From 5241d085b2ac1f77057a25a20a3efdbd913e56ac Mon Sep 17 00:00:00 2001
From: Gauthier Roy <gauthierroy83@gmail.com>
Date: Thu, 30 Jan 2020 11:09:23 +0100
Subject: [PATCH] more dynamique version of putting the ribbon
---
__pycache__/RotTable.cpython-36.pyc | Bin 2593 -> 2593 bytes
algogenetique.py | 6 +++---
croisement.py | 12 ++++++------
first_plot.png | Bin 2411 -> 2412 bytes
individu.py | 6 ++----
population.py | 9 +++++----
6 files changed, 16 insertions(+), 17 deletions(-)
diff --git a/__pycache__/RotTable.cpython-36.pyc b/__pycache__/RotTable.cpython-36.pyc
index 459e4374c306aebaf525b830c17f00a8820843ca..1f5e29dc3b0df2b0334882e4c8d69ed8515fe145 100644
GIT binary patch
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delta 210
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diff --git a/algogenetique.py b/algogenetique.py
index 9db84ff..8fe1a62 100644
--- a/algogenetique.py
+++ b/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")
diff --git a/croisement.py b/croisement.py
index e9942ef..8e160e4 100644
--- a/croisement.py
+++ b/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)
diff --git a/first_plot.png b/first_plot.png
index f9294f16e9e6dc29674f763436bc6927a5b6bbb7..0e536fb530ea40bc82a9395940755ad27a030609 100644
GIT binary patch
delta 30
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delta 29
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diff --git a/individu.py b/individu.py
index c75d145..6e06018 100644
--- a/individu.py
+++ b/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
diff --git a/population.py b/population.py
index 2c89349..036717f 100644
--- a/population.py
+++ b/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()
--
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