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generic_pivoted_approximant.py
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R6746 RationalROMPy
generic_pivoted_approximant.py
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# Copyright (C) 2018 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
copy
import
deepcopy
as
copy
from
rrompy.reduction_methods.base.generic_approximant
import
(
GenericApproximant
)
from
rrompy.sampling
import
(
SamplingEnginePivoted
,
SamplingEnginePivotedPOD
,
SamplingEnginePivotedPODGlobal
)
from
rrompy.utilities.poly_fitting.polynomial
import
polybases
as
ppb
from
rrompy.utilities.poly_fitting.radial_basis
import
polybases
as
rbpb
from
rrompy.utilities.poly_fitting.moving_least_squares
import
(
polybases
as
mlspb
)
from
rrompy.utilities.base.types
import
paramList
,
ListAny
from
rrompy.utilities.base
import
verbosityManager
as
vbMng
from
rrompy.utilities.numerical.degree
import
reduceDegreeN
from
rrompy.utilities.exception_manager
import
(
RROMPyException
,
RROMPyAssert
,
RROMPyWarning
)
__all__
=
[
'GenericPivotedApproximantNoMatch'
,
'GenericPivotedApproximant'
,
'PODGlobal'
]
PODGlobal
=
2
class
GenericPivotedApproximantBase
(
GenericApproximant
):
def
__init__
(
self
,
directionPivot
:
ListAny
,
*
args
,
**
kwargs
):
self
.
_preInit
()
if
len
(
directionPivot
)
>
1
:
raise
RROMPyException
((
"Exactly 1 pivot parameter allowed in pole "
"matching."
))
from
rrompy.parameter.parameter_sampling
import
QuadratureSampler
as
QS
QSBase
=
QS
([[
0
],
[
1
]],
"UNIFORM"
)
self
.
_addParametersToList
([
"cutOffTolerance"
,
"radialDirectionalWeightsMarginal"
,
"interpRcondMarginal"
],
[
np
.
inf
,
[
1.
],
-
1
],
[
"samplerPivot"
,
"SMarginal"
,
"samplerMarginal"
],
[
QSBase
,
[
1
],
QSBase
])
del
QS
self
.
_directionPivot
=
directionPivot
super
()
.
__init__
(
*
args
,
**
kwargs
)
self
.
_postInit
()
def
setupSampling
(
self
):
"""Setup sampling engine."""
RROMPyAssert
(
self
.
_mode
,
message
=
"Cannot setup sampling engine."
)
if
not
hasattr
(
self
,
"_POD"
)
or
self
.
_POD
is
None
:
return
if
self
.
POD
:
if
self
.
POD
==
PODGlobal
:
SamplingEngine
=
SamplingEnginePivotedPODGlobal
else
:
SamplingEngine
=
SamplingEnginePivotedPOD
else
:
SamplingEngine
=
SamplingEnginePivoted
self
.
samplingEngine
=
SamplingEngine
(
self
.
HFEngine
,
self
.
directionPivot
,
sample_state
=
self
.
approx_state
,
verbosity
=
self
.
verbosity
)
def
initializeModelData
(
self
,
datadict
):
if
"directionPivot"
in
datadict
.
keys
():
from
.trained_model.trained_model_pivoted_data
import
(
TrainedModelPivotedData
)
return
(
TrainedModelPivotedData
(
datadict
[
"mu0"
],
datadict
.
pop
(
"projMat"
),
datadict
[
"scaleFactor"
],
datadict
.
pop
(
"rescalingExp"
),
datadict
[
"directionPivot"
]),
[
"mu0"
,
"scaleFactor"
,
"directionPivot"
,
"mus"
])
else
:
return
super
()
.
initializeModelData
(
datadict
)
@property
def
npar
(
self
):
"""Number of parameters."""
if
hasattr
(
self
,
"_temporaryPivot"
):
return
self
.
nparPivot
return
super
()
.
npar
@property
def
mus
(
self
):
"""Value of mus. Its assignment may reset snapshots."""
return
self
.
