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Raphael Sturgis
skais
Commits
57d2a200
Commit
57d2a200
authored
3 years ago
by
Raphael Sturgis
Browse files
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moved histogram functions
parent
4c46b543
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1 merge request
!6
Develop
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2
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2 changed files
skais/ais/ais_points.py
+6
-68
6 additions, 68 deletions
skais/ais/ais_points.py
skais/learn/point_representation.py
+71
-0
71 additions, 0 deletions
skais/learn/point_representation.py
with
77 additions
and
68 deletions
skais/ais/ais_points.py
+
6
−
68
View file @
57d2a200
...
@@ -8,7 +8,7 @@ from scipy.stats import stats
...
@@ -8,7 +8,7 @@ from scipy.stats import stats
from
skais.ais.ais_trajectory
import
AISTrajectory
from
skais.ais.ais_trajectory
import
AISTrajectory
# TODO: remove
def
compute_trajectories
(
df
,
time_gap
,
min_size
=
50
,
size_limit
=
500
,
interpolation_time
=
None
):
def
compute_trajectories
(
df
,
time_gap
,
min_size
=
50
,
size_limit
=
500
,
interpolation_time
=
None
):
n_sample
=
len
(
df
.
index
)
n_sample
=
len
(
df
.
index
)
result
=
[]
result
=
[]
...
@@ -26,6 +26,7 @@ def compute_trajectories(df, time_gap, min_size=50, size_limit=500, interpolatio
...
@@ -26,6 +26,7 @@ def compute_trajectories(df, time_gap, min_size=50, size_limit=500, interpolatio
return
result
return
result
# TODO: remove
@jit
(
nopython
=
True
)
@jit
(
nopython
=
True
)
def
compute_trajectory
(
times
,
time_gap
,
size_limit
):
def
compute_trajectory
(
times
,
time_gap
,
size_limit
):
n_samples
=
len
(
times
)
n_samples
=
len
(
times
)
...
@@ -92,32 +93,7 @@ class AISPoints:
...
@@ -92,32 +93,7 @@ class AISPoints:
f
"
standardization]
"
)
f
"
standardization]
"
)
return
normalization_type
,
normalization_dict
return
normalization_type
,
normalization_dict
def
histogram
(
self
,
features
,
bins
=
10
,
ranges
=
None
,
label
=
None
,
y_field
=
'
label
'
):
# TODO: rename
if
label
is
not
None
:
tmp
=
self
.
df
[
self
.
df
[
y_field
]
==
label
]
else
:
tmp
=
self
.
df
dat
=
tmp
[
features
]
h
=
np
.
histogramdd
(
dat
.
to_numpy
(),
bins
,
ranges
)[
0
]
if
h
.
sum
()
==
0
:
return
np
.
full
(
h
.
shape
,
1
/
h
.
size
)
else
:
return
h
/
h
.
sum
()
def
disjointed_histogram
(
self
,
features
,
bins
,
ranges
,
label
=
None
,
y_field
=
'
label
'
):
if
label
is
not
None
:
tmp
=
self
.
df
[
self
.
df
[
y_field
]
==
label
]
else
:
tmp
=
self
.
df
if
type
(
bins
)
==
int
:
bins
=
[
bins
for
_
in
features
]
histograms
=
[]
for
feature
,
bin
,
f_range
in
zip
(
features
,
bins
,
ranges
):
histograms
.
append
(
np
.
histogram
(
tmp
[
feature
],
bin
,
f_range
))
return
histograms
def
compute_diff_heading_cog
(
self
):
def
compute_diff_heading_cog
(
self
):
self
.
df
[
"
diff
"
]
=
self
.
df
.
apply
(
lambda
x
:
180
-
abs
(
abs
(
x
[
'
heading
'
]
-
x
[
'
cog
'
])
-
180
),
self
.
df
[
"
diff
"
]
=
self
.
df
.
apply
(
lambda
x
:
180
-
abs
(
abs
(
x
[
'
heading
'
]
-
x
[
'
cog
'
])
-
180
),
axis
=
1
)
axis
=
1
)
...
