Saataa andagii !
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4 changed files with 87 additions and 37 deletions
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@ -21,8 +21,7 @@ def max_i(array: list[int]) -> int:
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max_i = i
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return max_i
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def new_population(pm, ng, n, ts, tm, alpha, fm): # -> set(list(set(str)), str, int, int, int, float, float, float, int)
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def new_population(pm, ng, n, ts, tm, alpha, fm):
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"""
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fonction qui renvoie une nouvelle population
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"""
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@ -43,7 +42,7 @@ def new_population(pm, ng, n, ts, tm, alpha, fm): # -> set(list(set(str)), str,
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return population
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def new_individual(): # -> set(str)
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def new_individual():
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"""
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fonction qui renvoie un nouvel individu
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"""
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@ -66,6 +65,7 @@ def fitness1(individual, pm) -> int:
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"""
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sum = 0
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for i in range(len(individual["chromozome"])):
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if i < len(pm) and i < len(individual["chromozome"]):
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sum += abs(ord(individual["chromozome"][i]) - ord(pm[i]))
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return -sum
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@ -76,7 +76,7 @@ def fitness2(individual, pm, alpha) -> int:
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match = 0
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missed_placed = 0
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for i in range(len(individual["chromozome"])):
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if i > len(pm):
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if i >= len(pm):
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missed_placed += len(individual["chromozome"]) - len(pm)
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break
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elif individual["chromozome"][i] == pm[i]:
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@ -104,13 +104,17 @@ def get_fitness(population, individual) -> int:
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case _:
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return fitness1(individual, population["pm"])
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def get_fitness_list(population):
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fitness_list = []
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for individual in population["individuals"]:
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fitness_list.append(get_fitness(population, individual))
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return fitness_list
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def get_best(population):
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"""
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Methode qui renvoie le meilleur individu de la population
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"""
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fitness_list = []
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for individual in population["individuals"]:
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fitness_list.append(get_fitness(population, individual))
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fitness_list = get_fitness_list(population)
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return population["individuals"][max_i(fitness_list)]
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def print_best(population) -> None:
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@ -119,49 +123,57 @@ def print_best(population) -> None:
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"""
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print(get_best(population)["chromozome"])
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def mutate(individual) -> None:
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"""
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Methode qui change un des caractères du chromozome
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"""
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new = list(individual["chromozome"])
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if random.randint(1, 2) == 1: new.insert(random.randint(0, len(new) - 1), chr(random.randint(0, 255)))
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else : new[random.randint(0, len(new) - 1)] = chr(random.randint(0, 255))
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individual["chromozome"] = "".join(new)
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def select(population) -> None:
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"""
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Methode qui sélectionne les meilleurs individus
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"""
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fitness_list = []
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for individual in population["individuals"]:
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fitness_list.append(get_fitness(population, individual))
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fitness_list = get_fitness_list(population)
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for i in range(int((1 - population["ts"]) * population["n"])):
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least = min_i(fitness_list)
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fitness_list.pop(least)
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population["individuals"].pop(least)
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def get_two_random_individuals(population):
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i = random.randint(0, len(population["individuals"]) - 1)
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j = random.randint(0, len(population["individuals"]) - 1)
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while i == j:
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j = random.randint(0, len(population["individuals"]) - 1)
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return (population["individuals"][i], population['individuals'][j])
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def reproduct(population) -> None:
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"""
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Methode qui reproduit les individus entre eux jusqu'à obtenir une population de taille N
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"""
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new = []
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while len(population["individuals"]) + len(new) != population["n"]:
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i = random.randint(0, len(population["individuals"]) - 1)
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j = random.randint(0, len(population["individuals"]) - 1)
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while i == j:
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j = random.randint(0, len(population["individuals"]) - 1)
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indivi_1 = population["individuals"][i]
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indivi_2 = population['individuals'][j]
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indivi_1, indivi_2 = get_two_random_individuals(population)
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avg = (len(indivi_1) + len(indivi_2)) // 2
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cut = random.randint(avg // 3, 2 * avg // 3)
