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aima-python/tests/test_learning.py at master · aliraee/aima-python · GitHub
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import
pytest
from
learning
import
*
random
.
seed
(
"aima-python"
)
def
test_exclude
():
iris
=
DataSet
(
name
=
'iris'
,
exclude
=
[
3
])
assert
iris
.
inputs
==
[
0
,
1
,
2
]
def
test_parse_csv
():
iris
=
open_data
(
'iris.csv'
).
read
()
assert
parse_csv
(
iris
)[
0
]
==
[
5.1
,
3.5
,
1.4
,
0.2
,
'setosa'
]
def
test_weighted_mode
():
assert
weighted_mode
(
'abbaa'
, [
1
,
2
,
3
,
1
,
2
])
==
'b'
def
test_weighted_replicate
():
assert
weighted_replicate
(
'ABC'
, [
1
,
2
,
1
],
4
)
==
[
'A'
,
'B'
,
'B'
,
'C'
]
def
test_means_and_deviation
():
iris
=
DataSet
(
name
=
'iris'
)
means
,
deviations
=
iris
.
find_means_and_deviations
()
assert
round
(
means
[
'setosa'
][
0
],
3
)
==
5.006
assert
round
(
means
[
'versicolor'
][
0
],
3
)
==
5.936
assert
round
(
means
[
'virginica'
][
0
],
3
)
==
6.588
assert
round
(
deviations
[
'setosa'
][
0
],
3
)
==
0.352
assert
round
(
deviations
[
'versicolor'
][
0
],
3
)
==
0.516
assert
round
(
deviations
[
'virginica'
][
0
],
3
)
==
0.636
def
test_plurality_learner
():
zoo
=
DataSet
(
name
=
'zoo'
)
pl
=
PluralityLearner
(
zoo
)
assert
pl
([
1
,
0
,
0
,
1
,
0
,
0
,
0
,
1
,
1
,
1
,
0
,
0
,
4
,
1
,
0
,
1
])
==
'mammal'
def
test_k_nearest_neighbors
():
iris
=
DataSet
(
name
=
'iris'
)
knn
=
NearestNeighborLearner
(
iris
,
k
=
3
)
assert
knn
([
5
,
3
,
1
,
0.1
])
==
'setosa'
assert
knn
([
6
,
5
,
3
,
1.5
])
==
'versicolor'
assert
knn
([
7.5
,
4
,
6
,
2
])
==
'virginica'
def
test_decision_tree_learner
():
iris
=
DataSet
(
name
=
'iris'
)
dtl
=
DecisionTreeLearner
(
iris
)
assert
dtl
([
5
,
3
,
1
,
0.1
])
==
'setosa'
assert
dtl
([
6
,
5
,
3
,
1.5
])
==
'versicolor'
assert
dtl
([
7.5
,
4
,
6
,
2
])
==
'virginica'
def
test_svc
():
iris
=
DataSet
(
name
=
'iris'
)
classes
=
[
'setosa'
,
'versicolor'
,
'virginica'
]
iris
.
classes_to_numbers
(
classes
)
n_samples
,
n_features
=
len
(
iris
.
examples
),
iris
.
target
X
,
y
=
(
np
.
array
([
x
[:
n_features
]
for
x
in
iris
.
examples
]),
np
.
array
([
x
[
n_features
]
for
x
in
iris
.
examples
]))
svm
=
MultiClassLearner
(
SVC
()).
fit
(
X
,
y
)
assert
svm
.
predict
([[
5.0
,
3.1
,
0.9
,
0.1
]])
==
0
assert
svm
.
predict
([[
5.1
,
3.5
,
1.0
,
0.0
]])
==
0
assert
svm
.
predict
([[
4.9
,
3.3
,
1.1
,
0.1
]])
==
0
assert
svm
.
predict
([[
6.0
,
3.0
,
4.0
,
1.1
]])
==
1
assert
svm
.
predict
([[
6.1
,
2.2
,
3.5
,
1.0
]])
==
1
assert
svm
.
predict
([[
5.9
,
2.5
,
3.3
,
1.1
]])
==
1
assert
svm
.
predict
([[
7.5
,
4.1
,
6.2
,
2.3
]])
==
2
assert
svm
.
predict
([[
7.3
,
4.0
,
6.1
,
2.4
]])
==
2
assert
svm
.
