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corrigés du mastermind hors publication, liens vers les fichiers utiles

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admin
2026-09-11 10:41:55 +02:00
parent df87229b88
commit 348877eb6c
9 changed files with 108 additions and 108 deletions
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Projets/src/algo_gen.py → Projets/src/_algo_gen.py
+1 -1
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@@ -1,4 +1,4 @@
from problem_sup import *
from _problem_sup import *
BEST_KEPT = 5
Projets/src/combinaison.py → Projets/src/_combinaison.py
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Projets/src/problem_1.py → Projets/src/_problem_1.py
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Projets/src/problem_2.py → Projets/src/_problem_2.py
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Projets/src/problem_4.py → Projets/src/_problem_4.py
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Projets/src/problem_sup.py → Projets/src/_problem_sup.py
+84 -84
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@@ -1,84 +1,84 @@
from problem_interface import *
from combinaison import *
class Problem_5(Problem_Interface) :
"""
classe probleme n°5 : Combinaison Coffre-Fort
"""
def __init__(self, calcul):
self.__size = len(calcul)
self.__combinaison = calcul
def create_individual(self):
'''
create a randomly generated indidivual for this problem
:return: a randomly generated individual gfor this problem
:rtype: an Individual object
'''
return Combinaison(self.__size)
def evaluate_fitness(self, individual):
'''
compute the fitness of individual for this problem
:param individual: the individual to consider
:type individual: an Individual object
:return: the fitness of individual for this problem
:rtype: an Individual object
'''
return sum([1 if calcul[chiffre]==self.__combinaison[chiffre] else 0 for chiffre in range(len(calcul))])
def sort_population(self, population):
'''
sort population from best fitted to worst fitted individuals.
Depending on the problem, it can correspond to ascending or descending order with respect to the fitness function.
side effect : population is modified by this method
:param population: the list of individuals to sort
:type population: list(Individual)
'''
population.sort(key = lambda x: x.get_score())
def best_individual(self, population):
'''
return the best fitted individual from population.
Depending on the problem, it can correspond to the individual with the highest or the lowest fitness value.
:param population: the list of individuals to sort
:type population: list(Individual)
:return: the best fitted individual of population
:rtype: an Individual object
'''
self.sort_population(population)
return population[len(population) -1 ]
def tournament(self, first, second):
'''
perform a rounament between two individuals, the winner is the most fitted one, it is returned
:param first: an individual
:type first: an Individual object
:param second: an individual
:type second: an Individual object
:return: the winner of the tournament
:rtype: Individual object
'''
if first.get_score() < second.get_score():
res = first
else:
res = second
return res
from problem_interface import *
from _combinaison import *
class Problem_5(Problem_Interface) :
"""
classe probleme n°5 : Combinaison Coffre-Fort
"""
def __init__(self, calcul):
self.__size = len(calcul)
self.__combinaison = calcul
def create_individual(self):
'''
create a randomly generated indidivual for this problem
:return: a randomly generated individual gfor this problem
:rtype: an Individual object
'''
return Combinaison(self.__size)
def evaluate_fitness(self, individual):
'''
compute the fitness of individual for this problem
:param individual: the individual to consider
:type individual: an Individual object
:return: the fitness of individual for this problem
:rtype: an Individual object
'''
return sum([1 if calcul[chiffre]==self.__combinaison[chiffre] else 0 for chiffre in range(len(calcul))])
def sort_population(self, population):
'''
sort population from best fitted to worst fitted individuals.
Depending on the problem, it can correspond to ascending or descending order with respect to the fitness function.
side effect : population is modified by this method
:param population: the list of individuals to sort
:type population: list(Individual)
'''
population.sort(key = lambda x: x.get_score())
def best_individual(self, population):
'''
return the best fitted individual from population.
Depending on the problem, it can correspond to the individual with the highest or the lowest fitness value.
:param population: the list of individuals to sort
:type population: list(Individual)
:return: the best fitted individual of population
:rtype: an Individual object
'''
self.sort_population(population)
return population[len(population) -1 ]
def tournament(self, first, second):
'''
perform a rounament between two individuals, the winner is the most fitted one, it is returned
:param first: an individual
:type first: an Individual object
:param second: an individual
:type second: an Individual object
:return: the winner of the tournament
:rtype: Individual object
'''
if first.get_score() < second.get_score():
res = first
else:
res = second
return res
Projets/src/test.py
+15 -15
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@@ -1,16 +1,16 @@
from algo_gen import *
from problem_4 import *
if __name__=="__main__":
p = Problem_4([
['Lille', 15, 15],
['Tourcoing', 10, 20],
['Paris', 5, 20],
['Alsace', 5, 30],
['Londre', 30, 15],
['Strasburg', 30, 30],
['Moskou', 5, 5],
['San Francisco', 10, 15]
])
a = AlgoGen(p, 1000, 0, 0.1)
from _algo_gen import *
from _problem_4 import *
if __name__=="__main__":
p = Problem_4([
['Lille', 15, 15],
['Tourcoing', 10, 20],
['Paris', 5, 20],
['Alsace', 5, 30],
['Londre', 30, 15],
['Strasburg', 30, 30],
['Moskou', 5, 5],
['San Francisco', 10, 15]
])
a = AlgoGen(p, 1000, 0, 0.1)
a.solve(20)