ultra-mastermind-implementation/lib/ultra_mastermind_pp_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):
	"""
	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, 4, 30)

	return population

def new_individual():
	"""
	fonction qui renvoie un nouvel individu
	"""
	return {
		"chromozome": ""
	}

def randomize(individual, min_l, max_l) -> str:
	"""
	Methode qui change la valeur d'un chromozome pour une valeur aléatoire
	"""
	new = ""
	for i in range(random.randint(min_l, max_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"])):
		if i < len(pm) and i < 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 i >= len(pm):
			missed_placed += len(individual["chromozome"]) - len(pm)
			break
		elif individual["chromozome"][i] == pm[i]:
			match += 1
		else:
			missed_placed += 1
	if len(pm) > len(individual["chromozome"]):
		missed_placed += len(pm) - len(individual["chromozome"])
	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_fitness_list(population):
	fitness_list = []
	for individual in population["individuals"]:
		fitness_list.append(get_fitness(population, individual))
	return fitness_list

def get_best(population):
	"""
	Methode qui renvoie le meilleur individu de la population
	"""
	fitness_list = get_fitness_list(population)
	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 select(population) -> None:
	"""
	Methode qui sélectionne les meilleurs individus
	"""
	fitness_list = get_fitness_list(population)
	for i in range(int((1 - population["ts"]) * population["n"])):
		least = min_i(fitness_list)
		fitness_list.pop(least)
		population["individuals"].pop(least)

def get_two_random_individuals(population):
	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)
	return (population["individuals"][i], population['individuals'][j])

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"]:
		indivi_1, indivi_2 = get_two_random_individuals(population)

		avg = (len(indivi_1) + len(indivi_2)) // 2
		cut = random.randint(avg // 3, 2 * avg // 3)
		while cut > len(indivi_1) or cut > len(indivi_2): 
			cut = random.randint(avg // 3, 2 * avg // 3)

		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(individual) -> None:
	"""
	Methode qui change un des caractères du chromozome
	"""
	new = list(individual["chromozome"])
	dice = random.randint(1,3)
	if dice == 1 and len(new) < 30:
		new.insert(random.randint(0, len(new) - 1), chr(random.randint(0, 255)))
	elif dice == 2 and len(new) > 4:
		new.pop(random.randint(0, len(new) - 1))
	else :
		new[random.randint(0, len(new) - 1)] = chr(random.randint(0, 255))
	individual["chromozome"] = "".join(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)