diff --git a/.DS_Store b/.DS_Store
index ce584036d310ff852c50dff6063d64ba8881c0a3..eb0f78382c04c8e58afb90d646790e2b7fb5c130 100644
Binary files a/.DS_Store and b/.DS_Store differ
diff --git a/RotTable.py b/RotTable.py
index e3486c1b21452cfed789090694fdb3d372884c98..9f841efe35b5e7de98443db1b6948ec1a040ef33 100644
--- a/RotTable.py
+++ b/RotTable.py
@@ -21,9 +21,40 @@ class RotTable:
"TA": [36, 0.9, 0, 1.1, 2, 0],\
"TC": [36.9, 5.3, -120, 0.9, 6, 0],\
"TG": [34.5, 3.5, 64, 0.9, 34, 0],\
- "TT": [35.62, 7.2, -154, 0.06, 0.6, 0]\
+ "TT": [35.62, 7.2, 154, 0.06, 0.6, 0]\
}
+ __CORRESPONDANCE = {\
+ "AA": "TT",\
+ "AC": "GT",\
+ "AG": "CT",\
+ "AT": "AT",\
+ "CA": "TG",\
+ "CC": "GG",\
+ "CG": "CG",\
+ "CT": "AG",\
+ "GA": "TC",\
+ "GC": "GC",\
+ "GG": "CC",\
+ "GT": "AC",\
+ "TA": "TA",\
+ "TC": "GA",\
+ "TG": "CA",\
+ "TT": "AA"\
+ }
+
+ __SOUS_CORRESPONDANCE = {\
+ "AA": "TT",\
+ "AC": "GT",\
+ "AG": "CT",\
+ "AT": "AT",\
+ "CA": "TG",\
+ "CC": "GG",\
+ "CG": "CG",\
+ "GA": "TC",\
+ "GC": "GC",\
+ "TA": "TA"\
+ }
# get the angles in each axis (x, y, z), considering the deviation
def __init__(self):
self.rot_table = {}
@@ -34,13 +65,18 @@ class RotTable:
# get a random deviation, considering the "limits" given in the last 3 columns
# of __ORIGINAL_ROT_TABLE
def alea(self):
- for dinucleotide in RotTable.__ORIGINAL_ROT_TABLE:
+ for dinucleotide in RotTable.__SOUS_CORRESPONDANCE:
for i in range(2):
- self.rot_table[dinucleotide][i] += numpy.random.uniform(low = -RotTable.__ORIGINAL_ROT_TABLE[dinucleotide][i+3], high= RotTable.__ORIGINAL_ROT_TABLE[dinucleotide][i+3])
+ delta = numpy.random.uniform(low = -RotTable.__ORIGINAL_ROT_TABLE[dinucleotide][i+3], high= RotTable.__ORIGINAL_ROT_TABLE[dinucleotide][i+3])
+ self.rot_table[dinucleotide][i] += delta
+ self.rot_table[RotTable.__SOUS_CORRESPONDANCE[dinucleotide]][i] += delta
# return __ORIGINAL_ROT_TABLE
def orta(self):
return self.__ORIGINAL_ROT_TABLE
+
+ def corr(self):
+ return self.__CORRESPONDANCE
###################
# WRITING METHODS #
@@ -53,17 +89,17 @@ class RotTable:
###################
def getTwist(self, dinucleotide):
- return RotTable.__ORIGINAL_ROT_TABLE[dinucleotide][0]
+ return self.rot_table[dinucleotide][0]
def getWedge(self, dinucleotide):
- return RotTable.__ORIGINAL_ROT_TABLE[dinucleotide][1]
+ return self.rot_table[dinucleotide][1]
def getDirection(self, dinucleotide):
- return RotTable.__ORIGINAL_ROT_TABLE[dinucleotide][2]
+ return self.rot_table[dinucleotide][2]
###################
-# table1 = RotTable()
-# print(table1.orta())
+#table1 = RotTable()
+#print(table1.orta())
-# print(table1.rot_table["AA"])
+# print(table1.rot_table)
diff --git a/Traj3D.py b/Traj3D.py
index eee71819327b28c570771dcdca2b603043a014b9..92cc9720555ee8c29c954b2ea9a40cfc814917aa 100644
--- a/Traj3D.py
+++ b/Traj3D.py
@@ -65,3 +65,9 @@ class Traj3D:
ax.plot(x,y,z)
plt.show()
plt.savefig(filename)
+
+# from RotTable import RotTable
+# table = RotTable()
+# test = Traj3D()
+# test.compute("AAAGGATCTTCTTGAGATCCTTTTTTTCTGCGCGTAATCTGCTGCCAGTAAACGAAAAAACCGCCTGGGGAGGCGGTTTAGTCGAA", table)
+# test.draw("first_plot")
\ No newline at end of file
diff --git a/__pycache__/Initialisation.cpython-37.pyc b/__pycache__/Initialisation.cpython-37.pyc
deleted file mode 100644
index e986e03a10e3574402c013796cf8296d4a58eb32..0000000000000000000000000000000000000000
