lucien/ultra-mastermind-implementation
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Saataa andagii !

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Lukian 2025-01-07 23:05:40 +01:00
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commit 757ea77cc5
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4
README.md
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@ -5,6 +5,7 @@
### Linux
- `git clone https://git.leizour.fr/lucien/ultra-mastermind-implementation`
- `cd ultra-mastermind-implementation`
- `python -m venv .venv`
- `source .venv/bin/activate`
- `pip install -r requirements.txt`
@ -20,8 +21,9 @@
#### Second method
- `git clone https://git.leizour.fr/lucien/ultra-mastermind-implementation`
- `cd ultra-mastermind-implementation`
- `python -m venv .venv`
- `./.venv/bin/activate`
- `.venv/bin/activate`
- `pip install -r requirements.txt`
- `python main.py`

103
analyse.py Normal file
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@ -0,0 +1,103 @@
# fichier de tests du projet
import matplotlib.pyplot as plt
import random
# 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
PM = "Hello, world!"
NG = 4000
N = 400
TS = 0.7
TM = 0.25
ALPHA = 0.5
FITNESS_METHOD = 3
# Variation de la taille de la phrase
length_ng = []
length = []
for i in range(5, 26, 3):
print(f"Step {i}:")
length.append(i)
vals = []
for j in range(5):
print(f" Part {j}")
PM = "".join([chr(random.randint(0, 255)) for _ in range(i)])
pop = libppimp.new_population(PM, NG, N, TS, TM, ALPHA, FITNESS_METHOD)
ng = libppimp.run(pop)
vals.append(ng)
length_ng.append(sum(vals) / len(vals))
plt.plot(length, length_ng)
plt.title("Nombre de générations nécéssaires en fonction de la taille de la phrase.")
plt.xlabel("Taille de la phrase")
plt.ylabel("Nombre de générations")
plt.show()
# Variation de la taille de la population
n_ng = []
n = []
for i in range(50, 1000, 100):
print(f"Step {i}:")
n.append(i)
vals = []
for j in range(5):
print(f" Part {j}")
pop = libppimp.new_population(PM, NG, i, TS, TM, ALPHA, FITNESS_METHOD)
ng = libppimp.run(pop)
vals.append(ng)
n_ng.append(sum(vals) / len(vals))
plt.plot(n, n_ng)
plt.title("Nombre de générations nécéssaires en fonction de la taille de la population.")
plt.xlabel("Taille de la population")
plt.ylabel("Nombre de générations")
plt.show()
# Variation du taut de sélection
ts_ng = []
ts = []
for i in range(1, 9, 1):
print(f"Step {i / 10}:")
ts.append(i / 10)
vals = []
for j in range(5):
print(f" Part {j}")
pop = libppimp.new_population(PM, NG, N, i / 10, TM, ALPHA, FITNESS_METHOD)
ng = libppimp.run(pop)
vals.append(ng)
ts_ng.append(sum(vals) / len(vals))
plt.plot(ts, ts_ng)
plt.title("Nombre de générations nécéssaires en fonction du taut de sélection.")
plt.xlabel("Taut de sélection")
plt.ylabel("Nombre de générations")
plt.show()
# Variation du taut de mutation
tm_ng = []
tm = []
for i in range(10, 40, 5):
print(f"Step {i / 100}:")
tm.append(i / 100)
vals = []
for j in range(5):
print(f" Part {j}")
pop = libppimp.new_population(PM, NG, N, TS, i / 100, ALPHA, FITNESS_METHOD)
ng = libppimp.run(pop)
vals.append(ng)
tm_ng.append(sum(vals) / len(vals))
plt.plot(tm, tm_ng)
plt.title("Nombre de générations nécéssaires en fonction du taut de mutation.")
plt.xlabel("Taut de mutation")
plt.ylabel("Nombre de générations")
plt.show()

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8
lib/ultra_mastermind_pp_imp.py
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@ -165,7 +165,7 @@ def mutate(individual) -> None:
Methode qui change un des caractères du chromozome
"""
new = list(individual["chromozome"])
dice = random.randint(1,3)
dice = random.randint(1, 6)
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:
@ -186,12 +186,14 @@ def mutate_pop(population) -> None:
mutate(population["individuals"][to_mutate])
mutated.append(to_mutate)
def run(population) -> None:
def run(population) -> int:
"""
Boucle principale
"""
for i in range(population["ng"]):
if get_best(population)["chromozome"] == population["pm"]:
return i
select(population)
reproduct(population)
mutate_pop(population)
print_best(population)
return population["ng"]

1
main.py
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@ -31,6 +31,7 @@ def main() -> None:
# imperative version ++
pop = libppimp.new_population(PM, NG, N, TS, TM, ALPHA, FITNESS_METHOD)
libppimp.run(pop)
print(libppimp.get_best(pop)["chromozome"])
if __name__ == "__main__":
main()

59
tests.py
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@ -1,59 +0,0 @@
# 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()