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index efb81b9..699e73b 100644
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+++ b/.idea/workspace.xml
@@ -4,8 +4,9 @@
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diff --git a/__pycache__/definitions.cpython-37.pyc b/__pycache__/definitions.cpython-37.pyc
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diff --git a/__pycache__/station.cpython-37.pyc b/__pycache__/station.cpython-37.pyc
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diff --git a/definitions.py b/definitions.py
index e037fad..541f6da 100644
--- a/definitions.py
+++ b/definitions.py
@@ -65,7 +65,13 @@ FLOWER_DANDELION_MAXIMUM_STATE = 1
FONT_COLOR = (255, 255, 255)
FONT_SIZE = 23
FONT = pygame.font.Font(os.path.join('resources/font', 'Minecraft.ttf'), FONT_SIZE)
-FPS = 10
+FPS = 1
+GENETIC_ALGORITHM_COSTS_AMOUNT = 13
+GENETIC_ALGORITHM_NUMBER_OF_BEST_INDIVIDUALS = 4
+GENETIC_ALGORITHM_NUMBER_OF_CART_MOVES = 1000
+GENETIC_ALGORITHM_NUMBER_OF_GENERATIONS = 10
+GENETIC_ALGORITHM_NUMBER_OF_INDIVIDUALS = 10
+GENETIC_ALGORITHM_NUMBER_OF_INDIVIDUALS_ZERO = 10
HEIGHT_AMOUNT, WIDTH_AMOUNT = 10, 10
HEIGHT_MAP, WIDTH_MAP = BLOCK_SIZE * HEIGHT_AMOUNT, BLOCK_SIZE * WIDTH_AMOUNT
HEIGHT, WIDTH = HEIGHT_MAP + BLOCK_SIZE, WIDTH_MAP
diff --git a/geneticalgorithm.py b/geneticalgorithm.py
index 34f8aa0..aaf36dd 100644
--- a/geneticalgorithm.py
+++ b/geneticalgorithm.py
@@ -11,8 +11,9 @@ import random
import station
import treelearn
def create_genetic_algorithm():
- if os.path.exists("resources/genetic_algorithm/optimal.pkl"): #jeżeli drzewo jest zapisane w pliku to odczytaj
- astar_costs = pickle.load(open(os.path.join('resources/genetic_algorithm', "optimal.pkl"), "rb"))
+ if os.path.exists("resources/genetic_algorithm/optimalastar.pkl"): #jeżeli algorytm genetyczny utworzył plik wcześcniej to odczytaj
+ astar_costs = pickle.load(open(os.path.join('resources/genetic_algorithm', "optimalastar.pkl"), "rb"))
+ #kolejność alfabetyczna
definitions.BEETROOTS_ADULT_COST = astar_costs[0]
definitions.BEETROOTS_GROW_COST = astar_costs[1]
definitions.CARROTS_ADULT_COST = astar_costs[2]
@@ -26,20 +27,58 @@ def create_genetic_algorithm():
definitions.STATION_COST = astar_costs[10]
definitions.WHEAT_ADULT_COST = astar_costs[11]
definitions.WHEAT_GROW_COST = astar_costs[12]
- print(definitions.BEETROOTS_ADULT_COST)
- print(definitions.BEETROOTS_GROW_COST)
- else:
- astar_costs = [definitions.BEETROOTS_ADULT_COST, definitions.BEETROOTS_GROW_COST, definitions.CARROTS_ADULT_COST, definitions.CARROTS_GROW_COST, definitions.DIRT_COST, definitions.FARMLAND_DRY_COST, definitions.FARMLAND_WET_COST, definitions.FLOWER_DANDELION_COST, definitions.POTATOES_ADULT_COST, definitions.POTATOES_GROW_COST, definitions.STATION_COST, definitions.WHEAT_ADULT_COST, definitions.WHEAT_GROW_COST]
- astar_costs = make_generations(astar_costs)
- pickle.dump(astar_costs, open(os.path.join('resources/genetic_algorithm', "optimal.pkl"), "wb"))
-
-def fitness(astar_costs):
- ans = solution(astar_costs)
- if ans == 0:
+ else: #w przeciwnym razie ucz algorytmem genetycznym
+ astar_costs = [definitions.BEETROOTS_ADULT_COST, definitions.BEETROOTS_GROW_COST, definitions.CARROTS_ADULT_COST, definitions.CARROTS_GROW_COST, definitions.DIRT_COST, definitions.FARMLAND_DRY_COST, definitions.FARMLAND_WET_COST, definitions.FLOWER_DANDELION_COST, definitions.POTATOES_ADULT_COST, definitions.POTATOES_GROW_COST, definitions.STATION_COST, definitions.WHEAT_ADULT_COST, definitions.WHEAT_GROW_COST] #kolejność alfabetyczna
+ astar_costs = evolve(astar_costs)
+ pickle.dump(astar_costs, open(os.path.join('resources/genetic_algorithm', "optimalastar.pkl"), "wb"))
+def evolve(astar_costs):
+ first_generation = [] #pierwsza generacja
+ overall_solutions = [] #rozwiązania końcowe
+ solutions = [] #rozwiązania danej generacji
