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smadani2
nulling-python
Commits
af27dd8e
Commit
af27dd8e
authored
4 years ago
by
Sepehr Madani
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Adds class attributes to Chromosome
parent
9c8e1be3
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algorithms/genetic_algorithm.py
+38
-31
38 additions, 31 deletions
algorithms/genetic_algorithm.py
with
38 additions
and
31 deletions
algorithms/genetic_algorithm.py
+
38
−
31
View file @
af27dd8e
from
random
import
randrange
,
random
,
choice
,
sample
from
cmath
import
exp
,
phase
from
copy
import
deepcopy
from
math
import
log10
,
pi
,
nan
from
math
import
log10
,
pi
,
nan
,
cos
,
sin
from
typing
import
List
from
time
import
time_ns
from
utils.pattern
import
compute_pattern
from
utils.pattern
import
compute_pattern
,
compute_single_pattern
from
.base_algorithm
import
BaseAlgorithm
class
Chromosome
:
def
__init__
(
self
,
n
,
bit_count
):
self
.
gene
=
[
Chromosome
.
new_gene
(
bit_count
)
for
_
in
range
(
n
)]
N
=
None
BIT_COUNT
=
None
MUTATION_FACTOR
=
None
@classmethod
def
init_consts
(
cls
,
N
,
bit_count
,
mutation_factor
):
cls
.
N
=
N
cls
.
BIT_COUNT
=
bit_count
cls
.
MUTATION_FACTOR
=
mutation_factor
@classmethod
def
new_gene
(
cls
):
return
randrange
(
0
,
2
**
cls
.
BIT_COUNT
)
def
__init__
(
self
):
self
.
gene
=
[
Chromosome
.
new_gene
()
for
_
in
range
(
self
.
N
)]
self
.
pattern
=
nan
self
.
needs_update
=
True
...
...
@@ -23,9 +36,11 @@ class Chromosome:
"""
Evaluates a score based on chromosome
'
s pattern
"""
return
-
20
*
log10
(
abs
(
self
.
pattern
))
@staticmethod
def
new_gene
(
bit_count
):
return
randrange
(
0
,
2
**
bit_count
)
def
mutate
(
self
):
for
ii
in
range
(
self
.
N
):
if
random
()
<=
self
.
MUTATION_FACTOR
:
self
.
gene
[
ii
]
=
Chromosome
.
new_gene
()
self
.
needs_update
=
True
class
GeneticAlgorithm
(
BaseAlgorithm
):
...
...
@@ -45,15 +60,18 @@ class GeneticAlgorithm(BaseAlgorithm):
self
.
mutation_factor
=
options
.
mutation_factor
self
.
overwrite_mutations
=
options
.
overwrite_mutations
Chromosome
.
init_consts
(
self
.
N
,
self
.
bit_count
,
self
.
mutation_factor
)
self
.
stop_criterion
=
options
.
stop_criterion
# time, target, iter
self
.
gen_to_repeat
=
options
.
gen_to_repeat
self
.
time_limit
=
options
.
time_limit
self
.
stop_after_score
=
options
.
stop_after_score
self
.
generations
=
0
self
.
chromosomes
=
[]
self
.
min_gene_deviation
=
2
*
sin
(
pi
/
2
**
self
.
bit_resolution
)
self
.
buckets
=
None
if
options
.
use_buckets
:
self
.
bucket_count
=
options
.
bucket_count
...
...
@@ -67,7 +85,7 @@ class GeneticAlgorithm(BaseAlgorithm):
if
self
.
buckets
is
not
None
:
assert
len
(
self
.
null_degrees
)
==
1
assert
self
.
bucket_count
&
1
==
0
assert
self
.
stop_criterion
in
[
"
time
"
,
"
target
"
,
"
iter
"
]
assert
self
.
stop_criterion
in
[
"
time
"
,
"
target
"
,
"
iter
"
]
def
solve
(
self
):
self
.
initialize_sample
()
...
...
@@ -151,31 +169,20 @@ class GeneticAlgorithm(BaseAlgorithm):
Overwrites the previous chromosomes if overwrite_mutations is True.
"""
if
self
.
overwrite_mutations
:
# For all except the best chromosome
for
chromosome
in
self
.
chromosomes
[
1
:]:
chromosome
.
needs_update
=
True
for
idx
in
range
(
self
.
N
):
if
random
()
<=
self
.
mutation_factor
:
chromosome
.
gene
[
idx
]
=
Chromosome
.
new_gene
(
self
.
bit_count
)
chromosome
.
mutate
()
else
:
# For all except the best chromosome
for
original
in
range
(
1
,
self
.
sample_size
):
mutated
=
original
+
self
.
sample_size
-
1
self
.
chromosomes
[
mutated
].
needs_update
=
True
for
ii
in
range
(
self
.
N
):
if
random
()
<=
self
.
mutation_factor
:
self
.
chromosomes
[
mutated
].
gene
[
ii
]
=
Chromosome
.
new_gene
(
self
.
bit_count
)
else
:
self
.
chromosomes
[
mutated
].
gene
[
ii
]
=
self
.
chromosomes
[
original
].
gene
[
ii
]
for
idx
,
original
in
enumerate
(
self
.
chromosomes
[
1
:
self
.
sample_size
+
1
]):
mutated
=
self
.
chromosomes
[
idx
+
self
.
sample_size
-
1
]
mutated
.
gene
=
original
.
gene
.
copy
()
mutated
.
needs_update
=
True
mutated
.
mutate
()
def
make_weights
(
self
,
chromosome
):
"""
Returns e^{iθ} value for a chromosome
'
s θs
"""
weights
=
[]
for
bits
in
chromosome
.
gene
:
angle
=
(
bits
-
(
2
**
self
.
bit_count
-
1
)
/
2
)
*
2
*
pi
/
(
2
**
self
.
bit_resolution
)
weights
.
append
(
complex
(
cos
(
angle
),
sin
(
angle
)))
angles
=
[(
bits
-
(
2
**
self
.
bit_count
-
1
)
/
2
)
*
2
*
pi
/
(
2
**
self
.
bit_resolution
)
for
bits
in
chromosome
.
gene
]
weights
=
[
complex
(
cos
(
theta
),
sin
(
theta
))
for
theta
in
angles
]
return
weights
def
crossover
(
self
,
p1
,
p2
,
c1
,
c2
):
...
...
@@ -206,11 +213,11 @@ class GeneticAlgorithm(BaseAlgorithm):
self
.
chromosomes
.
clear
()
if
self
.
overwrite_mutations
:
self
.
chromosomes
=
[
Chromosome
(
self
.
N
,
self
.
bit_count
)
for
_
in
range
(
self
.
sample_size
)
Chromosome
()
for
_
in
range
(
self
.
sample_size
)
]
else
:
self
.
chromosomes
=
[
Chromosome
(
self
.
N
,
self
.
bit_count
)
for
_
in
range
(
self
.
sample_size
*
2
-
1
)
Chromosome
()
for
_
in
range
(
self
.
sample_size
*
2
-
1
)
]
if
self
.
buckets
is
not
None
:
...
...
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