiplier2 = {'corn': 1.25, 'potato': 1.19, 'tomato': 1.22, 'carrot': 1.15}
|
||||
|
||||
|
||||
def calculate_yields(garden):
|
||||
rows = len(garden)
|
||||
cols = len(garden[0])
|
||||
|
||||
total_yields = 0
|
||||
|
||||
for i in range(rows):
|
||||
for j in range(cols):
|
||||
plant = garden[i][j]
|
||||
yield_count = initial_yields[plant]
|
||||
|
||||
# Sprawdzanie sąsiadów
|
||||
neighbors = [
|
||||
(i - 1, j), (i + 1, j), (i, j - 1), (i, j + 1)
|
||||
]
|
||||
|
||||
for ni, nj in neighbors:
|
||||
if 0 <= ni < rows and 0 <= nj < cols:
|
||||
neighbor_plant = garden[ni][nj]
|
||||
yield_count += yield_reduction[plant][neighbor_plant]
|
||||
|
||||
yield_count *= yield_multiplier[plant]
|
||||
total_yields += yield_count
|
||||
|
||||
return total_yields
|
||||
|
||||
|
||||
def calculate_yields2(garden):
|
||||
rows = len(garden)
|
||||
cols = len(garden[0])
|
||||
|
||||
total_yields = 0
|
||||
|
||||
for i in range(rows):
|
||||
for j in range(cols):
|
||||
plant = garden[i][j]
|
||||
yield_count = initial_yields[plant]
|
||||
|
||||
# Sprawdzanie sąsiadów
|
||||
neighbors = [
|
||||
(i - 1, j), (i + 1, j), (i, j - 1), (i, j + 1)
|
||||
]
|
||||
neighbor_flag = False
|
||||
for ni, nj in neighbors:
|
||||
if 0 <= ni < rows and 0 <= nj < cols:
|
||||
neighbor_plant = garden[ni][nj]
|
||||
|
||||
if yield_reduction2[plant][neighbor_plant] is not None: # jeśli jest wartość None to plony dla tej rośliny będą wyzerowane
|
||||
yield_count += yield_reduction2[plant][neighbor_plant]
|
||||
else:
|
||||
neighbor_flag = True
|
||||
|
||||
if not neighbor_flag:
|
||||
yield_count *= yield_multiplier2[plant]
|
||||
total_yields += yield_count
|
||||
|
||||
return total_yields
|
||||
|
||||
|
||||
def generate_garden(rows=20, cols=12):
|
||||
return [[random.choice(plants) for _ in range(cols)] for _ in range(rows)]
|
||||
|
||||
|
||||
def generate_garden_with_yields(t, rows=NUM_Y, cols=NUM_X):
|
||||
garden = generate_garden(rows, cols)
|
||||
if t == 1:
|
||||
total_yields = calculate_yields(garden)
|
||||
else:
|
||||
total_yields = calculate_yields2(garden)
|
||||
return [garden, total_yields]
|
||||
|
||||
|
||||
def generate():
|
||||
s1 = 0
|
||||
s2 = 0
|
||||
n = 150
|
||||
for i in range(n):
|
||||
x = generate_garden_with_yields(1)
|
||||
s1 += x[1]
|
||||
y = generate_garden_with_yields(2)
|
||||
s2 += y[1]
|
||||
return [s1/n, s2/n]
|
||||
|
||||
|
||||
data = generate()
|
||||
# print(data)
|
||||
|
||||
# Odczyt z pliku
|
||||
with open(f'pole_pop{population}_iter{iterat}_{roulette}.json', 'r') as file:
|
||||
garden_data = json.load(file)
|
||||
|
||||
# print("Odczytane dane ogrodu:")
|
||||
# for row in garden_data:
|
||||
# print(row)
|
||||
|
||||
print("Wygenerowane przy pomocy GA: ", calculate_yields(garden_data))
|
||||
print(f"Przeciętny ogród wygenerowany randomowo ma {data[0]} plonów")
|
||||
print("Uśredniony przyrost plonów (ile razy więcej plonów): ", calculate_yields(garden_data)/data[0])
|
||||
|
||||
|
||||
|
||||
# Odczyt z pliku
|
||||
with open(f'pole2_pop{population}_iter{iterat}_{roulette}.json', 'r') as file:
|
||||
garden_data2 = json.load(file)
|
||||
|
||||
# print("Odczytane dane ogrodu:")
|
||||
# for row in garden_data2:
|
||||
# print(row)
|
||||
|
||||
print("Wygenerowane: przy pomocy GA2", calculate_yields2(garden_data2))
|
||||
print(f"Przeciętny ogród wygenerowany randomowo ma {data[1]} plonów")
|
||||
print("Uśredniony przyrost plonów (ile razy więcej plonów): ", calculate_yields2(garden_data2)/data[1])
|
||||
@ -1,208 +0,0 @@
|
||||
import copy
|
||||
import json
|
||||
import random
|
||||
from displayControler import NUM_X, NUM_Y
|
||||
|
||||
# Definiowanie stałych dla roślin i plonów
|
||||
plants = ['corn', 'potato', 'tomato', 'carrot']
|
||||
initial_yields = {'corn': 38, 'potato': 40, 'tomato': 43, 'carrot': 45}
|
||||
yield_reduction = {
|
||||
'corn': {'corn': -4.5, 'potato': -3, 'tomato': -7, 'carrot': -7},
|
||||
'potato': {'corn': -7, 'potato': -5, 'tomato': -10, 'carrot': -6},
|
||||
'tomato': {'corn': -4, 'potato': -5, 'tomato': -7, 'carrot': -7},
|
||||
'carrot': {'corn': -11, 'potato': -5, 'tomato': -4, 'carrot': -7}
|
||||
}
|
||||
yield_multiplier = {'corn': 1.25, 'potato': 1.17, 'tomato': 1.22, 'carrot': 1.13}
|
||||
|
||||
|
||||
# Generowanie listy 20x12 z losowo rozmieszczonymi roślinami
|
||||
def generate_garden(rows=20, cols=12):
|
||||
return [[random.choice(plants) for _ in range(cols)] for _ in range(rows)]
|
||||
|
||||
|
||||
# Funkcja do obliczania liczby plonów
|
||||
def calculate_yields(garden):
|
||||
rows = len(garden)
|
||||
cols = len(garden[0])
|
||||
|
||||
total_yields = 0
|
||||
|
||||
for i in range(rows):
|
||||
for j in range(cols):
|
||||
plant = garden[i][j]
|
||||
yield_count = initial_yields[plant]
|
||||
|
||||
# Sprawdzanie sąsiadów
|
||||
neighbors = [
|
||||
(i - 1, j), (i + 1, j), (i, j - 1), (i, j + 1)
|
||||
]
|
||||
|
||||
for ni, nj in neighbors:
|
||||
if 0 <= ni < rows and 0 <= nj < cols:
|
||||
neighbor_plant = garden[ni][nj]
|
||||
yield_count += yield_reduction[plant][neighbor_plant]
|
||||
|
||||
yield_count *= yield_multiplier[plant]
|
||||
total_yields += yield_count
|
||||
|
||||
return total_yields
|
||||
|
||||
|
||||
# Funkcja do generowania planszy/ogrodu i zapisywania go jako lista z liczbą plonów
|
||||
def generate_garden_with_yields(rows=NUM_Y, cols=NUM_X):