_mus
@mus.setter
def
mus
(
self
,
mus
):
musOld
=
copy
(
self
.
mus
)
if
hasattr
(
self
,
'_mus'
)
else
None
if
(
musOld
is
None
or
len
(
mus
)
!=
len
(
musOld
)
or
not
mus
==
musOld
):
self
.
resetSamples
()
self
.
_mus
=
mus
@property
def
cutOffTolerance
(
self
):
"""Value of cutOffTolerance."""
return
self
.
_cutOffTolerance
@cutOffTolerance.setter
def
cutOffTolerance
(
self
,
cutOffTolerance
):
self
.
_cutOffTolerance
=
cutOffTolerance
self
.
_approxParameters
[
"cutOffTolerance"
]
=
self
.
cutOffTolerance
@property
def
SMarginal
(
self
):
"""Value of SMarginal."""
return
self
.
_SMarginal
@SMarginal.setter
def
SMarginal
(
self
,
SMarginal
):
if
SMarginal
<=
0
:
raise
RROMPyException
(
"SMarginal must be positive."
)
if
hasattr
(
self
,
"_SMarginal"
)
and
self
.
_SMarginal
is
not
None
:
Sold
=
self
.
SMarginal
else
:
Sold
=
-
1
self
.
_SMarginal
=
SMarginal
self
.
_approxParameters
[
"SMarginal"
]
=
self
.
SMarginal
if
Sold
!=
self
.
SMarginal
:
self
.
resetSamples
()
@property
def
radialDirectionalWeightsMarginal
(
self
):
"""Value of radialDirectionalWeightsMarginal."""
return
self
.
_radialDirectionalWeightsMarginal
@radialDirectionalWeightsMarginal.setter
def
radialDirectionalWeightsMarginal
(
self
,
radialDirWeightsMarginal
):
if
not
hasattr
(
radialDirWeightsMarginal
,
"__len__"
):
radialDirWeightsMarginal
=
[
radialDirWeightsMarginal
]
self
.
_radialDirectionalWeightsMarginal
=
radialDirWeightsMarginal
self
.
_approxParameters
[
"radialDirectionalWeightsMarginal"
]
=
(
self
.
radialDirectionalWeightsMarginal
)
@property
def
interpRcondMarginal
(
self
):
"""Value of interpRcondMarginal."""
return
self
.
_interpRcondMarginal
@interpRcondMarginal.setter
def
interpRcondMarginal
(
self
,
interpRcondMarginal
):
self
.
_interpRcondMarginal
=
interpRcondMarginal
self
.
_approxParameters
[
"interpRcondMarginal"
]
=
(
self
.
interpRcondMarginal
)
@property
def
directionPivot
(
self
):
"""Value of directionPivot. Its assignment may reset snapshots."""
return
self
.
_directionPivot
@directionPivot.setter
def
directionPivot
(
self
,
directionPivot
):
if
hasattr
(
self
,
'_directionPivot'
):
directionPivotOld
=
copy
(
self
.
directionPivot
)
else
:
directionPivotOld
=
None
if
(
directionPivotOld
is
None
or
len
(
directionPivot
)
!=
len
(
directionPivotOld
)
or
not
directionPivot
==
directionPivotOld
):
self
.
resetSamples
()
self
.
_directionPivot
=
directionPivot
@property
def
directionMarginal
(
self
):
return
[
x
for
x
in
range
(
self
.
HFEngine
.
npar
)
\
if
x
not
in
self
.
directionPivot
]
@property
def
nparPivot
(
self
):
return
len
(
self
.
directionPivot
)
@property
def
nparMarginal
(
self
):
return
self
.
npar
-
self
.
nparPivot
@property
def
rescalingExpPivot
(
self
):
return
[
self
.
HFEngine
.
rescalingExp
[
x
]
for
x
in
self
.
directionPivot
]
@property
def
rescalingExpMarginal
(
self
):
return
[
self
.
