X-Git-Url: http://git.salome-platform.org/gitweb/?a=blobdiff_plain;f=src%2FdaComposant%2FdaAlgorithms%2FNonLinearLeastSquares.py;h=eebcf2afb37acebc5e44b99af0b57a2f8f672d48;hb=59c9144c3b491494cb2beeb10093f232a5e13f2a;hp=53a90aabe797a17c28f1dcfbe36270d77d5c4ae8;hpb=e82cfdd97cfaa50b8d9af16a3b2e06f9fd78a2dd;p=modules%2Fadao.git diff --git a/src/daComposant/daAlgorithms/NonLinearLeastSquares.py b/src/daComposant/daAlgorithms/NonLinearLeastSquares.py index 53a90aa..eebcf2a 100644 --- a/src/daComposant/daAlgorithms/NonLinearLeastSquares.py +++ b/src/daComposant/daAlgorithms/NonLinearLeastSquares.py @@ -1,28 +1,28 @@ -#-*-coding:iso-8859-1-*- +# -*- coding: utf-8 -*- # -# Copyright (C) 2008-2015 EDF R&D +# Copyright (C) 2008-2020 EDF R&D # -# This library 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 2.1 of the License. +# This library 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 2.1 of the License. # -# This library 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. +# This library 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 this library; if not, write to the Free Software -# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA +# You should have received a copy of the GNU Lesser General Public +# License along with this library; if not, write to the Free Software +# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA # -# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com +# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com # -# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D +# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D import logging from daCore import BasicObjects -import numpy, scipy.optimize +import numpy, scipy.optimize, scipy.version # ============================================================================== class ElementaryAlgorithm(BasicObjects.Algorithm): @@ -32,7 +32,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): name = "Minimizer", default = "LBFGSB", typecast = str, - message = "Minimiseur utilisé", + message = "Minimiseur utilisé", listval = ["LBFGSB","TNC", "CG", "NCG", "BFGS", "LM"], ) self.defineRequiredParameter( @@ -46,99 +46,144 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): name = "CostDecrementTolerance", default = 1.e-7, typecast = float, - message = "Diminution relative minimale du cout lors de l'arrêt", + message = "Diminution relative minimale du coût lors de l'arrêt", + minval = 0., ) self.defineRequiredParameter( name = "ProjectedGradientTolerance", default = -1, typecast = float, - message = "Maximum des composantes du gradient projeté lors de l'arrêt", + message = "Maximum des composantes du gradient projeté lors de l'arrêt", minval = -1, ) self.defineRequiredParameter( name = "GradientNormTolerance", default = 1.e-05, typecast = float, - message = "Maximum des composantes du gradient lors de l'arrêt", + message = "Maximum des composantes du gradient lors de l'arrêt", + minval = 0., ) self.defineRequiredParameter( name = "StoreInternalVariables", default = False, typecast = bool, - message = "Stockage des variables internes ou intermédiaires du calcul", + message = "Stockage des variables internes ou intermédiaires du calcul", ) self.defineRequiredParameter( name = "StoreSupplementaryCalculations", default = [], typecast = tuple, - message = "Liste de calculs supplémentaires à stocker et/ou effectuer", - listval = ["BMA", "OMA", "OMB", "Innovation"] + message = "Liste de calculs supplémentaires à stocker et/ou effectuer", + listval = [ + "Analysis", + "BMA", + "CostFunctionJ", + "CostFunctionJAtCurrentOptimum", + "CostFunctionJb", + "CostFunctionJbAtCurrentOptimum", + "CostFunctionJo", + "CostFunctionJoAtCurrentOptimum", + "CurrentIterationNumber", + "CurrentOptimum", + "CurrentState", + "IndexOfOptimum", + "Innovation", + "InnovationAtCurrentState", + "OMA", + "OMB", + "SimulatedObservationAtBackground", + "SimulatedObservationAtCurrentOptimum", + "SimulatedObservationAtCurrentState", + "SimulatedObservationAtOptimum", + ] ) + self.defineRequiredParameter( # Pas