Page MenuHomec4science
Files F67921245

trained_model_pivoted_rational.py
No OneTemporary
Actions

File Metadata

Created
Tue, Jun 25, 01:49

trained_model_pivoted_rational.py
View Options

# 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 .trained_model_pivoted_general import TrainedModelPivotedGeneral
from .trained_model_rational import TrainedModelRational
from rrompy.utilities.base.types import Np1D, List, paramList, sampList, HFEng
from rrompy.utilities.base import verbosityManager as vbMng
from rrompy.utilities.poly_fitting.heaviside import rational2heaviside
from rrompy.utilities.exception_manager import RROMPyAssert
from rrompy.parameter import checkParameterList
from rrompy.sampling import sampleList
__all__ = ['TrainedModelPivotedRational']
class TrainedModelPivotedRational(TrainedModelPivotedGeneral,
TrainedModelRational):
"""
ROM approximant evaluation for pivoted Rational approximant (with pole
matching).
Attributes:
Data: dictionary with all that can be pickled.
"""
def initializeFromRational(self, HFEngine:HFEng,
matchingWeight : float = 1., POD : bool = True):
"""Initialize Heaviside representation."""
RROMPyAssert(self.data.nparPivot, 1, "Number of pivot parameters")
poles, coeffs = [], []
for Q, P in zip(self.data.Qs, self.data.Ps):
cfs, pls, basis = rational2heaviside(P, Q)
poles += [pls]
coeffs += [cfs]
self.initializeFromLists(poles, coeffs, basis, HFEngine,
matchingWeight, POD)
self.data._temporary = False
def getPVal(self, mu : paramList = []) -> sampList:
"""
Evaluate rational numerator at arbitrary parameter.
Args:
mu: Target parameter.
"""
RROMPyAssert(self.data.nparPivot, 1, "Number of pivot parameters")
if not self.data._temporary: return super().getPVal(mu)
mu = checkParameterList(mu, self.data.npar)[0]
muP = checkParameterList(mu.data[:, self.data.directionPivot],
self.data.nparPivot)[0]
muM = checkParameterList(mu.data[:, self.data.directionMarginal],
self.data.nparMarginal)[0]
muP = checkParameterList(muP, self.data.nparPivot)[0]
vbMng(self, "INIT", "Evaluating numerator at mu = {}.".format(muP), 17)
muPC = self.centerNormalizePivot(muP)
pP = [sampleList(P(muPC)) for P in self.data.Ps]
vbMng(self, "DEL", "Done evaluating numerator.", 17)
return self.interpolateMarginal(muM, pP)
def getQVal(self, mu:Np1D, der : List[int] = None,
scl : Np1D = None) -> Np1D:
"""
Evaluate rational denominator at arbitrary parameter.
Args:
mu: Target parameter.
der(optional): Derivatives to take before evaluation.
"""
RROMPyAssert(self.data.nparPivot, 1, "Number of pivot parameters")
if not self.data._temporary: return super().getQVal(mu, der, scl)
mu = checkParameterList(mu, self.data.npar)[0]
muP = checkParameterList(mu.data[:, self.data.directionPivot],
self.data.nparPivot)[0]
muM = checkParameterList(mu.data[:, self.data.directionMarginal],
self.data.nparMarginal)[0]
if der is None:
derP, derM = None, None
else:
derP = [der[x] for x in self.data.directionPivot]
derM = [der[x] for x in self.data.directionMarginal]
if scl is None:
sclP, sclM = None, None
else:
sclP = [scl[x] for x in self.data.directionPivot]
sclM = [scl[x] for x in self.data.directionMarginal]
muP = checkParameterList(muP, self.data.nparPivot)[0]
vbMng(self, "INIT", "Evaluating denominator at mu = {}.".format(muP),
17)
muPC = self.centerNormalizePivot(muP)
qP = [Q(muPC, derP, sclP).reshape(1, -1) for Q in self.data.Qs]
vbMng(self, "DEL", "Done evaluating denominator.", 17)
return self.interpolateMarginal(muM, qP, derM, sclM)

Event Timeline

c4science · Help