Binary files a/__pycache__/Initialisation.cpython-37.pyc and /dev/null differ
diff --git a/__pycache__/RotTable.cpython-36.pyc b/__pycache__/RotTable.cpython-36.pyc
index dbb2e4134f6503a6df95598a7c5505e659fc5c85..a8064bb45dbaad46db289f9e730b7dd816423ae4 100644
Binary files a/__pycache__/RotTable.cpython-36.pyc and b/__pycache__/RotTable.cpython-36.pyc differ
diff --git a/__pycache__/RotTable.cpython-37.pyc b/__pycache__/RotTable.cpython-37.pyc
index 306bef3707a8dfe8d997aa52208ffe660f6a4161..34a0caf41c6c790ba653935d986362949db3c6b6 100644
Binary files a/__pycache__/RotTable.cpython-37.pyc and b/__pycache__/RotTable.cpython-37.pyc differ
diff --git a/__pycache__/Traj3D.cpython-36.pyc b/__pycache__/Traj3D.cpython-36.pyc
index ecd80201694553fe02c71577fca8c84a37602d91..b5c1da4e7f16d56be5828446b2f8423d03027c1c 100644
Binary files a/__pycache__/Traj3D.cpython-36.pyc and b/__pycache__/Traj3D.cpython-36.pyc differ
diff --git a/__pycache__/Traj3D.cpython-37.pyc b/__pycache__/Traj3D.cpython-37.pyc
index 9089d353fd2661eb0e1c444fe75a6b707a6ecd10..868cf3c5285c75f887b4c574b83184755ea94f88 100644
Binary files a/__pycache__/Traj3D.cpython-37.pyc and b/__pycache__/Traj3D.cpython-37.pyc differ
diff --git a/__pycache__/croisement.cpython-37.pyc b/__pycache__/croisement.cpython-37.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..ecad689e2e6d0e89bac238cab0e024e5e204b30d
Binary files /dev/null and b/__pycache__/croisement.cpython-37.pyc differ
diff --git a/__pycache__/individu.cpython-37.pyc b/__pycache__/individu.cpython-37.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..37d35b2ce8c0d3c05331c63b2112883e7ee05733
Binary files /dev/null and b/__pycache__/individu.cpython-37.pyc differ
diff --git a/__pycache__/population.cpython-37.pyc b/__pycache__/population.cpython-37.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..0a6a330d28c49e773950856fc4d57e01af3ba167
Binary files /dev/null and b/__pycache__/population.cpython-37.pyc differ
diff --git a/algogenetique.py b/algogenetique.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a1874e03d214a4c3f2f9804b275af26d35d8f5e
--- /dev/null
+++ b/algogenetique.py
@@ -0,0 +1,38 @@
+import mathutils
+import math
+import numpy
+import RotTable
+from individu import Individu
+from population import Population, afficher
+import croisement
+from Traj3D import *
+from random import random
+import matplotlib.pyplot as plt
+
+
+
+def main(N,tmax,pmutation, proportion,brin="plasmid_8k.fasta"):
+ '''lineList = [line.rstrip('\n') for line in open(brin)]
+ brin = ''.join(lineList[1:])'''
+ L=[]
+ People=Population(N)
+ for i in range(tmax):
+ print(i)
+ max=0
+ best=None
+ People.reproduction(p = proportion, proba_mutation= pmutation)
+ for individu in People.indiv:
+ if individu.score>max:
+ best=individu
+ max=individu.score
+ L.append(max)
+
+ plt.plot([i for i in range(tmax)], L)
+ plt.show()
+ return(best)
+
+
+main(100,100,0,50)
+
+
+
diff --git a/croisement.py b/croisement.py
index 48a399f3e03e49d46867b53a1695e99657f13c67..96f85a8a89e1dcaa97cbed0f06233942ee36f211 100644
--- a/croisement.py
+++ b/croisement.py
@@ -26,30 +26,71 @@ def croisement_un_point(parent1, parent2):
enfant1 = Individu(RotTable())
enfant2 = Individu(RotTable())
comp = 0
- point_crois= numpy.random.random_integers(0,16)
+ point_crois= numpy.random.random_integers(0,8)
for doublet in ROT_TABLE:
+ if doublet == "GA":
