Merge branch 'master' of...

Merge branch 'master' of https://gitlab-research.centralesupelec.fr/2019santosgc/ei-jeux-evolutionnaires

algogenetique.py

@@ -8,30 +8,59 @@ import croisement
 from Traj3D import *
 from random import random
 import matplotlib.pyplot as plt
+import time
 
+# Debut du decompte du temps
+start_time = time.time()
 
 
-def main(N,tmax,pmutation, proportion,brin="plasmid_8k.fasta"):
+def main(N,tmax,pmutation, proportion):
+    
     L=[]
+    lineList = [line.rstrip('\n') for line in open("plasmid_8k.fasta")]
+    brin = ''.join(lineList[1:])
     People=Population(N)
+    S1=[]
+    for individu in People.indiv:
+        individu.evaluate()
+        S1.append(int(individu.score))
+    maximum=int(max(S1))
     for i in range(tmax):
-        #print(i)
-        max=0
-        best=None
+        mini=People.indiv[0].score
+        best=People.indiv[0]
         People.reproduction(p = proportion, proba_mutation= pmutation)
         for individu in People.indiv:
-            if individu.score>max:
+            if individu.score<mini:
                 best=individu
-                max=individu.score
-        L.append(1/max)
-        print(i,":",1/max)
+                mini=individu.score
+        L.append(mini)
+        print(i,":",mini)
 
+    plt.subplot(221)
     plt.plot([i for i in range(tmax)], L)
+    
+
+    plt.subplot(223)
+    plt.hist(S1, range = (0, maximum+10), bins = 20, color = 'red')
+
+    S2=[individu.score for individu in People.indiv]
+    print("Score final: ",best.score)
+
+
+    plt.subplot(224)
+    plt.hist(S2, range = (0,maximum+10), bins = 20, color = 'blue')
     plt.show()
-    return(best, People)
+   
+
+    return(best,People)
+
+lineList = [line.rstrip('\n') for line in open("plasmid_8k.fasta")]
+brin = ''.join(lineList[1:])
+best,People = main(200,10,0.01,100)
+test = Traj3D()
+test.compute(brin, best.table)
+test.draw("first_plot")
 
 
-best,People = main(60,100,0.01,25)
-traj = Traj3D()
-traj.compute(best.brin,best.table)
-traj.draw("plot")
+# Affichage du temps d execution
+print("Temps d'execution : %s secondes " % (time.time() - start_time))

individu.py

@@ -9,40 +9,40 @@ 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
         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 = self.brin[0:numb_ajout]
         last_seq = self.brin[-numb_ajout:]
 
         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 = 1/max(list_distance)
+        self.score = max(list_distance)
 
-        return 1/max(list_distance)
+        #return max(list_distance)
 
 
     def mutation_with_numbers(self, proba = P1):
@@ -79,14 +79,32 @@ class Individu():
                     else :
                         #sur l'axe z il y a un moins
                         table_rotations[doublet2][coord] = - table_rotations[doublet][coord]
+        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())

population.py

-
 from random import *
 from individu import Individu
 from RotTable import RotTable
@@ -14,8 +13,6 @@ 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()
         return self
 
     def selection_p_best(self,p=None):
@@ -66,7 +63,7 @@ class Population:
             p = (self.n)//2
         meilleur = self.indiv[0]
         for individu in self.indiv :
-            if meilleur.score < individu.score:
+            if meilleur.score > individu.score:
                 #print("meilleur, individu: ", meilleur.score, individu.score)
                 meilleur = individu
         newself = [meilleur]
@@ -82,7 +79,7 @@ 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)
@@ -166,6 +163,8 @@ class Population:
             y=copy.deepcopy(newself[t])
             couple_enfant = enfant(x,y)
             for child in couple_enfant :
+                lineList = [line.rstrip('\n') for line in open("plasmid_8k.fasta")]
+                brin = ''.join(lineList[1:])
                 child.mutation(proba_mutation)
                 child.evaluate()
             newself.append(couple_enfant[0])
@@ -204,5 +203,6 @@ def test():
 
 
 
+