|
||||
garden = generate_garden(rows, cols)
|
||||
total_yields = calculate_yields(garden)
|
||||
return [garden, total_yields]
|
||||
|
||||
|
||||
# Funkcja do generowania linii cięcia i zapisywania jej jako liczba roślin w kolumnie z pierwszej planszy/ogrodu
|
||||
def line():
|
||||
path = []
|
||||
flag = False
|
||||
x = random.randint(4, 8)
|
||||
position = (0, x)
|
||||
path.append(position)
|
||||
while not flag: # wybór punktu dopóki nie wybierze się skrajnego
|
||||
# prawdopodobieństwo "ruchu" -> 0.6: w prawo, 0.2: w góre, 0.2: w dół
|
||||
p = [(position[0] + 1, position[1]), (position[0], position[1] + 1), (position[0], position[1] - 1)]
|
||||
w = [0.6, 0.2, 0.2]
|
||||
position2 = random.choices(p, w)[0]
|
||||
if position2 not in path: # sprawdzenie czy dany punkt nie był już wybrany aby nie zapętlać się
|
||||
path.append(position2)
|
||||
position = position2
|
||||
if position[0] == NUM_X or position[1] == 0 or position[1] == NUM_Y: # sprawdzenie czy osiągnięto skrajny punkt
|
||||
flag = True
|
||||
info = [] # przeformatowanie sposobu zapisu na liczbę roślin w kolumnie, które będzię się dzidziczyło z pierwszej planszy/ogrodu
|
||||
for i in range(len(path) - 1):
|
||||
if path[i + 1][0] - path[i][0] == 1:
|
||||
info.append(NUM_Y - path[i][1])
|
||||
if len(info) < NUM_X: # uzupełnienie informacji o dziedziczeniu z planszy/ogrodu
|
||||
if path[-1:][0][1] == 0:
|
||||
x = NUM_Y
|
||||
else:
|
||||
x = 0
|
||||
while len(info) < NUM_X:
|
||||
info.append(x)
|
||||
# return path, info
|
||||
return info
|
||||
|
||||
|
||||
# Funkcja do generowania potomstwa
|
||||
def divide_gardens(garden1, garden2):
|
||||
info = line()
|
||||
new_garden1 = [[] for _ in range(NUM_Y)]
|
||||
new_garden2 = [[] for _ in range(NUM_Y)]
|
||||
for i in range(NUM_X):
|
||||
for j in range(NUM_Y):
|
||||
# do utworzonych kolumn w nowych planszach/ogrodach dodajemy dziedziczone rośliny
|
||||
if j < info[i]:
|
||||
new_garden1[j].append(garden1[j][i])
|
||||
new_garden2[j].append(garden2[j][i])
|
||||
else:
|
||||
new_garden1[j].append(garden2[j][i])
|
||||
new_garden2[j].append(garden1[j][i])
|
||||
|
||||
return [new_garden1, calculate_yields(new_garden1)], [new_garden2, calculate_yields(new_garden2)]
|
||||
|
||||
|
||||
# Funkcja do mutacji danej planszy/ogrodu
|
||||
def mutation(garden, not_used):
|
||||
new_garden = copy.deepcopy(garden)
|
||||
for i in range(NUM_X):
|
||||
x = random.randint(0, 11) # wybieramy, w którym wierszu w i-tej kolumnie zmieniamy roślinę na inną
|
||||
other_plants = [plant for plant in plants if plant != new_garden[x][i]]
|
||||
new_garden[x][i] = random.choice(other_plants)
|
||||
return [new_garden, calculate_yields(new_garden)]
|
||||
|
||||
|
||||
# Funkcja do generowania pierwszego pokolenia
|
||||
def generate(n):
|
||||
generation = []
|
||||
for i in range(n * 3):
|
||||
generation.append(generate_garden_with_yields())
|
||||
generation.sort(reverse=True, key=lambda x: x[1])
|
||||
return generation[:n]
|
||||
|
||||
|
||||
# Funkcja do implementacji ruletki (sposobu wyboru) - sumuje wszystkie plony generacji
|
||||
def sum_yields(x):
|
||||
s = 0
|
||||
for i in range(len(x)):
|
||||
s += x[i][1]
|
||||
return s
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
roulette = True
|
||||
attemps = 150
|
||||
iterat = 2500
|
||||
population = 120
|
||||
best = []
|
||||
for a in range(attemps):
|
||||
generation = generate(population)
|
||||
print(generation[0][1])
|
||||
for i in range(iterat): # ile iteracji - nowych pokoleń
|
||||
print(a, i)
|
||||
new_generation = generation[:(population // 7)] # dziedziczenie x najlepszych osobników
|
||||
j = 0
|
||||
while j < (
|
||||
population - (
|
||||
population // 7)): # dobór reszty osobników do pełnej liczby populacji danego pokolenia
|
||||
if roulette: # zasada ruletki -> "2 rzuty kulką"
|
||||
s = sum_yields(generation) # suma wszystkich plnów całego pokolenia
|
||||
z = []
|
||||
if s == 0: # wtedy każdy osobnik ma takie same szanse
|
||||
z.append(random.randint(0, population - 1))
|
||||
z.append(random.randint(0, population - 1))
|
||||
else:
|
||||
weights = [] # wagi prawdopodobieństwa dla każdego osobnika generacji
|
||||
pos = [] # numery od 0 do 49 odpowiadające numerom osobnikom w generacji
|
||||
for i in range(population):
|
||||
weights.append(generation[i][1] / s)
|
||||
pos.append(i)
|
||||
z.append(random.choices(pos, weights)[0]) # wybranie osobnika według wag prawdopodobieństwa
|
||||
z.append(random.choices(pos, weights)[0]) # wybranie osobnika według wag prawdopodobieństwa
|
||||
else: # metoda rankingu
|
||||
z = random.sample(range(0, int(population // 1.7)), 2)
|
||||
|
||||
# krzyzowanie 90% szans, mutacja 10% szans
|
||||
function = [divide_gardens, mutation]
|
||||
weight = [0.9, 0.1]
|
||||
fun = random.choices(function, weight)[0]
|
||||
h = fun(generation[z[0]][0], generation[z[1]][0])
|
||||
if len(h[0]) == 2:
|
||||
new_generation.append(h[0])
|
||||
new_generation.append(h[1])
|
||||
j += 2
|
||||
else:
|
||||
new_generation.append(h)
|
||||
j += 1
|
||||
|
||||
new_generation.sort(reverse=True, key=lambda x: x[1]) # sortowanie malejąco listy według wartości plonów
|
||||
generation = new_generation[:population]
|
||||
|
||||
best.append(generation[0])
|
||||
|
||||
best.sort(reverse=True, key=lambda x: x[1])
|
||||
|
||||
# Zapis do pliku
|
