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hackathon
brain_age_from_eeg
Commits
bb4e067a
Commit
bb4e067a
authored
May 24, 2017
by
Alex Fout
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parent
855ffc8d
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feature_extractors.py
feature_extractors.py
+15
15
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feature_extractors.py
View file @
bb4e067a
...
...
@@ 31,12 +31,12 @@ def coherence(raw_matrix):
def
rms
(
raw_matrix
):
"""
Calculates the root mean square value of a time series
Calculates the root mean square value of a time series
:param raw_matrix: data matrix where rows are examples and columns are raw features
:type raw_matrix: ndarray
:return: feature matrix where rows are examples and columns are calculated features
:rtype: ndarray
"""
"""
rmsValues
=
[]
for
i
in
range
(
raw_matrix
.
shape
[
1
]):
x
=
raw_matrix
[:,
i
]
...
...
@@ 46,12 +46,12 @@ def rms(raw_matrix):
def
meanAbs
(
raw_matrix
):
"""
Calculates the mean of absolute values
Calculates the mean of absolute values
:param raw_matrix: data matrix where rows are examples and columns are raw features
:type raw_matrix: ndarray
:return: feature matrix where rows are examples and columns are calculated features
:rtype: ndarray
"""
"""
meanAbsValues
=
[]
for
i
in
range
(
raw_matrix
.
shape
[
1
]):
x
=
raw_matrix
[:,
i
]
...
...
@@ 61,12 +61,12 @@ def meanAbs(raw_matrix):
def
std
(
raw_matrix
):
"""
standard deviation of a time series
standard deviation of a time series
:param raw_matrix: data matrix where rows are examples and columns are raw features
:type raw_matrix: ndarray
:return: feature matrix where rows are examples and columns are calculated features
:rtype: ndarray
"""
"""
stdValues
=
[]
for
i
in
range
(
raw_matrix
.
shape
[
1
]):
x
=
raw_matrix
[:,
i
]
...
...
@@ 76,20 +76,20 @@ def std(raw_matrix):
def
subBandRatio
(
raw_matrix
,
nBands
=
6
):
"""
The ratio of the mean of absolute values, between adjacent columns
Note: This measure was used in a paper where the columns of the matrix represent the
frequency bands
The ratio of the mean of absolute values, between adjacent columns
Note: This measure was used in a paper where the columns of the matrix represent the
frequency bands
:param raw_matrix: data matrix where rows are examples and columns are raw features
:type raw_matrix: ndarray
:return: feature matrix where rows are examples and columns are calculated features
:rtype: ndarray
"""
ratio
=
[]
channel
=
int
(
raw_matrix
.
shape
[
1
]
/
nBands
)
"""
ratio
=
[]
channel
=
int
(
raw_matrix
.
shape
[
1
]
/
nBands
)
for
i
in
range
(
channel
):
x
=
raw_matrix
[:,
i
*
nBands
:(
i
+
1
)
*
nBands
]
for
j
in
range
(
x
.
shape
[
1
]

1
):
ratio
.
append
(
np
.
mean
(
np
.
abs
(
x
[:,
j
]))
/
np
.
mean
(
np
.
abs
(
x
[:,
j
+
1
])))
x
=
raw_matrix
[:,
i
*
nBands
:(
i
+
1
)
*
nBands
]
for
j
in
range
(
x
.
shape
[
1
]

1
):
ratio
.
append
(
np
.
mean
(
np
.
abs
(
x
[:,
j
]))
/
np
.
mean
(
np
.
abs
(
x
[:,
j
+
1
])))
return
np
.
hstack
(
ratio
)
extractors
=
{
...
...
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