HFEngine
.
rescalingExp
[
x
]
for
x
in
self
.
directionMarginal
]
@property
def
muBounds
(
self
):
"""Value of muBounds."""
return
self
.
samplerPivot
.
lims
@property
def
muBoundsMarginal
(
self
):
"""Value of muBoundsMarginal."""
return
self
.
samplerMarginal
.
lims
@property
def
samplerPivot
(
self
):
"""Value of samplerPivot."""
return
self
.
_samplerPivot
@samplerPivot.setter
def
samplerPivot
(
self
,
samplerPivot
):
if
'generatePoints'
not
in
dir
(
samplerPivot
):
raise
RROMPyException
(
"Pivot sampler type not recognized."
)
if
hasattr
(
self
,
'_samplerPivot'
)
and
self
.
_samplerPivot
is
not
None
:
samplerOld
=
self
.
samplerPivot
self
.
_samplerPivot
=
samplerPivot
self
.
_approxParameters
[
"samplerPivot"
]
=
self
.
samplerPivot
.
__str__
()
if
not
'samplerOld'
in
locals
()
or
samplerOld
!=
self
.
samplerPivot
:
self
.
resetSamples
()
@property
def
samplerMarginal
(
self
):
"""Value of samplerMarginal."""
return
self
.
_samplerMarginal
@samplerMarginal.setter
def
samplerMarginal
(
self
,
samplerMarginal
):
if
'generatePoints'
not
in
dir
(
samplerMarginal
):
raise
RROMPyException
(
"Marginal sampler type not recognized."
)
if
(
hasattr
(
self
,
'_samplerMarginal'
)
and
self
.
_samplerMarginal
is
not
None
):
samplerOld
=
self
.
samplerMarginal
self
.
_samplerMarginal
=
samplerMarginal
self
.
_approxParameters
[
"samplerMarginal"
]
=
(
self
.
samplerMarginal
.
__str__
())
if
not
'samplerOld'
in
locals
()
or
samplerOld
!=
self
.
samplerMarginal
:
self
.
resetSamples
()
def
setSamples
(
self
,
samplingEngine
):
"""Copy samplingEngine and samples."""
self
.
mus
=
copy
(
samplingEngine
.
mus
[
0
])
for
sEj
in
samplingEngine
.
mus
[
1
:]:
self
.
mus
.
append
(
sEj
)
super
()
.
setSamples
(
samplingEngine
)
def
computeScaleFactor
(
self
):
"""Compute parameter rescaling factor."""
RROMPyAssert
(
self
.
_mode
,
message
=
"Cannot compute rescaling factor."
)
self
.
scaleFactorPivot
=
.
5
*
np
.
abs
(
self
.
muBounds
[
0
]
**
self
.
rescalingExpPivot
-
self
.
muBounds
[
1
]
**
self
.
rescalingExpPivot
)
self
.
scaleFactorMarginal
=
.
5
*
np
.
abs
(
self
.
muBoundsMarginal
[
0
]
**
self
.
rescalingExpMarginal
-
self
.
muBoundsMarginal
[
1
]
**
self
.
rescalingExpMarginal
)
self
.
scaleFactor
=
np
.
empty
(
self
.
npar
)
self
.
scaleFactor
[
self
.
directionPivot
]
=
self
.
scaleFactorPivot
self
.
scaleFactor
[
self
.
directionMarginal
]
=
self
.
scaleFactorMarginal
def
normApprox
(
self
,
mu
:
paramList
)
->
float
:
_PODOld
=
self
.
POD
self
.
_POD
=
self
.
POD
==
PODGlobal
result
=
super
()
.
normApprox
(
mu
)
self
.
_POD
=
_PODOld
return
result
class
GenericPivotedApproximantNoMatch
(
GenericPivotedApproximantBase
):
"""
ROM pivoted approximant (without pole matching) computation for parametric
problems (ABSTRACT).
