rational_interpolant_pivoted.py
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Created: Tue, May 14, 08:13

rational_interpolant_pivoted.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 rrompy.reduction_methods.base import checkRobustTolerance
	from .generic_pivoted_approximant import GenericPivotedApproximant
	from rrompy.reduction_methods.standard.rational_interpolant import (
	RationalInterpolant as RI)
	from rrompy.utilities.poly_fitting.polynomial import (
	polybases as ppb, polyfitname,
	polyvander as pvP, polyvanderTotal as pvTP,
	PolynomialInterpolator as PI)
	from rrompy.utilities.poly_fitting.radial_basis import (polybases as rbpb,
	RadialBasisInterpolator as RBI)
	from rrompy.utilities.poly_fitting.moving_least_squares import (
	polybases as mlspb,
	MovingLeastSquaresInterpolator as MLSI)
	from rrompy.utilities.base.types import (Np1D, Np2D, HFEng, DictAny, Tuple,
	List, ListAny, paramVal)
	from rrompy.utilities.base import verbosityManager as vbMng, freepar as fp
	from rrompy.utilities.numerical import (multifactorial, customPInv, dot,
	fullDegreeN, totalDegreeN,
	degreeTotalToFull, fullDegreeMaxMask,
	totalDegreeMaxMask,
	nextDerivativeIndices,
	hashDerivativeToIdx as hashD,
	hashIdxToDerivative as hashI)
	from rrompy.utilities.exception_manager import (RROMPyException, RROMPyAssert,
	RROMPyWarning)
	from rrompy.parameter import checkParameter

	__all__ = ['RationalInterpolantPivoted']

	class RationalInterpolantPivoted(GenericPivotedApproximant):
	"""
	ROM pivoted rational interpolant (with pole matching) computation for
	parametric problems.

	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;
	- 'matchingWeight': weight for pole matching optimization; defaults
	to 1;
	- 'cutOffTolerance': tolerance for ignoring parasitic poles;
	defaults to np.inf;
	- 'cutOffType': rule for tolerance computation for parasitic poles;
	defaults to 'MAGNITUDE';
	- '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;
	- 'polybasisPivot': type of polynomial basis for pivot
	interpolation; defaults to 'MONOMIAL';
	- 'polybasisMarginal': type of polynomial basis for marginal
	interpolation; allowed values include 'MONOMIAL', 'CHEBYSHEV'
	and 'LEGENDRE'; defaults to 'MONOMIAL';
	- 'M': degree of rational interpolant numerator; defaults to 0;
	- 'N': degree of rational interpolant denominator; defaults to 0;
	- 'polydegreetype': type of polynomial degree; defaults to 'TOTAL';
	- 'MMarginal': degree of marginal interpolant; defaults to 0;
	- 'polydegreetypeMarginal': type of polynomial degree for marginal;
	defaults to 'TOTAL';
	- 'radialDirectionalWeightsPivot': radial basis weights for pivot
	numerator; defaults to 0, i.e. identity;
	- 'radialDirectionalWeightsMarginal': radial basis weights for
	marginal interpolant; defaults to 0, i.e. identity;
	- 'nNearestNeighborPivot': number of pivot nearest neighbors
	considered if polybasisPivot allows; defaults to -1;
	- 'nNearestNeighborMarginal': number of marginal nearest neighbors
	considered if polybasisMarginal allows; defaults to -1;
	- 'interpRcondPivot': tolerance for pivot interpolation; defaults
	to None;
	- 'interpRcondMarginal': tolerance for marginal interpolation;
	defaults to None;
	- 'robustTol': tolerance for robust rational denominator
	management; defaults to 0.
	Defaults to empty dict.
	force_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.
	musPivot: Array of pivot 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;
	- 'matchingWeight': weight for pole matching optimization;
	- 'cutOffTolerance': tolerance for ignoring parasitic poles;
	- 'cutOffType': rule for tolerance computation for parasitic poles;
	- 'polybasisPivot': type of polynomial basis for pivot
	interpolation;
	- 'polybasisMarginal': type of polynomial basis for marginal
	interpolation;
	- 'M': degree of rational interpolant numerator;
	- 'N': degree of rational interpolant denominator;
	- 'polydegreetype': type of polynomial degree;
	- 'MMarginal': degree of marginal interpolant;
	- 'polydegreetypeMarginal': type of polynomial degree for marginal;
	- 'radialDirectionalWeightsPivot': radial basis weights for pivot
	numerator;
	- 'radialDirectionalWeightsMarginal': radial basis weights for
	marginal interpolant;
	- 'nNearestNeighborPivot': number of pivot nearest neighbors
	considered if polybasisPivot allows;
	- 'nNearestNeighborMarginal': number of marginal nearest neighbors
	considered if polybasisMarginal allows;
	- 'interpRcondPivot': tolerance for pivot interpolation;
	- 'interpRcondMarginal': tolerance for marginal interpolation;
	- 'robustTol': tolerance for robust rational denominator
	management.
	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.
	force_state: Whether to approximate state.
	verbosity: Verbosity level.
	POD: Whether to compute POD of snapshots.
	matchingWeight: Weight for pole matching optimization.
	cutOffTolerance: Tolerance for ignoring parasitic poles.
	cutOffType: Rule for tolerance computation for parasitic poles.
	S: Total number of pivot samples current approximant relies upon.
	sampler: Pivot sample point generator.
	polybasisPivot: Type of polynomial basis for pivot interpolation.
	polybasisMarginal: Type of polynomial basis for marginal interpolation.
	M: Numerator degree of approximant.
	N: Denominator degree of approximant.
	polydegreetype: Type of polynomial degree.
	MMarginal: Degree of marginal interpolant.
	polydegreetypeMarginal: Type of polynomial degree for marginal.
	radialDirectionalWeightsPivot: Radial basis weights for pivot
	numerator.
	radialDirectionalWeightsMarginal: Radial basis weights for marginal
	interpolant.
	nNearestNeighborPivot: Number of pivot nearest neighbors considered if
	polybasisPivot allows.
	nNearestNeighborMarginal: Number of marginal nearest neighbors
	considered if polybasisMarginal allows.
	interpRcondPivot: Tolerance for pivot interpolation.
	interpRcondMarginal: Tolerance for marginal interpolation.
	robustTol: Tolerance for robust rational denominator management.
	muBoundsPivot: 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.
	Q: Numpy 1D vector containing complex coefficients of approximant
	denominator.
	P: Numpy 2D vector whose columns are FE dofs of coefficients of
	approximant numerator.
	"""