||||
# for i in range(len(best)):
|
||||
# print(best[i][1], calculate_yields(best[i][0]))
|
||||
#
|
||||
#
|
||||
# with open(f'pole_pop{population}_iter{iterat}_{roulette}.json', 'w') as file: # zapis planszy/ogrodu do pliku json
|
||||
# json.dump(best[0][0], file, indent=4)
|
||||
#
|
||||
# print("Dane zapisane do pliku")
|
||||
|
||||
# Odczyt z pliku
|
||||
# with open(f'pole_pop{population}_iter{iterat}_{roulette}.json', 'r') as file:
|
||||
# garden_data = json.load(file)
|
||||
#
|
||||
# print("Odczytane dane ogrodu:")
|
||||
# for row in garden_data:
|
||||
# print(row)
|
||||
#
|
||||
# print(calculate_yields(garden_data))
|
||||
# if best[0][0] == garden_data:
|
||||
# print("POPRAWNE: ", calculate_yields(garden_data), calculate_yields(best[0][0]))
|
||||
@ -1,213 +0,0 @@
|
||||
import copy
|
||||
import json
|
||||
import random
|
||||
from displayControler import NUM_X, NUM_Y
|
||||
|
||||
# Definiowanie stałych dla roślin i plonów
|
||||
plants = ['corn', 'potato', 'tomato', 'carrot']
|
||||
initial_yields = {'corn': 38, 'potato': 40, 'tomato': 43, 'carrot': 45}
|
||||
yield_reduction = {
|
||||
'corn': {'corn': None, 'potato': -4, 'tomato': -2, 'carrot': -4},
|
||||
'potato': {'corn': None, 'potato': -5, 'tomato': -5, 'carrot': -2},
|
||||
'tomato': {'corn': -5, 'potato': -3, 'tomato': -7, 'carrot': None},
|
||||
'carrot': {'corn': -3, 'potato': -6, 'tomato': -4, 'carrot': -9}
|
||||
}
|
||||
yield_multiplier = {'corn': 1.25, 'potato': 1.19, 'tomato': 1.22, 'carrot': 1.15}
|
||||
|
||||
|
||||
# Generowanie listy 20x12 z losowo rozmieszczonymi roślinami
|
||||
def generate_garden(rows=20, cols=12):
|
||||
return [[random.choice(plants) for _ in range(cols)] for _ in range(rows)]
|
||||
|
||||
|
||||
# Funkcja do obliczania liczby plonów
|
||||
def calculate_yields(garden):
|
||||
rows = len(garden)
|
||||
cols = len(garden[0])
|
||||
|
||||
total_yields = 0
|
||||
|
||||
for i in range(rows):
|
||||
for j in range(cols):
|
||||
plant = garden[i][j]
|
||||
yield_count = initial_yields[plant]
|
||||
|
||||
# Sprawdzanie sąsiadów
|
||||
neighbors = [
|
||||
(i - 1, j), (i + 1, j), (i, j - 1), (i, j + 1)
|
||||
]
|
||||
neighbor_flag = False
|
||||
for ni, nj in neighbors:
|
||||
if 0 <= ni < rows and 0 <= nj < cols:
|
||||
neighbor_plant = garden[ni][nj]
|
||||
|
||||
if yield_reduction[plant][neighbor_plant] is not None: # jeśli jest wartość None to plony dla tej rośliny będą wyzerowane
|
||||
yield_count += yield_reduction[plant][neighbor_plant]
|
||||
else:
|
||||
neighbor_flag = True
|
||||
|
||||
if not neighbor_flag:
|
||||
yield_count *= yield_multiplier[plant]
|
||||
total_yields += yield_count
|
||||
|
||||
return total_yields
|
||||
|
||||
|
||||
# Funkcja do generowania planszy/ogrodu i zapisywania go jako lista z liczbą plonów
|
||||
def generate_garden_with_yields(rows=NUM_Y, cols=NUM_X):
|
||||
garden = generate_garden(rows, cols)
|
||||
total_yields = calculate_yields(garden)
|
||||
return [garden, total_yields]
|
||||
|
||||
|
||||
# Funkcja do generowania linii cięcia i zapisywania jej jako liczba roślin w kolumnie z pierwszej planszy/ogrodu
|
||||
def line():
|
||||
path = []
|
||||
flag = False
|
||||
x = random.randint(4, 8)
|
||||
position = (0, x)
|
||||
path.append(position)
|
||||
while not flag: # wybór punktu dopóki nie wybierze się skrajnego
|
||||
# prawdopodobieństwo "ruchu" -> 0.6: w prawo, 0.2: w góre, 0.2: w dół
|
||||
p = [(position[0] + 1, position[1]), (position[0], position[1] + 1), (position[0], position[1] - 1)]
|
||||
w = [0.6, 0.2, 0.2]
|
||||
position2 = random.choices(p, w)[0]
|
||||
if position2 not in path: # sprawdzenie czy dany punkt nie był już wybrany aby nie zapętlać się
|
||||
path.append(position2)
|
||||
position = position2
|
||||
if position[0] == NUM_X or position[1] == 0 or position[1] == NUM_Y: # sprawdzenie czy osiągnięto skrajny punkt
|
||||
flag = True
|
||||
info = [] # przeformatowanie sposobu zapisu na liczbę roślin w kolumnie, które będzię się dzidziczyło z pierwszej planszy/ogrodu
|
||||
for i in range(len(path) - 1):
|
||||
if path[i + 1][0] - path[i][0] == 1:
|
||||
info.append(NUM_Y - path[i][1])
|
||||
if len(info) < NUM_X: # uzupełnienie informacji o dziedziczeniu z planszy/ogrodu
|
||||
if path[-1:][0][1] == 0:
|
||||
x = NUM_Y
|
||||
else:
|
||||
x = 0
|
||||
while len(info) < NUM_X:
|
||||
info.append(x)
|
||||
# return path, info
|
||||
return info
|
||||
|
||||
|
||||
# Funkcja do generowania potomstwa
|
||||
def divide_gardens(garden1, garden2):
|
||||
info = line()
|
||||
new_garden1 = [[] for _ in range(NUM_Y)]
|
||||
new_garden2 = [[] for _ in range(NUM_Y)]
|
||||
for i in range(NUM_X):
|
||||
for j in range(NUM_Y):
|
||||
# do utworzonych kolumn w nowych planszach/ogrodach dodajemy dziedziczone rośliny
|
||||
if j < info[i]:
|
||||
new_garden1[j].append(garden1[j][i])
|
||||
new_garden2[j].append(garden2[j][i])
|
||||
else:
|
||||
new_garden1[j].append(garden2[j][i])
|
||||
new_garden2[j].append(garden1[j][i])
|
||||
|
||||
return [new_garden1, calculate_yields(new_garden1)], [new_garden2, calculate_yields(new_garden2)]
|
||||
|
||||
|
||||
# Funkcja do mutacji danej planszy/ogrodu
|
||||
def mutation(garden, not_used):
|
||||
new_garden = copy.deepcopy(garden)
|
||||
for i in range(NUM_X):
|
||||
x = random.randint(0, 11) # wybieramy, w którym wierszu w i-tej kolumnie zmieniamy roślinę na inną
