fix gen alg
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31
board.py
31
board.py
@ -19,7 +19,22 @@ class Board:
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self.carrot = pygame.image.load("board/carrot.png")
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def generate_board(self):
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self.board = [[random.choice([0,1,2,3,4,5,6,7,8,9]) for _ in range(rows)] for _ in range(cols)]
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# Najpierw wypełniamy całą planszę trawą (kod 2)
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self.board = [[2 for _ in range(rows)] for _ in range(cols)]
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# Losowo wybieramy 5 unikalnych pozycji dla chwastów
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weed_positions = random.sample([(row, col) for row in range(rows) for col in range(cols)], 5)
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# Umieszczamy chwasty na wylosowanych pozycjach
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for row, col in weed_positions:
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self.board[row][col] = 1 # 1 oznacza chwast
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# Teraz losowo umieszczamy inne elementy, omijając pozycje chwastów
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for row in range(rows):
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for col in range(cols):
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if (row, col) not in weed_positions:
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# Losujemy typ terenu, ale pomijamy kod 1 (chwast)
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self.board[row][col] = random.choice([0, 2, 3, 4, 5, 6, 7, 8, 9])
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def draw_cubes(self, win):
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@ -33,7 +48,7 @@ class Board:
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elif cube == 0:
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rock_scale = pygame.transform.scale(self.rock, (size, size))
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win.blit(self.dirt, cube_rect)
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win.blit(rock_scale, cube_rect)
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#win.blit(rock_scale, cube_rect)
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elif cube == 1:
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weed_scale = pygame.transform.scale(self.weeds, (size, size))
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win.blit(self.grass, cube_rect)
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@ -50,7 +65,6 @@ class Board:
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else:
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win.blit(self.dirt, cube_rect)
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def load_costs(self):
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self.costs = {
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0: 100, #kamien
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@ -78,6 +92,7 @@ class Board:
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def is_weed(self,row,col):
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return self.board[row][col] == 1
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def set_grass(self,row,col):
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self.board[row][col]=2
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@ -93,10 +108,10 @@ class Board:
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def set_carrot(self, row, col):
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self.board[row][col] = 11
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def get_dirt_positions(self):
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dirt_positions = []
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def get_weed_positions(self):
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weed_positions = []
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for row in range(rows):
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for col in range(cols):
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if self.is_dirt(row, col):
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dirt_positions.append([row, col])
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return dirt_positions
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if self.is_weed(row, col):
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weed_positions.append([row, col])
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return weed_positions
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130
gen_algorithm.py
130
gen_algorithm.py
@ -5,11 +5,10 @@ from constant import width, height, size, rows, cols
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from board import Board
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from tractor import Tractor
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routes_num = 20 # Ilość ścieżek
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board = Board()
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dirt_positions = board.get_dirt_positions()
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dirt_count = len(dirt_positions)
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weed_positions = board.get_weed_positions()
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weed_count = len(weed_positions)
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def manhattan(a, b):
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return abs(a[0] - b[0]) + abs(a[1] - b[1])
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@ -18,23 +17,20 @@ def find_routes(routes_num):
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population_set = [] # zapisujemy trasy - losowe ułóżenia
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for i in range(routes_num):
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# losowo wygenerowane kolejności na trasie
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single_route = np.random.choice(list(range(dirt_count)), dirt_count, replace=False)
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single_route = np.random.choice(list(range(weed_count)), weed_count, replace=False)
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population_set.append(single_route)
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return np.array(population_set)
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return np.array(population_set) #zwracamy 20 roznych losowych tras
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def sum_up_for_route(route_indices):
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sum = 0
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for i in range(len(route_indices) - 1):
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current_dirt = dirt_positions[route_indices[i]]
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next_dirt = dirt_positions[route_indices[i + 1]]
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sum += manhattan(current_dirt, next_dirt)
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return sum
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current_weed = weed_positions[route_indices[i]]
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next_weed = weed_positions[route_indices[i + 1]]
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sum += manhattan(current_weed, next_weed)
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return sum #zwracamy odleglosc (ilosc pol) dla danej trasy manhatanem
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def routes_sum(population_set): # zapisujemy na liście finalne sumy odległości dla każdej z opcji tras
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def routes_sum(population_set): # zapisujemy na liście finalne sumy odległości dla każdej z tras
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list_of_sums = np.zeros(routes_num)
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for i in range(routes_num):
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list_of_sums[i] = sum_up_for_route(population_set[i]) # wywołujemy dla każdej trasy na liście
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@ -42,29 +38,30 @@ def routes_sum(population_set): # zapisujemy na liście finalne sumy odległoś
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def calculate_fitness(distances):
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# Odwrotność odległości jako fitness
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# Dodajemy małą wartość (np. 1) aby uniknąć dzielenia przez zero
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# odwrotność odległości jako fitness
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# dodajemy małą wartość (np. 1) aby uniknąć dzielenia przez zero
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return 1 / (distances + 1)
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def selection(population_set, list_of_sums):
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# Oblicz wartości fitness dla każdej trasy
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fitness_values = calculate_fitness(list_of_sums)
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# Normalizuj wartości fitness, aby sumowały się do 1 (wymagane dla np.random.choice)
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#RULETKA - czesciowo faworyzuje rozwiaznaia, wiekszy fitness wieksze szanse
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# obliczamy wartości fitness (przystosowania) dla każdej trasy
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fitness_values = calculate_fitness(list_of_sums)#krotsze trasy maja miec wyzsze wartosci
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# normalizujemy wartości fitness, aby sumowały się do 1 (wymagane dla np.random.choice)
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probabilities = fitness_values / fitness_values.sum()
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# Wybierz rodziców na podstawie prawdopodobieństw (wartości fitness)
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# wybieramy indeksy rodziców na podstawie prawdopodobieństw
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progenitor_indices_a = np.random.choice(range(len(population_set)), len(population_set), p=probabilities, replace=True)
