Merge branch 'master' of...

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

algogenetique.py

@@ -11,25 +11,6 @@ import matplotlib.pyplot as plt
 import time
 from copy import deepcopy
 
-# def main(N,tmax,pmutation, proportion,brin="plasmid_8k.fasta"):
-#     '''lineList = [line.rstrip('\n') for line in open(brin)]
-# 	brin = ''.join(lineList[1:])'''
-#     L=[]
-#     People=Population(N)
-#     for i in range(tmax):
-#         print(i)
-#         max=0
-#         best=None
-#         People.reproduction(p = proportion, proba_mutation= pmutation)
-#         for individu in People.indiv:
-#             if individu.score>max:
-#                 best=individu
-#                 max=individu.score
-#         L.append(max)
-
-#     plt.plot([i for i in range(tmax)], L, label = str(pmutation))
-#     return(best)
-
 def main(N,tmax,pmutation, proportion, indice_selection, population_initiale, enfant = croisement_un_point):
 
     
@@ -68,8 +49,10 @@ def main(N,tmax,pmutation, proportion, indice_selection, population_initiale, en
     # 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)
+    S2=[individu.score for individu in People.indiv]
+    print("Score final: ",best.score)
+    print("Avg:", sum(S2)/len(S2))
+    print("Distance final: ",best.distance)
 
 
     # plt.subplot(224)
@@ -79,62 +62,12 @@ def main(N,tmax,pmutation, proportion, indice_selection, population_initiale, en
 
     return(best,People)
 
-# 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(brin, best.table)
-# test.draw("first_plot")
-
-
-
-def compare_mutation():
-    start_time = time.time()
-    plt.figure()
-    for i in range(1,5):
-        print("\n \n", i)
-        main(100,40,10**(-i),50)
-    plt.legend()
-    plt.xlabel("Nombre de générations")
-    plt.ylabel("Score du meilleur individu")
-    plt.title("Comparaison en fonction du taux de mutation")
-    print("Temps d'execution : %s secondes " % (time.time() - start_time))
-    plt.show()   
-
-
-def comparaison_selections():
-    liste_selections = ["selection_p_best", "selection_duel_pondere", "selection_duel", "selection_par_rang", "selection_proportionnelle"]
-    liste_time = []
-    # plt.figure()
-    People = Population(100)
-    # S2=[individu.score for individu in People.indiv]
-    # plt.hist(S2, range = (0,int(max(S2)+10)), bins = 20, color = 'blue')
-    # plt.show()
-    # plt.figure()
-    for i in range(5):
-        print("\n", liste_selections[i], "\n")
-        start_time = time.time()
-        best = main(100, 35, 0.001, 50, i, deepcopy(People), enfant = croisement_deux_points)[0]
-        liste_time.append((liste_selections[i], time.time() - start_time, best.score))
-    # plt.legend()
-    # plt.xlabel("Nombre de générations")
-    # plt.ylabel("Score du meilleur individu")
-    # plt.title("Comparaison en fonction de la méthode de sélection")
-    return numpy.array(liste_time)
-    # plt.show()   
-
-# def comparaisons_croisements():
-#     liste_croisements = ["croisement_un_point", "croisement_deux_points"]
-
-
-
-
-# compare_mutation()
-liste = []
-for i in range(5):
-    liste.append(comparaison_selections())
-    print(liste)
-print(liste)
+lineList = [line.rstrip('\n') for line in open("plasmid_8k.fasta")]
+brin = ''.join(lineList[1:])
+best,People = main(100,10,0.05,10)
+test = Traj3D()
+test.compute(brin, best.table)
+test.draw("first_plot")
 
 # [['selection_p_best' '22.637820959091187' '116.30569654472626']
 #  ['selection_duel_pondere' '22.636890172958374' '46.6242321955727']

individu.py

@@ -16,6 +16,7 @@ class Individu():
         self.brin = ''.join(lineList[1:])
         #self.brin = "AAAGGATCTTCTTGAGATCCTTTTTTTCTGCGCGTAATCTGCTGCCAGTAAACGAAAAAACCGCCTGGGGAGGCGGTTTAGTCGAA"
         self.score = None
+        self.distance = None
 
     def evaluate(self):
         ''' Evalue le score d'un individu sur un nombre numb_ajout de points'''
@@ -42,10 +43,10 @@ class Individu():
                 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)
-
+        self.distance = np.linalg.norm(traj_array[numb_ajout] - traj_array[-(numb_ajout+1)], ord=2)
         #return max(list_distance)