|
||||
other_plants = [plant for plant in plants if plant != new_garden[x][i]]
|
||||
new_garden[x][i] = random.choice(other_plants)
|
||||
return [new_garden, calculate_yields(new_garden)]
|
||||
|
||||
|
||||
# Funkcja do generowania pierwszego pokolenia
|
||||
def generate(n):
|
||||
generation = []
|
||||
for i in range(n * 3):
|
||||
generation.append(generate_garden_with_yields())
|
||||
generation.sort(reverse=True, key=lambda x: x[1])
|
||||
return generation[:n]
|
||||
|
||||
|
||||
# Funkcja do implementacji ruletki (sposobu wyboru) - sumuje wszystkie plony generacji
|
||||
def sum_yields(x):
|
||||
s = 0
|
||||
for i in range(len(x)):
|
||||
s += x[i][1]
|
||||
return s
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
roulette = True
|
||||
attemps = 20
|
||||
iterat = 2500
|
||||
population = 120
|
||||
best = []
|
||||
for a in range(attemps):
|
||||
generation = generate(population)
|
||||
print(generation[0][1])
|
||||
for i in range(iterat): # ile iteracji - nowych pokoleń
|
||||
print(a, i)
|
||||
new_generation = generation[:(population // 7)] # dziedziczenie x najlepszych osobników
|
||||
j = 0
|
||||
while j < (
|
||||
population - (
|
||||
population // 7)): # dobór reszty osobników do pełnej liczby populacji danego pokolenia
|
||||
if roulette: # zasada ruletki -> "2 rzuty kulką"
|
||||
s = sum_yields(generation) # suma wszystkich plnów całego pokolenia
|
||||
z = []
|
||||
if s == 0: # wtedy każdy osobnik ma takie same szanse
|
||||
z.append(random.randint(0, population - 1))
|
||||
z.append(random.randint(0, population - 1))
|
||||
else:
|
||||
weights = [] # wagi prawdopodobieństwa dla każdego osobnika generacji
|
||||
pos = [] # numery od 0 do 49 odpowiadające numerom osobnikom w generacji
|
||||
for i in range(population):
|
||||
weights.append(generation[i][1] / s)
|
||||
pos.append(i)
|
||||
z.append(random.choices(pos, weights)[0]) # wybranie osobnika według wag prawdopodobieństwa
|
||||
z.append(random.choices(pos, weights)[0]) # wybranie osobnika według wag prawdopodobieństwa
|
||||
else: # metoda rankingu
|
||||
z = random.sample(range(0, int(population // 1.7)), 2)
|
||||
|
||||
# krzyzowanie 90% szans, mutacja 10% szans
|
||||
function = [divide_gardens, mutation]
|
||||
weight = [0.9, 0.1]
|
||||
fun = random.choices(function, weight)[0]
|
||||
h = fun(generation[z[0]][0], generation[z[1]][0])
|
||||
if len(h[0]) == 2:
|
||||
new_generation.append(h[0])
|
||||
new_generation.append(h[1])
|
||||
j += 2
|
||||
else:
|
||||
new_generation.append(h)
|
||||
j += 1
|
||||
|
||||
new_generation.sort(reverse=True, key=lambda x: x[1]) # sortowanie malejąco listy według wartości plonów
|
||||
generation = new_generation[:population]
|
||||
|
||||
best.append(generation[0])
|
||||
|
||||
best.sort(reverse=True, key=lambda x: x[1])
|
||||
|
||||
# Zapis do pliku
|
||||
# for i in range(len(best)):
|
||||
# print(best[i][1], calculate_yields(best[i][0]))
|
||||
#
|
||||
#
|
||||
# with open(f'pole2_pop{population}_iter{iterat}_{roulette}.json', 'w') as file: # zapis planszy/ogrodu do pliku json
|
||||
# json.dump(best[0][0], file, indent=4)
|
||||
#
|
||||
# print("Dane zapisane do pliku")
|
||||
#
|
||||
# Odczyt z pliku
|
||||
# with open(f'pole2_pop{population}_iter{iterat}_{roulette}.json', 'r') as file:
|
||||
# garden_data = json.load(file)
|
||||
#
|
||||
# print("Odczytane dane ogrodu:")
|
||||
# for row in garden_data:
|
||||
# print(row)
|
||||
#
|
||||
# print(calculate_yields(garden_data))
|
||||
# if best[0][0] == garden_data:
|
||||
# print("POPRAWNE: ", calculate_yields(garden_data), calculate_yields(best[0][0]))
|
||||
@ -1,211 +0,0 @@
|
||||
import copy
|
||||
import json
|
||||
import random
|
||||
from displayControler import NUM_X, NUM_Y
|
||||
|
||||
# Definiowanie stałych dla roślin i plonów
|
||||
plants = ['corn', 'potato', 'tomato', 'carrot']
|
||||
initial_yields = {'corn': 38, 'potato': 40, 'tomato': 43, 'carrot': 45}
|
||||
yield_reduction = {
|
||||
'corn': {'corn': None, 'potato': 0, 'tomato': 0, 'carrot': 0},
|
||||
'potato': {'corn': None, 'potato': 0, 'tomato': 0, 'carrot': 0},
|
||||
'tomato': {'corn': 0, 'potato': 0, 'tomato': 0, 'carrot': None},
|
||||
'carrot': {'corn': 0, 'potato': 0, 'tomato': 0, 'carrot': 0}
|
||||
}
|
||||
yield_multiplier = {'corn': 1.25, 'potato': 1.19, 'tomato': 1.22, 'carrot': 1.13}
|
||||
|
||||
|
||||
# Generowanie listy 20x12 z losowo rozmieszczonymi roślinami
|
||||
def generate_garden(rows=20, cols=12):
|
||||
return [[random.choice(plants) for _ in range(cols)] for _ in range(rows)]
|
||||
|
||||
|
||||
# Funkcja do obliczania liczby plonów
|
||||
def calculate_yields(garden):
|
||||
rows = len(garden)
|
||||
cols = len(garden[0])
|
||||
|
||||
total_yields = 0
|
||||
|
||||
for i in range(rows):
|
||||
for j in range(cols):
|
||||
plant = garden[i][j]
|
||||
|
||||
# Sprawdzanie sąsiadów
|
||||
neighbors = [
|
||||
(i - 1, j), (i + 1, j), (i, j - 1), (i, j + 1)
|
||||
]
|
||||
neighbor_flag = False
|
||||
for ni, nj in neighbors:
|
||||
if 0 <= ni < rows and 0 <= nj < cols:
|
||||
neighbor_plant = garden[ni][nj]
|
||||
|
||||
if yield_reduction[plant][neighbor_plant] is None: # jeśli jest wartość None to plony dla tej rośliny będą wyzerowane
|
||||
neighbor_flag = True
|
||||
|
||||
if not neighbor_flag:
|
||||
total_yields += 1
|
||||
|
||||
return total_yields