+ for individual in range(definitions.GENETIC_ALGORITHM_NUMBER_OF_INDIVIDUALS_ZERO): #liczba osobników pierwszej generacji
+ for _ in range(definitions.GENETIC_ALGORITHM_COSTS_AMOUNT):
+ first_generation.append(random.uniform(0, 10))
+ solutions.append(first_generation) #generowanie losowych kosztów pól dla pierwszej generacji
+ for gen in range(definitions.GENETIC_ALGORITHM_NUMBER_OF_GENERATIONS): #liczba generacji
+ print(f"=== Generation {gen + 1} ===")
+ ranked_solutions = [] #rozwiązania z wynikiem
+ index = 0
+ for s in solutions: #przypisanie rozwiązaniom wyniku funkcji fitness
+ ranked_solutions.append((fitness(s, index), s))
+ index = index + 1
+ ranked_solutions.sort()
+ print(f"=== Gen {gen + 1} best solution ===")
+ print(ranked_solutions[0])
+ overall_solutions.append(ranked_solutions[0])
+ #TODO warunek stopu
+ best_solutions = ranked_solutions[:definitions.GENETIC_ALGORITHM_NUMBER_OF_BEST_INDIVIDUALS] #najlepsze osobniki w danej generacji
+ elements = []
+ for element in range(definitions.GENETIC_ALGORITHM_COSTS_AMOUNT):
+ elems = []
+ elements.append(elems)
+ for solution in best_solutions:
+ for element in range(definitions.GENETIC_ALGORITHM_COSTS_AMOUNT):
+ elements[element].append(solution[1][element])
+ next_generation = [] #nowa ganeracja
+ e = []
+ for individual in range(definitions.GENETIC_ALGORITHM_NUMBER_OF_INDIVIDUALS): #liczba osobników w kolejnej generacji
+ for el in range(definitions.GENETIC_ALGORITHM_COSTS_AMOUNT):
+ #mutacje
+ e.append(random.choice(elements[el]) * random.uniform(0.99, 1.01))
+ next_generation.append(e)
+ solutions = next_generation #zastąpnienie osobników nową generacją
+ overall_solutions.sort()
+ for _ in range(definitions.GENETIC_ALGORITHM_COSTS_AMOUNT): #przyspianie finalnych kosztów astara
+ astar_costs[_] = overall_solutions[0][1][_]
+ return astar_costs
+def fitness(astar_costs, index):
+ ans = harvest(astar_costs, index)
+ if ans == 0: #TODO
return 0
else:
return 1 / ans
-def solution(astar_costs):
+def harvest(astar_costs, index):
+ #kolejność alfabetyczna
definitions.BEETROOTS_ADULT_COST = astar_costs[0]
definitions.BEETROOTS_GROW_COST = astar_costs[1]
definitions.CARROTS_ADULT_COST = astar_costs[2]
@@ -68,7 +107,7 @@ def solution(astar_costs):
decision = [0] #początkowa decyzja o braku powrotu do stacji (0)
grow_flower_dandelion = False
random_movement = False
- for run in range(1000): #liczba ruchów wózka
+ for run in range(definitions.GENETIC_ALGORITHM_NUMBER_OF_CART_MOVES): #liczba ruchów wózka
if not move_list: #jeżeli są jakieś ruchy do wykonania w move_list
grow_flower_dandelion = True
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 miejsce)
@@ -89,92 +128,5 @@ def solution(astar_costs):
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
-
-
-
- print("Ile zebrał: ", station1.get_collected_plants("beetroot") + station1.get_collected_plants("carrot") + station1.get_collected_plants("potato") + station1.get_collected_plants("wheat"))
- return station1.get_collected_plants("beetroot") + station1.get_collected_plants("carrot") + station1.get_collected_plants("potato") + station1.get_collected_plants("wheat")
-
-
-
-def make_generations(astar_costs):
- solutions = []
- ans = []
- for s in range(10): #liczba osobników zerowej generacji
- solutions.append((random.uniform(0, 10), random.uniform(0, 10), random.uniform(0, 10), random.uniform(0, 10), random.uniform(0, 10), random.uniform(0, 10), random.uniform(0, 10), random.uniform(0, 10), random.uniform(0, 10), random.uniform(0, 10), random.uniform(0, 10), random.uniform(0, 10), random.uniform(0, 10)))
- for i in range(10): #liczba generacji
- ranked_solutions = []
- for s in solutions:
- ranked_solutions.append((fitness(s), s))
- ranked_solutions.sort()
- print(f"=== Gen {i + 1} best solution ===")
- #print(f"=== Gen {i} best solutions ===")