	def __init__(self, HFEngine:HFEng, mu0 : paramVal = None,
	directionPivot : ListAny = [0],
	approxParameters : DictAny = {}, force_state : bool = False,
	verbosity : int = 10, timestamp : bool = True):
	self._preInit()
	self._addParametersToList(["polybasisPivot", "M", "N",
	"polydegreetype",
	"radialDirectionalWeightsPivot",
	"nNearestNeighborPivot",
	"interpRcondPivot", "robustTol"],
	["MONOMIAL", 0, 0, "TOTAL", 1, -1, -1, 0])
	super().__init__(HFEngine = HFEngine, mu0 = mu0,
	directionPivot = directionPivot,
	approxParameters = approxParameters,
	force_state = force_state, verbosity = verbosity,
	timestamp = timestamp)
	self._postInit()

	@property
	def tModelType(self):
	from rrompy.reduction_methods.trained_model import \
	TrainedModelPivotedRational
	return TrainedModelPivotedRational

	def initializeModelData(self, datadict):
	from rrompy.reduction_methods.trained_model import \
	TrainedModelPivotedData
	return (TrainedModelPivotedData(datadict["mu0"],
	datadict.pop("projMat"),
	datadict["scaleFactor"],
	datadict.pop("rescalingExp"),
	datadict["directionPivot"]),
	["mu0", "scaleFactor", "directionPivot", "mus"])

	@property
	def polybasisPivot(self):
	"""Value of polybasisPivot."""
	return self._polybasisPivot
	@polybasisPivot.setter
	def polybasisPivot(self, polybasisPivot):
	try:
	polybasisPivot = polybasisPivot.upper().strip().replace(" ","")
	if polybasisPivot not in ppb + rbpb + mlspb:
	raise RROMPyException(
	"Prescribed pivot polybasis not recognized.")
	self._polybasisPivot = polybasisPivot
	except:
	RROMPyWarning(("Prescribed pivot polybasis not recognized. "
	"Overriding to 'MONOMIAL'."))
	self._polybasisPivot = "MONOMIAL"
	self._approxParameters["polybasisPivot"] = self.polybasisPivot

