Saataa andagii !

lib/ultra_mastermind_pp_imp.py

@@ -21,8 +21,7 @@ def max_i(array: list[int]) -> int:
 			max_i = i 
 	return max_i
 
-
+def new_population(pm, ng, n, ts, tm, alpha, fm):
-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
 	"""
@@ -43,7 +42,7 @@ def new_population(pm, ng, n, ts, tm, alpha, fm): #  -> set(list(set(str)), str,
 
 	return population
 
-def new_individual(): #  -> set(str)
+def new_individual():
 	"""
 	fonction qui renvoie un nouvel individu
 	"""
@@ -66,7 +65,8 @@ def fitness1(individual, pm) -> int:
 	"""
 	sum = 0
 	for i in range(len(individual["chromozome"])):
-		sum += abs(ord(individual["chromozome"][i]) - ord(pm[i]))
+		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:
@@ -76,7 +76,7 @@ def fitness2(individual, pm, alpha) -> int:
 	match = 0
 	missed_placed = 0
 	for i in range(len(individual["chromozome"])):
-		if i > len(pm):
+		if i >= len(pm):
 			missed_placed += len(individual["chromozome"]) - len(pm)
 			break
 		elif individual["chromozome"][i] == pm[i]:
@@ -104,13 +104,17 @@ def get_fitness(population, individual) -> int:
 		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 = []
+	fitness_list = get_fitness_list(population)
-	for individual in population["individuals"]:
-		fitness_list.append(get_fitness(population, individual))
 	return population["individuals"][max_i(fitness_list)]
 
 def print_best(population) -> None:
@@ -119,49 +123,57 @@ def print_best(population) -> None:
 	"""
 	print(get_best(population)["chromozome"])
 
-def mutate(individual) -> None:
-	"""
-	Methode qui change un des caractères du chromozome
-	"""
-	new = list(individual["chromozome"])
-	if random.randint(1, 2) == 1: new.insert(random.randint(0, len(new) - 1), chr(random.randint(0, 255)))
-	else : 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 = [] 
+	fitness_list = get_fitness_list(population)
-	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 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"]:
-		i = random.randint(0, len(population["individuals"]) - 1)
+		indivi_1, indivi_2 = get_two_random_individuals(population)
-		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]
 		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)
+		while cut > len(indivi_1) or cut > len(indivi_2): 
-		new_chromozome = indivi_1["chromozome"][:cut] + indivi_2["chromozome"][-cut:]
+			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

main.py

@@ -8,7 +8,7 @@ import lib.ultra_mastermind_pp_imp as libppimp
 # constants 
 PM = ""
 NG = 500
-N  = 200
+N  = 300
 TS = 0.5
 TM = 0.25
 ALPHA = 0.5

requirements.txt

@@ -1,2 +1,13 @@
+contourpy==1.3.1
+cycler==0.12.1
+fonttools==4.55.0
+kiwisolver==1.4.7
 Levenshtein==0.26.1
+matplotlib==3.9.2
+numpy==2.1.3
+packaging==24.2
+pillow==11.0.0
+pyparsing==3.2.0
+python-dateutil==2.9.0.post0
 RapidFuzz==3.10.1
+six==1.16.0

tests.py

@@ -5,6 +5,7 @@ import matplotlib.pyplot as plt
 # project libs importations
 import lib.ultra_mastermind_obj as libobj
 import lib.ultra_mastermind_imp as libimp
+import lib.ultra_mastermind_pp_imp as libppimp
 
 # Variation du nombre de générations
 PM = "Hello, world!"
@@ -13,20 +14,46 @@ N  = 400
 TS = 0.5
 TM = 0.25
 ALPHA = 0.5
-FITNESS_METHOD = 1
+FITNESS_METHOD = 3
 
 fitness_ng = []
 all_ng = []
 
-for i in range(1, 21):
+for i in range(1, 11):
-	NG = i * 100
+	NG = i * 200
 	all_ng.append(NG)
-	pop = libimp.new_population(PM, NG, N, TS, TM, ALPHA, FITNESS_METHOD)
+	pop = libppimp.new_population(PM, NG, N, TS, TM, ALPHA, FITNESS_METHOD)
-	libimp.run(pop)
+	libppimp.run(pop)
-	fitness_ng.append(libimp.get_fitness(pop, libimp.get_best(pop)))
+	fitness_ng.append(libppimp.get_fitness(pop, libppimp.get_best(pop)))
 
 plt.plot(all_ng, fitness_ng)
-plt.title("Fitness du meilleur individu en fonciton du nombre de générations")
+plt.title("Fitness du meilleur individu en fonction du nombre de générations")
 plt.xlabel("Nombre de générations")
 plt.ylabel("Fitness du meilleur individu")
 plt.show()
+
+# Variation du nombre de générations
+PM = "Hello, world!"
+NG = 500
+# N  = 400
+TS = 0.5
+TM = 0.25
+ALPHA = 0.5
+FITNESS_METHOD = 3
+
+fitness_n = []
+all_n = []
+
+for i in range(1, 11):
+	N = i * 100
+	all_n.append(N)
+	pop = libppimp.new_population(PM, NG, N, TS, TM, ALPHA, FITNESS_METHOD)
+	libppimp.run(pop)
+	fitness_n.append(libppimp.get_fitness(pop, libppimp.get_best(pop)))
+
+plt.plot(all_n, fitness_n)
+plt.title("Fitness du meilleur individu en fonction de la taille de population")
+plt.xlabel("Taille de population")
+plt.ylabel("Fitness du meilleur individu")
+plt.show()
+