|
||||
|
||||
|
||||
# Funkcja do generowania planszy/ogrodu i zapisywania go jako lista z liczbą plonów
|
||||
def generate_garden_with_yields(rows=NUM_Y, cols=NUM_X):
|
||||
garden = generate_garden(rows, cols)
|
||||
total_yields = calculate_yields(garden)
|
||||
return [garden, total_yields]
|
||||
|
||||
|
||||
# Funkcja do generowania linii cięcia i zapisywania jej jako liczba roślin w kolumnie z pierwszej planszy/ogrodu
|
||||
def line():
|
||||
path = []
|
||||
flag = False
|
||||
x = random.randint(4, 8)
|
||||
position = (0, x)
|
||||
path.append(position)
|
||||
while not flag: # wybór punktu dopóki nie wybierze się skrajnego
|
||||
# prawdopodobieństwo "ruchu" -> 0.6: w prawo, 0.2: w góre, 0.2: w dół
|
||||
p = [(position[0] + 1, position[1]), (position[0], position[1] + 1), (position[0], position[1] - 1)]
|
||||
w = [0.6, 0.2, 0.2]
|
||||
position2 = random.choices(p, w)[0]
|
||||
if position2 not in path: # sprawdzenie czy dany punkt nie był już wybrany aby nie zapętlać się
|
||||
path.append(position2)
|
||||
position = position2
|
||||
if position[0] == NUM_X or position[1] == 0 or position[1] == NUM_Y: # sprawdzenie czy osiągnięto skrajny punkt
|
||||
flag = True
|
||||
info = [] # przeformatowanie sposobu zapisu na liczbę roślin w kolumnie, które będzię się dzidziczyło z pierwszej planszy/ogrodu
|
||||
for i in range(len(path) - 1):
|
||||
if path[i + 1][0] - path[i][0] == 1:
|
||||
info.append(NUM_Y - path[i][1])
|
||||
if len(info) < NUM_X: # uzupełnienie informacji o dziedziczeniu z planszy/ogrodu
|
||||
if path[-1:][0][1] == 0:
|
||||
x = NUM_Y
|
||||
else:
|
||||
x = 0
|
||||
while len(info) < NUM_X:
|
||||
info.append(x)
|
||||
# return path, info
|
||||
return info
|
||||
|
||||
|
||||
# Funkcja do generowania potomstwa
|
||||
def divide_gardens(garden1, garden2):
|
||||
info = line()
|
||||
new_garden1 = [[] for _ in range(NUM_Y)]
|
||||
new_garden2 = [[] for _ in range(NUM_Y)]
|
||||
for i in range(NUM_X):
|
||||
for j in range(NUM_Y):
|
||||
# do utworzonych kolumn w nowych planszach/ogrodach dodajemy dziedziczone rośliny
|
||||
if j < info[i]:
|
||||
new_garden1[j].append(garden1[j][i])
|
||||
new_garden2[j].append(garden2[j][i])
|
||||
else:
|
||||
new_garden1[j].append(garden2[j][i])
|
||||
new_garden2[j].append(garden1[j][i])
|
||||
|
||||
return [new_garden1, calculate_yields(new_garden1)], [new_garden2, calculate_yields(new_garden2)]
|
||||
|
||||
|
||||
# Funkcja do mutacji danej planszy/ogrodu
|
||||
def mutation(garden, not_used):
|
||||
new_garden = copy.deepcopy(garden)
|
||||
for i in range(NUM_X):
|
||||
x = random.randint(0, 11) # wybieramy, w którym wierszu w i-tej kolumnie zmieniamy roślinę na inną
|
||||
other_plants = [plant for plant in plants if plant != new_garden[x][i]]
|
||||
new_garden[x][i] = random.choice(other_plants)
|
||||
return [new_garden, calculate_yields(new_garden)]
|
||||
|
||||
|
||||
# Funkcja do generowania pierwszego pokolenia
|
||||
def generate(n):
|
||||
generation = []
|
||||
for i in range(n * 3):
|
||||
generation.append(generate_garden_with_yields())
|
||||
generation.sort(reverse=True, key=lambda x: x[1])
|
||||
return generation[:n]
|
||||
|
||||
|
||||
# Funkcja do implementacji ruletki (sposobu wyboru) - sumuje wszystkie plony generacji
|
||||
def sum_yields(x):
|
||||
s = 0
|
||||
for i in range(len(x)):
|
||||
s += x[i][1]
|
||||
return s
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
roulette = True
|
||||
attemps = 1
|
||||
population = 120
|
||||
best = []
|
||||
iter = 0
|
||||
for a in range(attemps):
|
||||
generation = generate(population)
|
||||
print(generation[0][1])
|
||||
while generation[0][1] != NUM_X*NUM_Y: # ile iteracji - nowych pokoleń
|
||||
iter += 1
|
||||
print(iter)
|
||||
print(generation[0][1])
|
||||
new_generation = generation[:(population // 7)] # dziedziczenie x najlepszych osobników
|
||||
j = 0
|
||||
while j < (
|
||||
population - (
|
||||
population // 7)): # dobór reszty osobników do pełnej liczby populacji danego pokolenia
|
||||
if roulette: # zasada ruletki -> "2 rzuty kulką"
|
||||
s = sum_yields(generation) # suma wszystkich plnów całego pokolenia
|
||||
z = []
|
||||
if s == 0: # wtedy każdy osobnik ma takie same szanse
|
||||
z.append(random.randint(0, population - 1))
|
||||
z.append(random.randint(0, population - 1))
|
||||
else:
|
||||
weights = [] # wagi prawdopodobieństwa dla każdego osobnika generacji
|
||||
pos = [] # numery od 0 do 49 odpowiadające numerom osobnikom w generacji
|
||||
for i in range(population):
|
||||
weights.append(generation[i][1] / s)
|
||||
pos.append(i)
|
||||
z.append(random.choices(pos, weights)[0]) # wybranie osobnika według wag prawdopodobieństwa
|
||||
z.append(random.choices(pos, weights)[0]) # wybranie osobnika według wag prawdopodobieństwa
|
||||
else: # metoda rankingu
|
||||
z = random.sample(range(0, int(population // 1.7)), 2)
|
||||
|
||||
# krzyzowanie 90% szans, mutacja 10% szans
|
||||
function = [divide_gardens, mutation]
|
||||
weight = [0.9, 0.1]
|
||||
fun = random.choices(function, weight)[0]
|
||||
h = fun(generation[z[0]][0], generation[z[1]][0])
|
||||
if len(h[0]) == 2:
|
||||
new_generation.append(h[0])
|
||||
new_generation.append(h[1])
|
||||
j += 2
|
||||
else:
|
||||
new_generation.append(h)
|
||||
j += 1
|
||||
|
||||
new_generation.sort(reverse=True, key=lambda x: x[1]) # sortowanie malejąco listy według wartości plonów