	@property
	def polybasisPivot0(self):
	if "_" in self.polybasisPivot:
	return self.polybasisPivot.split("_")[0]
	return self.polybasisPivot

	@property
	def radialDirectionalWeightsPivot(self):
	"""Value of radialDirectionalWeightsPivot."""
	return self._radialDirectionalWeightsPivot
	@radialDirectionalWeightsPivot.setter
	def radialDirectionalWeightsPivot(self, radialDirectionalWeightsPivot):
	self._radialDirectionalWeightsPivot = radialDirectionalWeightsPivot
	self._approxParameters["radialDirectionalWeightsPivot"] = (
	self.radialDirectionalWeightsPivot)

	@property
	def nNearestNeighborPivot(self):
	"""Value of nNearestNeighborPivot."""
	return self._nNearestNeighborPivot
	@nNearestNeighborPivot.setter
	def nNearestNeighborPivot(self, nNearestNeighborPivot):
	self._nNearestNeighborPivot = nNearestNeighborPivot
	self._approxParameters["nNearestNeighborPivot"] = (
	self.nNearestNeighborPivot)

	@property
	def interpRcondPivot(self):
	"""Value of interpRcondPivot."""
	return self._interpRcondPivot
	@interpRcondPivot.setter
	def interpRcondPivot(self, interpRcondPivot):
	self._interpRcondPivot = interpRcondPivot
	self._approxParameters["interpRcondPivot"] = self.interpRcondPivot

	@property
	def M(self):
	"""Value of M."""
	return self._M
	@M.setter
	def M(self, M):
	if M < 0: raise RROMPyException("M must be non-negative.")
	self._M = M
	self._approxParameters["M"] = self.M

	@property
	def N(self):
	"""Value of N."""
	return self._N
	@N.setter
	def N(self, N):
	if N < 0: raise RROMPyException("N must be non-negative.")
	self._N = N
	self._approxParameters["N"] = self.N

	@property
	def polydegreetype(self):
	"""Value of polydegreetype."""
	return self._polydegreetype
	@polydegreetype.setter
	def polydegreetype(self, polydegreetype):
	try:
	polydegreetype = polydegreetype.upper().strip().replace(" ","")
	if polydegreetype not in ["TOTAL", "FULL"]:
	raise RROMPyException(("Prescribed polydegreetype not "
	"recognized."))
	self._polydegreetype = polydegreetype
	except:
	RROMPyWarning(("Prescribed polydegreetype not recognized. "
	"Overriding to 'TOTAL'."))
	self._polydegreetype = "TOTAL"
	self._approxParameters["polydegreetype"] = self.polydegreetype

	@property
	def robustTol(self):
	"""Value of tolerance for robust rational denominator management."""
	return self._robustTol
	@robustTol.setter
	def robustTol(self, robustTol):
	if robustTol < 0.:
	RROMPyWarning(("Overriding prescribed negative robustness "
	"tolerance to 0."))
	robustTol = 0.
	self._robustTol = robustTol
	self._approxParameters["robustTol"] = self.robustTol

	def resetSamples(self):
	"""Reset samples."""
	super().resetSamples()
	self._musPUniqueCN = None
	self._derPIdxs = None
	self._reorderP = None

	def _setupPivotInterpolationIndices(self):
	"""Setup parameters for polyvander."""
	RROMPyAssert(self._mode,
	message = "Cannot setup interpolation indices.")
	if (self._musPUniqueCN is None
	or len(self._reorderP) != len(self.musPivot)):
	self._musPUniqueCN, musPIdxsTo, musPIdxs, musPCount = (
	self.trainedModel.centerNormalizePivot(self.musPivot).unique(
	return_index = True, return_inverse = True,
	return_counts = True))
	self._musPUnique = self.mus[musPIdxsTo]
	self._derPIdxs = [None] * len(self._musPUniqueCN)
	self._reorderP = np.empty(len(musPIdxs), dtype = int)
	filled = 0
	for j, cnt in enumerate(musPCount):
	self._derPIdxs[j] = nextDerivativeIndices([],
	self.nparPivot, cnt)
	jIdx = np.nonzero(musPIdxs == j)[0]
	self._reorderP[jIdx] = np.arange(filled, filled + cnt)
	filled += cnt