Args:
HFEngine: HF problem solver.
mu0(optional): Default parameter. Defaults to 0.
directionPivot(optional): Pivot components. Defaults to [0].
approxParameters(optional): Dictionary containing values for main
parameters of approximant. Recognized keys are:
- 'POD': whether to compute POD of snapshots; defaults to True;
- 'scaleFactorDer': scaling factors for derivative computation;
defaults to 'AUTO';
- 'cutOffTolerance': tolerance for ignoring parasitic poles;
defaults to np.inf;
- 'S': total number of pivot samples current approximant relies
upon;
- 'samplerPivot': pivot sample point generator;
- 'SMarginal': total number of marginal samples current approximant
relies upon;
- 'samplerMarginal': marginal sample point generator;
- 'radialDirectionalWeightsMarginal': radial basis weights for
marginal interpolant; defaults to 1;
- 'interpRcondMarginal': tolerance for marginal interpolation;
defaults to None.
Defaults to empty dict.
approx_state(optional): Whether to approximate state. Defaults to
False.
verbosity(optional): Verbosity level. Defaults to 10.
Attributes:
HFEngine: HF problem solver.
mu0: Default parameter.
directionPivot: Pivot components.
mus: Array of snapshot parameters.
musMarginal: Array of marginal snapshot parameters.
approxParameters: Dictionary containing values for main parameters of
approximant. Recognized keys are in parameterList.
parameterListSoft: Recognized keys of soft approximant parameters:
- 'POD': whether to compute POD of snapshots;
- 'scaleFactorDer': scaling factors for derivative computation;
- 'cutOffTolerance': tolerance for ignoring parasitic poles;
- 'radialDirectionalWeightsMarginal': radial basis weights for
marginal interpolant;
- 'interpRcondMarginal': tolerance for marginal interpolation.
parameterListCritical: Recognized keys of critical approximant
parameters:
- 'S': total number of pivot samples current approximant relies
upon;
- 'samplerPivot': pivot sample point generator;
- 'SMarginal': total number of marginal samples current approximant
relies upon;
- 'samplerMarginal': marginal sample point generator.
approx_state: Whether to approximate state.
verbosity: Verbosity level.
POD: Whether to compute POD of snapshots.
scaleFactorDer: Scaling factors for derivative computation.
cutOffTolerance: Tolerance for ignoring parasitic poles.
S: Total number of pivot samples current approximant relies upon.
samplerPivot: Pivot sample point generator.
SMarginal: Total number of marginal samples current approximant relies
upon.
samplerMarginal: Marginal sample point generator.
radialDirectionalWeightsMarginal: Radial basis weights for marginal
interpolant.
interpRcondMarginal: Tolerance for marginal interpolation.
muBounds: list of bounds for pivot parameter values.
muBoundsMarginal: list of bounds for marginal parameter values.
samplingEngine: Sampling engine.
uHF: High fidelity solution(s) with parameter(s) lastSolvedHF as
sampleList.
lastSolvedHF: Parameter(s) corresponding to last computed high fidelity
solution(s) as parameterList.
uApproxReduced: Reduced approximate solution(s) with parameter(s)
lastSolvedApprox as sampleList.
lastSolvedApproxReduced: Parameter(s) corresponding to last computed
reduced approximate solution(s) as parameterList.
uApprox: Approximate solution(s) with parameter(s) lastSolvedApprox as
sampleList.
lastSolvedApprox: Parameter(s) corresponding to last computed
approximate solution(s) as parameterList.
"""
@property
def
tModelType
(
self
):
from
.trained_model.trained_model_pivoted_rational_nomatch
import
(
TrainedModelPivotedRationalNoMatch
)
return
TrainedModelPivotedRationalNoMatch
def
_finalizeMarginalization
(
self
):
vbMng
(
self
,
"INIT"
,
"Recompressing by cut off."
