minimiser distance, histogrammes et brin affiché

algogenetique.py

@@ -8,32 +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"):
-    '''lineList = [line.rstrip('\n') for line in open(brin)]
-	brin = ''.join(lineList[1:])'''
+
+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(brin)
+        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(max)
+                mini=individu.score
+        L.append(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)
 
-best,People = main(100,100,0.01,50)
+lineList = [line.rstrip('\n') for line in open("plasmid_8k.fasta")]
+brin = ''.join(lineList[1:])
+best,People = main(10,10,0.01,5)
 test = Traj3D()
-test.compute("AAAGGATCTTCTTGAGATCCTTTTTTTCTGCGCGTAATCTGCTGCCAGTAAACGAAAAAACCGCCTGGGGAGGCGGTTTAGTCGAA", best.table)
+test.compute(brin, best.table)
 test.draw("first_plot")
 
+
+# Affichage du temps d execution
+print("Temps d'execution : %s secondes " % (time.time() - start_time))

individu.py

@@ -37,9 +37,9 @@ class Individu():
                 list_distance += [distance_first_nuc, distance_last_nuc]
 
 
-        self.score = 1/max(list_distance)
+        self.score = max(list_distance)
 
-        return 1/max(list_distance)
+        return max(list_distance)
 
 
     def mutation(self, proba = P1):

population.py

@@ -67,7 +67,7 @@ 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]
@@ -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)
@@ -167,8 +167,10 @@ 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("AAAGGATCTTCTTGAGATCCTTTTTTTCTGCGCGTAATCTGCTGCCAGTAAACGAAAAAACCGCCTGGGGAGGCGGTTTAGTCGAA")
+                child.evaluate(brin)
             newself.append(couple_enfant[0])
             newself.append(couple_enfant[1])
         self = self.modifier_population(newself)