diff --git a/__pycache__/RotTable.cpython-36.pyc b/__pycache__/RotTable.cpython-36.pyc
index a8064bb45dbaad46db289f9e730b7dd816423ae4..459e4374c306aebaf525b830c17f00a8820843ca 100644
Binary files a/__pycache__/RotTable.cpython-36.pyc and b/__pycache__/RotTable.cpython-36.pyc differ
diff --git a/algogenetique.py b/algogenetique.py
index 7c49cd2af0e82ae43dddb7524e47373f1c611d4c..138d8a15f2efd56a7fa7bf34bc150bc638c53cbf 100644
--- a/algogenetique.py
+++ b/algogenetique.py
@@ -8,7 +8,7 @@ import croisement
from Traj3D import *
from random import random
import matplotlib.pyplot as plt
-import time
+import time
# def main(N,tmax,pmutation, proportion,brin="plasmid_8k.fasta"):
# '''lineList = [line.rstrip('\n') for line in open(brin)]
@@ -29,8 +29,8 @@ import time
# plt.plot([i for i in range(tmax)], L, label = str(pmutation))
# return(best)
-
def main(N,tmax,pmutation, proportion):
+
L=[]
lineList = [line.rstrip('\n') for line in open("plasmid_8k.fasta")]
brin = ''.join(lineList[1:])
@@ -41,7 +41,7 @@ def main(N,tmax,pmutation, proportion):
# 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)
diff --git a/individu.py b/individu.py
index 20958d3de778f360a6da39856777dffe233336bf..08744e8f0dc9a3ca276c4ff3b75c3ab6dd5cb4be 100644
--- a/individu.py
+++ b/individu.py
@@ -9,37 +9,41 @@ P1 = 0.015
class Individu():
def __init__(self, table):
+ lineList = [line.rstrip('\n') for line in open("plasmid_8k.fasta")]
+ brin = ''.join(lineList[1:])
self.table = table
- self.score = self.evaluate("AAAGGATCTTCTTGAGATCCTTTTTTTCTGCGCGTAATCTGCTGCCAGTAAACGAAAAAACCGCCTGGGGAGGCGGTTTAGTCGAA")
-
- def evaluate(self, brin):
+ lineList = [line.rstrip('\n') for line in open("plasmid_8k.fasta")]
+ self.brin = ''.join(lineList[1:])
+ #self.brin = "AAAGGATCTTCTTGAGATCCTTTTTTTCTGCGCGTAATCTGCTGCCAGTAAACGAAAAAACCGCCTGGGGAGGCGGTTTAGTCGAA"
+ self.score = self.evaluate()
+
+ def evaluate(self):
traj = Traj3D()
- numb_ajout = 3
+ numb_ajout = 6
- fisrt_seq = brin[0:numb_ajout]
- last_seq = brin[-numb_ajout:]
+ fisrt_seq = self.brin[0:numb_ajout]
+ last_seq = self.brin[-numb_ajout:]
- traj.compute(last_seq + brin + fisrt_seq, self.table)
+ traj.compute(last_seq + self.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]
+ begining = traj_array[0:2*numb_ajout, 0:3]
+ end = traj_array[-2*numb_ajout:, 0:3]
- 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)
+ for i in range(numb_ajout):
- list_distance += [distance_first_nuc, distance_last_nuc]
+ nuc_coordonate_beg = begining[i]
+ nuc_coordonate_end = end[i]
+ distance_nuc = np.linalg.norm(nuc_coordonate_beg - nuc_coordonate_end, ord=2)
+ list_distance += [distance_nuc]
self.score = max(list_distance)
- return max(list_distance)
+ #return max(list_distance)
def mutation(self, proba = P1):
@@ -56,15 +60,34 @@ class Individu():
#sur l'axe z il y a un moins
table_rotations[doublet2][coord] = - table_rotations[doublet][coord]
+ def mutation_with_numbers(self, proba = P1, number_of_mutations = 5):
+ table_rotations = self.table.rot_table
+ for i in range(0,number_of_mutations):
+ tir = random()
+ if tir < proba :
+ doubletNumber = randrange(0,8)
+ counter = 0
+ for doublet in table_rotations:
+ if counter==doubletNumber:
+ break
+ counter+=1
+ for coord in range(3):
+ 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])
+ 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)
# 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())
diff --git a/population.py b/population.py
index 104f66bb97b274d923f7dcf554ea4fe3599ef717..7d2507349ff1cdf2d946cdb03a6cdf7eca96da7a 100644
--- a/population.py
+++ b/population.py
@@ -15,7 +15,7 @@ class Population:
self.n = len(liste_individus)
self.indiv = liste_individus
for i in range(0,self.n):
- self.indiv[i].evaluate("AAAGGATCTTCTTGAGATCCTTTTTTTCTGCGCGTAATCTGCTGCCAGTAAACGAAAAAACCGCCTGGGGAGGCGGTTTAGTCGAA")
+ self.indiv[i].evaluate()
return self
@@ -68,7 +68,7 @@ class Population:
meilleur = self.indiv[0]
for individu in self.indiv :
if meilleur.score > individu.score:
- print("meilleur, individu: ", meilleur.score, individu.score)
+ #print("meilleur, individu: ", meilleur.score, individu.score)
meilleur = individu
newself = [meilleur]
vu=set()
@@ -83,7 +83,7 @@ class Population:
x=self.indiv[m]
y=self.indiv[t]
- if x.score<y.score:
+ if x.score<=y.score:
newself.append(x)
else:
newself.append(y)
@@ -170,7 +170,7 @@ class Population:
lineList = [line.rstrip('\n') for line in open("plasmid_8k.fasta")]
brin = ''.join(lineList[1:])
child.mutation(proba_mutation)
- child.evaluate(brin)
+ child.evaluate()
newself.append(couple_enfant[0])
newself.append(couple_enfant[1])
self = self.modifier_population(newself)
@@ -186,7 +186,7 @@ def test():
popu = Population(4)
print("\n POPULATION INITIALE \n")
for individu in popu.indiv :
- individu.evaluate("AAAGGATCTTCTTGAGATCCTTTTTTTCTGCGCGTAATCTGCTGCCAGTAAACGAAAAAACCGCCTGGGGAGGCGGTTTAGTCGAA")
+ individu.evaluate()
afficher(popu)
popu.reproduction(selection = popu.selection_duel)
print("\n REPRODUCTION \n")