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nearest_neighbor.py
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Wed, May 8, 05:47

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/>.
#
from copy import deepcopy as copy
import numpy as np
from .generic_standard_approximant import GenericStandardApproximant
from rrompy.utilities.base import verbosityManager as vbMng
from rrompy.utilities.poly_fitting.nearest_neighbor import (
NearestNeighborInterpolator as NNI)
from rrompy.utilities.exception_manager import RROMPyAssert
__all__ = ['NearestNeighbor']
class NearestNeighbor(GenericStandardApproximant):
"""
ROM nearest neighbor approximant computation for parametric problems.
Args:
HFEngine: HF problem solver.
mu0(optional): Default parameter. 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';
- 'S': total number of samples current approximant relies upon;
- 'sampler': sample point generator;
- 'nNeighbors': number of nearest neighbors; defaults to 1;
- 'radialDirectionalWeights': directional weights for computation
of parameter distance; defaults to 1.
Defaults to empty dict.
approx_state(optional): Whether to approximate state. Defaults and must
be True.
verbosity(optional): Verbosity level. Defaults to 10.
Attributes:
HFEngine: HF problem solver.
mu0: Default parameter.
mus: Array of 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;
- 'nNeighbors': number of nearest neighbors;
- 'radialDirectionalWeights': directional weights for computation
of parameter distance.
parameterListCritical: Recognized keys of critical approximant
parameters:
- 'S': total number of samples current approximant relies upon;
- 'sampler': 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.
S: Number of solution snapshots over which current approximant is
based upon.
sampler: Sample point generator.
nNeighbors: Number of nearest neighbors.
radialDirectionalWeights: Directional weights for computation of
parameter distance.
muBounds: list of bounds for 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(["nNeighbors", "radialDirectionalWeights"],
[1, [1.]])
super().__init__(*args, **kwargs)
self._postInit()
@property
def tModelType(self):
from .trained_model.trained_model_nearest_neighbor import (
TrainedModelNearestNeighbor)
return TrainedModelNearestNeighbor
@property
def nNeighbors(self):
"""Value of nNeighbors."""
return self._nNeighbors
@nNeighbors.setter
def nNeighbors(self, nNeighbors):
self._nNeighbors = max(1, nNeighbors)
self._approxParameters["nNeighbors"] = self.nNeighbors
@property
def radialDirectionalWeights(self):
"""Value of radialDirectionalWeights."""
return self._radialDirectionalWeights
@radialDirectionalWeights.setter
def radialDirectionalWeights(self, radialDirectionalWeights):
if hasattr(radialDirectionalWeights, "__len__"):
radialDirectionalWeights = list(radialDirectionalWeights)
else:
radialDirectionalWeights = [radialDirectionalWeights]
self._radialDirectionalWeights = radialDirectionalWeights
self._approxParameters["radialDirectionalWeights"] = (
self.radialDirectionalWeights)
def setupApprox(self) -> int:
"""Compute RB projection matrix."""
if self.checkComputedApprox(): return -1
RROMPyAssert(self._mode, message = "Cannot setup approximant.")
vbMng(self, "INIT", "Setting up {}.". format(self.name()), 5)
self.computeSnapshots()
setData = self.trainedModel is None
self._setupTrainedModel(self.samplingEngine.samples.data)
if setData: self.trainedModel.data.NN = NNI()
if self.POD:
suppVals = self.samplingEngine.RPOD.T
else:
suppVals = np.eye(len(self.mus))
self.trainedModel.data.NN.setupByInterpolation(self.mus, suppVals,
self.nNeighbors,
self.radialDirectionalWeights)
self.trainedModel.data.approxParameters = copy(self.approxParameters)
vbMng(self, "DEL", "Done setting up approximant.", 5)
return 0

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