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progenitor_indices_b = np.random.choice(range(len(population_set)), len(population_set), p=probabilities, replace=True)
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# Wybierz rzeczywiste trasy
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# finalne trasy
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progenitor_a = population_set[progenitor_indices_a]
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progenitor_b = population_set[progenitor_indices_b]
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return np.array([progenitor_a, progenitor_b])
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return np.array([progenitor_a, progenitor_b]) #zwracami listy przodkow-rodzicow
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def one_point_crossover(parent_a, parent_b): #krzyzowanie jednopunktowe
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crossover_point = np.random.randint(1, len(parent_a))
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child = np.concatenate((parent_a[:crossover_point], [x for x in parent_b if x not in parent_a[:crossover_point]]))
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return child
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return child #loosyw punkt przeciecia ktory skleja nam nowa trase, wieksza szans na lepsza tarse
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def population_mating(progenitor_list):
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new_population_set = []
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@ -72,24 +69,17 @@ def population_mating(progenitor_list):
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progenitor_a, progenitor_b = progenitor_list[0][i], progenitor_list[1][i]
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child = one_point_crossover(progenitor_a, progenitor_b)
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new_population_set.append(child)
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return new_population_set
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return new_population_set # lista potomkow po krzyzowaniu
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def mutation_of_child(child):
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for i in range(dirt_count): # dla każdego elementu dajemy losową szansę zamiany int *rate
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x = np.random.randint(0, dirt_count)
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y = np.random.randint(0, dirt_count)
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child[x], child[y] = child[y], child[x] # zamiana miejscami
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return child
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'''def mutation_of_child(child, mutation_rate=0.1):#procent moze pomoc w niezaklucaniu trasy gdy jesy duza trasa ale idk
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def mutation_of_child(child, mutation_rate=0.2):#procent moze pomoc w niezaklucaniu trasy gdy jesy duza trasa ale idk
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num_mutations = int(len(child) * mutation_rate)
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for _ in range(num_mutations):
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x = np.random.randint(0, len(child))
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x = np.random.randint(0, len(child))#losowa szansa zamiany - mutacja
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y = np.random.randint(0, len(child))
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child[x], child[y] = child[y], child[x]
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return child'''
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return child#zwrocenie bardziej roznorodnych potomkow
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def mutate_population(new_population_set):
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@ -100,6 +90,23 @@ def mutate_population(new_population_set):
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if __name__ == '__main__':
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pygame.init()
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WIN = pygame.display.set_mode((width, height))
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pygame.display.set_caption('Trasa Traktora')
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clock = pygame.time.Clock()
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board = Board()
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board.load_images()
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weed_positions = [(col, row) for col in range(cols) for row in range(rows) if board.is_weed(col, row)]
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weed_count = len(weed_positions)
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board.set_grass(9, 9) # pozycja startowa
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tractor = Tractor(9, 9) # Start traktora
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# Inicjalizacja final_route
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final_route = [0, float('inf'), np.array([])]
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# [0]: indeks iteracji, [1]: najlepsza suma odległości, [2]: najlepsza trasa
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population_set = find_routes(routes_num)
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list_of_sums = routes_sum(population_set)
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@ -107,6 +114,7 @@ if __name__ == '__main__':
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new_population_set = population_mating(progenitor_list)
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final_mutated_population = mutate_population(new_population_set)
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final_route = [-1, np.inf, np.array([])] # format listy
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for i in range(20):
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list_of_sums = routes_sum(final_mutated_population)
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# zapisujemy najlepsze rozwiązanie
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@ -115,12 +123,64 @@ if __name__ == '__main__':
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final_route[1] = list_of_sums.min()
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final_route[2] = np.array(final_mutated_population)[list_of_sums.min() == list_of_sums]
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progenitor_list = selection(population_set, list_of_sums)
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new_population_set = population_mating(progenitor_list)
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final_mutated_population = mutate_population(new_population_set)
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print(f"Najlepsza trasa znaleziona w iteracji: {final_route[0]}")
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print(f"Minimalna suma odległości: {final_route[1]}")
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print(f"Kolejne pola: {final_route[2]}")
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run = True
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current_target_index = 0
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best_routes = final_route[2] #tablica z najlepszymi trasami
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visited_fields = []
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while run:
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clock.tick(2) # FPS
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for event in pygame.event.get():
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if event.type == pygame.QUIT:
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run = False
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for route in best_routes:
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if current_target_index < len(route):
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current_weed = weed_positions[route[current_target_index]]
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# ruch w kierunku bieżącego celu
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if tractor.col < current_weed[0]:
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tractor.col += 1
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tractor.direction = "right"
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elif tractor.col > current_weed[0]:
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tractor.col -= 1
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tractor.direction = "left"
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elif tractor.row < current_weed[1]:
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tractor.row += 1
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tractor.direction = "down"
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elif tractor.row > current_weed[1]:
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tractor.row -= 1
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tractor.direction = "up"
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current_position = (tractor.col, tractor.row)
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if current_position not in visited_fields:
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visited_fields.append(current_position)
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# Jeśli traktor dotarł do celu
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if (tractor.col, tractor.row) == current_weed:
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current_target_index += 1
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# Aktualizacja planszy
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if board.is_weed(tractor.col, tractor.row):
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board.set_carrot(tractor.col, tractor.row)
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board.draw_cubes(WIN)
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tractor.draw(WIN)
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pygame.display.update()
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print("Odwiedzone pola:")
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for field in visited_fields:
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print(field)
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pygame.quit()
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