@@ -129,48 +105,10 @@ class AISPoints:
...
@@ -129,48 +105,10 @@ class AISPoints:
self
.
df
=
self
.
df
[
self
.
df
[
"
heading
"
]
<=
360
]
self
.
df
=
self
.
df
[
self
.
df
[
"
heading
"
]
<=
360
]
self
.
df
=
self
.
df
[
self
.
df
[
"
heading
"
]
>=
0
]
self
.
df
=
self
.
df
[
self
.
df
[
"
heading
"
]
>=
0
]
def
histogram_joint_x_y
(
self
,
x_fields
=
[
"
sog
"
,
"
diff
"
],
x_nb_bins
=
10
,
x_range
=
[[
0
,
30
],
[
0
,
180
]]
,
y_nb_bins
=
2
,
y_fields
=
'
label
'
,
y_range
=
[
0
,
1
]):
return
self
.
histogram
(
x_fields
+
[
y_fields
],
bins
=
[
x_nb_bins
for
i
in
x_fields
]
+
[
y_nb_bins
],
ranges
=
x_range
+
[
y_range
])
def
histogram_x_knowing_y
(
self
,
x_fields
=
[
"
sog
"
,
"
diff
"
],
x_nb_bins
=
10
,
x_range
=
[[
0
,
30
],
[
0
,
180
]]
,
y_nb_bins
=
2
,
y_field
=
'
label
'
):
result
=
[]
for
i
in
range
(
y_nb_bins
):
layer
=
self
.
histogram
(
x_fields
,
bins
=
x_nb_bins
,
ranges
=
x_range
,
label
=
i
,
y_field
=
y_field
)
result
.
append
(
layer
)
return
np
.
stack
(
result
,
axis
=
len
(
x_fields
))
def
disjointed_histogram_x_knowing_y
(
self
,
features
,
x_nb_bins
=
10
,
x_range
=
[[
0
,
1
]]
,
y_nb_bins
=
4
,
y_field
=
'
label
'
):
out
=
[]
for
feature
,
f_range
in
zip
(
features
,
x_range
):
result
=
[]
for
i
in
range
(
y_nb_bins
):
layer
,
_
=
np
.
histogram
(
self
.
df
[
self
.
df
[
y_field
]
==
i
][
feature
].
to_numpy
(),
bins
=
x_nb_bins
,
range
=
f_range
)
if
layer
.
sum
()
==
0
:
layer
=
np
.
full
(
layer
.
shape
,
1
)
result
.
append
(
layer
)
out
.
append
(
np
.
stack
(
result
))
return
out
def
histogram_y_knowing_x
(
self
,
x_fields
=
[
"
sog
"
,
"
diff
"
],
x_nb_bins
=
10
,
x_range
=
[[
0
,
30
],
[
0
,
180
]]
,
y_nb_bins
=
2
,
y_field
=
'
label
'
,
y_range
=
[
0
,
1
]):
h_joint
=
self
.
histogram_joint_x_y
(
x_fields
,
x_nb_bins
,
x_range
,
y_nb_bins
,
y_field
,
y_range
)
y_hist
=
self
.
histogram
(
features
=
y_field
,
bins
=
y_nb_bins
,
ranges
=
[
y_range
])
result
=
np
.
zeros
(
h_joint
.
shape
)
for
idx
,
x
in
np
.
ndenumerate
(
h_joint
):
if
h_joint
[
idx
[:
-
1
]].
sum
()
==
0
:
result
[
idx
]
=
y_hist
[
idx
[
-
1
]]
else
:
result
[
idx
]
=
x
/
h_joint
[
idx
[:
-
1
]].
sum
()
return
result
# TODO: redo
def
get_trajectories
(
self
,
time_gap
=
30
,
min_size
=
50
,
interpolation_time
=
None
):
def
get_trajectories
(
self
,
time_gap
=
30
,
min_size
=
50
,
interpolation_time
=
None
):
if
'
ts
'
in
self
.
df
:
if
'
ts
'
in
self
.
df
:
...
...
This diff is collapsed.