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while cut > len(indivi_1) or cut > len(indivi_2): cut = random.randint(avg // 3, 2 * avg // 3)
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new_chromozome = indivi_1["chromozome"][:cut] + indivi_2["chromozome"][-cut:]
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while cut > len(indivi_1) or cut > len(indivi_2):
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cut = random.randint(avg // 3, 2 * avg // 3)
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new_chromozome = indivi_1["chromozome"][:cut] + indivi_2["chromozome"][cut:]
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child = new_individual()
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child["chromozome"] = new_chromozome
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new.append(child)
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population["individuals"] += new
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def mutate(individual) -> None:
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"""
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Methode qui change un des caractères du chromozome
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"""
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new = list(individual["chromozome"])
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dice = random.randint(1,3)
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if dice == 1 and len(new) < 30:
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new.insert(random.randint(0, len(new) - 1), chr(random.randint(0, 255)))
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elif dice == 2 and len(new) > 4:
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new.pop(random.randint(0, len(new) - 1))
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else :
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new[random.randint(0, len(new) - 1)] = chr(random.randint(0, 255))
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individual["chromozome"] = "".join(new)
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def mutate_pop(population) -> None:
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"""
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Methode qui mute une partie de la population selon le taut de mutation
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2
main.py
2
main.py
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@ -8,7 +8,7 @@ import lib.ultra_mastermind_pp_imp as libppimp
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# constants
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PM = ""
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NG = 500
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N = 200
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N = 300
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TS = 0.5
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TM = 0.25
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ALPHA = 0.5
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@ -1,2 +1,13 @@
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contourpy==1.3.1
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cycler==0.12.1
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fonttools==4.55.0
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kiwisolver==1.4.7
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Levenshtein==0.26.1
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matplotlib==3.9.2
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numpy==2.1.3
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packaging==24.2
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pillow==11.0.0
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pyparsing==3.2.0
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python-dateutil==2.9.0.post0
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RapidFuzz==3.10.1
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six==1.16.0
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41
tests.py
41
tests.py
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@ -5,6 +5,7 @@ import matplotlib.pyplot as plt
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# project libs importations
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import lib.ultra_mastermind_obj as libobj
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import lib.ultra_mastermind_imp as libimp
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import lib.ultra_mastermind_pp_imp as libppimp
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# Variation du nombre de générations
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PM = "Hello, world!"
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@ -13,20 +14,46 @@ N = 400
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TS = 0.5
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TM = 0.25
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ALPHA = 0.5
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FITNESS_METHOD = 1
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FITNESS_METHOD = 3
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fitness_ng = []
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all_ng = []
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for i in range(1, 21):
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NG = i * 100
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for i in range(1, 11):
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NG = i * 200
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all_ng.append(NG)
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pop = libimp.new_population(PM, NG, N, TS, TM, ALPHA, FITNESS_METHOD)
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libimp.run(pop)
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fitness_ng.append(libimp.get_fitness(pop, libimp.get_best(pop)))
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pop = libppimp.new_population(PM, NG, N, TS, TM, ALPHA, FITNESS_METHOD)
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libppimp.run(pop)
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fitness_ng.append(libppimp.get_fitness(pop, libppimp.get_best(pop)))
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plt.plot(all_ng, fitness_ng)
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plt.title("Fitness du meilleur individu en fonciton du nombre de générations")
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plt.title("Fitness du meilleur individu en fonction du nombre de générations")
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plt.xlabel("Nombre de générations")
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plt.ylabel("Fitness du meilleur individu")
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plt.show()
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# Variation du nombre de générations
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PM = "Hello, world!"
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NG = 500
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# N = 400
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TS = 0.5
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TM = 0.25
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ALPHA = 0.5
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FITNESS_METHOD = 3
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fitness_n = []
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all_n = []
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for i in range(1, 11):
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N = i * 100
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all_n.append(N)
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pop = libppimp.new_population(PM, NG, N, TS, TM, ALPHA, FITNESS_METHOD)
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libppimp.run(pop)
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fitness_n.append(libppimp.get_fitness(pop, libppimp.get_best(pop)))
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plt.plot(all_n, fitness_n)
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plt.title("Fitness du meilleur individu en fonction de la taille de population")
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plt.xlabel("Taille de population")
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plt.ylabel("Fitness du meilleur individu")
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plt.show()
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