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trained_model_pivoted_rational_nearest_neighbor.py
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Tue, Jul 23, 13:38

trained_model_pivoted_rational_nearest_neighbor.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 .trained_model_pivoted_rational import TrainedModelPivotedRational
from rrompy.utilities.base.types import Np1D, paramVal, paramList
from rrompy.utilities.base import verbosityManager as vbMng
from rrompy.utilities.poly_fitting.nearest_neighbor import (
NearestNeighborInterpolator as NNI)
from rrompy.parameter import checkParameterList
__all__ = ['TrainedModelPivotedRationalNearestNeighbor']
class TrainedModelPivotedRationalNearestNeighbor(TrainedModelPivotedRational):
"""
ROM approximant evaluation for pivoted approximants based on interpolation
of rational approximants (with pole matching) via local average.
Attributes:
Data: dictionary with all that can be pickled.
"""
def centerNormalizeMarginal(self, mu : paramList = [],
mu0 : paramVal = None) -> paramList:
return checkParameterList(mu, self.data.nparMarginal)[0]
def setupMarginalInterp(self, nN:bool, rDWM : Np1D = None):
vbMng(self, "INIT", "Starting computation of marginal interpolator.",
12)
self.data.marginalInterp = []
for ipts, pts in enumerate(self.data.suppEffPts):
nn = NNI()
if len(pts) == 0:
nn.setupByInterpolation(self.data.musMarginal[[0]],
np.ones((1, 0)), 1, rDWM)
else:
nn.setupByInterpolation(self.data.musMarginal[pts],
np.eye(len(pts)), nN, rDWM)
self.data.marginalInterp += [copy(nn)]
vbMng(self, "DEL", "Done computing marginal interpolator.", 12)

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