de type + name = "Bounds", + message = "Liste des valeurs de bornes", + ) + self.requireInputArguments( + mandatory= ("Xb", "Y", "HO", "R"), + ) + self.setAttributes(tags=( + "Optimization", + "NonLinear", + "Variational", + )) def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None): - self._pre_run() - if logging.getLogger().level < logging.WARNING: - self.__iprint, self.__disp = 1, 1 - self.__message = scipy.optimize.tnc.MSG_ALL - else: - self.__iprint, self.__disp = -1, 0 - self.__message = scipy.optimize.tnc.MSG_NONE - # - # Paramètres de pilotage - # ---------------------- - self.setParameters(Parameters) - # - if self._parameters.has_key("Bounds") and (type(self._parameters["Bounds"]) is type([]) or type(self._parameters["Bounds"]) is type(())) and (len(self._parameters["Bounds"]) > 0): - Bounds = self._parameters["Bounds"] - logging.debug("%s Prise en compte des bornes effectuee"%(self._name,)) - else: - Bounds = None + self._pre_run(Parameters, Xb, Y, U, HO, EM, CM, R, B, Q) # # Correction pour pallier a un bug de TNC sur le retour du Minimum - if self._parameters.has_key("Minimizer") == "TNC": + if "Minimizer" in self._parameters and self._parameters["Minimizer"] == "TNC": self.setParameterValue("StoreInternalVariables",True) # - # Opérateur d'observation - # ----------------------- + # Opérateurs + # ---------- Hm = HO["Direct"].appliedTo Ha = HO["Adjoint"].appliedInXTo # - # Utilisation éventuelle d'un vecteur H(Xb) précalculé + # Utilisation éventuelle d'un vecteur H(Xb) précalculé # ---------------------------------------------------- - if HO["AppliedToX"] is not None and HO["AppliedToX"].has_key("HXb"): - HXb = HO["AppliedToX"]["HXb"] + if HO["AppliedInX"] is not None and "HXb" in HO["AppliedInX"]: + HXb = Hm( Xb, HO["AppliedInX"]["HXb"] ) else: HXb = Hm( Xb ) HXb = numpy.asmatrix(numpy.ravel( HXb )).T - # - # Calcul de l'innovation - # ---------------------- if Y.size != HXb.size: raise ValueError("The size %i of observations Y and %i of observed calculation H(X) are different, they have to be identical."%(Y.size,HXb.size)) if max(Y.shape) != max(HXb.shape): raise ValueError("The shapes %s of observations Y and %s of observed calculation H(X) are different, they have to be identical."%(Y.shape,HXb.shape)) - d = Y - HXb # - # Précalcul des inversions de B et R + # Précalcul des inversions de B et R # ---------------------------------- RI = R.getI() if self._parameters["Minimizer"] == "LM": RdemiI = R.choleskyI() # - # Définition de la fonction-coût + # Définition de la fonction-coût # ------------------------------ def CostFunction(x): _X = numpy.asmatrix(numpy.ravel( x )).T + if self._parameters["StoreInternalVariables"] or \ + self._toStore("CurrentState") or \ + self._toStore("CurrentOptimum"): + self.StoredVariables["CurrentState"].store( _X ) _HX = Hm( _X ) _HX = numpy.asmatrix(numpy.ravel( _HX )).T + _Innovation = Y - _HX + if self._toStore("SimulatedObservationAtCurrentState") or \ + self._toStore("SimulatedObservationAtCurrentOptimum"): + self.StoredVariables["SimulatedObservationAtCurrentState"].store( _HX ) + if self._toStore("InnovationAtCurrentState"): + self.StoredVariables["InnovationAtCurrentState"].store( _Innovation ) + # Jb = 0. - Jo = 0.5 * (Y - _HX).T * RI * (Y - _HX) - J = float( Jb ) + float( Jo ) - if self._parameters["StoreInternalVariables"]: - self.StoredVariables["CurrentState"].store( _X ) + Jo = float( 0.5 * _Innovation.T * RI * _Innovation ) + J = Jb + Jo + # + self.StoredVariables["CurrentIterationNumber"].store( len(self.StoredVariables["CostFunctionJ"]) ) self.StoredVariables["CostFunctionJb"].store( Jb ) self.StoredVariables["CostFunctionJo"].store( Jo ) self.StoredVariables["CostFunctionJ" ].store( J ) + if self._toStore("IndexOfOptimum") or \ + self._toStore("CurrentOptimum") or \ + self._toStore("CostFunctionJAtCurrentOptimum") or \ + self._toStore("CostFunctionJbAtCurrentOptimum") or \ + self._toStore("CostFunctionJoAtCurrentOptimum") or \ + self._toStore("SimulatedObservationAtCurrentOptimum"): + IndexMin = numpy.argmin( self.StoredVariables["CostFunctionJ"][nbPreviousSteps:] ) + nbPreviousSteps + if self._toStore("IndexOfOptimum"): + self.StoredVariables["IndexOfOptimum"].store( IndexMin ) + if self._toStore("CurrentOptimum"): + self.StoredVariables["CurrentOptimum"].store( self.StoredVariables["CurrentState"][IndexMin] ) + if self._toStore("SimulatedObservationAtCurrentOptimum"): + self.StoredVariables["SimulatedObservationAtCurrentOptimum"].store( self.StoredVariables["SimulatedObservationAtCurrentState"][IndexMin] ) + if self._toStore("CostFunctionJbAtCurrentOptimum"): + self.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJb"][IndexMin] ) + if self._toStore("CostFunctionJoAtCurrentOptimum"): + self.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJo"][IndexMin] ) + if self._toStore("CostFunctionJAtCurrentOptimum"): + self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( self.StoredVariables["CostFunctionJ" ][IndexMin] ) return J # def GradientOfCostFunction(x): @@ -154,16 +199,18 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): _X = numpy.asmatrix(numpy.ravel( x )).T _HX = Hm( _X ) _HX = numpy.asmatrix(numpy.ravel( _HX )).T + _Innovation = Y - _HX Jb = 0. - Jo = 0.5 * (Y - _HX).T * RI * (Y - _HX) - J = float( Jb ) + float( Jo ) - if self._parameters["StoreInternalVariables"]: + Jo = float( 0.5 * _Innovation.T * RI * _Innovation ) + J = Jb + Jo + if self._parameters["StoreInternalVariables"] or \ + self._toStore("CurrentState"): self.StoredVariables["CurrentState"].store( _X ) self.StoredVariables["CostFunctionJb"].store( Jb ) self.StoredVariables["CostFunctionJo"].store( Jo ) self.StoredVariables["CostFunctionJ" ].store( J ) # - return numpy.ravel( RdemiI*(Y - _HX) ) + return numpy.ravel( RdemiI*_Innovation ) # def GradientOfCostFunctionLM(x): _X = numpy.asmatrix(numpy.ravel( x )).T @@ -174,28 +221,30 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): GradJ = numpy.asmatrix( numpy.ravel( GradJb ) + numpy.ravel( GradJo ) ).T return - RdemiI*HO["Tangent"].asMatrix( _X ) # - # Point de démarrage de l'optimisation : Xini = Xb + # Point de démarrage de l'optimisation : Xini = Xb # ------------------------------------ - if type(Xb) is type(numpy.matrix([])): - Xini = Xb.A1.tolist() - else: - Xini = list(Xb) + Xini = numpy.ravel(Xb) # # Minimisation de la fonctionnelle # -------------------------------- nbPreviousSteps = self.StoredVariables["CostFunctionJ"].stepnumber() # if self._parameters["Minimizer"] == "LBFGSB": - Minimum, J_optimal, Informations = scipy.optimize.fmin_l_bfgs_b( + # Minimum, J_optimal, Informations = scipy.optimize.fmin_l_bfgs_b( + if "0.19" <= scipy.version.version <= "1.1.0": + import lbfgsbhlt as optimiseur + else: + import scipy.optimize as optimiseur + Minimum, J_optimal, Informations = optimiseur.fmin_l_bfgs_b( func = CostFunction, x0 = Xini, fprime = GradientOfCostFunction, args = (), - bounds = Bounds, + bounds = self._parameters["Bounds"], maxfun = self._parameters["MaximumNumberOfSteps"]-1, factr = self._parameters["CostDecrementTolerance"]*1.e14, pgtol = self._parameters["ProjectedGradientTolerance"], - iprint = self.