	def _setupDenominator(self):
	"""Compute rational denominator."""
	RROMPyAssert(self._mode, message = "Cannot setup denominator.")
	vbMng(self, "INIT", "Starting computation of denominator.", 7)
	NinvD = None
	N0 = copy(self.N)
	qs = []
	self.verbosity -= 10
	for j in range(len(self.musMarginal)):
	self._N = N0
	while self.N > 0:
	if NinvD != self.N:
	invD, fitinvP = self._computeInterpolantInverseBlocks()
	NinvD = self.N
	if self.POD:
	ev, eV = RI.findeveVGQR(self, self.samplingEngine.RPOD[j],
	invD)
	else:
	ev, eV = RI.findeveVGExplicit(self,
	self.samplingEngine.samples[j], invD)
	nevBad = checkRobustTolerance(ev, self.robustTol)
	if nevBad <= 1: break
	if self.catchInstability:
	raise RROMPyException(("Instability in denominator "
	"computation: eigenproblem is "
	"poorly conditioned."))
	RROMPyWarning(("Smallest {} eigenvalues below tolerance. "
	"Reducing N by 1.").format(nevBad))
	self.N = self.N - 1
	if self.N <= 0:
	self._N = 0
	eV = np.ones((1, 1))
	q = PI()
	q.npar = self.nparPivot
	q.polybasis = self.polybasisPivot0
	if self.polydegreetype == "TOTAL":
	q.coeffs = degreeTotalToFull(tuple([self.N + 1] * q.npar),
	q.npar, eV[:, 0])
	else:
	q.coeffs = eV[:, 0].reshape([self.N + 1] * q.npar)
	qs = qs + [copy(q)]
	self.verbosity += 10
	vbMng(self, "DEL", "Done computing denominator.", 7)
	return qs, fitinvP

	def _setupNumerator(self):
	"""Compute rational numerator."""
	RROMPyAssert(self._mode, message = "Cannot setup numerator.")
	vbMng(self, "INIT", "Starting computation of numerator.", 7)
	Qevaldiag = np.zeros((len(self.musPivot), len(self.musPivot)),
	dtype = np.complex)
	verb = self.trainedModel.verbosity
	self.trainedModel.verbosity = 0
	self._setupPivotInterpolationIndices()
	cfun = totalDegreeN if self.polydegreetype == "TOTAL" else fullDegreeN
	M = copy(self.M)
	while len(self.musPivot) < cfun(M, self.nparPivot): M -= 1
	if M < self.M:
	RROMPyWarning(("M too large compared to S. Reducing M by "
	"{}").format(self.M - M))
	self.M = M
	tensor_idx = 0
	ps = []
	for k, muM in enumerate(self.musMarginal):
	self._M = M
	idxGlob = 0
	for j, derIdxs in enumerate(self._derPIdxs):
	mujEff = [fp] * self.npar
	for jj, kk in enumerate(self.directionPivot):
	mujEff[kk] = self._musPUnique[j, jj]
	for jj, kk in enumerate(self.directionMarginal):
	mujEff[kk] = muM(0, jj)
	mujEff = checkParameter(mujEff, self.npar)
	nder = len(derIdxs)
	idxLoc = np.arange(len(self.musPivot))[
	(self._reorderP >= idxGlob)
	* (self._reorderP < idxGlob + nder)]
	idxGlob += nder
	Qval = [0] * nder
	for der in range(nder):
	derIdx = hashI(der, self.nparPivot)
	derIdxEff = [0] * self.npar
	sclEff = [0] * self.npar
	for jj, kk in enumerate(self.directionPivot):
	derIdxEff[kk] = derIdx[jj]
	sclEff[kk] = self.scaleFactorPivot[jj] ** -1.
	Qval[der] = (self.trainedModel.getQVal(mujEff, derIdxEff,
	scl = sclEff)
	/ multifactorial(derIdx))
	for derU, derUIdx in enumerate(derIdxs):
	for derQ, derQIdx in enumerate(derIdxs):
	diffIdx = [x - y for (x, y) in zip(derUIdx, derQIdx)]
	if all([x >= 0 for x in diffIdx]):
	diffj = hashD(diffIdx)
	Qevaldiag[idxLoc[derU], idxLoc[derQ]] = Qval[diffj]
	while self.M >= 0:
	if self.polybasisPivot in ppb:
	p = PI()
	wellCond, msg = p.setupByInterpolation(
	self._musPUniqueCN, Qevaldiag, self.M,
	self.polybasisPivot, self.verbosity >= 5,
	self.polydegreetype == "TOTAL",
	{"derIdxs": self._derPIdxs,
	"reorder": self._reorderP,
	"scl": np.power(self.scaleFactorPivot, -1.)},
	{"rcond": self.interpRcondPivot})
	elif self.polybasisPivot in rbpb:
	p = RBI()
	wellCond, msg = p.setupByInterpolation(
	self._musPUniqueCN, Qevaldiag, self.M,
	self.polybasisPivot,
	self.radialDirectionalWeightsPivot,
	self.verbosity >= 5,
	self.polydegreetype == "TOTAL",
	{"derIdxs": self._derPIdxs,
	"reorder": self._reorderP,
	"scl": np.power(self.scaleFactorPivot, -1.),
	"nNearestNeighbor" : self.nNearestNeighborPivot},
	{"rcond": self.interpRcondPivot})
	else:# if self.polybasisPivot in mlspb:
	p = MLSI()
	wellCond, msg = p.setupByInterpolation(
	self._musPUniqueCN, Qevaldiag, self.M,
	self.polybasisPivot,
	self.radialDirectionalWeightsPivot,
	self.verbosity >= 5,
	self.polydegreetype == "TOTAL",
	{"derIdxs": self._derPIdxs,
	"reorder": self._reorderP,
	"scl": np.power(self.scaleFactorPivot, -1.),
	"nNearestNeighbor" : self.nNearestNeighborPivot})
	vbMng(self, "MAIN", msg, 5)
	if wellCond: break
	if self.catchInstability:
	raise RROMPyException(("Instability in numerator "
	"computation: polyfit is "
	"poorly conditioned."))
	RROMPyWarning(("Polyfit is poorly conditioned. "
	"Reducing M by 1."))
	self.M = self.M - 1
	tensor_idx_new = tensor_idx + Qevaldiag.shape[1]
	if self.POD:
	p.postmultiplyTensorize(self.samplingEngine.RPODCoalesced.T[
	tensor_idx : tensor_idx_new, :])
	else:
	p.pad(tensor_idx, len(self.mus) - tensor_idx_new)
	tensor_idx = tensor_idx_new
	ps = ps + [copy(p)]
	self.trainedModel.verbosity = verb
	vbMng(self, "DEL", "Done computing numerator.", 7)
	return ps