|
||||
generation = new_generation[:population]
|
||||
|
||||
best.append(generation[0])
|
||||
|
||||
best.sort(reverse=True, key=lambda x: x[1])
|
||||
|
||||
# Zapis do pliku
|
||||
# for i in range(len(best)):
|
||||
# print(best[i][1], calculate_yields(best[i][0]))
|
||||
#
|
||||
#
|
||||
# with open(f'pole3_pop{population}_{iter}_{roulette}.json', 'w') as file: # zapis planszy/ogrodu do pliku json
|
||||
# json.dump(best[0][0], file, indent=4)
|
||||
#
|
||||
# print("Dane zapisane do pliku")
|
||||
#
|
||||
# Odczyt z pliku
|
||||
# with open(f'pole3_pop{population}_{iter}_{roulette}.json', 'r') as file:
|
||||
# garden_data = json.load(file)
|
||||
#
|
||||
# print("Odczytane dane ogrodu:")
|
||||
# for row in garden_data:
|
||||
# print(row)
|
||||
#
|
||||
# print(calculate_yields(garden_data))
|
||||
# if best[0][0] == garden_data:
|
||||
# print("POPRAWNE: ", calculate_yields(garden_data), calculate_yields(best[0][0]))
|
||||
22
Image.py
22
Image.py
@ -80,25 +80,3 @@ def getRandomImageFromDataBase():
|
||||
image = pygame.image.load(imgPath)
|
||||
image=pygame.transform.scale(image,(dCon.CUBE_SIZE,dCon.CUBE_SIZE))
|
||||
return image, label, imgPath
|
||||
|
||||
def getSpedifiedImageFromDatabase(label):
|
||||
folderPath = f"dataset/test/{label}"
|
||||
files = os.listdir(folderPath)
|
||||
random_image = random.choice(files)
|
||||
imgPath = os.path.join(folderPath, random_image)
|
||||
|
||||
while imgPath in imagePathList:
|
||||
for event in pygame.event.get():
|
||||
if event.type == pygame.QUIT:
|
||||
quit()
|
||||
label = random.choice(neuralnetwork.labels)
|
||||
folderPath = f"dataset/test/{label}"
|
||||
files = os.listdir(folderPath)
|
||||
random_image = random.choice(files)
|
||||
imgPath = os.path.join(folderPath, random_image)
|
||||
|
||||
imagePathList.append(imgPath)
|
||||
|
||||
image = pygame.image.load(imgPath)
|
||||
image=pygame.transform.scale(image,(dCon.CUBE_SIZE,dCon.CUBE_SIZE))
|
||||
return image, label, imgPath
|
||||
|
||||
8
Pole.py
8
Pole.py
@ -62,14 +62,6 @@ class Pole:
|
||||
continue
|
||||
else:
|
||||
self.slot_dict[coordinates].set_random_plant(nn)
|
||||
def setPlantsByList(self, plantList):
|
||||
pygame.display.update()
|
||||
time.sleep(3)
|
||||
for coordinates in self.slot_dict:
|
||||
if(coordinates==(0,0)):
|
||||
continue
|
||||
else:
|
||||
self.slot_dict[coordinates].set_specifided_plant(plantList[coordinates[1]][coordinates[0]])
|
||||
|
||||
def change_color_of_slot(self,coordinates,color): #Coordinates must be tuple (x,y) (left top slot has cord (0,0) ), color has to be from defined in Colors.py or custom in RGB value (R,G,B)
|
||||
self.get_slot_from_cord(coordinates).color_change(color)
|
||||
|
||||
7
Slot.py
7
Slot.py
@ -49,11 +49,6 @@ class Slot:
|
||||
# print(self.plant_image)
|
||||
self.plant=Roslina.Roslina(self.label)
|
||||
self.set_image()
|
||||
|
||||
def set_specifided_plant(self, plant):
|
||||
self.plant_image, self.label, self.imagePath = self.specified_plant_dataset(plant)
|
||||
self.plant=Roslina.Roslina(self.label)
|
||||
self.set_image()
|
||||
|
||||
def set_image(self):
|
||||
if self.plant_image is None:
|
||||
@ -80,8 +75,6 @@ class Slot:
|
||||
return self.image_loader.return_random_plant()
|
||||
def random_plant_dataset(self):
|
||||
return Image.getRandomImageFromDataBase()
|
||||
def specified_plant_dataset(self, plant):
|
||||
return Image.getSpedifiedImageFromDatabase(plant)
|
||||
|
||||
def return_plant(self):
|
||||
return self.plant
|
||||
|
||||
24
Tractor.py
24
Tractor.py
@ -30,7 +30,6 @@ class Tractor:
|
||||
DIRECTION_SOUTH = 'S'
|
||||
DIRECTION_WEST = 'W'
|
||||
DIRECTION_EAST = 'E'
|
||||
|
||||
def __init__(self,slot,screen, osprzet,clock,bfs2_flag):
|
||||
self.tractor_images = {
|
||||
Tractor.DIRECTION_NORTH: pygame.transform.scale(pygame.image.load('images/traktorN.png'),
|
||||
@ -194,7 +193,8 @@ class Tractor:
|
||||
self.turn_left()
|
||||
print("podlanych slotów: ", str(counter))
|
||||
|
||||
def snake_move_predict_plant(self, pole, model, headers, actions = None):
|
||||
def snake_move_predict_plant(self, pole, model):
|
||||
headers=['Coords','Real plant','Predicted plant','Result','Fertilizer']
|
||||
print(format_string_nn.format(*headers))
|
||||
initPos = (self.slot.x_axis, self.slot.y_axis)
|
||||
count = 0
|
||||
@ -207,11 +207,9 @@ class Tractor:
|
||||
predictedLabel = nn.predictLabel(self.slot.imagePath, model)
|
||||
|
||||
#print(str("Coords: ({:02d}, {:02d})").format(self.slot.x_axis, self.slot.y_axis), "real:", self.slot.label, "predicted:", predictedLabel, "correct" if (self.slot.label == predictedLabel) else "incorrect", 'nawożę za pomocą:', nn.fertilizer[predictedLabel])
|
||||
# print(format_string_nn.format(f"{self.slot.x_axis,self.slot.y_axis}",self.slot.label,predictedLabel,"correct" if (self.slot.label == predictedLabel) else "incorrect",nn.fertilizer[predictedLabel]))
|
||||
for a in actions:
|
||||
a(predictedLabel)
|
||||
print(format_string_nn.format(f"{self.slot.x_axis,self.slot.y_axis}",self.slot.label,predictedLabel,"correct" if (self.slot.label == predictedLabel) else "incorrect",nn.fertilizer[predictedLabel]))
|
||||
if self.slot.label != predictedLabel:
|
||||
# self.slot.mark_visited()
|
||||
self.slot.mark_visited()
|
||||
count += 1
|
||||
self.move_forward(pole, False)
|
