.vscode/settings.json

@@ -1,9 +0,0 @@
-{
-    "python.analysis.extraPaths": [
-        "./sources",
-        "/usr/local/lib/python3.10/site-packages",
-        "/Users/alexeybrown/development",
-        "/Users/alexeybrown/development/flutter/",
-        "./agent"
-    ]
-}

README.md

@@ -0,0 +1,10 @@
+# si23traktor
+
+Projekt zaliczeniowy przedmiotu Sztuczna Inteligencja w semestrze letnim 2023.
+
+Skład zespołu:
+- Jakub Chmielecki
+- Mikołaj Mazur
+- Szymon Szczubkowski
+- Tobiasz Przybylski
+- Aliaksei Shauchenka

agent/methods/genetic_algorithm.py

@@ -1,113 +0,0 @@
-import numpy as np
-import random
-import math
-
-def create_initial_population(num_cities, population_size, list):
-    population = []
-    for _ in range(population_size):
-        chromosome = list.copy()
-        chromosome.remove((1, 1))  # Usuń punkt (1, 1) z listy
-        random.shuffle(chromosome)
-        chromosome.insert(0, (1, 1))  # Dodaj punkt (1, 1) na początku trasy
-        population.append(chromosome)
-    return population
-
-def calculate_distance(city1, city2):
-    x1, y1 = city1
-    x2, y2 = city2
-    distance = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
-    return distance
-
-def calculate_fitness(individual):
-    total_distance = 0
-
-    num_cities = len(individual)
-
-    for i in range(num_cities - 1):
-        city1 = individual[i]
-        city2 = individual[i + 1]
-        distance = calculate_distance(city1, city2)
-        total_distance += distance
-
-    fitness = 1 / total_distance
-
-    return fitness
-
-def crossover(parent1, parent2):
-    child = [(1, 1)] + [None] * (len(parent1) - 1)  # Inicjalizacja dziecka z punktem (1, 1) na początku
-    start_index = random.randint(1, len(parent1) - 1)
-    end_index = random.randint(start_index + 1, len(parent1))
-    # Skopiuj fragment miast od parent1 do dziecka
-    child[start_index:end_index] = parent1[start_index:end_index]
-    # Uzupełnij brakujące miasta z parent2
-    remaining_cities = [city for city in parent2 if city not in child]
-    child[1:start_index] = remaining_cities[:start_index - 1]
-    child[end_index:] = remaining_cities[start_index - 1:]
-    return child
-
-def mutate(individual, mutation_rate):
-    for i in range(1, len(individual)):  # Rozpoczynamy od indeksu 1, aby pominąć punkt (1, 1)
-        if random.random() < mutation_rate:
-            j = random.randint(1, len(individual) - 1)  # Wybieramy indeks od 1 do ostatniego indeksu
-            individual[i], individual[j] = individual[j], individual[i]
-    return individual
-
-def genetic_algorithm(list):
-    chromosome_length = 21
-    max_generations = 200
-    population_size = 200
-    crossover_rate = 0.25
-    mutation_rate = 0.1
-    num_cities = chromosome_length
-    population = create_initial_population(num_cities, population_size, list)
-
-    best_individual = None
-    best_fitness = float('-inf')
-
-    for generation in range(max_generations):
-        # # Oblicz wartości fitness dla każdego osobnika w populacji
-        # fitness_values = [calculate_fitness(individual) for individual in population]
-        # population = [x for _, x in sorted(zip(fitness_values, population), reverse=True)]
-        # fitness_values.sort(reverse=True)
-        # max_fitness_index = np.argmax(fitness_values)
-        # # Wybierz najlepszego osobnika z ostatniej populacji
-        # if fitness_values[max_fitness_index] > best_fitness:
-        #     best_fitness = fitness_values[max_fitness_index]
-        #     best_individual = population[max_fitness_index]
-       
-        # # Twórz nową populację z krzyżówek
-        # new_population = []
-        # for _ in range(int(population_size / 2)):
-        #     parent1, parent2 = random.choices(population[:population_size // 2], k=2)
-        #     child1 = crossover(parent1, parent2)
-        #     child2 = crossover(parent2, parent1)
-        #     new_population.extend([child1, child2])
-        # # Dokonaj mutacji na nowej populacji
-        # new_population = [mutate(individual, mutation_rate) for individual in new_population]
-        # population = new_population
-
-        # Oblicz wartości fitness dla każdego osobnika w populacji
-        fitness_values = [calculate_fitness(individual) for individual in population]
-        population = [x for _, x in sorted(zip(fitness_values, population), reverse=True)]
-        fitness_values.sort(reverse=True)
-        best_individuals = population[:10]  # Wybierz k najlepszych osobników
-        new_population = best_individuals.copy()
-
-        # Twórz nową populację z krzyżówek i mutacji
-        while len(new_population) < population_size:
-            parent1, parent2 = random.choices(best_individuals, k=2)  # Wybierz rodziców spośród najlepszych osobników
-            child = crossover(parent1, parent2)  # Krzyżowanie
-            child = mutate(child, mutation_rate)  # Mutacja
-            new_population.append(child)
-
-        for individual in best_individuals:
-            fitness = calculate_fitness(individual)
-            if fitness > best_fitness:
-                best_fitness = fitness
-                best_individual = individual
-
-        population = new_population[:population_size]
-
-        
-    print("Best path:", best_individual)
-    return best_individual