	def setupApprox(self):
	"""
	Compute rational interpolant.
	SVD-based robust eigenvalue management.
	"""
	if self.checkComputedApprox():
	return
	RROMPyAssert(self._mode, message = "Cannot setup approximant.")
	vbMng(self, "INIT", "Setting up {}.". format(self.name()), 5)
	self.computeSnapshots()
	pMat = self.samplingEngine.samplesCoalesced.data
	pMatEff = dot(self.HFEngine.C, pMat) if self.force_state else pMat
	if self.trainedModel is None:
	self.trainedModel = self.tModelType()
	self.trainedModel.verbosity = self.verbosity
	self.trainedModel.timestamp = self.timestamp
	datadict = {"mu0": self.mu0, "projMat": pMatEff,
	"scaleFactor": self.scaleFactor,
	"rescalingExp": self.HFEngine.rescalingExp,
	"directionPivot": self.directionPivot}
	self.trainedModel.data = self.initializeModelData(datadict)[0]
	else:
	self.trainedModel = self.trainedModel
	self.trainedModel.data.projMat = copy(pMatEff)
	self.trainedModel.data.musPivot = copy(self.musPivot)
	self.trainedModel.data.musMarginal = copy(self.musMarginal)
	self.trainedModel.data.marginalInterp = self._setupMarginalInterp()
	if self.N > 0:
	Qs = self._setupDenominator()[0]
	else:
	Q = PI()
	Q.npar = self.nparPivot
	Q.coeffs = np.ones(tuple([1] * Q.npar),
	dtype = self.musMarginal.dtype)
	Q.polybasis = self.polybasisPivot0
	Qs = [Q for _ in range(len(self.musMarginal))]
	self.trainedModel.data._temporary = 1
	self.trainedModel.data.Qs = Qs
	self.trainedModel.data.Ps = self._setupNumerator()
	vbMng(self, "INIT", "Matching poles.", 10)
	self.trainedModel.initializeFromRational(self.HFEngine,
	self.matchingWeight, self.POD,
	self.force_state)
	del self.trainedModel.data._temporary
	vbMng(self, "DEL", "Done matching poles.", 10)
	if not np.isinf(self.cutOffTolerance):
	vbMng(self, "INIT", "Recompressing by cut-off.", 10)
	msg = self.trainedModel.recompressByCutOff([-1., 1.],
	self.cutOffTolerance,
	self.cutOffType)
	vbMng(self, "DEL", "Done recompressing." + msg, 10)
	self.trainedModel.data.approxParameters = copy(self.approxParameters)
	vbMng(self, "DEL", "Done setting up approximant.", 5)

