Saataa andagii !

lib/ultra_mastermind_imp.py

@@ -51,21 +51,24 @@ def new_individual(): #  -> set(str)
 		"chromozome": ""
 	}
 
+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 select(population) -> None:
 	"""
 	Methode qui sélectionne les meilleurs individus
 	"""
 	fitness_list = [] 
 	for individual in population["individuals"]:
-		match population["fm"]:
+		fitness_list.append(get_fitness(population, individual))
-			case 1:
-				fitness_list.append(fitness1(individual, population["pm"]))
-			case 2:
-				fitness_list.append(fitness2(individual, population["pm"], population["alpha"]))
-			case 3:
-				fitness_list.append(fitness3(individual, population["pm"]))
-			case _:
-				fitness_list.append(fitness1(individual, population["pm"]))
 
 	for i in range(int((1 - population["ts"]) * population["n"])):
 		least = min_i(fitness_list)
@@ -103,22 +106,20 @@ def mutate_pop(population) -> None:
 		mutate(population["individuals"][to_mutate])
 		mutated.append(to_mutate)
 
+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
 	"""
-	fitness_list = []
+	print(get_best(population)["chromozome"])
-	for individual in population["individuals"]:
-		match population["fm"]:
-			case 1:
-				fitness_list.append(fitness1(individual, population["pm"]))
-			case 2:
-				fitness_list.append(fitness2(individual, population["pm"], population["alpha"]))
-			case 3:
-				fitness_list.append(fitness3(individual, population["pm"]))
-			case _:
-				fitness_list.append(fitness1(individual, population["pm"]))
-	print(population["individuals"][max_i(fitness_list)]["chromozome"])
 
 def run(population) -> None:
 	"""

main.py

@@ -15,9 +15,11 @@ FITNESS_METHOD = 1
 
 # main function
 def main() -> None:
-    pop = libobj.Population(pm = PM, ng = NG, n = N, ts = TS, tm = TM, alpha = ALPHA, fm = FITNESS_METHOD)
+    # object version
-    pop.run()
+    # pop = libobj.Population(pm = PM, ng = NG, n = N, ts = TS, tm = TM, alpha = ALPHA, fm = FITNESS_METHOD)
+    # pop.run()
 
+    # imperative version
     pop = libimp.new_population(PM, NG, N, TS, TM, ALPHA, FITNESS_METHOD)
     libimp.run(pop)
 

requirements.txt

@@ -0,0 +1,2 @@
+Levenshtein==0.26.1
+RapidFuzz==3.10.1

tests.py

@@ -1,5 +1,32 @@
 # fichier de tests du projet
 
+import matplotlib.pyplot as plt
+
 # project libs importations
-import lib.ultra_mastermind_obj
+import lib.ultra_mastermind_obj as libobj
-import lib.ultra_mastermind_imp
+import lib.ultra_mastermind_imp as libimp
+
+# Variation du nombre de générations
+PM = "Hello, world!"
+# NG = 2000
+N  = 400
+TS = 0.5
+TM = 0.25
+ALPHA = 0.5
+FITNESS_METHOD = 1
+
+fitness_ng = []
+all_ng = []
+
+for i in range(1, 21):
+	NG = i * 100
+	all_ng.append(NG)
+	pop = libimp.new_population(PM, NG, N, TS, TM, ALPHA, FITNESS_METHOD)
+	libimp.run(pop)
+	fitness_ng.append(libimp.get_fitness(pop, libimp.get_best(pop)))
+
+plt.plot(all_ng, fitness_ng)
+plt.title("Fitness du meilleur individu en fonciton du nombre de générations")
+plt.xlabel("Nombre de générations")
+plt.ylabel("Fitness du meilleur individu")
+plt.show()