,
10
)
msg
=
self
.
trainedModel
.
recompressByCutOff
(
self
.
cutOffTolerance
,
self
.
samplerPivot
.
normalFoci
(),
self
.
samplerPivot
.
groundPotential
())
vbMng
(
self
,
"DEL"
,
"Done recompressing."
+
msg
,
10
)
self
.
trainedModel
.
setupMarginalInterp
(
self
,
self
.
radialDirectionalWeightsMarginal
)
self
.
trainedModel
.
data
.
approxParameters
=
copy
(
self
.
approxParameters
)
class
GenericPivotedApproximant
(
GenericPivotedApproximantBase
):
"""
ROM pivoted approximant (with pole matching) computation for parametric
problems (ABSTRACT).
Args:
HFEngine: HF problem solver.
mu0(optional): Default parameter. Defaults to 0.
directionPivot(optional): Pivot components. Defaults to [0].
approxParameters(optional): Dictionary containing values for main
parameters of approximant. Recognized keys are:
- 'POD': whether to compute POD of snapshots; defaults to True;
- 'scaleFactorDer': scaling factors for derivative computation;
defaults to 'AUTO';
- 'matchingWeight': weight for pole matching optimization; defaults
to 1;
- 'cutOffTolerance': tolerance for ignoring parasitic poles;
defaults to np.inf;
- 'cutOffSharedRatio': required ratio of marginal points to share
resonance in cut off strategy; defaults to 1.;
- 'S': total number of pivot samples current approximant relies
upon;
- 'samplerPivot': pivot sample point generator;
- 'SMarginal': total number of marginal samples current approximant
relies upon;
- 'samplerMarginal': marginal sample point generator;
- 'polybasisMarginal': type of polynomial basis for marginal
interpolation; allowed values include 'MONOMIAL', 'CHEBYSHEV'
and 'LEGENDRE'; defaults to 'MONOMIAL';
- 'MMarginal': degree of marginal interpolant; defaults to 'AUTO',
i.e. maximum allowed;
- 'polydegreetypeMarginal': type of polynomial degree for marginal;
defaults to 'TOTAL';
- 'radialDirectionalWeightsMarginal': radial basis weights for
marginal interpolant; defaults to 1;
- 'interpRcondMarginal': tolerance for marginal interpolation;
defaults to None.
Defaults to empty dict.
approx_state(optional): Whether to approximate state. Defaults to
False.
verbosity(optional): Verbosity level. Defaults to 10.
Attributes:
HFEngine: HF problem solver.
mu0: Default parameter.
directionPivot: Pivot components.
mus: Array of snapshot parameters.
musMarginal: Array of marginal snapshot parameters.
approxParameters: Dictionary containing values for main parameters of
approximant. Recognized keys are in parameterList.
parameterListSoft: Recognized keys of soft approximant parameters:
- 'POD': whether to compute POD of snapshots;
- 'scaleFactorDer': scaling factors for derivative computation;
- 'matchingWeight': weight for pole matching optimization;
- 'cutOffTolerance': tolerance for ignoring parasitic poles;
- 'cutOffSharedRatio': required ratio of marginal points to share
resonance in cut off strategy;
- 'polybasisMarginal': type of polynomial basis for marginal
interpolation;
- 'MMarginal': degree of marginal interpolant;
- 'polydegreetypeMarginal': type of polynomial degree for marginal;
- 'radialDirectionalWeightsMarginal': radial basis weights for
marginal interpolant;
- 'interpRcondMarginal': tolerance for marginal interpolation.
parameterListCritical: Recognized keys of critical approximant
parameters:
- 'S': total number of pivot samples current approximant relies
upon;
- 'samplerPivot': pivot sample point generator;
- 'SMarginal': total number of marginal samples current approximant
relies upon;
- 'samplerMarginal': marginal sample point generator.
approx_state: Whether to approximate state.
verbosity: Verbosity level.
