X-Git-Url: http://git.salome-platform.org/gitweb/?a=blobdiff_plain;f=src%2FdaComposant%2FdaAlgorithms%2FNonLinearLeastSquares.py;h=8098f9315770554bc428dbc27d1c36de70359fdc;hb=b02fec5bed967ec37e545b67dc841d15dd9c3e78;hp=03cf56bf7202fef2a074a7120c6596cc14104a33;hpb=090e7c8476a6998a88750af5ce0240377ee02043;p=modules%2Fadao.git diff --git a/src/daComposant/daAlgorithms/NonLinearLeastSquares.py b/src/daComposant/daAlgorithms/NonLinearLeastSquares.py index 03cf56b..8098f93 100644 --- a/src/daComposant/daAlgorithms/NonLinearLeastSquares.py +++ b/src/daComposant/daAlgorithms/NonLinearLeastSquares.py @@ -1,6 +1,6 @@ #-*-coding:iso-8859-1-*- # -# Copyright (C) 2008-2012 EDF R&D +# Copyright (C) 2008-2014 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 @@ -18,22 +18,11 @@ # # See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com # +# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D import logging -from daCore import BasicObjects, PlatformInfo -m = PlatformInfo.SystemUsage() - -import numpy -import scipy.optimize - -if logging.getLogger().level < 30: - iprint = 1 - message = scipy.optimize.tnc.MSG_ALL - disp = 1 -else: - iprint = -1 - message = scipy.optimize.tnc.MSG_NONE - disp = 0 +from daCore import BasicObjects +import numpy, scipy.optimize # ============================================================================== class ElementaryAlgorithm(BasicObjects.Algorithm): @@ -44,14 +33,14 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): default = "LBFGSB", typecast = str, message = "Minimiseur utilisé", - listval = ["LBFGSB","TNC", "CG", "NCG", "BFGS"], + listval = ["LBFGSB","TNC", "CG", "NCG", "BFGS", "LM"], ) self.defineRequiredParameter( name = "MaximumNumberOfSteps", default = 15000, typecast = int, message = "Nombre maximal de pas d'optimisation", - minval = -1 + minval = -1, ) self.defineRequiredParameter( name = "CostDecrementTolerance", @@ -72,14 +61,28 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): typecast = float, message = "Maximum des composantes du gradient lors de l'arrêt", ) + self.defineRequiredParameter( + name = "StoreInternalVariables", + default = False, + typecast = bool, + 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"] + ) - def run(self, Xb=None, Y=None, H=None, M=None, R=None, B=None, Q=None, Parameters=None): - """ - Calcul de l'estimateur moindres carrés pondérés non linéaires - (assimilation variationnelle sans ébauche) - """ - logging.debug("%s Lancement"%self._name) - logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("Mo"))) + 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 # ---------------------- @@ -91,20 +94,22 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): else: Bounds = None # + # Correction pour pallier a un bug de TNC sur le retour du Minimum + if self._parameters.has_key("Minimizer") == "TNC": + self.setParameterValue("StoreInternalVariables",True) + # # Opérateur d'observation # ----------------------- - Hm = H["Direct"].appliedTo - Ha = H["Adjoint"].appliedInXTo + Hm = HO["Direct"].appliedTo + Ha = HO["Adjoint"].appliedInXTo # # Utilisation éventuelle d'un vecteur H(Xb) précalculé # ---------------------------------------------------- - if H["AppliedToX"] is not None and H["AppliedToX"].has_key("HXb"): - logging.debug("%s Utilisation de HXb"%self._name) - HXb = H["AppliedToX"]["HXb"] + if HO["AppliedToX"] is not None and HO["AppliedToX"].has_key("HXb"): + HXb = HO["AppliedToX"]["HXb"] else: - logging.debug("%s Calcul de Hm(Xb)"%self._name) HXb = Hm( Xb ) - HXb = numpy.asmatrix(HXb).flatten().T + HXb = numpy.asmatrix(numpy.ravel( HXb )).T # # Calcul de l'innovation # ---------------------- @@ -113,66 +118,73 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): 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 - logging.debug("%s Innovation d = %s"%(self._name, d)) # # Précalcul des