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kernel.py
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Thu, Nov 28, 09:50
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text/x-python
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Sat, Nov 30, 09:50 (2 d)
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R6746 RationalROMPy
kernel.py
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# Copyright (C) 2018-2020 by the RROMPy authors
#
# This file is part of RROMPy.
#
# RROMPy is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# RROMPy is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with RROMPy. If not, see <http://www.gnu.org/licenses/>.
#
import
numpy
as
np
from
sklearn.gaussian_process.kernels
import
(
WhiteKernel
,
RBF
,
Matern
,
RationalQuadratic
,
ExpSineSquared
)
from
rrompy.utilities.base.types
import
Np1D
,
Tuple
from
rrompy.utilities.exception_manager
import
RROMPyException
__all__
=
[
'kernels'
]
def
setThetaAuto
(
val
,
bounds
,
name
:
str
=
"theta"
,
log
:
bool
=
True
):
if
val
is
None
:
if
bounds
is
None
:
raise
RROMPyException
((
"Must specify at least one of {0} and "
"{0}_bounds."
.
format
(
name
)))
val
=
10.
**
np
.
mean
(
np
.
log10
(
bounds
))
if
log
else
np
.
mean
(
bounds
)
if
bounds
is
None
:
bounds
=
"fixed"
return
(
val
,
bounds
)
def
addNoise
(
kernel
,
noise_level
:
float
=
None
,
noise_level_bounds
:
Tuple
[
float
]
=
None
):
try
:
noise_level
,
noise_level_bounds
=
setThetaAuto
(
noise_level
,
noise_level_bounds
,
"noise_level"
)
except
RROMPyException
:
return
1.0
*
kernel
return
1.0
*
kernel
+
WhiteKernel
(
noise_level
=
noise_level
,
noise_level_bounds
=
noise_level_bounds
)
def
radialGaussianKernel
(
length_scale
:
Np1D
=
None
,
length_scale_bounds
:
Tuple
[
float
]
=
None
,
noise_level
:
float
=
None
,
noise_level_bounds
:
Tuple
[
float
]
=
None
):
length_scale
,
length_scale_bounds
=
setThetaAuto
(
length_scale
,
length_scale_bounds
,
"length_scale"
)
return
addNoise
(
RBF
(
length_scale
=
length_scale
,
length_scale_bounds
=
length_scale_bounds
),
noise_level
,
noise_level_bounds
)
def
maternKernel
(
length_scale
:
Np1D
=
None
,
length_scale_bounds
:
Tuple
[
float
]
=
None
,
nu
:
float
=
1.5
,
noise_level
:
float
=
None
,
noise_level_bounds
:
Tuple
[
float
]
=
None
):
length_scale
,
length_scale_bounds
=
setThetaAuto
(
length_scale
,
length_scale_bounds
,
"length_scale"
)
return
addNoise
(
Matern
(
length_scale
=
length_scale
,
length_scale_bounds
=
length_scale_bounds
,
nu
=
nu
),
noise_level
,
noise_level_bounds
)
def
rationalQuadraticKernel
(
length_scale
:
Np1D
=
None
,
alpha
:
float
=
None
,
length_scale_bounds
:
Tuple
[
float
]
=
None
,
alpha_bounds
:
Tuple
[
float
]
=
None
,
noise_level
:
float
=
None
,
noise_level_bounds
:
Tuple
[
float
]
=
None
):
length_scale
,
length_scale_bounds
=
setThetaAuto
(
length_scale
,
length_scale_bounds
,
"length_scale"
)
alpha
,
alpha_bounds
=
setThetaAuto
(
alpha
,
alpha_bounds
,
"alpha"
,
False
)
return
addNoise
(
RationalQuadratic
(
length_scale
=
length_scale
,
alpha
=
alpha
,
length_scale_bounds
=
length_scale_bounds
,
alpha_bounds
=
alpha_bounds
),
noise_level
,
noise_level_bounds
)
def
expSineSquaredKernel
(
length_scale
:
Np1D
=
None
,
periodicity
:
float
=
None
,
length_scale_bounds
:
Tuple
[
float
]
=
None
,
periodicity_bounds
:
Tuple
[
float
]
=
None
,
noise_level
:
float
=
None
,
noise_level_bounds
:
Tuple
[
float
]
=
None
):
length_scale
,
length_scale_bounds
=
setThetaAuto
(
length_scale
,
length_scale_bounds
,
"length_scale"
)
periodicity
,
periodicity_bounds
=
setThetaAuto
(
periodicity
,
periodicity_bounds
,
"periodicity"
,
False
)
return
addNoise
(
ExpSineSquared
(
length_scale
=
length_scale
,
periodicity
=
periodicity
,
length_scale_bounds
=
length_scale_bounds
,
periodicity_bounds
=
periodicity_bounds
),
noise_level
,
noise_level_bounds
)
kernels
=
{
"GAUSSIAN"
:
radialGaussianKernel
,
"MATERN"
:
maternKernel
,
"RATIONALQUADRATIC"
:
rationalQuadraticKernel
,
"EXPSINESQUARED"
:
expSineSquaredKernel
}
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