board.py

@@ -2,6 +2,7 @@ import pygame
 from constant import size, rows, cols
 import random
 from tractor import Tractor
+
 class Board:
     def __init__(self):
         self.board = []
@@ -10,7 +11,6 @@ class Board:
         self.load_costs()
 
 
-
     def load_images(self):
         self.grass = pygame.image.load("board/grass.png")
         self.dirt = pygame.image.load("board/dirt.png")
@@ -20,53 +20,38 @@ class Board:
         self.carrot = pygame.image.load("board/carrot.png")
 
     def generate_board(self):
-        # Najpierw wypełniamy całą planszę trawą (kod 2)
-        self.board = [[2 for _ in range(rows)] for _ in range(cols)]
-
-        # Losowo wybieramy 5 unikalnych pozycji dla chwastów
-        weed_positions = random.sample([(row, col) for row in range(rows) for col in range(cols)], 5)
-
-        # Umieszczamy chwasty na wylosowanych pozycjach
-        for row, col in weed_positions:
-            self.board[row][col] = 1  # 1 oznacza chwast
-
-        # Teraz losowo umieszczamy inne elementy, omijając pozycje chwastów
-        for row in range(rows):
-            for col in range(cols):
-                if (row, col) not in weed_positions:
-                    # Losujemy typ terenu, ale pomijamy kod 1 (chwast)
-                    self.board[row][col] = random.choice([0, 2, 3, 4, 5, 6, 7, 8, 9])
+        self.board = [[random.choice([0,1,2,3,4,5,6,7,8,9]) for _ in range(rows)] for _ in range(cols)]
 
     def draw_cubes(self, win):
 
-
         for row in range(rows):
             for col in range(cols):
                 cube_rect = pygame.Rect(row * size, col * size, size, size)
-                cube = self.board[row][col]
+                cube=self.board[row][col]
 
-                if row == 4 and col == 4:
+                if row==4 and col==4:
                     win.blit(self.grass, cube_rect)
                 elif cube == 0:
                     rock_scale = pygame.transform.scale(self.rock, (size, size))
                     win.blit(self.dirt, cube_rect)
-                    #win.blit(rock_scale, cube_rect)
+                    win.blit(rock_scale,  cube_rect)
                 elif cube == 1:
-                    weed_scale = pygame.transform.scale(self.weeds, (size, size))
+                    weed_scale = pygame.transform.scale(self.weeds, (size,size))
                     win.blit(self.grass, cube_rect)
                     win.blit(weed_scale, cube_rect)
-                elif cube in (2, 3, 4, 5):
-                    win.blit(self.grass, cube_rect)
+                elif cube in(2,3,4,5):
+                    win.blit(self.grass,  cube_rect)
                 elif cube == 10:
                     win.blit(self.soil, cube_rect)
                 elif cube == 11:
-                    carrot_scale = pygame.transform.scale(self.carrot, (size, size))
-                    win.blit(self.carrot, cube_rect)
+                    carrot_scale = pygame.transform.scale(self.carrot, (size,size))
+                    win.blit(self.carrot, cube_rect) 
                     win.blit(carrot_scale, cube_rect)
-
+                       
                 else:
-                    win.blit(self.dirt, cube_rect)
-
+                    win.blit(self.dirt,  cube_rect)
+                    
+                    
     def load_costs(self):
             self.costs = {
                 0: 100,  #kamien
@@ -78,15 +63,15 @@ class Board:
                 6: 3,   #ziemia
                 7: 3,   #ziemia
                 8: 3,   #ziemia
-                9: 3,   #ziemia
+                9: 3,   #ziemia    
                 10: 4,  #zyzna
                 11: 10  #marchewka
             }
 
     def get_cost(self, row, col):
         tile_type = self.board[row][col]
-        return self.costs.get(tile_type, 1)
-
+        return self.costs.get(tile_type, 1)  
+            
     def is_rock(self, row, col):
         tractor = Tractor(row, col)
         return self.board[row][col] == 0 and not (row == tractor.row and col == tractor.col)
@@ -94,26 +79,17 @@ class Board:
     def is_weed(self,row,col):
         return self.board[row][col] == 1
 
