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chsieh16
cs598mp-fall2021-proj
Commits
4810bdc3
Commit
4810bdc3
authored
3 years ago
by
aastorg2
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can add positive and negative examples to learner files
parent
74c5446a
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dtree_learner.py
+58
-35
58 additions, 35 deletions
dtree_learner.py
with
58 additions
and
35 deletions
dtree_learner.py
+
58
−
35
View file @
4810bdc3
from
typing
import
List
,
Tuple
import
numpy
as
np
import
os
from
learner_base
import
LearnerBase
def
construct_sample_to_feature_func
(
a_mat
:
np
.
ndarray
,
b_vec
:
np
.
ndarray
):
perc_dim
,
state_dim
=
a_mat
.
shape
def
sample_to_feature_vec
(
sample
):
assert
len
(
sample
)
==
state_dim
+
perc_dim
state
=
sample
[
0
:
state_dim
]
perc
=
sample
[
state_dim
:
state_dim
+
perc_dim
]
perc_bar
=
perc
-
(
np
.
dot
(
state
,
a_mat
.
T
)
+
b_vec
)
return
perc_bar
return
sample_to_feature_vec
import
csv
class
DTreeLearner
(
LearnerBase
):
def
__init__
(
self
,
state_dim
:
int
,
perc_dim
:
int
,
timeout
:
int
=
10000
)
->
None
:
super
().
__init__
()
#learners/C50exact/c5.0dbg -I 1 -m 1 -f FileLocation/pre
self
.
_state_dim
:
int
=
state_dim
self
.
_perc_dim
:
int
=
perc_dim
self
.
_s2f_func_list
:
List
=
[]
dir_name
=
"
tempLocation
"
check_temp_dir
:
bool
=
os
.
path
.
isdir
(
dir_name
)
if
not
check_temp_dir
:
os
.
makedirs
(
dir_name
)
self
.
data_file
=
dir_name
+
"
/pre.data
"
self
.
names_file
=
dir_name
+
"
/pre.names
"
open
(
self
.
data_file
,
'
w
'
).
close
()
# create empty file
open
(
self
.
names_file
,
'
w
'
).
close
()
@property
def
state_dim
(
self
)
->
int
:
return
self
.
_state_dim
...
...
@@ -51,8 +49,7 @@ class DTreeLearner(LearnerBase):
feature_vec_list
.
append
(
feature_vec
)
print
(
"
Positive feature vectors (dbar, psibar):
"
,
feature_vec_list
)
# TODO save list of feature vectors to data as positive examples
self
.
write_to_file
(
self
.
data_file
,
feature_vec_list
,
"
true
"
)
def
add_negative_examples
(
self
,
*
args
)
->
None
:
feature_vec_list
=
[]
...
...
@@ -63,14 +60,54 @@ class DTreeLearner(LearnerBase):
feature_vec_list
.
append
(
feature_vec
)
print
(
"
Negative feature vectors (dbar, psibar):
"
,
feature_vec_list
)
self
.
write_to_file
(
self
.
data_file
,
feature_vec_list
,
"
false
"
)
def
write_to_file
(
self
,
file
:
str
,
feature_vec_list
,
label
:
str
):
with
open
(
file
,
'
a
'
)
as
d_file
:
data_out
=
csv
.
writer
(
d_file
)
for
f
in
feature_vec_list
:
print
(
f
)
data_out
.
writerow
(
f
+
[
label
])
# TODO save list of feature vectors to data as negative examples
def
learn
(
self
)
->
Tuple
:
# TODO read result from Dtree learner
pass
def
construct_sample_to_feature_func
(
a_mat
:
np
.
ndarray
,
b_vec
:
np
.
ndarray
):
perc_dim
,
state_dim
=
a_mat
.
shape
def
sample_to_feature_vec
(
sample
):
assert
len
(
sample
)
==
state_dim
+
perc_dim
state
=
sample
[
0
:
state_dim
]
perc
=
sample
[
state_dim
:
state_dim
+
perc_dim
]
perc_bar
=
perc
-
(
np
.
dot
(
state
,
a_mat
.
T
)
+
b_vec
)
return
perc_bar
return
sample_to_feature_vec
def
test_sample_to_feature
():
# tuple
a_mat
=
np
.
array
([[
0.
,
-
1.
,
0.
],
[
0.
,
0.
,
-
1
]])
b_vec
=
np
.
zeros
(
2
)
#construct_sample_to_feature_func: returns a function
#map: lin_trans(a_mat and b_vec pair) -> func
sample_to_feature_func
=
construct_sample_to_feature_func
(
a_mat
,
b_vec
)
#map = {name1:sample_to_feature_func}
sample
=
np
.
array
([
1.
,
2.
,
3.
,
-
2.
,
-
3.
])
# sample_to_feature_func will compute dBar and psiBar
feature_vec
=
sample_to_feature_func
(
sample
)
print
(
"
sample:
"
+
str
(
feature_vec
))
assert
np
.
array_equal
(
feature_vec
,
np
.
array
([
0.
,
0.
]))
sample
=
np
.
array
([
1.
,
2.
,
3.
,
-
1.
,
-
2.
])
feature_vec
=
sample_to_feature_func
(
sample
)
print
(
"
sample:
"
+
str
(
feature_vec
))
assert
np
.
array_equal
(
feature_vec
,
np
.
array
([
1.
,
1.
]))
def
test_dtree_learner
():
a_mat
=
np
.
array
([[
0.
,
-
1.
,
0.
],
...
...
@@ -85,7 +122,7 @@ def test_dtree_learner():
(
1.
,
2.
,
3.
,
-
1.
,
-
2.
)
]
learner
.
add_positive_examples
(
*
pos_examples
)
neg_examples
=
[
(
10.
,
1.0
,
1.0
,
0.5
,
0.5
),
(
10.
,
1.0
,
1.0
,
1.5
,
1.5
),
...
...
@@ -93,23 +130,9 @@ def test_dtree_learner():
]
learner
.
add_negative_examples
(
*
neg_examples
)
learner
.
learn
()
def
test_sample_to_feature
():
a_mat
=
np
.
array
([[
0.
,
-
1.
,
0.
],
[
0.
,
0.
,
-
1
]])
b_vec
=
np
.
zeros
(
2
)
sample_to_feature_func
=
construct_sample_to_feature_func
(
a_mat
,
b_vec
)
sample
=
np
.
array
([
1.
,
2.
,
3.
,
-
2.
,
-
3.
])
feature_vec
=
sample_to_feature_func
(
sample
)
assert
np
.
array_equal
(
feature_vec
,
np
.
array
([
0.
,
0.
]))
sample
=
np
.
array
([
1.
,
2.
,
3.
,
-
1.
,
-
2.
])
feature_vec
=
sample_to_feature_func
(
sample
)
assert
np
.
array_equal
(
feature_vec
,
np
.
array
([
1.
,
1.
]))
# learner.learn()
if
__name__
==
"
__main__
"
:
#test_sample_to_feature()
test_dtree_learner
()
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