+ break
if comp < point_crois:
enfant1.table.rot_table[doublet] = parent1.table.rot_table[doublet]
+ correspondent_doublet1 = enfant1.table.corr()[doublet]
+ enfant1.table.rot_table[correspondent_doublet1] = parent1.table.rot_table[correspondent_doublet1]
+ enfant1.table.rot_table[correspondent_doublet1][2] *= -1
+
enfant2.table.rot_table[doublet] = parent2.table.rot_table[doublet]
+ correspondent_doublet2 = enfant2.table.corr()[doublet]
+ enfant2.table.rot_table[correspondent_doublet2] = parent2.table.rot_table[correspondent_doublet2]
+ enfant2.table.rot_table[correspondent_doublet2][2] *= -1
+
else :
enfant1.table.rot_table[doublet] = parent2.table.rot_table[doublet]
+ correspondent_doublet1 = enfant1.table.corr()[doublet]
+ enfant1.table.rot_table[correspondent_doublet1] = parent2.table.rot_table[correspondent_doublet1]
+ enfant1.table.rot_table[correspondent_doublet1][2] *= -1
+
enfant2.table.rot_table[doublet] = parent1.table.rot_table[doublet]
+ correspondent_doublet2 = enfant2.table.corr()[doublet]
+ enfant2.table.rot_table[correspondent_doublet2] = parent1.table.rot_table[correspondent_doublet1]
+ enfant2.table.rot_table[correspondent_doublet2][2] *= -1
+
comp += 1
return enfant1, enfant2
def croisement_deux_points(parent1, parent2):
- enfant1 = RotTable()
- enfant2 = RotTable()
+ enfant1 = Individu(RotTable())
+ enfant2 = Individu(RotTable())
comp = 0
- point_crois1= numpy.random.random_integers(0,16)
- point_crois2= numpy.random.random_integers(0,16)
+ point_crois1= numpy.random.random_integers(0,8)
+ point_crois2= numpy.random.random_integers(0,8)
for doublet in ROT_TABLE:
if comp < min(point_crois1,point_crois2) or comp > max(point_crois1,point_crois2):
- enfant1.rot_table[doublet] = parent1.rot_table[doublet]
- enfant2.rot_table[doublet] = parent2.rot_table[doublet]
+ enfant1.table.rot_table[doublet] = parent1.table.rot_table[doublet]
+ correspondent_doublet1 = enfant1.table.corr()[doublet]
+ enfant1.table.rot_table[correspondent_doublet1] = parent1.table.rot_table[correspondent_doublet1]
+ enfant1.table.rot_table[correspondent_doublet1][2] *= -1
+
+ enfant2.table.rot_table[doublet] = parent2.table.rot_table[doublet]
+ correspondent_doublet2 = enfant2.table.corr()[doublet]
+ enfant2.table.rot_table[correspondent_doublet2] = parent2.table.rot_table[correspondent_doublet2]
+ enfant2.table.rot_table[correspondent_doublet2][2] *= -1
+
else :
- enfant1.rot_table[doublet] = parent2.rot_table[doublet]
- enfant2.rot_table[doublet] = parent1.rot_table[doublet]
+ enfant1.table.rot_table[doublet] = parent2.table.rot_table[doublet]
+ correspondent_doublet1 = enfant1.table.corr()[doublet]
+ enfant1.table.rot_table[correspondent_doublet1] = parent2.table.rot_table[correspondent_doublet1]
+ enfant1.table.rot_table[correspondent_doublet1][2] *= -1
+
+ enfant2.table.rot_table[doublet] = parent1.table.rot_table[doublet]
+ correspondent_doublet2 = enfant2.table.corr()[doublet]
+ enfant2.table.rot_table[correspondent_doublet2] = parent1.table.rot_table[correspondent_doublet1]
+ enfant2.table.rot_table[correspondent_doublet2][2] *= -1
comp += 1
- return enfant1, enfant2
\ No newline at end of file
+ return enfant1, enfant2
+
+# parent1 = Individu(RotTable())
+# parent2 = Individu(RotTable())
+# print("parent1: ", parent1.table.rot_table)
+# print("parent2: ", parent2.table.rot_table)
