diff --git a/.vscode/settings.json b/.vscode/settings.json
index 395ba2d31e295f66eef5e2bd3db334975a462037..31e7ddfdf3472dbdd1b04dd753c498bc2995fd54 100644
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@@ -1,3 +1,3 @@
{
- "python.pythonPath": "D:\\Programmes\\Anaconda3\\python.exe"
+ "python.pythonPath": "/Users/gauthierroy/anaconda3/bin/python"
}
\ No newline at end of file
diff --git a/RotTable.py b/RotTable.py
index 912cbb001760b2ced0c934ca28b8f7aefec53628..9f841efe35b5e7de98443db1b6948ec1a040ef33 100644
--- a/RotTable.py
+++ b/RotTable.py
@@ -99,6 +99,7 @@ class RotTable:
###################
-table1 = RotTable()
+#table1 = RotTable()
+#print(table1.orta())
# 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__/RotTable.cpython-36.pyc b/__pycache__/RotTable.cpython-36.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a8064bb45dbaad46db289f9e730b7dd816423ae4
Binary files /dev/null and b/__pycache__/RotTable.cpython-36.pyc differ
diff --git a/__pycache__/Traj3D.cpython-36.pyc b/__pycache__/Traj3D.cpython-36.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..b5c1da4e7f16d56be5828446b2f8423d03027c1c
Binary files /dev/null and b/__pycache__/Traj3D.cpython-36.pyc differ
diff --git a/algogenetique.py b/algogenetique.py
index cbcde6366a75846200845f298aabc3b3588fda1c..8a1874e03d214a4c3f2f9804b275af26d35d8f5e 100644
--- a/algogenetique.py
+++ b/algogenetique.py
@@ -16,30 +16,23 @@ def main(N,tmax,pmutation, proportion,brin="plasmid_8k.fasta"):
brin = ''.join(lineList[1:])'''
L=[]
People=Population(N)
- # afficher(People)
for i in range(tmax):
- print("\n \n NOUVELLE GENERATION \n \n")
+ print(i)
max=0
best=None
- for individu in People.indiv:
- individu.evaluate("AAAGGATCTTCTTGAGATCCTTTTTTTCTGCGCGTAATCTGCTGCCAGTAAACGAAAAAACCGCCTGGGGAGGCGGTTTAGTCGAA")
People.reproduction(p = proportion, proba_mutation= pmutation)
- # for individu in People.indiv:
- # individu.mutation(pmutation)
for individu in People.indiv:
- individu.evaluate("AAAGGATCTTCTTGAGATCCTTTTTTTCTGCGCGTAATCTGCTGCCAGTAAACGAAAAAACCGCCTGGGGAGGCGGTTTAGTCGAA")
if individu.score>max:
best=individu
max=individu.score
- afficher(People)
L.append(max)
- #print(L)
+
plt.plot([i for i in range(tmax)], L)
plt.show()
return(best)
-main(10,50,0,5)
+main(100,100,0,50)
diff --git a/sample.png b/first_plot.png
similarity index 96%
rename from sample.png
rename to first_plot.png
index e091d954c547a80658df7854ba7835a2e39f1b11..0e536fb530ea40bc82a9395940755ad27a030609 100644
Binary files a/sample.png and b/first_plot.png differ
diff --git a/individu.py b/individu.py
index 3ef8ae652a28fe19037ae25fad68465ad44a22bb..24193810b0c780eb4bc09bdd034a82094d59f376 100644
--- a/individu.py
+++ b/individu.py
@@ -1,38 +1,45 @@
from RotTable import RotTable
from Traj3D import Traj3D
import numpy as np
-from math import sqrt
+from math import sqrt, inf
from random import random
P1 = 0.015
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 = []
- first_nucleotide = traj_array[0, 0:3]
- last_nucleotide = traj_array[-1, 0:3]
- distance = sqrt(sum((first_nucleotide - last_nucleotide) ** 2))
+ 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_name = brin[0]
- last_name = brin[-1]
+ 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)
- #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))
+ list_distance += [distance_first_nuc, distance_last_nuc]
- self.score = 1/distance
+
+ self.score = 1/max(list_distance)
+
+ return 1/max(list_distance)
def mutation(self, proba = P1):
@@ -57,4 +64,10 @@ class Individu():
# table = RotTable()
# test = Individu(table)
# test.evaluate("AAAGGATCTTCTTGAGATCCTTTTTTTCTGCGCGTAATCTGCTGCCAGTAAACGAAAAAACCGCCTGGGGAGGCGGTTTAGTCGAA")
-# print(test.score)
\ No newline at end of file
+# 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 dc51f8c876c286ad1822991da190f5dcf3804b40..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 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
@@ -45,27 +68,29 @@ 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]
- print("\n \n \nmeilleur", meilleur.table.rot_table, "\n \nscore", meilleur.score)
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:
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
@@ -134,19 +159,21 @@ class Population:
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")
@@ -165,8 +192,6 @@ def test():
#test()
-
-