gen alg 2

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 = []
@@ -93,3 +92,11 @@ class Board:
 
     def set_carrot(self, row, col):
         self.board[row][col] = 11
+
+    def get_dirt_positions(self):
+        dirt_positions = []
+        for row in range(rows):
+            for col in range(cols):
+                if self.is_dirt(row, col):
+                    dirt_positions.append([row, col])
+        return dirt_positions

gen_algorithm.py

@@ -0,0 +1,126 @@
+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()
+dirt_positions = board.get_dirt_positions()
+dirt_count = len(dirt_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(dirt_count)), dirt_count, replace=False)
+        population_set.append(single_route)
+    return np.array(population_set)
+
+
+def sum_up_for_route(route_indices):
+    sum = 0
+    for i in range(len(route_indices) - 1):
+        current_dirt = dirt_positions[route_indices[i]]
+        next_dirt = dirt_positions[route_indices[i + 1]]
+        sum += manhattan(current_dirt, next_dirt)
+    return sum
+
+
+def routes_sum(population_set):  # zapisujemy na liście finalne sumy odległości dla każdej z opcji 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):
+    # Oblicz wartości fitness dla każdej trasy
+    fitness_values = calculate_fitness(list_of_sums)
+    # Normalizuj wartości fitness, aby sumowały się do 1 (wymagane dla np.random.choice)
+    probabilities = fitness_values / fitness_values.sum()
+    # Wybierz rodziców na podstawie prawdopodobieństw (wartości fitness)
+    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)
+    # Wybierz rzeczywiste trasy
+    progenitor_a = population_set[progenitor_indices_a]
+    progenitor_b = population_set[progenitor_indices_b]
+
+    return np.array([progenitor_a, progenitor_b])
+
+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
+
+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
+
+def mutation_of_child(child):
+    for i in range(dirt_count):  # dla każdego elementu dajemy losową szansę zamiany int *rate
+        x = np.random.randint(0, dirt_count)
+        y = np.random.randint(0, dirt_count)
+
+        child[x], child[y] = child[y], child[x]  # zamiana miejscami
+
+    return child
+
+'''def mutation_of_child(child, mutation_rate=0.1):#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))
+        y = np.random.randint(0, len(child))
+        child[x], child[y] = child[y], child[x]
+    return child'''
+
+
+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__':
+
+    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]}")
+    print(f"Kolejne pola: {final_route[2]}")
+