-
     def set_grass(self,row,col):
         self.board[row][col]=2
 
     def is_dirt(self,row,col):
         return self.board[row][col] in (6,7,8,9)
-
+        
     def is_soil(self, row, col):
         return self.board[row][col] == 10
-
+    
     def set_soil(self, row, col):
         self.board[row][col] = 10
-
+        
     def set_carrot(self, row, col):
-        self.board[row][col] = 11
-
-    def get_weed_positions(self):
-        weed_positions = []
-        for row in range(rows):
-            for col in range(cols):
-                if self.is_weed(row, col):
-                    weed_positions.append([row, col])
-        return weed_positions
+        self.board[row][col] = 11   

gen_algorithm.py

@@ -1,188 +0,0 @@
-import numpy as np
-import random
-import pygame
-from constant import width, height, size, rows, cols
-from board import Board
-from tractor import Tractor
-
-routes_num = 20  # Ilość ścieżek
-board = Board()
-weed_positions = board.get_weed_positions()
-weed_count = len(weed_positions)
-
-def manhattan(a, b):
-    return abs(a[0] - b[0]) + abs(a[1] - b[1])
-
-
-def find_routes(routes_num):
-    population_set = []  # zapisujemy trasy - losowe ułóżenia
-    for i in range(routes_num):
-        # losowo wygenerowane kolejności na trasie
-        single_route = np.random.choice(list(range(weed_count)), weed_count, replace=False)
-        population_set.append(single_route)
-    return np.array(population_set) #zwracamy 20 roznych losowych tras
-
-def sum_up_for_route(route_indices):
-
-    sum = 0
-    for i in range(len(route_indices) - 1):
-        current_weed = weed_positions[route_indices[i]]
-        next_weed = weed_positions[route_indices[i + 1]]
-        sum += manhattan(current_weed, next_weed)
-    return sum #zwracamy odleglosc (ilosc pol) dla danej trasy manhatanem
-
-def routes_sum(population_set):  # zapisujemy na liście finalne sumy odległości dla każdej z tras
-    list_of_sums = np.zeros(routes_num)
-    for i in range(routes_num):
-        list_of_sums[i] = sum_up_for_route(population_set[i])  # wywołujemy dla każdej trasy na liście
-
-    return list_of_sums
-
-
-def calculate_fitness(distances):
-    # odwrotność odległości jako fitness
-    # dodajemy małą wartość (np. 1) aby uniknąć dzielenia przez zero
-    return 1 / (distances + 1)
-
-
-def selection(population_set, list_of_sums):
-    #RULETKA - czesciowo faworyzuje rozwiaznaia, wiekszy fitness wieksze szanse
-    # obliczamy wartości fitness  (przystosowania) dla każdej trasy
-    fitness_values = calculate_fitness(list_of_sums)#krotsze trasy maja miec wyzsze wartosci
-    # normalizujemy wartości fitness, aby sumowały się do 1 (wymagane dla np.random.choice)
-    probabilities = fitness_values / fitness_values.sum()
-    # wybieramy indeksy rodziców na podstawie prawdopodobieństw
-    progenitor_indices_a = np.random.choice(range(len(population_set)), len(population_set), p=probabilities,  replace=True)
-    progenitor_indices_b = np.random.choice(range(len(population_set)), len(population_set), p=probabilities, replace=True)
-    # finalne  trasy
-    progenitor_a = population_set[progenitor_indices_a]
-    progenitor_b = population_set[progenitor_indices_b]
-
-    return np.array([progenitor_a, progenitor_b]) #zwracami listy przodkow-rodzicow
-
-def one_point_crossover(parent_a, parent_b): #krzyzowanie jednopunktowe
-    crossover_point = np.random.randint(1, len(parent_a))
-    child = np.concatenate((parent_a[:crossover_point], [x for x in parent_b if x not in parent_a[:crossover_point]]))
-    return child #loosyw punkt przeciecia ktory skleja nam nowa trase, wieksza szans na lepsza tarse
-
-def population_mating(progenitor_list):
-    new_population_set = []
-    for i in range(len(progenitor_list[0])):
-        progenitor_a, progenitor_b = progenitor_list[0][i], progenitor_list[1][i]
-        child = one_point_crossover(progenitor_a, progenitor_b)
-        new_population_set.append(child)
-    return new_population_set # lista potomkow po krzyzowaniu
-
-
-
-def mutation_of_child(child, mutation_rate=0.2):#procent moze pomoc w niezaklucaniu trasy gdy jesy duza trasa ale idk