__iprint, + iprint = self._parameters["optiprint"], ) nfeval = Informations['funcalls'] rc = Informations['warnflag'] @@ -205,11 +254,11 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): x0 = Xini, fprime = GradientOfCostFunction, args = (), - bounds = Bounds, + bounds = self._parameters["Bounds"], maxfun = self._parameters["MaximumNumberOfSteps"], pgtol = self._parameters["ProjectedGradientTolerance"], ftol = self._parameters["CostDecrementTolerance"], - messages = self.__message, + messages = self._parameters["optmessages"], ) elif self._parameters["Minimizer"] == "CG": Minimum, fopt, nfeval, grad_calls, rc = scipy.optimize.fmin_cg( @@ -219,7 +268,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): args = (), maxiter = self._parameters["MaximumNumberOfSteps"], gtol = self._parameters["GradientNormTolerance"], - disp = self.__disp, + disp = self._parameters["optdisp"], full_output = True, ) elif self._parameters["Minimizer"] == "NCG": @@ -230,7 +279,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): args = (), maxiter = self._parameters["MaximumNumberOfSteps"], avextol = self._parameters["CostDecrementTolerance"], - disp = self.__disp, + disp = self._parameters["optdisp"], full_output = True, ) elif self._parameters["Minimizer"] == "BFGS": @@ -241,7 +290,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): args = (), maxiter = self._parameters["MaximumNumberOfSteps"], gtol = self._parameters["GradientNormTolerance"], - disp = self.__disp, + disp = self._parameters["optdisp"], full_output = True, ) elif self._parameters["Minimizer"] == "LM": @@ -264,7 +313,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): # # Correction pour pallier a un bug de TNC sur le retour du Minimum # ---------------------------------------------------------------- - if self._parameters["StoreInternalVariables"]: + if self._parameters["StoreInternalVariables"] or self._toStore("CurrentState"): Minimum = self.StoredVariables["CurrentState"][IndexMin] # # Obtention de l'analyse @@ -273,20 +322,37 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): # self.StoredVariables["Analysis"].store( Xa.A1 ) # - # Calculs et/ou stockages supplémentaires + if self._toStore("OMA") or \ + self._toStore("SimulatedObservationAtOptimum"): + if self._toStore("SimulatedObservationAtCurrentState"): + HXa = self.StoredVariables["SimulatedObservationAtCurrentState"][IndexMin] + elif self._toStore("SimulatedObservationAtCurrentOptimum"): + HXa = self.StoredVariables["SimulatedObservationAtCurrentOptimum"][-1] + else: + HXa = Hm( Xa ) + # + # + # Calculs et/ou stockages supplémentaires # --------------------------------------- - if "Innovation" in self._parameters["StoreSupplementaryCalculations"]: + if self._toStore("Innovation") or \ + self._toStore("OMB"): + d = Y - HXb + if self._toStore("Innovation"): self.StoredVariables["Innovation"].store( numpy.ravel(d) ) - if "BMA" in self._parameters["StoreSupplementaryCalculations"]: + if self._toStore("BMA"): self.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(Xa) ) - if "OMA" in self._parameters["StoreSupplementaryCalculations"]: - self.StoredVariables["OMA"].store( numpy.ravel(Y) - numpy.ravel(Hm(Xa)) ) - if "OMB" in self._parameters["StoreSupplementaryCalculations"]: + if self._toStore("OMA"): + self.StoredVariables["OMA"].store( numpy.ravel(Y) - numpy.ravel(HXa) ) + if self._toStore("OMB"): self.StoredVariables["OMB"].store( numpy.ravel(d) ) + if self._toStore("SimulatedObservationAtBackground"): + self.StoredVariables["SimulatedObservationAtBackground"].store( numpy.ravel(HXb) ) + if self._toStore("SimulatedObservationAtOptimum"): + self.StoredVariables["SimulatedObservationAtOptimum"].store( numpy.ravel(HXa) ) # self._post_run(HO) return 0 # ============================================================================== if __name__ == "__main__": - print '\n AUTODIAGNOSTIC \n' + print('\n AUTODIAGNOSTIC\n')