ultra-mastermind-implementa.../tests.py
2024-12-17 10:55:50 +01:00

59 lines
1.3 KiB
Python

# fichier de tests du projet
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!"
# NG = 2000
N = 400
TS = 0.5
TM = 0.25
ALPHA = 0.5
FITNESS_METHOD = 3
fitness_ng = []
all_ng = []
for i in range(1, 11):
NG = i * 200
all_ng.append(NG)
pop = libppimp.new_population(PM, NG, N, TS, TM, ALPHA, FITNESS_METHOD)
libppimp.run(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 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()