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bob
bob.learn.em
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!41
Python implementation of k-means
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Merged
Python implementation of k-means
py-kmeans
into
pure-python
Overview
1
Commits
14
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5
Changes
1
Merged
Yannick DAYER
requested to merge
py-kmeans
into
pure-python
3 years ago
Overview
1
Commits
14
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k-means in Python using dask arrays, and init function from dask-ml.
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e40c0ea7
Tests for python k-means
· e40c0ea7
Yannick DAYER
authored
3 years ago
bob/learn/em/test/test_kmeans.py
+
79
−
66
Options
@@ -8,75 +8,88 @@
"""
Tests the KMeans machine
"""
import
os
import
numpy
import
tempfile
import
bob.io.base
from
bob.learn.em.clustering.kmeans
import
KMeansMachine
from
bob.learn.em.cluster
import
KMeansMachine
from
bob.learn.em.cluster
import
KMeansTrainer
import
dask.array
as
da
def
equals
(
x
,
y
,
epsilon
):
return
(
abs
(
x
-
y
)
<
epsilon
)
return
abs
(
x
-
y
)
<
epsilon
def
test_KMeansMachine
():
# Test a KMeansMachine
means
=
numpy
.
array
([[
3
,
70
,
0
],
[
4
,
72
,
0
]],
'
float64
'
)
mean
=
numpy
.
array
([
3
,
70
,
1
],
'
float64
'
)
# Initializes a KMeansMachine
km
=
KMeansMachine
(
2
,
3
)
km
.
means
=
means
assert
km
.
shape
==
(
2
,
3
)
# Sets and gets
assert
(
km
.
means
==
means
).
all
()
assert
(
km
.
get_mean
(
0
)
==
means
[
0
,:]).
all
()
assert
(
km
.
get_mean
(
1
)
==
means
[
1
,:]).
all
()
km
.
set_mean
(
0
,
mean
)
assert
(
km
.
get_mean
(
0
)
==
mean
).
all
()
# Distance and closest mean
eps
=
1e-10
assert
equals
(
km
.
get_distance_from_mean
(
mean
,
0
),
0
,
eps
)
assert
equals
(
km
.
get_distance_from_mean
(
mean
,
1
),
6
,
eps
)
(
index
,
dist
)
=
km
.
get_closest_mean
(
mean
)
assert
index
==
0
assert
equals
(
dist
,
0
,
eps
)
assert
equals
(
km
.
get_min_distance
(
mean
),
0
,
eps
)
# Copy constructor and comparison operators
km2
=
km
.
copy
()
assert
km2
==
km
assert
(
km2
!=
km
)
==
False
assert
km2
.
is_similar_to
(
km
)
means2
=
numpy
.
array
([[
3
,
70
,
0
],
[
4
,
72
,
2
]],
'
float64
'
)
km2
.
means
=
means2
assert
(
km2
==
km
)
==
False
assert
(
km2
!=
km
)
assert
(
km2
.
is_similar_to
(
km
))
==
False
def
test_KMeansMachine2
():
kmeans
=
bob
.
learn
.
em
.
clustering
.
kmeans
.
KMeansMachine
(
2
,
2
)
kmeans
.
means
=
numpy
.
array
([[
1.2
,
1.3
],[
0.2
,
-
0.3
]])
data
=
numpy
.
array
([
[
1.
,
1
],
[
1.2
,
3
],
[
0
,
0
],
[
0.3
,
0.2
],
[
0.2
,
0
]
])
variances
,
weights
=
kmeans
.
get_variances_and_weights_for_each_cluster
(
data
)
variances_result
=
numpy
.
array
([[
0.01
,
1.
