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Nadin Thibault
EI - Jeux Evolu
Commits
23a40076
Commit
23a40076
authored
2 years ago
by
Boucaut Marius
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parent
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2 changed files
3dna/Constantes.py
+4
-4
4 additions, 4 deletions
3dna/Constantes.py
3dna/RotTable.py
+21
-18
21 additions, 18 deletions
3dna/RotTable.py
with
25 additions
and
22 deletions
3dna/Constantes.py
+
4
−
4
View file @
23a40076
ALPHA
=
3
#
Nombre de redécoupage de la séquence dans la fonction evaluate2
ALPHA
=
3
#
Nombre de redécoupage de la séquence dans la fonction evaluate2
p
=
0.2
Pm
=
0.3
size
=
128
nb_iter
=
30
0
AGRANDISSEMENT
=
5
#
C'est le nombre de point qu'on utilise pour recoller
nb_iter
=
2
0
AGRANDISSEMENT
=
5
#
C'est le nombre de point qu'on utilise pour recoller
Prise_en_compte_angle
=
5
nom_selection
=
"
selection_tournoi
"
nom_croisement
=
"
croisement_milieu
"
List_of_genes
=
[
"
AA
"
,
"
AC
"
,
"
AG
"
,
"
AT
"
,
"
TA
"
,
"
CA
"
,
"
CC
"
,
"
CG
"
,
"
GC
"
,
"
CT
"
]
#
les gènes à modifier
"
TA
"
,
"
CA
"
,
"
CC
"
,
"
CG
"
,
"
GC
"
,
"
CT
"
]
#
les gènes à modifier
Symetrique
=
{
"
AA
"
:
"
TT
"
,
"
AC
"
:
"
TG
"
,
"
AG
"
:
"
TC
"
,
"
AT
"
:
None
,
"
TA
"
:
None
,
"
CA
"
:
"
GT
"
,
"
CC
"
:
"
GG
"
,
"
CG
"
:
None
,
"
GC
"
:
None
,
"
CT
"
:
"
GA
"
}
# les symétriques de ces gènes
pas
=
10
This diff is collapsed.
Click to expand it.
3dna/RotTable.py
+
21
−
18
View file @
23a40076
...
...
@@ -82,7 +82,7 @@ class RotTable:
zf
=
traj
.
getTraj
()[
n
-
1
][
2
]
self
.
dist_carre
=
(
xf
-
x0
)
**
2
+
(
yf
-
y0
)
**
2
+
(
zf
-
z0
)
**
2
return
math
.
sqrt
(
self
.
dist_carre
)
def
angle
(
self
,
molecule
):
traj
=
Traj3D
(
False
)
traj
.
compute
(
molecule
+
molecule
[:
AGRANDISSEMENT
],
self
)
...
...
@@ -90,23 +90,26 @@ class RotTable:
Y
=
traj
.
getTraj
()[
1
]
Z
=
traj
.
getTraj
()[
-
2
]
T
=
traj
.
getTraj
()[
-
1
]
def
prod_scalaire
(
X
,
Y
,
Z
,
T
):
s
=
0
def
prod_scalaire
(
X
,
Y
,
Z
,
T
):
s
=
0
for
i
in
range
(
3
):
s
+=
(
Y
[
i
]
-
X
[
i
])
*
(
T
[
i
]
-
Z
[
i
])
s
+=
(
Y
[
i
]
-
X
[
i
])
*
(
T
[
i
]
-
Z
[
i
])
return
s
def
norme
(
X
,
Y
):
s
=
0
def
norme
(
X
,
Y
):
s
=
0
for
i
in
range
(
3
):
s
+=
(
Y
[
i
]
-
X
[
i
])
**
2
s
+=
(
Y
[
i
]
-
X
[
i
])
**
2
return
s
return
math
.
acos
(
prod_scalaire
(
X
,
Y
,
Z
,
T
)
/
(
norme
(
X
,
Y
)
*
norme
(
Z
,
T
)))
def
evaluate
(
self
,
molecule
):
# renvoie la distance au carrée séparant les deux extrémités de la molecule telle que générée par la table self
def
prod_scalaire
(
X
,
Y
,
Z
,
T
):
s
=
0
return
math
.
acos
(
prod_scalaire
(
X
,
Y
,
Z
,
T
)
/
(
norme
(
X
,
Y
)
*
norme
(
Z
,
T
)))
def
evaluate
(
self
,
molecule
):
"""
prend en compte produit scalaire et distance
"""
def
prod_scalaire
(
X
,
Y
,
Z
,
T
):
s
=
0
for
i
in
range
(
3
):
s
+=
(
Y
[
i
]
-
X
[
i
])
*
(
T
[
i
]
-
Z
[
i
])
s
+=
(
Y
[
i
]
-
X
[
i
])
*
(
T
[
i
]
-
Z
[
i
])
return
s
traj
=
Traj3D
(
False
)
traj
.
compute
(
molecule
+
molecule
[:
AGRANDISSEMENT
],
self
)
...
...
@@ -125,9 +128,9 @@ class RotTable:
Y
=
traj
.
getTraj
()[
1
]
Z
=
traj
.
getTraj
()[
-
2
]
T
=
traj
.
getTraj
()[
-
1
]
return
(
res
/
AGRANDISSEMENT
-
prod_scalaire
(
X
,
Y
,
Z
,
T
)
*
Prise_en_compte_angle
)
return
(
res
/
AGRANDISSEMENT
-
prod_scalaire
(
X
,
Y
,
Z
,
T
)
*
Prise_en_compte_angle
)
def
evaluate
2
(
self
,
molecule
):
def
evaluate
_en_plusieurs_points
(
self
,
molecule
):
n
=
len
(
molecule
)
summ
=
self
.
evaluate
(
molecule
)
for
k
in
range
(
ALPHA
):
...
...
@@ -166,7 +169,7 @@ class RotTable:
Entrée :
- self : une RotTable
Sortie :
- nouv_personne : une RotTable avec un gene modifié
- nouv_personne : une RotTable avec un gene modifié
aléatoirement uniformément sur l
'
espace des valeurs admissibles
'''
Pm
=
0.3
selected_gene
=
List_of_genes
[
rd
.
randint
(
0
,
len
(
List_of_genes
)
-
1
)]
...
...
@@ -189,7 +192,7 @@ class RotTable:
baseDirection
-
deltaDirection
,
baseDirection
+
deltaDirection
)
self
.
setDirection
(
selected_gene
,
newDirection
)
def
mutate_
v2
(
self
,
molecule
):
def
mutate_
proximite
(
self
,
molecule
):
Pm
=
0.2
# à choisir dans [0,001 ; 0,01]
nb_feature
=
3
# 2 suffiraient dans notre cas mais on généralise
parametre_preced
=
self
.
getTable
()
...
...
@@ -213,7 +216,7 @@ class RotTable:
baseDirection
+
deltaDirection
,
parametre_preced
[
selected_gene
][
2
]
+
deltaDirection
/
5
))
self
.
setDirection
(
selected_gene
,
newDirection
)
def
mutate_
v3
(
self
,
molecule
):
def
mutate_
degressif
(
self
,
molecule
):
Pm
=
0.2
# à choisir dans [0,001 ; 0,01]
score
=
max
(
self
.
evaluate
(
molecule
)
/
(
6000
*
AGRANDISSEMENT
),
1
)
...
...
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