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trained_model.py
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Tue, Jul 30, 03:55
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text/x-python
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Thu, Aug 1, 03:55 (2 d)
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R6746 RationalROMPy
trained_model.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/>.
#
from
abc
import
abstractmethod
from
rrompy.utilities.base.types
import
Np1D
,
paramList
,
sampList
from
rrompy.parameter
import
checkParameterList
from
rrompy.sampling
import
emptySampleList
__all__
=
[
'TrainedModel'
]
class
TrainedModel
:
"""
ABSTRACT
ROM approximant evaluation.
Attributes:
Data: dictionary with all that can be pickled.
"""
def
name
(
self
)
->
str
:
return
self
.
__class__
.
__name__
def
__str__
(
self
)
->
str
:
return
self
.
name
()
def
__repr__
(
self
)
->
str
:
return
self
.
__str__
()
+
" at "
+
hex
(
id
(
self
))
def
reset
(
self
):
self
.
lastSolvedApproxReduced
=
None
self
.
lastSolvedApprox
=
None
@property
def
npar
(
self
):
"""Number of parameters."""
return
self
.
data
.
mu0
.
shape
[
1
]
@abstractmethod
def
getApproxReduced
(
self
,
mu
:
paramList
=
[])
->
sampList
:
"""
Evaluate reduced representation of approximant at arbitrary parameter.
(ABSTRACT)
Args:
mu: Target parameter.
"""
pass
def
getApprox
(
self
,
mu
:
paramList
=
[])
->
sampList
:
"""
Evaluate approximant at arbitrary parameter.
Args:
mu: Target parameter.
"""
mu
=
checkParameterList
(
mu
,
self
.
data
.
npar
)[
0
]
if
(
not
hasattr
(
self
,
"lastSolvedApprox"
)
or
self
.
lastSolvedApprox
!=
mu
):
uApproxR
=
self
.
getApproxReduced
(
mu
)
self
.
uApprox
=
emptySampleList
()
for
i
in
range
(
len
(
mu
)):
uApp
=
self
.
data
.
projMat
.
dot
(
uApproxR
[
i
])
if
i
==
0
:
self
.
uApprox
.
reset
((
len
(
uApp
),
len
(
mu
)),
dtype
=
uApp
.
dtype
)
self
.
uApprox
[
i
]
=
uApp
self
.
lastSolvedApprox
=
mu
return
self
.
uApprox
@abstractmethod
def
getPoles
(
self
)
->
Np1D
:
"""
Obtain approximant poles.
Returns:
Numpy complex vector of poles.
"""
pass
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