- print(ranked_solutions[0])
- ans.append(ranked_solutions[0])
- print(ans)
-
- #można dodać warunek stopu
-
- best_solutions = ranked_solutions[:4] #najlepsze 4 osobniki w danej generacji
- elements1 = []
- elements2 = []
- elements3 = []
- elements4 = []
- elements5 = []
- elements6 = []
- elements7 = []
- elements8 = []
- elements9 = []
- elements10 = []
- elements11 = []
- elements12 = []
- elements13 = []
- for s in best_solutions:
- elements1.append(s[1][0])
- elements2.append(s[1][1])
- elements3.append(s[1][2])
- elements4.append(s[1][3])
- elements5.append(s[1][4])
- elements6.append(s[1][5])
- elements7.append(s[1][6])
- elements8.append(s[1][7])
- elements9.append(s[1][8])
- elements10.append(s[1][9])
- elements11.append(s[1][10])
- elements12.append(s[1][11])
- elements13.append(s[1][12])
-
- new_gen = []
- for i in range(10): # liczba osobników w kolejnej generacji
- e1 = random.choice(elements1) * random.uniform(0.99, 1.01) # mutacje
- e2 = random.choice(elements2) * random.uniform(0.99, 1.01)
- e3 = random.choice(elements3) * random.uniform(0.99, 1.01)
- e4 = random.choice(elements4) * random.uniform(0.99, 1.01)
- e5 = random.choice(elements5) * random.uniform(0.99, 1.01)
- e6 = random.choice(elements6) * random.uniform(0.99, 1.01)
- e7 = random.choice(elements7) * random.uniform(0.99, 1.01)
- e8 = random.choice(elements8) * random.uniform(0.99, 1.01)
- e9 = random.choice(elements9) * random.uniform(0.99, 1.01)
- e10 = random.choice(elements10) * random.uniform(0.99, 1.01)
- e11 = random.choice(elements11) * random.uniform(0.99, 1.01)
- e12 = random.choice(elements12) * random.uniform(0.99, 1.01)
- e13 = random.choice(elements13) * random.uniform(0.99, 1.01)
-
- new_gen.append((e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12, e13))
-
- solutions = new_gen
- ans.sort()
- astar_costs[0] = ans[0][1][0]
- astar_costs[1] = ans[0][1][1]
- astar_costs[2] = ans[0][1][2]
- astar_costs[3] = ans[0][1][3]
- astar_costs[4] = ans[0][1][4]
- astar_costs[5] = ans[0][1][5]
- astar_costs[6] = ans[0][1][6]
- astar_costs[7] = ans[0][1][7]
- astar_costs[8] = ans[0][1][8]
- astar_costs[9] = ans[0][1][9]
- astar_costs[10] = ans[0][1][10]
- astar_costs[11] = ans[0][1][11]
- astar_costs[12] = ans[0][1][12]
- return astar_costs
\ No newline at end of file
+ print("individual no.", index + 1, "score:", station1.get_all_collected_plants())
+ return station1.get_all_collected_plants()
\ No newline at end of file
diff --git a/plant.py b/plant.py
index c332553..4810b92 100644
--- a/plant.py
+++ b/plant.py
@@ -14,7 +14,7 @@ class Plant:
def set_state(self, state):
self.state = state
@staticmethod
- def get_closest_mature_plant(istate, map): # TO DO, pobiera współrzędne najbliższej dojrzałej rośliny od miejsca, w którym znajduje się traktor
+ def get_closest_mature_plant(istate, map): #pobiera miejsce najbliższej dojrzałej rośliny od miejsca, w którym znajduje się wózek
x = None #x'owa pola
y = None #y'kowa pola
min = None
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diff --git a/station.py b/station.py
index c87ed4c..4bb9c90 100644
--- a/station.py
+++ b/station.py
@@ -1,6 +1,8 @@
class Station:
def __init__(self, collected_plants):
self.collected_plants = collected_plants #collected_plants to słownik, przechowuje informacje o oddanych plonach
+ def get_all_collected_plants(self): #zwraca łączną ilość zebranych plonów (suma plonów wszystkich roślin)
+ return self.collected_plants["beetroot"] + self.collected_plants["carrot"] + self.collected_plants["potato"] + self.collected_plants["wheat"]
def get_collected_plants(self, name): #zwraca łączną ilość oddanych plonów dla podanej rośliny (name)
return self.collected_plants[name]
def set_collected_plants(self, name, value): #dla podanej rośliny (name) ustawia łączną ilość oddanych plonów (value)