merge with carlos branch

algogenetique.py

@@ -8,14 +8,14 @@ import croisement
 from Traj3D import *
 from random import random
 import matplotlib.pyplot as plt
-import time 
+import time
 
 # Debut du decompte du temps
 start_time = time.time()
 
 
-
 def main(N,tmax,pmutation, proportion):
+
     L=[]
     lineList = [line.rstrip('\n') for line in open("plasmid_8k.fasta")]
     brin = ''.join(lineList[1:])
@@ -35,7 +35,6 @@ def main(N,tmax,pmutation, proportion):
                 best=individu
                 mini=individu.score
         L.append(mini)
-
     plt.subplot(221)
     plt.plot([i for i in range(tmax)], L)
     

individu.py

@@ -9,37 +9,38 @@ 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")
+        self.score = inf
     
     def evaluate(self, brin):
         traj = Traj3D()
 
-        numb_ajout = 3
+        numb_ajout = 6
 
         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]
+        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):
@@ -61,10 +62,10 @@ class Individu():
 # 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

@@ -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)