	def _computeInterpolantInverseBlocks(self) -> Tuple[List[Np2D], Np2D]:
	"""
	Compute inverse factors for minimal interpolant target functional.
	"""
	RROMPyAssert(self._mode, message = "Cannot solve eigenvalue problem.")
	self._setupPivotInterpolationIndices()
	cfun = totalDegreeN if self.polydegreetype == "TOTAL" else fullDegreeN
	N = copy(self.N)
	while len(self.musPivot) < cfun(N, self.nparPivot): N -= 1
	if N < self.N:
	RROMPyWarning(("N too large compared to S. Reducing N by "
	"{}").format(self.N - N))
	self.N = N
	while self.N >= 0:
	if self.polydegreetype == "TOTAL":
	TE, _, argIdxs = pvTP(self._musPUniqueCN, self.N,
	self.polybasisPivot0, self._derPIdxs,
	self._reorderP,
	scl = np.power(self.scaleFactorPivot, -1.))
	TE = TE[:, argIdxs]
	idxsB = totalDegreeMaxMask(self.N, self.nparPivot)
	else: #if self.polydegreetype == "FULL":
	TE = pvP(self._musPUniqueCN, [self.N] * self.nparPivot,
	self.polybasisPivot0, self._derPIdxs, self._reorderP,
	scl = np.power(self.scaleFactorPivot, -1.))
	idxsB = fullDegreeMaxMask(self.N, self.nparPivot)
	fitOut = customPInv(TE, rcond = self.interpRcondPivot,
	full = True)
	vbMng(self, "MAIN",
	("Fitting {} samples with degree {} through {}... "
	"Conditioning of pseudoinverse system: {:.4e}.").format(
	TE.shape[0], self.N,
	polyfitname(self.polybasisPivot0),
	fitOut[1][1][0] / fitOut[1][1][-1]),
	5)
	if fitOut[1][0] == TE.shape[1]:
	fitinvP = fitOut[0][idxsB, :]
	break
	RROMPyWarning("Polyfit is poorly conditioned. Reducing N by 1.")
	self.N -= 1
	if self.N < 0:
	raise RROMPyException(("Instability in computation of "
	"denominator. Aborting."))
	TN, _, argIdxs = pvTP(self._musPUniqueCN, self.N, self.polybasisPivot0,
	self._derPIdxs, self._reorderP,
	scl = np.power(self.scaleFactorPivot, -1.))
	TN = TN[:, argIdxs]
	invD = [None] * (len(idxsB))
	for k in range(len(idxsB)):
	pseudoInv = np.diag(fitinvP[k, :])
	idxGlob = 0
	for j, derIdxs in enumerate(self._derPIdxs):
	nder = len(derIdxs)
	idxGlob += nder
	if nder > 1:
	idxLoc = np.arange(len(self.musPivot))[
	(self._reorderP >= idxGlob - nder)
	* (self._reorderP < idxGlob)]
	invLoc = fitinvP[k, idxLoc]
	pseudoInv[np.ix_(idxLoc, idxLoc)] = 0.
	for diffj, diffjIdx in enumerate(derIdxs):
	for derQ, derQIdx in enumerate(derIdxs):
	derUIdx = [x - y for (x, y) in
	zip(diffjIdx, derQIdx)]
	if all([x >= 0 for x in derUIdx]):
	derU = hashD(derUIdx)
	pseudoInv[idxLoc[derU], idxLoc[derQ]] = (
	invLoc[diffj])
	invD[k] = dot(pseudoInv, TN)
	return invD, fitinvP

	def getResidues(self, args, *kwargs) -> Np1D:
	"""
	Obtain approximant residues.

	Returns:
	Matrix with residues as columns.
	"""
	return self.trainedModel.getResidues(args, *kwargs)

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