fix gen alg

board.py

@@ -2,7 +2,6 @@ import pygame
 from constant import size, rows, cols
 import random
 from tractor import Tractor
-
 class Board:
     def __init__(self):
         self.board = []
@@ -11,6 +10,7 @@ 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")
@@ -20,10 +20,26 @@ class Board:
         self.carrot = pygame.image.load("board/carrot.png")
 
     def generate_board(self):
-        self.board = [[random.choice([0,1,2,3,4,5,6,7,8,9]) for _ in range(rows)] for _ in range(cols)]
+        # 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])
 
     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)
@@ -34,7 +50,7 @@ class Board:
                 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))
                     win.blit(self.grass, cube_rect)
@@ -51,7 +67,6 @@ class Board:
                 else:
                     win.blit(self.dirt, cube_rect)
 
-                    
     def load_costs(self):
             self.costs = {
                 0: 100,  #kamien
@@ -79,6 +94,7 @@ 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
 
@@ -93,3 +109,11 @@ class Board:
 
     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

gen_algorithm.py

@@ -0,0 +1,188 @@
+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()