Merge branch 'master' of...

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

.vscode/settings.json

 {
-    "python.pythonPath": "D:\\Programmes\\Anaconda3\\python.exe"
+    "python.pythonPath": "/Users/gauthierroy/anaconda3/bin/python"
 }
\ No newline at end of file

RotTable.py

@@ -99,6 +99,7 @@ class RotTable:
 
     ###################
 
-table1 = RotTable()
+#table1 = RotTable()
+#print(table1.orta())
 
 # print(table1.rot_table)

Traj3D.py

@@ -65,3 +65,9 @@ class Traj3D:
         ax.plot(x,y,z)
         plt.show()
         plt.savefig(filename)
+
+# from RotTable import RotTable
+# table = RotTable()
+# test = Traj3D()
+# test.compute("AAAGGATCTTCTTGAGATCCTTTTTTTCTGCGCGTAATCTGCTGCCAGTAAACGAAAAAACCGCCTGGGGAGGCGGTTTAGTCGAA", table)
+# test.draw("first_plot")
\ No newline at end of file

algogenetique.py

@@ -16,30 +16,23 @@ def main(N,tmax,pmutation, proportion,brin="plasmid_8k.fasta"):
 	brin = ''.join(lineList[1:])'''
     L=[]
     People=Population(N)
-    # afficher(People)
     for i in range(tmax):
-        print("\n \n NOUVELLE GENERATION \n \n")
+        print(i)
         max=0
         best=None
-        for individu in People.indiv:
-            individu.evaluate("AAAGGATCTTCTTGAGATCCTTTTTTTCTGCGCGTAATCTGCTGCCAGTAAACGAAAAAACCGCCTGGGGAGGCGGTTTAGTCGAA")
         People.reproduction(p = proportion, proba_mutation= pmutation)
-        # for individu in People.indiv:
-        #     individu.mutation(pmutation)
         for individu in People.indiv:
-            individu.evaluate("AAAGGATCTTCTTGAGATCCTTTTTTTCTGCGCGTAATCTGCTGCCAGTAAACGAAAAAACCGCCTGGGGAGGCGGTTTAGTCGAA")
             if individu.score>max:
                 best=individu
                 max=individu.score
-        # afficher(People)
         L.append(max)
-        #print(L)
+
     plt.plot([i for i in range(tmax)], L)
     plt.show()
     return(best)
 
 
-main(6,8,0.01,2)
+main(100,100,0.1,50)
 
 
 

individu.py

 from RotTable import RotTable
 from Traj3D import Traj3D
 import numpy as np
-from math import sqrt
+from math import sqrt, inf
 from random import random
 
 P1 = 0.015
@@ -10,27 +10,36 @@ class Individu():
 
     def __init__(self, table):
         self.table = table
-        self.score = None
+        self.score = self.evaluate("AAAGGATCTTCTTGAGATCCTTTTTTTCTGCGCGTAATCTGCTGCCAGTAAACGAAAAAACCGCCTGGGGAGGCGGTTTAGTCGAA")
     
     def evaluate(self, brin):
         traj = Traj3D()
-        traj.compute(brin, self.table)
+
+        numb_ajout = 3
+
+        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]
+
+                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)
 
-        first_nucleotide = traj_array[0, 0:3]
-        last_nucleotide = traj_array[-1, 0:3]
-        distance = sqrt(sum((first_nucleotide - last_nucleotide) ** 2))
+                list_distance += [distance_first_nuc, distance_last_nuc]
 
-        first_name = brin[0]
-        last_name = brin[-1]
 
-        #rot_computed = self.table.rot_table[last_name+first_name]
-        #rot_traj = first_nucleotide - last_nucleotide
-        # print(rot_traj)
-        # print(rot_computed)
-        #diff_angle = sum(abs(rot_computed - rot_traj))
+        self.score = 1/max(list_distance)
 
-        self.score = 1/distance
+        return 1/distance
 
 
     def mutation(self, proba = P1):
@@ -58,7 +67,7 @@ class Individu():
 # print(test.score)
 