inversions de B et R # ---------------------------------- - # if B is not None: - # BI = B.I - # elif self._parameters["B_scalar"] is not None: - # BI = 1.0 / self._parameters["B_scalar"] - # else: - # raise ValueError("Background error covariance matrix has to be properly defined!") - # - if R is not None: - RI = R.I - elif self._parameters["R_scalar"] is not None: - RI = 1.0 / self._parameters["R_scalar"] - else: - raise ValueError("Observation error covariance matrix has to be properly defined!") + RI = R.getI() + if self._parameters["Minimizer"] == "LM": + RdemiI = R.choleskyI() # # Définition de la fonction-coût # ------------------------------ def CostFunction(x): - _X = numpy.asmatrix(x).flatten().T - logging.debug("%s CostFunction X = %s"%(self._name, numpy.asmatrix( _X ).flatten())) + _X = numpy.asmatrix(numpy.ravel( x )).T _HX = Hm( _X ) - _HX = numpy.asmatrix(_HX).flatten().T + _HX = numpy.asmatrix(numpy.ravel( _HX )).T Jb = 0. Jo = 0.5 * (Y - _HX).T * RI * (Y - _HX) J = float( Jb ) + float( Jo ) - logging.debug("%s CostFunction Jb = %s"%(self._name, Jb)) - logging.debug("%s CostFunction Jo = %s"%(self._name, Jo)) - logging.debug("%s CostFunction J = %s"%(self._name, J)) - self.StoredVariables["CurrentState"].store( _X.A1 ) + if self._parameters["StoreInternalVariables"]: + self.StoredVariables["CurrentState"].store( _X ) self.StoredVariables["CostFunctionJb"].store( Jb ) self.StoredVariables["CostFunctionJo"].store( Jo ) self.StoredVariables["CostFunctionJ" ].store( J ) - return float( J ) + return J # def GradientOfCostFunction(x): - _X = numpy.asmatrix(x).flatten().T - logging.debug("%s GradientOfCostFunction X = %s"%(self._name, numpy.asmatrix( _X ).flatten())) + _X = numpy.asmatrix(numpy.ravel( x )).T _HX = Hm( _X ) - _HX = numpy.asmatrix(_HX).flatten().T + _HX = numpy.asmatrix(numpy.ravel( _HX )).T GradJb = 0. GradJo = - Ha( (_X, RI * (Y - _HX)) ) - GradJ = numpy.asmatrix( GradJb ).flatten().T + numpy.asmatrix( GradJo ).flatten().T - logging.debug("%s GradientOfCostFunction GradJb = %s"%(self._name, numpy.asmatrix( GradJb ).flatten())) - logging.debug("%s GradientOfCostFunction GradJo = %s"%(self._name, numpy.asmatrix( GradJo ).flatten())) - logging.debug("%s GradientOfCostFunction GradJ = %s"%(self._name, numpy.asmatrix( GradJ ).flatten())) + GradJ = numpy.asmatrix( numpy.ravel( GradJb ) + numpy.ravel( GradJo ) ).T return GradJ.A1 # + def CostFunctionLM(x): + _X = numpy.asmatrix(numpy.ravel( x )).T + _HX = Hm( _X ) + _HX = numpy.asmatrix(numpy.ravel( _HX )).T + 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 ) + self.StoredVariables["CostFunctionJb"].store( Jb ) + self.StoredVariables["CostFunctionJo"].store( Jo ) + self.StoredVariables["CostFunctionJ" ].store( J ) + # + return numpy.ravel( RdemiI*(Y - _HX) ) + # + def GradientOfCostFunctionLM(x): + _X = numpy.asmatrix(numpy.ravel( x )).T + _HX = Hm( _X ) + _HX = numpy.asmatrix(numpy.ravel( _HX )).T + GradJb = 0. + GradJo = - Ha( (_X, RI * (Y - _HX)) ) + GradJ = numpy.asmatrix( numpy.ravel( GradJb ) + numpy.ravel( GradJo ) ).T + return - RdemiI*HO["Tangent"].asMatrix( _X ) + # # Point de démarrage de l'optimisation : Xini = Xb # ------------------------------------ if type(Xb) is type(numpy.matrix([])): Xini = Xb.A1.tolist() else: Xini = list(Xb) - logging.debug("%s Point de démarrage Xini = %s"%(self._name, Xini)) # # Minimisation de la fonctionnelle # -------------------------------- + nbPreviousSteps = self.StoredVariables["CostFunctionJ"].stepnumber() + # if self._parameters["Minimizer"] == "LBFGSB": Minimum, J_optimal, Informations = scipy.optimize.fmin_l_bfgs_b( func = CostFunction, @@ -183,7 +195,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): maxfun = self._parameters["MaximumNumberOfSteps"]-1, factr = self._parameters["CostDecrementTolerance"]*1.e14, pgtol = self._parameters["ProjectedGradientTolerance"], - iprint = iprint, + iprint = self.