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import
pytest
import
math
import
random
from
utils
import
open_data
from
learning
import
*
random
.
seed
(
"aima-python"
)
def
test_euclidean
():
distance
=
euclidean_distance
([
1
,
2
], [
3
,
4
])
assert
round
(
distance
,
2
)
==
2.83
distance
=
euclidean_distance
([
1
,
2
,
3
], [
4
,
5
,
6
])
assert
round
(
distance
,
2
)
==
5.2
distance
=
euclidean_distance
([
0
,
0
,
0
], [
0
,
0
,
0
])
assert
distance
==
0
def
test_rms_error
():
assert
rms_error
([
2
,
2
], [
2
,
2
])
==
0
assert
rms_error
((
0
,
0
), (
0
,
1
))
==
math
.
sqrt
(
0.5
)
assert
rms_error
((
1
,
0
), (
0
,
1
))
==
1
assert
rms_error
((
0
,
0
), (
0
,
-
1
))
==
math
.
sqrt
(
0.5
)
assert
rms_error
((
0
,
0.5
), (
0
,
-
0.5
))
==
math
.
sqrt
(
0.5
)
def
test_manhattan_distance
():
assert
manhattan_distance
([
2
,
2
], [
2
,
2
])
==
0
assert
manhattan_distance
([
0
,
0
], [
0
,
1
])
==
1
assert
manhattan_distance
([
1
,
0
], [
0
,
1
])
==
2
assert
manhattan_distance
([
0
,
0
], [
0
,
-
1
])
==
1
assert
manhattan_distance
([
0
,
0.5
], [
0
,
-
0.5
])
==
1
def
test_mean_boolean_error
():
assert
mean_boolean_error
([
1
,
1
], [
0
,
0
])
==
1
assert
mean_boolean_error
([
0
,
1
], [
1
,
0
])
==
1
assert
mean_boolean_error
([
1
,
1
], [
0
,
1
])
==
0.5
assert
mean_boolean_error
([
0
,
0
], [
0
,
0
])
==
0
assert
mean_boolean_error
([
1
,
1
], [
1
,
1
])
==
0
def
test_mean_error
():
assert
mean_error
([
2
,
2
], [
2
,
2
])
==
0
assert
mean_error
([
0
,
0
], [
0
,
1
])
==
0.5
assert
mean_error
([
1
,
0
], [
0
,
1
])
==
1
assert
mean_error
([
0
,
0
], [
0
,
-
1
])
==
0.5
assert
mean_error
([
0
,
0.5
], [
0
,
-
0.5
])
==
0.5
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_naive_bayes
():
iris
=
DataSet
(
name
=
"iris"
)
# Discrete
nBD
=
NaiveBayesLearner
(
iris
,
continuous
=
False
)
assert
nBD
([
5
,
3
,
1
,
0.1
])
==
"setosa"
assert
nBD
([
6
,
3
,
4
,
1.1
])
==
"versicolor"
assert
nBD
([
7.7
,
3
,
6
,
2
])
==
"virginica"
# Continuous
nBC
=
NaiveBayesLearner
(
iris
,
continuous
=
True
)
assert
nBC
([
5
,
3
,
1
,
0.1
])
==
"setosa"
assert
nBC
([
6
,
5
,
3
,
1.5
])
==
"versicolor"
assert
nBC
([
7
,
3
,
6.5
,
2
])
==
"virginica"
# Simple
data1
=
'a'
*
50
+
'b'
*
30
+
'c'
*
15
dist1
=
CountingProbDist
(
data1
)
data2
=
'a'
*
30
+
'b'
*
45
+
'c'
*
20
dist2
=
CountingProbDist
(
data2
)
data3
=
'a'
*
20
+
'b'
*
20
+
'c'
*
35
dist3
=
CountingProbDist
(
data3
)
dist
=
{(
'First'
,
0.5
):
dist1
, (
'Second'
,
0.3
):
dist2
, (
'Third'
,
0.2
):
dist3
}
nBS
=
NaiveBayesLearner
(
dist
,
simple
=
True
)
assert
nBS
(
'aab'
)
==
'First'
assert
nBS
([
'b'
,
'b'
])
==
'Second'
assert
nBS
(
'ccbcc'
)
==
'Third'
def
test_k_nearest_neighbors
():
iris
=
DataSet
(
name
=
"iris"
)
kNN
=
NearestNeighborLearner
(
iris
,
k
=
3
)
assert
kNN
([
5
,
3
,
1
,
0.1
])
==
"setosa"
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_truncated_svd
():
test_mat
=
[[
17
,
0
],
[
0
,
11
]]
_
,
_
,
eival
=
truncated_svd
(
test_mat
)
assert
isclose
(
abs
(
eival
[
0
]),
17
)
assert
isclose
(
abs
(
eival
[
1
]),
11
)
test_mat
=
[[
17
,
0
],
[
0
,
-
34
]]
_
,
_
,
eival
=
truncated_svd
(
test_mat
)
assert
isclose
(
abs
(
eival
[
0
]),
34
)
assert
isclose
(
abs
(
eival
[
1
]),
17
)
test_mat
=
[[
1
,
0
,
0
,
0
,
2
],
[
0
,
0
,
3
,
0
,
0
],
[
0
,
0
,
0
,
0
,
0
],
[
0
,
2
,
0
,
0
,
0
]]
_
,
_
,
eival
=
truncated_svd
(
test_mat
)
assert
isclose
(
abs
(
eival
[
0
]),
3
)
assert
isclose
(
abs
(
eival
[
1
]),
5
**
0.5
)
test_mat
=
[[
3
,
2
,
2
],
[
2
,
3
,
-
2
]]
_
,
_
,
eival
=
truncated_svd
(
test_mat
)
assert
isclose
(
abs
(
eival
[
0
]),
5
)
assert
isclose
(
abs
(
eival
[
1
]),
3
)
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_random_forest
():
iris
=
DataSet
(
name
=
"iris"
)
rF
=
RandomForest
(
iris
)
assert
rF
([
5
,
3
,
1
,
0.1
])
==
"setosa"
assert
rF
([
6
,
5
,
3
,
1
])
==
"versicolor"
assert
rF
([
7.5
,
4
,
6
,
2
])
==
"virginica"
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
)
<
0.2
def
test_perceptron
():
iris
=
DataSet
(
name
=
"iris"
)
iris
.
classes_to_numbers
()
classes_number
=
len
(
iris
.
values
[
iris
.
target
])
perceptron
=
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
(
perceptron
,
tests
)
>
1
/
2
assert
err_ratio
(
perceptron
,
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
weight
>=
min_value
and
weight
<=
max_value
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