more dynamique version of putting the ribbon

algogenetique.py

@@ -14,10 +14,10 @@ import time
 start_time = time.time()
 
 
-def main(N,tmax,pmutation, proportion):
+def main(N,tmax,pmutation, proportion, filename):
     
     #Creation of the initial population
-    People=Population(N)
+    People=Population(N, filename)
     L=[]
 
     #Evaluating the initial population for the histogram
@@ -72,7 +72,7 @@ def main(N,tmax,pmutation, proportion):
 #Testing our solution and printing the result in 3D
 lineList = [line.rstrip('\n') for line in open("plasmid_8k.fasta")]
 brin = ''.join(lineList[1:])
-best,People = main(10,1000,0.05,5)
+best,People = main(100,10,0.1,5, "plasmid_8k.fasta")
 test = Traj3D()
 test.compute(brin, best.table)
 test.draw("first_plot")

croisement.py

@@ -2,12 +2,12 @@ import numpy
 from RotTable import RotTable
 from individu import Individu
 
-def croisement_un_point(parent1, parent2):
+def croisement_un_point(parent1, parent2, filename):
     '''Croise les tables de rotation des parents pour former deux enfants en respectant les symétries du problème'''
     ''' Retourne deux enfants'''
 
-    enfant1 = Individu(RotTable())
-    enfant2 = Individu(RotTable())
+    enfant1 = Individu(RotTable(), filename)
+    enfant2 = Individu(RotTable(), filename)
     comp = 0
     point_crois= numpy.random.random_integers(0,8)
     list_dinucleotides = sorted(RotTable().orta())
@@ -45,12 +45,12 @@ def croisement_un_point(parent1, parent2):
     return enfant1, enfant2
 
 
-def croisement_deux_points(parent1, parent2):
+def croisement_deux_points(parent1, parent2, filename):
     ''' Croise les tables de rotationd des deux parents en croisant à deux points et respectant les symétries du problème'''
     ''' Retourne deux enfants'''
 
-    enfant1 = Individu(RotTable())
-    enfant2 = Individu(RotTable())
+    enfant1 = Individu(RotTable(), filename)
+    enfant2 = Individu(RotTable(), filename)
     comp = 0
     point_crois1= numpy.random.random_integers(0,8)
     point_crois2= numpy.random.random_integers(0,8)

individu.py

@@ -9,12 +9,10 @@ P1 = 0.015
 class Individu():
     ''' Un individu est caractérisé par sa table de rotations (individu.table)'''
     
-    def __init__(self, table):
+    def __init__(self, table, filename):
         self.table = table
-        lineList = [line.rstrip('\n') for line in open("plasmid_8k.fasta")]
+        lineList = [line.rstrip('\n') for line in open(filename)]
         self.brin = ''.join(lineList[1:])
-        #self.brin = "AAAGGATCTTCTTGAGATCCTTTTTTTCTGCGCGTAATCTGCTGCCAGTAAACGAAAAAACCGCCTGGGGAGGCGGTTTAGTCGAA"
-        # (sequence used for test)
         self.score = None
         self.distance = None
 

population.py

@@ -10,8 +10,9 @@ class Population:
 
 
     #Class initialization
-    def __init__(self,n):
-        self.indiv=[Individu(RotTable()) for k in range (n)]
+    def __init__(self,n,filename):
+        self.filename = filename
+        self.indiv=[Individu(RotTable(), self.filename) for k in range (n)]
         self.n = n
     
     #Updates the current individuals in the population  
@@ -132,7 +133,7 @@ class Population:
         if proba_mutation == None :
             proba_mutation = 0.001
         if selection == None :
-            selection = self.selection_duel
+            selection = self.selection_par_rang
         else :
             selection = liste_selections[selection]
         if p == None :
@@ -150,7 +151,7 @@ class Population:
             y=copy.deepcopy(newself[t])
 
             #Creation of the childs
-            couple_enfant = enfant(x,y)
+            couple_enfant = enfant(x,y, self.filename)
             for child in couple_enfant :
                 child.mutation_close_values(proba_mutation, number_of_mutations = 2)
                 child.evaluate()