POD: Whether to compute POD of snapshots.
scaleFactorDer: Scaling factors for derivative computation.
matchingWeight: Weight for pole matching optimization.
cutOffTolerance: Tolerance for ignoring parasitic poles.
cutOffSharedRatio: Required ratio of marginal points to share resonance
in cut off strategy.
S: Total number of pivot samples current approximant relies upon.
samplerPivot: Pivot sample point generator.
SMarginal: Total number of marginal samples current approximant relies
upon.
samplerMarginal: Marginal sample point generator.
polybasisMarginal: Type of polynomial basis for marginal interpolation.
MMarginal: Degree of marginal interpolant.
polydegreetypeMarginal: Type of polynomial degree for marginal.
radialDirectionalWeightsMarginal: Radial basis weights for marginal
interpolant.
interpRcondMarginal: Tolerance for marginal interpolation.
muBounds: list of bounds for pivot parameter values.
muBoundsMarginal: list of bounds for marginal parameter values.
samplingEngine: Sampling engine.
uHF: High fidelity solution(s) with parameter(s) lastSolvedHF as
sampleList.
lastSolvedHF: Parameter(s) corresponding to last computed high fidelity
solution(s) as parameterList.
uApproxReduced: Reduced approximate solution(s) with parameter(s)
lastSolvedApprox as sampleList.
lastSolvedApproxReduced: Parameter(s) corresponding to last computed
reduced approximate solution(s) as parameterList.
uApprox: Approximate solution(s) with parameter(s) lastSolvedApprox as
sampleList.
lastSolvedApprox: Parameter(s) corresponding to last computed
approximate solution(s) as parameterList.
"""
def
__init__
(
self
,
*
args
,
**
kwargs
):
self
.
_preInit
()
self
.
_addParametersToList
([
"matchingWeight"
,
"cutOffSharedRatio"
,
"polybasisMarginal"
,
"MMarginal"
,
"polydegreetypeMarginal"
],
[
1.
,
1.
,
"MONOMIAL"
,
"AUTO"
,
"TOTAL"
])
super
()
.
__init__
(
*
args
,
**
kwargs
)
self
.
_postInit
()
@property
def
tModelType
(
self
):
from
.trained_model.trained_model_pivoted_rational
import
(
TrainedModelPivotedRational
)
return
TrainedModelPivotedRational
@property
def
matchingWeight
(
self
):
"""Value of matchingWeight."""
return
self
.
_matchingWeight
@matchingWeight.setter
def
matchingWeight
(
self
,
matchingWeight
):
self
.
_matchingWeight
=
matchingWeight
self
.
_approxParameters
[
"matchingWeight"
]
=
self
.
matchingWeight
@property
def
cutOffSharedRatio
(
self
):
"""Value of cutOffSharedRatio."""
return
self
.
_cutOffSharedRatio
@cutOffSharedRatio.setter
def
cutOffSharedRatio
(
self
,
cutOffSharedRatio
):
if
cutOffSharedRatio
>
1.
:
RROMPyWarning
(
"Cut off shared ratio too large. Clipping to 1."
)
cutOffSharedRatio
=
1.
elif
cutOffSharedRatio
<
0.
:
RROMPyWarning
(
"Cut off shared ratio too small. Clipping to 0."
)
cutOffSharedRatio
=
0.
self
.
_cutOffSharedRatio
=
cutOffSharedRatio
self
.
_approxParameters
[
"cutOffSharedRatio"
]
=
self
.
cutOffSharedRatio
@property
def
polybasisMarginal
(
self
):
"""Value of polybasisMarginal."""
return
self
.
_polybasisMarginal
@polybasisMarginal.setter
def
polybasisMarginal
(
self
,
polybasisMarginal
):
try
:
polybasisMarginal
=
polybasisMarginal
.
upper
()
.
strip
()
.
replace
(
" "
,
""
)
if
polybasisMarginal
not
in
ppb
+
rbpb
+
mlspb
:
raise
RROMPyException
(
"Prescribed marginal polybasis not recognized."
)
self
.