||||
if i % 2 == 0 and i != dCon.NUM_Y - 1:
|
||||
@ -222,19 +220,7 @@ class Tractor:
|
||||
self.turn_left()
|
||||
self.move_forward(pole, False)
|
||||
self.turn_left()
|
||||
print(f"Dobrze rozpoznanych roślin: {20*12-count}, źle rozpoznanych roślin: {count}")
|
||||
|
||||
def fertilize_slot(self, predictedLabel):
|
||||
print(format_string_nn.format(f"{self.slot.x_axis,self.slot.y_axis}",self.slot.label,predictedLabel,"correct" if (self.slot.label == predictedLabel) else "incorrect",nn.fertilizer[predictedLabel]))
|
||||
if self.slot.label != predictedLabel:
|
||||
self.slot.mark_visited()
|
||||
|
||||
def irigate_slot_NN(self, predictedLabel):
|
||||
attributes=self.get_attributes()
|
||||
decision = drzewo.makeDecision(attributes)
|
||||
print(format_string_nn.format(f"{self.slot.x_axis,self.slot.y_axis}",self.slot.label,predictedLabel,"correct" if (self.slot.label == predictedLabel) else "incorrect",decision))
|
||||
condition.cycle()
|
||||
self.waterLevel = random.randint(0, 100)
|
||||
print(f"Dobrze nawiezionych roślin: {20*12-count}, źle nawiezionych roślin: {count}")
|
||||
|
||||
def snake_move(self,pole,x,y):
|
||||
next_slot_coordinates=(x,y)
|
||||
|
||||
@ -77,7 +77,7 @@ def saveModel(model, path):
|
||||
def loadModel(path):
|
||||
print("Loading model")
|
||||
model = getModel()
|
||||
model.load_state_dict(torch.load(path, map_location=torch.device('cpu'))) # musiałem tutaj dodać to ładowanie z mapowaniem na cpu bo u mnie CUDA nie działa wy pewnie możecie to usunąć
|
||||
model.load_state_dict(torch.load(path))
|
||||
return model
|
||||
|
||||
def trainNewModel(n_iter=100, batch_size=256):
|
||||
|
||||
@ -1,266 +0,0 @@
|
||||
[
|
||||
[
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"tomato",
|
||||
"corn",
|
||||
"carrot",
|
||||
"potato",
|
||||
"potato",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato"
|
||||
],
|
||||
[
|
||||
"corn",
|
||||
"carrot",
|
||||
"potato",
|
||||
"potato",
|
||||
"carrot",
|
||||
"potato",
|
||||
"potato",
|
||||
"carrot",
|
||||
"potato",
|
||||
"potato",
|
||||
"carrot",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"carrot",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn"
|
||||
],
|
||||
[
|
||||
"carrot",
|
||||
"potato",
|
||||
"potato",
|
||||
"carrot",
|
||||
"corn",
|
||||
"carrot",
|
||||
"potato",
|
||||
"carrot",
|
||||
"potato",
|
||||
"carrot",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato"
|
||||
],
|
||||
[
|
||||
"potato",
|
||||
"potato",
|
||||
"carrot",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"carrot",
|
||||
"corn",
|
||||
"carrot",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn"
|
||||
],
|
||||
[
|
||||
"potato",
|
||||
"carrot",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato"
|
||||
],
|
||||
[
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn"
|
||||
],
|
||||
[
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato"
|
||||
],
|
||||
[
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn"
|
||||
],
|
||||
[
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"carrot",
|
||||
"corn",
|
||||
"tomato"
|
||||
],
|
||||
[
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"carrot",
|
||||
"potato",
|
||||
"tomato",
|
||||
"potato"
|
||||
],
|
||||
[
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"carrot",
|
||||
"corn",
|
||||
"carrot",
|
||||
"corn",
|
||||
"carrot",
|
||||
"potato",
|
||||
"potato",
|
||||
"tomato",
|
||||
"potato"
|
||||
],
|
||||
[
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"potato",
|
||||
"potato",
|
||||
"carrot",
|
||||
"corn",
|
||||
"tomato"
|
||||
]
|
||||
]
|
||||
@ -1,266 +0,0 @@
|
||||
[
|
||||
[
|
||||
"potato",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"potato",
|
||||
"potato",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"potato",
|
||||
"tomato",
|
||||
"potato",
|
||||
"carrot",
|
||||
"potato",
|
||||
"carrot",
|
||||
"potato",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"potato",
|
||||
"potato",
|
||||
"potato",
|
||||
"carrot"
|
||||
],
|
||||
[
|
||||
"tomato",
|
||||
"potato",
|
||||
"carrot",
|
||||
"potato",
|
||||
"tomato",
|
||||
"potato",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"potato",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"potato",
|
||||
"potato",
|
||||
"carrot",
|
||||
"potato"
|
||||
],
|
||||
[
|
||||
"corn",
|
||||
"carrot",
|
||||
"corn",
|
||||
"carrot",
|
||||
"potato",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"carrot",
|
||||
"potato",
|
||||
"potato",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"corn",
|
||||
"carrot",
|
||||
"potato",
|
||||
"potato",
|
||||
"tomato"
|
||||
],
|
||||
[
|
||||
"tomato",
|
||||
"potato",
|
||||
"carrot",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"corn",
|
||||
"carrot",
|
||||
"potato",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"corn",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"potato",
|
||||
"potato",
|
||||
"tomato",
|
||||
"tomato"
|
||||
],
|
||||
[
|
||||
"tomato",
|
||||
"potato",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"corn",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"potato",
|
||||
"tomato",
|
||||
"potato",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"potato",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"potato",
|
||||
"tomato",
|
||||
"tomato",
|
||||
"potato",