__iprint, ) nfeval = Informations['funcalls'] rc = Informations['warnflag'] @@ -197,7 +209,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): maxfun = self._parameters["MaximumNumberOfSteps"], pgtol = self._parameters["ProjectedGradientTolerance"], ftol = self._parameters["CostDecrementTolerance"], - messages = message, + messages = self.__message, ) elif self._parameters["Minimizer"] == "CG": Minimum, fopt, nfeval, grad_calls, rc = scipy.optimize.fmin_cg( @@ -207,7 +219,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): args = (), maxiter = self._parameters["MaximumNumberOfSteps"], gtol = self._parameters["GradientNormTolerance"], - disp = disp, + disp = self.__disp, full_output = True, ) elif self._parameters["Minimizer"] == "NCG": @@ -218,7 +230,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): args = (), maxiter = self._parameters["MaximumNumberOfSteps"], avextol = self._parameters["CostDecrementTolerance"], - disp = disp, + disp = self.__disp, full_output = True, ) elif self._parameters["Minimizer"] == "BFGS": @@ -229,35 +241,50 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): args = (), maxiter = self._parameters["MaximumNumberOfSteps"], gtol = self._parameters["GradientNormTolerance"], - disp = disp, + disp = self.__disp, full_output = True, ) + elif self._parameters["Minimizer"] == "LM": + Minimum, cov_x, infodict, mesg, rc = scipy.optimize.leastsq( + func = CostFunctionLM, + x0 = Xini, + Dfun = GradientOfCostFunctionLM, + args = (), + ftol = self._parameters["CostDecrementTolerance"], + maxfev = self._parameters["MaximumNumberOfSteps"], + gtol = self._parameters["GradientNormTolerance"], + full_output = True, + ) + nfeval = infodict['nfev'] else: raise ValueError("Error in Minimizer name: %s"%self._parameters["Minimizer"]) # + IndexMin = numpy.argmin( self.StoredVariables["CostFunctionJ"][nbPreviousSteps:] ) + nbPreviousSteps + MinJ = self.StoredVariables["CostFunctionJ"][IndexMin] + # # Correction pour pallier a un bug de TNC sur le retour du Minimum # ---------------------------------------------------------------- - StepMin = numpy.argmin( self.StoredVariables["CostFunctionJ"].valueserie() ) - MinJ = self.StoredVariables["CostFunctionJ"].valueserie(step = StepMin) - Minimum = self.StoredVariables["CurrentState"].valueserie(step = StepMin) - # - logging.debug("%s %s Step of min cost = %s"%(self._name, self._parameters["Minimizer"], StepMin)) - logging.debug("%s %s Minimum cost = %s"%(self._name, self._parameters["Minimizer"], MinJ)) - logging.debug("%s %s Minimum state = %s"%(self._name, self._parameters["Minimizer"], Minimum)) - logging.debug("%s %s Nb of F = %s"%(self._name, self._parameters["Minimizer"], nfeval)) - logging.debug("%s %s RetCode = %s"%(self._name, self._parameters["Minimizer"], rc)) + if self._parameters["StoreInternalVariables"]: + Minimum = self.StoredVariables["CurrentState"][IndexMin] # # Obtention de l'analyse # ---------------------- - Xa = numpy.asmatrix(Minimum).T - logging.debug("%s Analyse Xa = %s"%(self._name, Xa)) + Xa = numpy.asmatrix(numpy.ravel( Minimum )).T # self.StoredVariables["Analysis"].store( Xa.A1 ) - self.StoredVariables["Innovation"].store( d.A1 ) # - logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("MB"))) - logging.debug("%s Terminé"%self._name) + # Calculs et/ou stockages supplémentaires + # --------------------------------------- + if "Innovation" in self._parameters["StoreSupplementaryCalculations"]: + self.StoredVariables["Innovation"].store( numpy.ravel(d) ) + if "BMA" in self._parameters["StoreSupplementaryCalculations"]: + 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"]: + self.StoredVariables["OMB"].store( numpy.ravel(d) ) # + self._post_run(HO) return 0 # ==============================================================================