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Commit febc80c6 authored by Muller Sacha's avatar Muller Sacha
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Ajout de la fonction de comparaison selon la selection utilisee

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algogenetique.py
+ 49
− 9
View file @ febc80c6
......@@ -4,11 +4,12 @@ import numpy
import RotTable
from individu import Individu
from population import Population, afficher
import croisement
from croisement import *
from Traj3D import *
from random import random
import matplotlib.pyplot as plt
import time
from copy import deepcopy
# def main(N,tmax,pmutation, proportion,brin="plasmid_8k.fasta"):
# '''lineList = [line.rstrip('\n') for line in open(brin)]
......@@ -29,22 +30,22 @@ import time
# plt.plot([i for i in range(tmax)], L, label = str(pmutation))
# return(best)
def main(N,tmax,pmutation, proportion):
def main(N,tmax,pmutation, proportion, indice_selection, population_initiale, enfant = croisement_un_point):
L=[]
lineList = [line.rstrip('\n') for line in open("plasmid_8k.fasta")]
brin = ''.join(lineList[1:])
People=Population(N)
People=deepcopy(population_initiale)
# S1=[]
for individu in People.indiv:
individu.evaluate(brin)
individu.evaluate()
# S1.append(int(individu.score))
# maximum=int(max(S1))
for i in range(tmax):
#print(i)
print(i)
mini=People.indiv[0].score
best=People.indiv[0]
People.reproduction(p = proportion, proba_mutation= pmutation)
People.reproduction(p = proportion, proba_mutation= pmutation, selection = indice_selection, enfant = enfant)
for individu in People.indiv:
if individu.score<mini:
best=individu
......@@ -52,7 +53,8 @@ def main(N,tmax,pmutation, proportion):
L.append(mini)
# plt.subplot(221)
plt.plot([i for i in range(tmax)], L, label = str(pmutation))
liste_selections = ["selection_p_best", "selection_duel_pondere", "selection_duel", "selection_par_rang", "selection_proportionnelle"]
plt.plot([j for j in range(tmax)], L, label = liste_selections[indice_selection])
# plt.subplot(223)
......@@ -79,6 +81,7 @@ def main(N,tmax,pmutation, proportion):
def test_mutation():
start_time = time.time()
plt.figure()
for i in range(1,5):
print("\n \n", i)
......@@ -91,5 +94,42 @@ def test_mutation():
plt.show()
start_time = time.time()
test_mutation()
def comparaison_selections():
liste_selections = ["selection_p_best", "selection_duel_pondere", "selection_duel", "selection_par_rang", "selection_proportionnelle"]
liste_time = []
plt.figure()
People = Population(100)
for individu in People.indiv:
individu.evaluate()
S2=[individu.score for individu in People.indiv]
plt.hist(S2, range = (0,int(max(S2)+10)), bins = 20, color = 'blue')
plt.show()
plt.figure()
for i in range(5):
print("\n", liste_selections[i], "\n")
start_time = time.time()
best = main(100, 35, 0.001, 50, i, deepcopy(People))[0]
liste_time.append((liste_selections[i], time.time() - start_time, best.score))
plt.legend()
plt.xlabel("Nombre de générations")
plt.ylabel("Score du meilleur individu")
plt.title("Comparaison en fonction de la méthode de sélection")
print(numpy.array(liste_time))
plt.show()
# def comparaisons_croisements():
# liste_croisements = ["croisement_un_point", "croisement_deux_points"]
# test_mutation()
comparaison_selections()
# [['selection_p_best' '85.1275908946991']
# ['selection_duel_pondere' '85.47507500648499']
# ['selection_duel' '88.86001086235046']
# ['selection_par_rang' '87.76551222801208']
# ['selection_proportionnelle' '87.30216789245605']]
\ No newline at end of file
individu.py
+ 4
− 4
View file @ febc80c6
......@@ -84,10 +84,10 @@ class Individu():
# print(individu1.table.rot_table)
# individu1.mutation()
table = RotTable()
test = Individu(table)
test.evaluate("AAAGGATCTTCTTGAGATCCTTTTTTTCTGCGCGTAATCTGCTGCCAGTAAACGAAAAAACCGCCTGGGGAGGCGGTTTAGTCGAA")
print(test.score)
# table = RotTable()
# test = Individu(table)
# test.evaluate("AAAGGATCTTCTTGAGATCCTTTTTTTCTGCGCGTAATCTGCTGCCAGTAAACGAAAAAACCGCCTGGGGAGGCGGTTTAGTCGAA")
# print(test.score)
# qqun=Individu(RotTable())
......