+# enfant1, enfant2 = croisement_un_point(parent1, parent2)
+# print("enfant1: ", enfant1.table.rot_table)
+# print("enfant2: ", enfant2.table.rot_table)
\ No newline at end of file
diff --git a/first_plot.png b/first_plot.png
new file mode 100644
index 0000000000000000000000000000000000000000..0e536fb530ea40bc82a9395940755ad27a030609
Binary files /dev/null and b/first_plot.png differ
diff --git a/individu.py b/individu.py
index 0bc68e4926298da3172bec0b670ea360435d13ca..24193810b0c780eb4bc09bdd034a82094d59f376 100644
--- a/individu.py
+++ b/individu.py
@@ -1,7 +1,7 @@
from RotTable import RotTable
-from Traj3D import *
+from Traj3D import Traj3D
import numpy as np
-from math import sqrt
+from math import sqrt, inf
from random import random
P1 = 0.015
@@ -10,27 +10,36 @@ class Individu():
def __init__(self, table):
self.table = table
- self.score = None
+ self.score = self.evaluate("AAAGGATCTTCTTGAGATCCTTTTTTTCTGCGCGTAATCTGCTGCCAGTAAACGAAAAAACCGCCTGGGGAGGCGGTTTAGTCGAA")
def evaluate(self, brin):
traj = Traj3D()
- traj.compute(brin, self.table)
+
+ numb_ajout = 3
+
+ fisrt_seq = brin[0:numb_ajout]
+ last_seq = brin[-numb_ajout:]
+
+ traj.compute(last_seq + brin + fisrt_seq, self.table)
traj_array = np.array(traj.getTraj())
+ list_distance = []
+
+ for i in range(numb_ajout):
+ first_nuc_coordonate = traj_array[numb_ajout+i, 0:3]
+ first_nuc_coordonate_compute = traj_array[-(numb_ajout-i), 0:3]
+
+ last_nuc_coordonate = traj_array[-(2*numb_ajout-i), 0:3]
+ last_nuc_coordonate_compute = traj_array[i, 0:3]
- first_nucleotide = traj_array[0, 0:3]
- last_nucleotide = traj_array[-1, 0:3]
- distance = sqrt(sum((first_nucleotide - last_nucleotide) ** 2))
+ distance_first_nuc = np.linalg.norm(first_nuc_coordonate - first_nuc_coordonate_compute, ord=2)
+ distance_last_nuc = np.linalg.norm(last_nuc_coordonate - last_nuc_coordonate_compute, ord=2)
- first_name = brin[0]
- last_name = brin[-1]
+ list_distance += [distance_first_nuc, distance_last_nuc]
- rot_computed = self.table.rot_table[last_name+first_name]
- rot_traj = first_nucleotide - last_nucleotide
- # print(rot_traj)
- # print(rot_computed)
- diff_angle = sum(abs(rot_computed - rot_traj))
- self.score = 1/(distance + diff_angle)
+ self.score = 1/max(list_distance)
+
+ return 1/max(list_distance)
def mutation(self, proba = P1):
@@ -39,10 +48,14 @@ class Individu():
for coord in range(3):
tir = random()
if tir < proba :
- # print("mutation", doublet, coord)
- # print("table", table_rotations[doublet][coord])
table_rotations[doublet][coord] =np.random.uniform(low = self.table.orta()[doublet][coord] - self.table.orta()[doublet][coord + 3], high = self.table.orta()[doublet][coord] + self.table.orta()[doublet][coord + 3])
- # print("table", table_rotations[doublet][coord])
+ doublet2 = self.table.corr()[doublet]
+ if coord == 0 or coord == 1 :
+ table_rotations[doublet2][coord] = table_rotations[doublet][coord]
+ else :
+ #sur l'axe z il y a un moins
+ table_rotations[doublet2][coord] = - table_rotations[doublet][coord]
+
# individu1 = Individu(RotTable())
# print(individu1.table.rot_table)
@@ -52,3 +65,9 @@ class Individu():
# test = Individu(table)
# test.evaluate("AAAGGATCTTCTTGAGATCCTTTTTTTCTGCGCGTAATCTGCTGCCAGTAAACGAAAAAACCGCCTGGGGAGGCGGTTTAGTCGAA")
# print(test.score)
+
+
+# qqun=Individu(RotTable())
