From a13196da847f0b7bf8106be24fef97a93d577621 Mon Sep 17 00:00:00 2001
From: Kappes Marques Rodrigo <rodrigo.kappes@student-cs.fr>
Date: Thu, 30 Jan 2020 09:19:58 +0100
Subject: [PATCH] cleaned the population
---
__pycache__/individu.cpython-37.pyc | Bin 3877 -> 3877 bytes
__pycache__/population.cpython-37.pyc | Bin 5882 -> 5102 bytes
population.py | 52 +++++++++++---------------
3 files changed, 21 insertions(+), 31 deletions(-)
diff --git a/__pycache__/individu.cpython-37.pyc b/__pycache__/individu.cpython-37.pyc
index b5e349ab4911f800f7576808eaf3ee7522919618..3ef8c96f9616f2b5975f79ca003e59c0324ae6ae 100644
GIT binary patch
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diff --git a/__pycache__/population.cpython-37.pyc b/__pycache__/population.cpython-37.pyc
index a2d00a0f413706a094887ce7f0dfe4f1e70ccd5f..f5ff9c148a75706fba3a62595b561aadc6eb2256 100644
GIT binary patch
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diff --git a/population.py b/population.py
index 618126d..fbb42ef 100644
--- a/population.py
+++ b/population.py
@@ -5,16 +5,20 @@ from croisement import croisement_un_point, croisement_deux_points
import copy
class Population:
+
+ #Class initialization
def __init__(self,n):
self.indiv=[Individu(RotTable()) for k in range (n)]
self.n = n
-
+
+ #Updates the current individuals in the population
def modifier_population(self, liste_individus):
"""Fonction qui renvoie une nouvelle instance de population a partir d'une liste d'individus"""
self.n = len(liste_individus)
self.indiv = liste_individus
return self
+ #Select a fixed number of individuals in a population
def selection_p_best(self,p=None):
if p==None:
p=(self.n)//2
@@ -23,7 +27,7 @@ class Population:
individus_selectionnes = [element for element in liste_individus[:p]]
self = self.modifier_population(individus_selectionnes)
-
+ #Select via comparing two random individuals with some randomness involved and compared
def selection_duel_pondere(self,p=None):
if p == None :
p = (self.n)//2
@@ -33,7 +37,8 @@ class Population:
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
+ 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:
m = choice(non_vu)
@@ -50,6 +55,7 @@ class Population:
self = self.modifier_population(newself)
+ #Selection via comparing the player`s score randomly
def selection_duel(self,p=None):
if p == None :
p = (self.n)//2
@@ -75,8 +81,7 @@ class Population:
newself.append(y)
self = self.modifier_population(newself)
-
-
+ #Selection using a random method to select players based on their rank in score
def selection_par_rang(self,p = None):
if p == None :
p = (self.n)//2
@@ -97,6 +102,7 @@ class Population:
self = self.modifier_population(individus_selectionnes)
+ #Selecting using a random method that takes the score into ponderation
def selection_proportionnelle(self,p= None):
if p == None :
p = (self.n)//2
@@ -116,8 +122,12 @@ class Population:
newself.append(x)
self = self.modifier_population(newself)
+ #The Function that makes the selection and reduces the quantity of players
+ #and after reproduces and mutates the remaining ones into a new population
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]
+
+ #Values of variables if there are no initial conditions
if proba_mutation == None :
proba_mutation = 0.001
if selection == None :
@@ -126,14 +136,19 @@ class Population:
selection = liste_selections[selection]
if p == None :
p = (self.n)//2
+
+
vieille_taille = self.n
selection(p)
newself = [element for element in self.indiv]
while len(newself)<vieille_taille:
m=randrange(0,self.n)
t=randrange(0,self.n)
+ #We have to make a deep copy so we dont lose the values of the parents
x=copy.deepcopy(newself[m])
y=copy.deepcopy(newself[t])
+
+ #Creation of the childs
couple_enfant = enfant(x,y)
for child in couple_enfant :
child.mutation_close_values(proba_mutation, number_of_mutations = 2)
@@ -142,40 +157,15 @@ class Population:
newself.append(couple_enfant[1])
self = self.modifier_population(newself)
-
+#Prints the current population(used for debugging)
def afficher(popu):
for individu in popu.indiv :
print("\n individu \n")
# print(individu.table.rot_table)
print ("score", individu.score)
-
-def test():
- popu = Population(4)
- print("\n POPULATION INITIALE \n")
- for individu in popu.indiv :
- individu.evaluate()
- afficher(popu)
- popu.reproduction(selection = popu.selection_duel)
- print("\n REPRODUCTION \n")
- afficher(popu)
-
-#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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