predict
([[
7.0
,
3.3
,
6.1
,
2.5
]])
==
2
def
test_information_content
():
assert
information_content
([])
==
0
assert
information_content
([
4
])
==
0
assert
information_content
([
5
,
4
,
0
,
2
,
5
,
0
])
>
1.9
assert
information_content
([
5
,
4
,
0
,
2
,
5
,
0
])
<
2
assert
information_content
([
1.5
,
2.5
])
>
0.9
assert
information_content
([
1.5
,
2.5
])
<
1.0
def
test_random_forest
():
iris
=
DataSet
(
name
=
'iris'
)
rf
=
RandomForest
(
iris
)
tests
=
[([
5.0
,
3.0
,
1.0
,
0.1
],
'setosa'
),
([
5.1
,
3.3
,
1.1
,
0.1
],
'setosa'
),
([
6.0
,
5.0
,
3.0
,
1.0
],
'versicolor'
),
([
6.1
,
2.2
,
3.5
,
1.0
],
'versicolor'
),
([
7.5
,
4.1
,
6.2
,
2.3
],
'virginica'
),
([
7.3
,
3.7
,
6.1
,
2.5
],
'virginica'
)]
assert
grade_learner
(
rf
,
tests
)
>=
1
/
3
def
test_neural_network_learner
():
iris
=
DataSet
(
name
=
'iris'
)
classes
=
[
'setosa'
,
'versicolor'
,
'virginica'
]
iris
.
classes_to_numbers
(
classes
)
nnl
=
NeuralNetLearner
(
iris
, [
5
],
0.15
,
75
)
tests
=
[([
5.0
,
3.1
,
0.9
,
0.1
],
0
),
([
5.1
,
3.5
,
1.0
,
0.0
],
0
),
([
4.9
,
3.3
,
1.1
,
0.1
],
0
),
([
6.0
,
3.0
,
4.0
,
1.1
],
1
),
([
6.1
,
2.2
,
3.5
,
1.0
],
1
),
([
5.9
,
2.5
,
3.3
,
1.1
],
1
),
([
7.5
,
4.1
,
6.2
,
2.3
],
2
),
([
7.3
,
4.0
,
6.1
,
2.4
],
2
),
([
7.0
,
3.3
,
6.1
,
2.5
],
2
)]
assert
grade_learner
(
nnl
,
tests
)
>=
1
/
3
assert
err_ratio
(
nnl
,
iris
)
<
0.21
def
test_perceptron
():
iris
=
DataSet
(
name
=
'iris'
)
iris
.
classes_to_numbers
()
pl
=
PerceptronLearner
(
iris
)
tests
=
[([
5
,
3
,
1
,
0.1
],
0
),
([
5
,
3.5
,
1
,
0
],
0
),
([
6
,
3
,
4
,
1.1
],
1
),
([
6
,
2
,
3.5
,
1
],
1
),
([
7.5
,
4
,
6
,
2
],
2
),
([
7
,
3
,
6
,
2.5
],
2
)]
assert
grade_learner
(
pl
,
tests
)
>
1
/
2
assert
err_ratio
(
pl
,
iris
)
<
0.4
def
test_random_weights
():
min_value
=
-
0.5
max_value
=
0.5
num_weights
=
10
test_weights
=
random_weights
(
min_value
,
max_value
,
num_weights
)
assert
len
(
test_weights
)
==
num_weights
for
weight
in
test_weights
:
assert
min_value
<=
weight
<=
max_value
def
test_ada_boost
():
iris
=
DataSet
(
name
=
'iris'
)
iris
.
classes_to_numbers
()
wl
=
WeightedLearner
(
PerceptronLearner
)
ab
=
ada_boost
(
iris
,
wl
,
5
)
tests
=
[([
5
,
3
,
1
,
0.1
],
0
),
([
5
,
3.5
,
1
,
0
],
0
),
([
6
,
3
,
4
,
1.1
],
1
),
([
6
,
2
,
3.5
,
1
],
1
),
([
7.5
,
4
,
6
,
2
],
2
),
([
7
,
3
,
6
,
2.5
],
2
)]
assert
grade_learner
(
ab
,
tests
)
>
2
/
3
assert
err_ratio
(
ab
,
iris
)
<
0.25
if
__name__
==
"__main__"
:
pytest
.
main
()
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