+# qqun.table.rot_table={'AA': [35.576558502141, 7.433901511509349, -154], 'AC': [33.22048222654215, 5.25191751302917, 143], 'AG': [26.446029097301288, 6.052240462237622, -2], 'AT': [30.47045254036881, 1.333716025628036, 0], 'CA': [34.00734209585039, 33.70710613604862, -64], 'CC': [33.61019622767888, 3.713127032109607, -57], 'CG': [29.664061041382677, 6.725155507162601, 0], 'CT': [26.446029097301288, 6.052240462237622, 2], 'GA': [36.655773481637176, 10.45337581740701, 120], 'GC': [42.26984493493484, 3.5310453395352823, 180], 'GG': [33.61019622767888, 3.713127032109607, -57], 'GT': [33.22048222654215, 5.25191751302917, 143], 'TA': [36.951508786388914, -2.5174751178033303, 0], 'TC': [36.655773481637176, 10.45337581740701, -120], 'TG': [34.00734209585039, 33.70710613604862, -64], 'TT': [35.576558502141, 7.433901511509349, -154]}
+# qqun.evaluate("AAAGGATCTTCTTGAGATCCTTTTTTTCTGCGCGTAATCTGCTGCCAGTAAACGAAAAAACCGCCTGGGGAGGCGGTTTAGTCGAA")
+# print(qqun.score)
diff --git a/inutile.py b/inutile.py
index 1a2947e5d6d776eaab4c42387fcd36f075272bea..420a646fc6e25c17b5aec102de8321736ae1ebaf 100644
--- a/inutile.py
+++ b/inutile.py
@@ -31,7 +31,7 @@ class table_rotation(rotation):
self.dict = {doublet : rotation(doublet) for doublet in ORIGINAL_ROT_TABLE}
table1 = table_rotation()
-print(table1.dict["AA"].x)
+# print(table1.dict["AA"].x)
#table1.dict --> {'AA': <__main__.rotation object at 0x000001A722E1BAC8>, 'AC': <__main__.rotation object at 0x000001A722E1BB00>, 'AG': <__main__.rotation object at 0x000001A729A66A58>, 'AT': <__main__.rotation object at 0x000001A729A66A20>, 'CA': <__main__.rotation object at 0x000001A729A669E8>, 'CC': <__main__.rotation object at 0x000001A729A66A90>, 'CG': <__main__.rotation object at 0x000001A729A66B00>, 'CT': <__main__.rotation object at 0x000001A729A66B70>, 'GA': <__main__.rotation object at 0x000001A729B88D68>, 'GC': <__main__.rotation object at 0x000001A729B88DA0>, 'GG': <__main__.rotation object at 0x000001A729B88DD8>, 'GT': <__main__.rotation object at 0x000001A729B88E10>, 'TA': <__main__.rotation object at 0x000001A729B88E48>, 'TC': <__main__.rotation object at 0x000001A729B88E80>, 'TG': <__main__.rotation object at 0x000001A729B88EB8>, 'TT': <__main__.rotation object at 0x000001A729B88EF0>}
#table1.dict["AA"] ---> <__main__.rotation object at 0x000001A722E1BAC8> (qui est l'object rotation)
diff --git a/population.py b/population.py
index fdcb337769f967dce43c6738a3a2729364390cfe..828caac54389b4296f47305d41e41a990b3b09dd 100644
--- a/population.py
+++ b/population.py
@@ -1,8 +1,9 @@
-import random
-from random import random, randint, randrange
+
+from random import *
from individu import Individu
from RotTable import RotTable
-from croisement import *
+from croisement import croisement_un_point, croisement_deux_points
+import copy
class Population:
def __init__(self,n):
@@ -13,8 +14,30 @@ class Population:
"""Fonction qui renvoie une nouvelle instance de population a partir d'une liste d'individus"""
self.n = len(liste_individus)
self.indiv = liste_individus
+ for i in range(0,self.n):
+ self.indiv[i].evaluate("AAAGGATCTTCTTGAGATCCTTTTTTTCTGCGCGTAATCTGCTGCCAGTAAACGAAAAAACCGCCTGGGGAGGCGGTTTAGTCGAA")
+
return self
+ 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)
+ individus_selectionnes = [element for element in liste_individus[:p]]
+ self = self.modifier_population(individus_selectionnes)
+
+
def selection_duel_pondere(self,p=None):
if p == None :
