From f823574db19760b1582e68a5e263dc3b9d790f4c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Chlo=C3=A9=20Muller?= <chloe.muller@student-cs.fr>
Date: Wed, 29 Jan 2020 15:43:04 +0100
Subject: [PATCH] update

---
 .DS_Store                             | Bin 6148 -> 8196 bytes
 __pycache__/croisement.cpython-37.pyc | Bin 2413 -> 2706 bytes
 __pycache__/individu.cpython-37.pyc   | Bin 2770 -> 3987 bytes
 __pycache__/population.cpython-37.pyc | Bin 6118 -> 5908 bytes
 algogenetique.py                      |  47 +++++++++++++-------------
 individu.py                           |   2 +-
 6 files changed, 25 insertions(+), 24 deletions(-)

diff --git a/.DS_Store b/.DS_Store
index eb0f78382c04c8e58afb90d646790e2b7fb5c130..c6bf50dfd786e007f7389525a204a30892ff7c32 100644
GIT binary patch
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delta 135
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diff --git a/__pycache__/croisement.cpython-37.pyc b/__pycache__/croisement.cpython-37.pyc
index ffc251a785b743cc5adda1c078006796b1382246..32aa8054c9bf4394aa2cc4dd70c8392a65cdfb6c 100644
GIT binary patch
delta 1346
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delta 1044
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diff --git a/__pycache__/individu.cpython-37.pyc b/__pycache__/individu.cpython-37.pyc
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GIT binary patch
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diff --git a/__pycache__/population.cpython-37.pyc b/__pycache__/population.cpython-37.pyc
index 401ae0cbbc69372d8568b1bb8e951a92e401f59d..037163fdebd2a013e0b24fa10c0ea6f91cff2c10 100644
GIT binary patch
delta 1486
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diff --git a/algogenetique.py b/algogenetique.py
index 81f6b2e..7474d74 100644
--- a/algogenetique.py
+++ b/algogenetique.py
@@ -45,7 +45,7 @@ def main(N,tmax,pmutation, proportion, indice_selection, population_initiale, en
     for individu in People.indiv:
             if individu.score<mini:
                 mini=individu.score
-    L=[mini]
+    # L=[mini]
     for i in range(tmax):
         print(i)
         People.reproduction(p = proportion, proba_mutation= pmutation, selection = indice_selection, enfant = enfant)
@@ -56,13 +56,13 @@ def main(N,tmax,pmutation, proportion, indice_selection, population_initiale, en
                 best=individu
                 mini=individu.score
         
-        S2=[individu.score for individu in People.indiv]
-        avg = sum(S2)/len(S2)
-        L.append(mini)
+        # S2=[individu.score for individu in People.indiv]
+        # avg = sum(S2)/len(S2)
+        # L.append(mini)
 
     # plt.subplot(221)
-    liste_selections = ["selection_p_best", "selection_duel_pondere", "selection_duel", "selection_par_rang", "selection_proportionnelle"]
-    plt.plot([j for j in range(len(L))], L, label = liste_selections[indice_selection])
+    # liste_selections = ["selection_p_best", "selection_duel_pondere", "selection_duel", "selection_par_rang", "selection_proportionnelle"]
+    # plt.plot([j for j in range(len(L))], L, label = liste_selections[indice_selection])
     
 
     # plt.subplot(223)
@@ -105,25 +105,23 @@ def compare_mutation():
 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)
-    for individu in People.indiv:
-        individu.evaluate()
-    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()
+    # plt.figure()
+    People = Population(1000)
+    # 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))[0]
+        best = main(500, 5, 0.001, 500, i, deepcopy(People))[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")
-    print(numpy.array(liste_time))
-    plt.show()   
+    # 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"]
@@ -132,8 +130,11 @@ def comparaison_selections():
 
 
 # compare_mutation()
-
-comparaison_selections()
+liste = []
+for i in range(3):
+    liste.append(comparaison_selections())
+    print(liste)
+print(liste)
 
 # [['selection_p_best' '22.637820959091187' '116.30569654472626']
 #  ['selection_duel_pondere' '22.636890172958374' '46.6242321955727']
diff --git a/individu.py b/individu.py
index c68b98b..ec36561 100644
--- a/individu.py
+++ b/individu.py
@@ -23,7 +23,7 @@ class Individu():
         
         traj = Traj3D()
 
-        numb_ajout = 6
+        numb_ajout = 50
 
         fisrt_seq = self.brin[0:numb_ajout]
         last_seq = self.brin[-numb_ajout:]
-- 
GitLab