_polybasisMarginal
=
polybasisMarginal
except
:
RROMPyWarning
((
"Prescribed marginal polybasis not recognized. "
"Overriding to 'MONOMIAL'."
))
self
.
_polybasisMarginal
=
"MONOMIAL"
self
.
_approxParameters
[
"polybasisMarginal"
]
=
self
.
polybasisMarginal
@property
def
MMarginal
(
self
):
"""Value of MMarginal."""
return
self
.
_MMarginal
@MMarginal.setter
def
MMarginal
(
self
,
MMarginal
):
if
isinstance
(
MMarginal
,
str
):
MMarginal
=
MMarginal
.
strip
()
.
replace
(
" "
,
""
)
if
"-"
not
in
MMarginal
:
MMarginal
=
MMarginal
+
"-0"
self
.
_MMarginal_isauto
=
True
self
.
_MMarginal_shift
=
int
(
MMarginal
.
split
(
"-"
)[
-
1
])
MMarginal
=
0
if
MMarginal
<
0
:
raise
RROMPyException
(
"MMarginal must be non-negative."
)
self
.
_MMarginal
=
MMarginal
self
.
_approxParameters
[
"MMarginal"
]
=
self
.
MMarginal
def
_setMMarginalAuto
(
self
):
self
.
MMarginal
=
max
(
0
,
reduceDegreeN
(
len
(
self
.
musMarginal
),
len
(
self
.
musMarginal
),
self
.
nparMarginal
,
self
.
polydegreetypeMarginal
)
-
self
.
_MMarginal_shift
)
vbMng
(
self
,
"MAIN"
,
(
"Automatically setting MMarginal to "
"{}."
)
.
format
(
self
.
MMarginal
),
25
)
@property
def
polydegreetypeMarginal
(
self
):
"""Value of polydegreetypeMarginal."""
return
self
.
_polydegreetypeMarginal
@polydegreetypeMarginal.setter
def
polydegreetypeMarginal
(
self
,
polydegreetypeM
):
try
:
polydegreetypeM
=
polydegreetypeM
.
upper
()
.
strip
()
.
replace
(
" "
,
""
)
if
polydegreetypeM
not
in
[
"TOTAL"
,
"FULL"
]:
raise
RROMPyException
((
"Prescribed polydegreetypeMarginal not "
"recognized."
))
self
.
_polydegreetypeMarginal
=
polydegreetypeM
except
:
RROMPyWarning
((
"Prescribed polydegreetypeMarginal not recognized. "
"Overriding to 'TOTAL'."
))
self
.
_polydegreetypeMarginal
=
"TOTAL"
self
.
_approxParameters
[
"polydegreetypeMarginal"
]
=
(
self
.
polydegreetypeMarginal
)
def
_finalizeMarginalization
(
self
):
vbMng
(
self
,
"INIT"
,
"Recompressing by cut off."
,
10
)
msg
=
self
.
trainedModel
.
recompressByCutOff
(
self
.
cutOffTolerance
,
self
.
cutOffSharedRatio
,
self
.
samplerPivot
.
normalFoci
(),
self
.
samplerPivot
.
groundPotential
())
vbMng
(
self
,
"DEL"
,
"Done recompressing."
+
msg
,
10
)
interpPars
=
[
self
.
verbosity
>=
5
,
self
.
polydegreetypeMarginal
==
"TOTAL"
,
{}]
if
self
.
polybasisMarginal
in
ppb
+
rbpb
:
interpPars
+=
[{
"rcond"
:
self
.
interpRcondMarginal
}]
self
.
trainedModel
.
setupMarginalInterp
(
self
,
interpPars
,
hasattr
(
self
,
"_MMarginal_isauto"
),
self
.
radialDirectionalWeightsMarginal
,
hasattr
(
self
,
"_reduceDegreeNNoWarn"
))
self
.
trainedModel
.
data
.
approxParameters
=
copy
(
self
.
approxParameters
)
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