p = (self.n)//2
@@ -31,7 +54,7 @@ class Population:
y=self.indiv[t]
vu.add(t)
vu.add(m)
- p=random(0,1)
+ p=random()
if p>x.score/(x.score+y.score):
newself.append(y)
else:
@@ -42,25 +65,32 @@ class Population:
def selection_duel(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:
+ print("meilleur, individu: ", meilleur.score, individu.score)
+ meilleur = individu
+ newself = [meilleur]
vu=set()
t=randrange(0,self.n)
- m=randrange(0,self.n)
+ m=randrange(0,self.n)
+ 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)
- if x.score<=y.score:
+ if x.score>=y.score:
newself.append(x)
else:
newself.append(y)
self = self.modifier_population(newself)
+
+
def selection_par_rang(self,p = None):
if p == None :
p = (self.n)//2
@@ -115,33 +145,40 @@ class Population:
while len(newself)<p:
m=m=randrange(0, self.n)
x=self.indiv[m]
- p=random(0,1)
+ p=random()
if p<=x.score/somme:
newself.append(x)
self = self.modifier_population(newself)
- def reproduction(self,selection=None,enfant=croisement_un_point, p = None):
+ def reproduction(self,proba_mutation = None, selection=None,enfant=croisement_un_point, p = None):
+ if proba_mutation == None :
+ proba_mutation = 0.001
if selection == None :
selection = self.selection_duel
if p == None :
p = (self.n)//2
vieille_taille = self.n
selection(p)
- newself = list(self.indiv)
+ newself = [element for element in self.indiv]
while len(newself)<vieille_taille:
m=randrange(0,self.n)
t=randrange(0,self.n)
- x=newself[m]
- y=newself[t]
+ x=copy.deepcopy(newself[m])
+ y=copy.deepcopy(newself[t])
couple_enfant = enfant(x,y)
+ for child in couple_enfant :
+ child.mutation(proba_mutation)
+ child.evaluate("AAAGGATCTTCTTGAGATCCTTTTTTTCTGCGCGTAATCTGCTGCCAGTAAACGAAAAAACCGCCTGGGGAGGCGGTTTAGTCGAA")
newself.append(couple_enfant[0])
newself.append(couple_enfant[1])
self = self.modifier_population(newself)
+
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)
@@ -149,16 +186,11 @@ def test():
for individu in popu.indiv :
individu.evaluate("AAAGGATCTTCTTGAGATCCTTTTTTTCTGCGCGTAATCTGCTGCCAGTAAACGAAAAAACCGCCTGGGGAGGCGGTTTAGTCGAA")
afficher(popu)
- # popu.selection_par_rang()
- # print("\n SELECTION PAR RANG \n")
- # afficher(popu)
- popu.reproduction()
+ popu.reproduction(selection = popu.selection_duel)
print("\n REPRODUCTION \n")
afficher(popu)
-test()
-
-
+#test()
diff --git a/selection_par_rang.py b/selection_par_rang.py
deleted file mode 100644
index ded5567fe7094249e4e3f1e0222031f0010a0164..0000000000000000000000000000000000000000
--- a/selection_par_rang.py
+++ /dev/null
@@ -1,45 +0,0 @@
-from random import random
-
-def creer_population(self, liste_individus):
- self.n = len(liste_individus)
- self.indiv = set(liste_individus)
- return self
-
-def selection_par_rang(self, p = n//2):
- set_individus = self.indiv
- n = self.n
-
- def partitionner(tableau,debut,fin):
- echanger(tableau,debut,random.randint(debut,fin-1))
- partition=debut
- for i in range(debut+1,fin):
- 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))
-
- liste = list(set_individus)
- tri_rapide(liste)
- individus_selectionnes = []
-
- for _ in range(p):
- curseur = random()*n*(n+1)/2
- j = 1
- while j*(j+1)/2 < curseur :
- j+=1
- #on doit prendre l'individu avec le jème score
- individus_selectionnes.append(liste[j])
- self = creer_population(self, liste_individus)
-
-
-
\ No newline at end of file