-    num_mutations = int(len(child) * mutation_rate)
-    for _ in range(num_mutations):
-        x = np.random.randint(0, len(child))#losowa szansa zamiany - mutacja
-        y = np.random.randint(0, len(child))
-        child[x], child[y] = child[y], child[x]
-    return child#zwrocenie bardziej roznorodnych potomkow
-
-
-def mutate_population(new_population_set):
-    final_mutated_population = []
-    for child in new_population_set:
-        final_mutated_population.append(mutation_of_child(child))  # dodajemy zmutowane dziecko do finalnej listy
-    return final_mutated_population
-
-
-if __name__ == '__main__':
-    pygame.init()
-    WIN = pygame.display.set_mode((width, height))
-    pygame.display.set_caption('Trasa Traktora')
-    clock = pygame.time.Clock()
-
-    board = Board()
-    board.load_images()
-    weed_positions = [(col, row) for col in range(cols) for row in range(rows) if board.is_weed(col, row)]
-    weed_count = len(weed_positions)
-
-    board.set_grass(9, 9)  # pozycja startowa
-    tractor = Tractor(9, 9)  # Start traktora
-
-
-    # Inicjalizacja final_route
-    final_route = [0, float('inf'), np.array([])]
-    # [0]: indeks iteracji, [1]: najlepsza suma odległości, [2]: najlepsza trasa
-
-    population_set = find_routes(routes_num)
-    list_of_sums = routes_sum(population_set)
-    progenitor_list = selection(population_set, list_of_sums)
-    new_population_set = population_mating(progenitor_list)
-    final_mutated_population = mutate_population(new_population_set)
-    final_route = [-1, np.inf, np.array([])]  # format listy
-
-    for i in range(20):
-        list_of_sums = routes_sum(final_mutated_population)
-        # zapisujemy najlepsze rozwiązanie
-        if list_of_sums.min() < final_route[1]:
-            final_route[0] = i
-            final_route[1] = list_of_sums.min()
-            final_route[2] = np.array(final_mutated_population)[list_of_sums.min() == list_of_sums]
-
-        progenitor_list = selection(population_set, list_of_sums)
-        new_population_set = population_mating(progenitor_list)
-        final_mutated_population = mutate_population(new_population_set)
-
-    print(f"Najlepsza trasa znaleziona w iteracji: {final_route[0]}")
-    print(f"Minimalna suma odległości: {final_route[1]}")
-
-
-    run = True
-    current_target_index = 0
-    best_routes = final_route[2]  #tablica z najlepszymi trasami
-    visited_fields = []
-    while run:
-        clock.tick(2)  # FPS
-
-        for event in pygame.event.get():
-            if event.type == pygame.QUIT:
-                run = False
-
-        for route in best_routes:
-            if current_target_index < len(route):
-                current_weed = weed_positions[route[current_target_index]]
-
-
-                # ruch w kierunku bieżącego celu
-                if tractor.col < current_weed[0]:
-                    tractor.col += 1
-                    tractor.direction = "right"
-                elif tractor.col > current_weed[0]:
-                    tractor.col -= 1
-                    tractor.direction = "left"
-                elif tractor.row < current_weed[1]:
-                    tractor.row += 1
-                    tractor.direction = "down"
-                elif tractor.row > current_weed[1]:
-                    tractor.row -= 1
-                    tractor.direction = "up"
-
-                current_position = (tractor.col, tractor.row)
-                if current_position not in visited_fields:
-                    visited_fields.append(current_position)
-
-                # Jeśli traktor dotarł do celu
-                if (tractor.col, tractor.row) == current_weed:
-                    current_target_index += 1
-
-                # Aktualizacja planszy
-                if board.is_weed(tractor.col, tractor.row):
-                    board.set_carrot(tractor.col, tractor.row)
-
-
-
-        board.draw_cubes(WIN)
-        tractor.draw(WIN)
-        pygame.display.update()
-
-    print("Odwiedzone pola:")
-    for field in visited_fields:
-        print(field)
-
-    pygame.quit()