|
||||
"potato"
|
||||
],
|
||||
[
|
||||
"potato",
|
||||
"tomato",
|
||||
"potato",
|
||||
"potato",
|
||||
"tomato",
|
||||
"corn",
|
||||
"carrot",
|
||||
"potato",
|
||||
"potato",
|
||||
"tomato",
|
||||
"potato",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"potato",
|
||||
"carrot",
|
||||
"potato",
|
||||
"tomato",
|
||||
"potato",
|
||||
"carrot",
|
||||
"carrot"
|
||||
],
|
||||
[
|
||||
"potato",
|
||||
"potato",
|
||||
"tomato",
|
||||
"tomato",
|
||||
"corn",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"potato",
|
||||
"tomato",
|
||||
"potato",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"potato",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"potato",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"potato"
|
||||
],
|
||||
[
|
||||
"carrot",
|
||||
"potato",
|
||||
"tomato",
|
||||
"potato",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"potato",
|
||||
"potato",
|
||||
"tomato",
|
||||
"tomato",
|
||||
"potato",
|
||||
"carrot",
|
||||
"potato",
|
||||
"potato",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"potato",
|
||||
"carrot",
|
||||
"corn",
|
||||
"carrot"
|
||||
],
|
||||
[
|
||||
"carrot",
|
||||
"potato",
|
||||
"tomato",
|
||||
"potato",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"potato",
|
||||
"tomato",
|
||||
"potato",
|
||||
"potato",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"potato",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"potato",
|
||||
"tomato",
|
||||
"corn"
|
||||
],
|
||||
[
|
||||
"potato",
|
||||
"tomato",
|
||||
"tomato",
|
||||
"potato",
|
||||
"carrot",
|
||||
"potato",
|
||||
"potato",
|
||||
"carrot",
|
||||
"potato",
|
||||
"potato",
|
||||
"carrot",
|
||||
"potato",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"potato",
|
||||
"carrot",
|
||||
"corn",
|
||||
"tomato",
|
||||
"potato",
|
||||
"tomato"
|
||||
],
|
||||
[
|
||||
"potato",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"potato",
|
||||
"potato",
|
||||
"potato",
|
||||
"potato",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"potato",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"potato",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"corn",
|
||||
"tomato",
|
||||
"potato"
|
||||
],
|
||||
[
|
||||
"potato",
|
||||
"potato",
|
||||
"carrot",
|
||||
"corn",
|
||||
"carrot",
|
||||
"potato",
|
||||
"potato",
|
||||
"potato",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"potato",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"potato",
|
||||
"carrot",
|
||||
"corn",
|
||||
"carrot",
|
||||
"corn",
|
||||
"tomato"
|
||||
]
|
||||
]
|
||||
@ -1,266 +0,0 @@
|
||||
[
|
||||
[
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"potato",
|
||||
"potato",
|
||||
"carrot",
|
||||
"potato"
|
||||
],
|
||||
[
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"potato",
|
||||
"potato",
|
||||
"potato"
|
||||
],
|
||||
[
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"potato",
|
||||
"carrot"
|
||||
],
|
||||
[
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato"
|
||||
],
|
||||
[
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot"
|
||||
],
|
||||
[
|
||||
"corn",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato"
|
||||
],
|
||||
[
|
||||
"corn",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"potato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot"
|
||||
],
|
||||
[
|
||||
"tomato",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato"
|
||||
],
|
||||
[
|
||||
"carrot",
|
||||
"potato",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot"
|
||||
],
|
||||
[
|
||||
"potato",
|
||||
"potato",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato"
|
||||
],
|
||||
[
|
||||
"potato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"corn",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot"
|
||||
],
|
||||
[
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"corn",
|
||||
"corn",
|
||||
"tomato",
|
||||
"corn",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato",
|
||||
"tomato",
|
||||
"carrot",
|
||||
"tomato"
|
||||
]
|
||||
]
|
||||
19
readme.txt
19
readme.txt
@ -4,21 +4,4 @@ Required packages:
|
||||
pip install pygame
|
||||
pip install matplotlib
|
||||
pip install scikit-learn
|
||||
pip install pandas
|
||||
How to run:
|
||||
For BFS3:
|
||||
-in App.py: -change bfs3_flag to True (other flags need to be disabled) -ensure that in App.py in BFS3 you give goalTreasure
|
||||
-in Image.py change range in function return_random_plant to (0,5)
|
||||
For Astar:
|
||||
in App.py change Astar to True (other flags need to be disabled)
|
||||
-in Image.py change range in function return_random_plant to (0,7)
|
||||
For Astar2:
|
||||
-in App.py change Astar2 flag to True (other flags need to be disabled)
|
||||
-in Image.py change range in function return_random_plant to (0,7)
|
||||
For Tree:
|
||||
-in App.py change TreeFlag to True (other flags need to be disabled)
|
||||
-in Image.py change range in function return_random_plant to (0,5)
|
||||
For neuralnetwork:
|
||||
-in App.py change nnFlag to True (other flags need to be disabled)
|
||||
For final_show (neuralnetwork+tree+genetic algorithm)
|
||||
-in App.py change finalFlag to True (other flags need to be disabled)
|
||||
pip install pandas
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Loading…
Reference in New Issue
Block a user