gen alg 2
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__pycache__/board.cpython-311.pyc
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__pycache__/constant.cpython-311.pyc
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__pycache__/kolejka.cpython-311.pyc
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__pycache__/kolejka.cpython-311.pyc
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__pycache__/tractor.cpython-311.pyc
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gen_algorythm.py
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gen_algorythm.py
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import numpy as np
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import random
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import pygame
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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, które będziemy generować
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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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def manhattan(a, b):
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return abs(a[0] - b[0]) + abs(a[1] - b[1])
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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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population_set.append(single_route)
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return np.array(population_set)
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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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def routes_sum(population_set): # zapisujemy na liście finalne sumy odległości dla każdej z opcji 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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return list_of_sums
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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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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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probabilities = fitness_values / fitness_values.sum()
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# Wybierz rodziców na podstawie prawdopodobieństw (wartości fitness)
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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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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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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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def population_mating(progenitor_list):
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new_population_set = []
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for i in range(len(progenitor_list[0])):
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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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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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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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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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def mutate_population(new_population_set):
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final_mutated_population = []
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for child in new_population_set:
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final_mutated_population.append(mutation_of_child(child)) # dodajemy zmutowane dziecko do finalnej listy
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return final_mutated_population
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if __name__ == '__main__':
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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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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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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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if list_of_sums.min() < final_route[1]:
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final_route[0] = i
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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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