ultra-mastermind-implementation/lib/ultra_mastermind_imp.py

# Librairie du projet en version impératif

import random
import Levenshtein

def min_i(array: list[int]) -> int:
    min_val = array[0]
    min_i = 0
    for i in range(len(array)):
        if array[i] < min_val:
            min_val = array[i]
            min_i = i 
    return min_i

def max_i(array: list[int]) -> int:
    max_val = array[0]
    max_i = 0
    for i in range(len(array)):
        if array[i] > max_val:
            max_val = array[i]
            max_i = i 
    return max_i


def new_population(pm, ng, n, ts, tm, alpha, fm): #  -> set(list(set(str)), str, int, int, int, float, float, float, int)
    """
    fonction qui renvoie une nouvelle population
    """
    population = {
        "individuals": [new_individual() for i in range(n)],
        "pm": pm,
        "ng": ng,
        "l": len(pm),
        "n": n,
        "ts": ts,
        "tm": tm,
        "alpha": alpha,
        "fm": fm
    }

    for individual in population["individuals"]:
        randomize(individual, len(pm))

    return population

def new_individual(): #  -> set(str)
    """
    fonction qui renvoie un nouvel individu
    """
    return {
        "chromozome": ""
    }

def randomize(individual, l) -> str:
    """
    Methode qui change la valeur d'un chromozome pour une valeur aléatoire
    """
    new = ""
    for i in range(l):
        new += chr(random.randint(0, 255))
    individual["chromozome"] = new

def fitness1(individual, pm) -> int:
    """
    Première methode de fitness, fait la somme des différences entre les codages des caractères des deux chaînes.
    """
    sum = 0
    for i in range(len(individual["chromozome"])):
        sum += abs(ord(individual["chromozome"][i]) - ord(pm[i]))
    return -sum

def fitness2(individual, pm, alpha) -> int:
    """
    Deuxième methode de fitness qui compte les caractères bien placés et mal placés et qui renvoie un int pondéré par alpha
    """
    match = 0
    missed_placed = 0
    for i in range(len(individual["chromozome"])):
        if individual["chromozome"][i] == pm[i]:
            match += 1
        else:
            missed_placed += 1
    return match + alpha * missed_placed

def fitness3(individual, pm) -> int:
    """
    Troisième methode de fitness qui utilise la distance de Levenshtein
    """
    return -Levenshtein.distance(individual["chromozome"], pm)

def get_fitness(population, individual) -> int:
    match population["fm"]:
        case 1:
            return fitness1(individual, population["pm"])
        case 2:
            return fitness2(individual, population["pm"], population["alpha"])
        case 3:
            return fitness3(individual, population["pm"])
        case _:
            return fitness1(individual, population["pm"])

def get_best(population):
    """
    Methode qui renvoie le meilleur individu de la population
    """
    fitness_list = []
    for individual in population["individuals"]:
        fitness_list.append(get_fitness(population, individual))
    return population["individuals"][max_i(fitness_list)]

def print_best(population) -> None:
    """
    Methode qui affiche le meilleur individu de la population
    """
    print(get_best(population)["chromozome"])

def mutate(individual) -> None:
    """
    Methode qui change un des caractères du chromozome
    """
    new = list(individual["chromozome"])
    new[random.randint(0, len(new) - 1)] = chr(random.randint(0, 255))
    individual["chromozome"] = "".join(new)

def select(population) -> None:
    """
    Methode qui sélectionne les meilleurs individus
    """
    fitness_list = [] 
    for individual in population["individuals"]:
        fitness_list.append(get_fitness(population, individual))

    for i in range(int((1 - population["ts"]) * population["n"])):
        least = min_i(fitness_list)
        fitness_list.pop(least)
        population["individuals"].pop(least)

def reproduct(population) -> None:
    """
    Methode qui reproduit les individus entre eux jusqu'à obtenir une population de taille N
    """
    new = []
    while len(population["individuals"]) + len(new) != population["n"]:
        cut = random.randint(int(population["l"] / 3), int(2 * population["l"] / 3))
        i = random.randint(0, len(population["individuals"]) - 1)
        j = random.randint(0, len(population["individuals"]) - 1)
        while i == j:
            j = random.randint(0, len(population["individuals"]) - 1)
        indivi_1 = population["individuals"][i]
        indivi_2 = population['individuals'][j]
        new_chromozome = indivi_1["chromozome"][:cut] + indivi_2["chromozome"][cut:]
        child = new_individual()
        child["chromozome"] = new_chromozome
        new.append(child)
    population["individuals"] += new

def mutate_pop(population) -> None:
    """
    Methode qui mute une partie de la population selon le taut de mutation
    """
    mutated = []
    for i in range(int(population["tm"] * population["n"])):
        to_mutate = random.randint(0, population["n"] - 1)
        while to_mutate in mutated:
            to_mutate = random.randint(0, population["n"] - 1)
        mutate(population["individuals"][to_mutate])
        mutated.append(to_mutate)

def run(population) -> None:
    """
    Boucle principale
    """
    for i in range(population["ng"]):
        select(population)
        reproduct(population)
        mutate_pop(population)
    print_best(population)