],
[
0.01555556
,
0.00888889
]])
weights_result
=
numpy
.
array
([
0.4
,
0.6
])
assert
equals
(
weights_result
,
weights
,
1e-3
).
all
()
assert
equals
(
variances_result
,
variances
,
1e-3
).
all
()
# Test a KMeansMachine
means
=
numpy
.
array
([[
3
,
70
,
0
],
[
4
,
72
,
0
]],
"
float64
"
)
mean
=
numpy
.
array
([
3
,
70
,
1
],
"
float64
"
)
# Initializes a KMeansMachine
km
=
KMeansMachine
(
2
)
km
.
centroids_
=
means
# Distance and closest mean
eps
=
1e-10
assert
equals
(
km
.
transform
(
mean
)[
0
],
1
,
eps
),
km
.
transform
(
mean
)[
0
].
compute
()
assert
equals
(
km
.
transform
(
mean
)[
1
],
6
,
eps
),
km
.
transform
(
mean
)[
1
].
compute
()
(
index
,
dist
)
=
km
.
get_closest_centroid
(
mean
)
assert
index
==
0
assert
equals
(
dist
,
1
,
eps
)
assert
equals
(
km
.
get_min_distance
(
mean
),
1
,
eps
)
def
test_KMeansMachine_var_and_weight
():
kmeans
=
KMeansMachine
(
2
)
kmeans
.
centroids_
=
numpy
.
array
([[
1.2
,
1.3
],
[
0.2
,
-
0.3
]])
data
=
numpy
.
array
([[
1.0
,
1
],
[
1.2
,
3
],
[
0
,
0
],
[
0.3
,
0.2
],
[
0.2
,
0
]])
variances
,
weights
=
kmeans
.
get_variances_and_weights_for_each_cluster
(
data
)
variances_result
=
numpy
.
array
([[
0.01
,
1.0
],
[
0.01555556
,
0.00888889
]])
weights_result
=
numpy
.
array
([
0.4
,
0.6
])
assert
equals
(
weights_result
,
weights
,
1e-3
).
all
()
assert
equals
(
variances_result
,
variances
,
1e-3
).
all
()
def
test_kmeans_fit
():
da
.
random
.
seed
(
0
)
data1
=
da
.
random
.
normal
(
loc
=
1
,
size
=
(
2000
,
3
))
data2
=
da
.
random
.
normal
(
loc
=-
1
,
size
=
(
2000
,
3
))
data
=
da
.
concatenate
([
data1
,
data2
],
axis
=
0
)
machine
=
KMeansMachine
(
2
).
fit
(
data
)
expected
=
da
.
array
(
[[
-
0.99262315
,
-
1.05226141
,
-
1.00525245
],
[
1.00426431
,
1.00359693
,
1.05996704
]]
)
assert
da
.
isclose
(
machine
.
centroids_
,
expected
).
all
(),
machine
.
centroids_
.
compute
()
def
test_kmeans_fit_init_pp
():
da
.
random
.
seed
(
0
)
data1
=
da
.
random
.
normal
(
loc
=
1
,
size
=
(
2000
,
3
))
data2
=
da
.
random
.
normal
(
loc
=-
1
,
size
=
(
2000
,
3
))
data
=
da
.
concatenate
([
data1
,
data2
],
axis
=
0
)
trainer
=
KMeansTrainer
(
init_method
=
"
k-means++
"
)
machine
=
KMeansMachine
(
2
).
fit
(
data
,
trainer
=
trainer
)
expected
=
da
.
array
(
[[
-
0.99262315
,
-
1.05226141
,
-
1.00525245
],
[
1.00426431
,
1.00359693
,
1.05996704
]]
)
assert
da
.
isclose
(
machine
.
centroids_
,
expected
).
all
(),
machine
.
centroids_
.
compute
()
def
test_kmeans_fit_init_random
():
da
.
random
.
seed
(
0
)
data1
=
da
.
random
.
normal
(
loc
=
1
,
size
=
(
2000
,
3
))
data2
=
da
.
random
.
normal
(
loc
=-
1
,
size
=
(
2000
,
3
))
data
=
da
.
concatenate
([
data1
,
data2
],
axis
=
0
)
trainer
=
KMeansTrainer
(
init_method
=
"
random
"
,
random_state
=
0
)
machine
=
KMeansMachine
(
2
).
fit
(
data
,
trainer
=
trainer
)
expected
=
da
.
array
(
[[
-
0.99433738
,
-
1.05561588
,
-
1.01236246
],
[
0.99800688
,
0.99873325
,
1.05879539
]]
)
assert
da
.
isclose
(
machine
.
centroids_
,
expected
).
all
(),
machine
.
centroids_
.
compute
()
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