 
-qqun=Individu(RotTable())
-qqun.table.rot_table={'AA': [35.576558502141, 7.433901511509349, -154], 'AC': [33.22048222654215, 5.25191751302917, 143], 'AG': [26.446029097301288, 6.052240462237622, -2], 'AT': [30.47045254036881, 1.333716025628036, 0], 'CA': [34.00734209585039, 33.70710613604862, -64], 'CC': [33.61019622767888, 3.713127032109607, -57], 'CG': [29.664061041382677, 6.725155507162601, 0], 'CT': [26.446029097301288, 6.052240462237622, 2], 'GA': [36.655773481637176, 10.45337581740701, 120], 'GC': [42.26984493493484, 3.5310453395352823, 180], 'GG': [33.61019622767888, 3.713127032109607, -57], 'GT': [33.22048222654215, 5.25191751302917, 143], 'TA': [36.951508786388914, -2.5174751178033303, 0], 'TC': [36.655773481637176, 10.45337581740701, -120], 'TG': [34.00734209585039, 33.70710613604862, -64], 'TT': [35.576558502141, 7.433901511509349, -154]}
-qqun.evaluate("AAAGGATCTTCTTGAGATCCTTTTTTTCTGCGCGTAATCTGCTGCCAGTAAACGAAAAAACCGCCTGGGGAGGCGGTTTAGTCGAA")
-print(qqun.score)
+# qqun=Individu(RotTable())
+# qqun.table.rot_table={'AA': [35.576558502141, 7.433901511509349, -154], 'AC': [33.22048222654215, 5.25191751302917, 143], 'AG': [26.446029097301288, 6.052240462237622, -2], 'AT': [30.47045254036881, 1.333716025628036, 0], 'CA': [34.00734209585039, 33.70710613604862, -64], 'CC': [33.61019622767888, 3.713127032109607, -57], 'CG': [29.664061041382677, 6.725155507162601, 0], 'CT': [26.446029097301288, 6.052240462237622, 2], 'GA': [36.655773481637176, 10.45337581740701, 120], 'GC': [42.26984493493484, 3.5310453395352823, 180], 'GG': [33.61019622767888, 3.713127032109607, -57], 'GT': [33.22048222654215, 5.25191751302917, 143], 'TA': [36.951508786388914, -2.5174751178033303, 0], 'TC': [36.655773481637176, 10.45337581740701, -120], 'TG': [34.00734209585039, 33.70710613604862, -64], 'TT': [35.576558502141, 7.433901511509349, -154]}
+# qqun.evaluate("AAAGGATCTTCTTGAGATCCTTTTTTTCTGCGCGTAATCTGCTGCCAGTAAACGAAAAAACCGCCTGGGGAGGCGGTTTAGTCGAA")
+# print(qqun.score)

population.py

@@ -64,8 +64,7 @@ class Population:
                 print("meilleur, individu: ", meilleur.score, individu.score)
                 meilleur = individu
         newself = [meilleur]
-        # print("\n \n \nmeilleur", meilleur.table.rot_table, "\n \nscore", meilleur.score)
-                           
+        vu=set()                        
         t=randrange(0,self.n)
         m=randrange(0,self.n)
         non_vu = [i for i in range(0, self.n)]          
@@ -155,7 +154,7 @@ class Population:
             p = (self.n)//2
         vieille_taille = self.n
         selection(p)
-        newself = list(self.indiv)
+        newself = [element for element in self.indiv]       
         while len(newself)<vieille_taille:
             m=randrange(0,self.n)
             t=randrange(0,self.n)
@@ -164,10 +163,12 @@ class Population:
             couple_enfant = enfant(x,y)
             for child in couple_enfant :
                 child.mutation(proba_mutation)
+                child.evaluate("AAAGGATCTTCTTGAGATCCTTTTTTTCTGCGCGTAATCTGCTGCCAGTAAACGAAAAAACCGCCTGGGGAGGCGGTTTAGTCGAA")
             newself.append(couple_enfant[0])
             newself.append(couple_enfant[1])
         self = self.modifier_population(newself)
 
+
 def afficher(popu):
     for individu in popu.indiv :
         print("\n individu \n")
@@ -186,8 +187,6 @@ def test():
 
 #test()
 
-
-