population.py
+ 43
− 59
View file @ febc80c6
......@@ -22,18 +22,8 @@ class Population:
def selection_p_best(self,p=None):
if p==None:
p=(self.n)//2
def tri_rapide_aux(tableau,debut,fin):
if debut < fin-1:
positionPivot=partitionner(tableau,debut,fin)
tri_rapide_aux(tableau,debut,positionPivot)
tri_rapide_aux(tableau,positionPivot+1,fin)
def tri_rapide(tableau):
tri_rapide_aux(tableau,0,len(tableau))
liste_individus=self.indiv
tri_rapide(liste_individus)
liste_individus.sort(key = lambda individu : individu.score)
individus_selectionnes = [element for element in liste_individus[:p]]
self = self.modifier_population(individus_selectionnes)
......@@ -41,21 +31,23 @@ class Population:
def selection_duel_pondere(self,p=None):
if p == None :
p = (self.n)//2
newself=[]
vu=set()
meilleur = self.indiv[0]
for individu in self.indiv :
if meilleur.score > individu.score:
meilleur = individu
newself=[meilleur]
m=randrange(0,self.n)
t=randrange(0,self.n) #méthode des duels pondérée: si x=10 et y=1, y a une chance sur 11 de passer
non_vu = [i for i in range(0, self.n)]
while len(newself)<p:
while m in vu:
m=randrange(0,self.n)
while t in vu:
t=randrange(0,self.n)
m = choice(non_vu)
non_vu.remove(m)
t = choice(non_vu)
non_vu.remove(t)
x=self.indiv[m]
y=self.indiv[t]
vu.add(t)
vu.add(m)
p=random()
if p>x.score/(x.score+y.score):
proba=random()
if proba<x.score/(x.score+y.score):
newself.append(y)
else:
newself.append(x)
......@@ -68,10 +60,8 @@ class Population:
meilleur = self.indiv[0]
for individu in self.indiv :
if meilleur.score > individu.score:
#print("meilleur, individu: ", meilleur.score, individu.score)
meilleur = individu
newself = [meilleur]
vu=set()
newself = [meilleur]
t=randrange(0,self.n)
m=randrange(0,self.n)
non_vu = [i for i in range(0, self.n)]
......@@ -95,35 +85,11 @@ class Population:
if p == None :
p = (self.n)//2
liste_individus = self.indiv
n = self.n
def echanger(tableau, i, j):
tableau[i], tableau[j] = tableau[j], tableau[i]
def partitionner(tableau,debut,fin):
echanger(tableau,debut,randint(debut,fin-1))
partition=debut
for i in range(debut+1,fin):
# if tableau[i] < tableau[debut]:
if tableau[i].score<tableau[debut].score:
partition+=1
echanger(tableau,i,partition)
echanger(tableau,debut,partition)
return partition
def tri_rapide_aux(tableau,debut,fin):
if debut < fin-1:
positionPivot=partitionner(tableau,debut,fin)
tri_rapide_aux(tableau,debut,positionPivot)
tri_rapide_aux(tableau,positionPivot+1,fin)
def tri_rapide(tableau):
tri_rapide_aux(tableau,0,len(tableau))
tri_rapide(liste_individus)
individus_selectionnes = []
n = self.n
liste_individus.sort(key = lambda individu : individu.score, reverse = True)
individus_selectionnes = [liste_individus[-1]]
for _ in range(p):
for _ in range(p-1):
curseur = random()*n*(n+1)/2
# print("curseur", curseur)
j = 1
......@@ -135,26 +101,33 @@ class Population:
self = self.modifier_population(individus_selectionnes)
def selection_proportionelle(self,p= None):
def selection_proportionnelle(self,p= None):
if p == None :
p = (self.n)//2
newself=[]
meilleur = self.indiv[0]
for individu in self.indiv :
if meilleur.score > individu.score:
meilleur = individu
newself = [meilleur]
somme=0
for indiv in self.indiv:
somme=somme+indiv.score
somme+=1/indiv.score
while len(newself)<p:
m=m=randrange(0, self.n)
x=self.indiv[m]
p=random()
if p<=x.score/somme:
proba=random()
if proba<=x.score/somme:
newself.append(x)
self = self.modifier_population(newself)
def reproduction(self,proba_mutation = None, selection=None,enfant=croisement_un_point, p = None):
liste_selections = [self.selection_p_best, self.selection_duel_pondere, self.selection_duel, self.selection_par_rang, self.selection_proportionnelle]
if proba_mutation == None :
proba_mutation = 0.001
if selection == None :
selection = self.selection_duel
else :
selection = liste_selections[selection]
if p == None :
p = (self.n)//2
vieille_taille = self.n
......@@ -179,7 +152,7 @@ class Population:
def afficher(popu):
for individu in popu.indiv :
print("\n individu \n")
print(individu.table.rot_table)
# print(individu.table.rot_table)
print ("score", individu.score)
def test():
......@@ -194,10 +167,21 @@ def test():
#test()
def test2():
popu = Population(10)
for individu in popu.indiv :
lineList = [line.rstrip('\n') for line in open("plasmid_8k.fasta")]
brin = ''.join(lineList[1:])
individu.evaluate()
print("\n \n POPULATION INITIALE \n \n")
afficher(popu)
popu.selection_proportionnelle()
print("\n\nAPRES SELECTION \n\n")
afficher(popu)
# test2()
......
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