Click to expand it.
skais/learn/point_representation.py
0 → 100644
+
71
−
0
View file @
57d2a200
import
numpy
as
np
def
histogram
(
ais_points
,
features
,
bins
,
ranges
=
None
,
label
=
None
,
y_field
=
'
label
'
):
if
label
is
not
None
:
tmp
=
ais_points
.
df
[
ais_points
.
df
[
y_field
]
==
label
]
else
:
tmp
=
ais_points
.
df
dat
=
tmp
[
features
]
h
=
np
.
histogramdd
(
dat
.
to_numpy
(),
bins
,
ranges
)[
0
]
if
h
.
sum
()
==
0
:
return
np
.
full
(
h
.
shape
,
1
/
h
.
size
)
else
:
return
h
/
h
.
sum
()
def
disjointed_histogram
(
ais_points
,
features
,
bins
,
ranges
,
label
=
None
,
y_field
=
'
label
'
):
if
label
is
not
None
:
tmp
=
ais_points
.
df
[
ais_points
.
df
[
y_field
]
==
label
]
else
:
tmp
=
ais_points
.
df
if
type
(
bins
)
==
int
:
bins
=
[
bins
for
_
in
features
]
histograms
=
[]
for
feature
,
h_bin
,
f_range
in
zip
(
features
,
bins
,
ranges
):
histograms
.
append
(
np
.
histogram
(
tmp
[
feature
],
h_bin
,
f_range
))
return
histograms
def
histogram_joint_x_y
(
ais_points
,
x_fields
,
x_nb_bins
,
x_range
,
y_fields
,
y_nb_bins
,
y_range
):
return
histogram
(
ais_points
,
x_fields
+
[
y_fields
],
bins
=
[
x_nb_bins
for
_
in
x_fields
]
+
[
y_nb_bins
],
ranges
=
x_range
+
[
y_range
])
def
histogram_x_knowing_y
(
ais_points
,
x_fields
,
x_nb_bins
,
x_range
,
y_nb_bins
,
y_field
):
result
=
[]
for
i
in
range
(
y_nb_bins
):
layer
=
histogram
(
ais_points
,
x_fields
,
bins
=
x_nb_bins
,
ranges
=
x_range
,
label
=
i
,
y_field
=
y_field
)
result
.
append
(
layer
)
return
np
.
stack
(
result
,
axis
=
len
(
x_fields
))
def
disjointed_histogram_x_knowing_y
(
ais_points
,
features
,
x_nb_bins
,
x_range
,
y_nb_bins
,
y_field
):
out
=
[]
for
feature
,
f_range
in
zip
(
features
,
x_range
):
result
=
[]
for
i
in
range
(
y_nb_bins
):
layer
,
_
=
np
.
histogram
(
ais_points
.
df
[
ais_points
.
df
[
y_field
]
==
i
][
feature
].
to_numpy
(),
bins
=
x_nb_bins
,
range
=
f_range
)
if
layer
.
sum
()
==
0
:
layer
=
np
.
full
(
layer
.
shape
,
1
)
result
.
append
(
layer
)
out
.
append
(
np
.
stack
(
result
))
return
out
def
histogram_y_knowing_x
(
ais_points
,
x_fields
,
x_nb_bins
,
x_range
,
y_nb_bins
,
y_field
,
y_range
):
h_joint
=
histogram_joint_x_y
(
ais_points
,
x_fields
,
x_nb_bins
,
x_range
,
y_nb_bins
,
y_field
,
y_range
)
y_hist
=
histogram
(
ais_points
,
features
=
y_field
,
bins
=
y_nb_bins
,
ranges
=
[
y_range
])
result
=
np
.
zeros
(
h_joint
.
shape
)
for
idx
,
x
in
np
.
ndenumerate
(
h_joint
):
if
h_joint
[
idx
[:
-
1
]].
sum
()
==
0
:
result
[
idx
]
=
y_hist
[
idx
[
-
1
]]
else
:
result
[
idx
]
=
x
/
h_joint
[
idx
[:
-
1
]].
sum
()
return
result
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