From: Jean-Philippe ARGAUD Date: Fri, 3 Feb 2012 13:19:05 +0000 (+0100) Subject: Ajout de l'algorithme NonLinearLeastSquares et correction des autres X-Git-Tag: V6_5_0~27 X-Git-Url: http://git.salome-platform.org/gitweb/?a=commitdiff_plain;h=5c377b89d86e8d62aa005c31a016a3e138703202;p=modules%2Fadao.git Ajout de l'algorithme NonLinearLeastSquares et correction des autres --- diff --git a/src/daComposant/daAlgorithms/3DVAR.py b/src/daComposant/daAlgorithms/3DVAR.py index bc17491..4812750 100644 --- a/src/daComposant/daAlgorithms/3DVAR.py +++ b/src/daComposant/daAlgorithms/3DVAR.py @@ -66,13 +66,24 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): # # 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 logging.debug("%s Innovation d = %s"%(self._name, d)) # # Précalcul des inversion appellée dans les fonction-coût et gradient # ------------------------------------------------------------------- - BI = B.I - RI = R.I + if B is not None: + BI = B.I + elif Parameters["B_scalar"] is not None: + BI = 1.0 / Parameters["B_scalar"] + # + if R is not None: + RI = R.I + elif Parameters["R_scalar"] is not None: + RI = 1.0 / Parameters["R_scalar"] # # Définition de la fonction-coût # ------------------------------ @@ -135,10 +146,10 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): logging.debug("%s Nombre maximal de pas d'optimisation = %s"%(self._name, str(maxiter))) if Parameters.has_key("CostDecrementTolerance") and (Parameters["CostDecrementTolerance"] > 0): ftol = float(Parameters["CostDecrementTolerance"]) - factr = 1./ftol + factr = ftol * 1.e14 else: ftol = 1.e-7 - factr = 1./ftol + factr = ftol * 1.e14 logging.debug("%s Diminution relative minimale du cout lors de l'arret = %s"%(self._name, str(1./factr))) if Parameters.has_key("ProjectedGradientTolerance") and (Parameters["ProjectedGradientTolerance"] > -1): pgtol = float(Parameters["ProjectedGradientTolerance"]) @@ -152,11 +163,10 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): logging.debug("%s Maximum des composantes du gradient lors de l'arret = %s"%(self._name, str(gtol))) InnerMinimizerList = ["CG", "NCG", "BFGS"] if Parameters.has_key("InnerMinimizer") and (Parameters["InnerMinimizer"] in InnerMinimizerList): - InnerMinimizer = str( Parameters["Minimizer"] ) + InnerMinimizer = str( Parameters["InnerMinimizer"] ) else: InnerMinimizer = "BFGS" logging.debug("%s Minimiseur interne utilisé = %s"%(self._name, InnerMinimizer)) - logging.debug("%s Norme du gradient lors de l'arret = %s"%(self._name, str(gtol))) # # Minimisation de la fonctionnelle # -------------------------------- @@ -167,7 +177,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): fprime = GradientOfCostFunction, args = (), bounds = Bounds, - maxfun = maxiter, + maxfun = maxiter-1, factr = factr, pgtol = pgtol, iprint = iprint, diff --git a/src/daComposant/daAlgorithms/Blue.py b/src/daComposant/daAlgorithms/Blue.py index 0dcd6d4..ae15c23 100644 --- a/src/daComposant/daAlgorithms/Blue.py +++ b/src/daComposant/daAlgorithms/Blue.py @@ -63,6 +63,10 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): # # Calcul de l'innovation et de l'analyse # -------------------------------------- + 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 logging.debug("%s Innovation d = %s"%(self._name, d)) Xa = Xb + K*d diff --git a/src/daComposant/daAlgorithms/NonLinearLeastSquares.py b/src/daComposant/daAlgorithms/NonLinearLeastSquares.py new file mode 100644 index 0000000..27528d4 --- /dev/null +++ b/src/daComposant/daAlgorithms/NonLinearLeastSquares.py @@ -0,0 +1,263 @@ +#-*-coding:iso-8859-1-*- +# +# Copyright (C) 2008-2011 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 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 +# +# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com +# + +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 + +# ============================================================================== +class ElementaryAlgorithm(BasicObjects.Algorithm): + def __init__(self): + BasicObjects.Algorithm.__init__(self) + self._name = "NONLINEARLEASTSQUARES" + logging.debug("%s Initialisation"%self._name) + + 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"))) + # + # Opérateur d'observation + # ----------------------- + Hm = H["Direct"].appliedTo + Ht = H["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"] + else: + logging.debug("%s Calcul de Hm(Xb)"%self._name) + HXb = Hm( Xb ) + HXb = numpy.asmatrix(HXb).flatten().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 + logging.debug("%s Innovation d = %s"%(self._name, d)) + # + # Précalcul des inversion appellée dans les fonction-coût et gradient + # ------------------------------------------------------------------- + # if B is not None: + # BI = B.I + # elif Parameters["B_scalar"] is not None: + # BI = 1.0 / Parameters["B_scalar"] + # + if R is not None: + RI = R.I + elif Parameters["R_scalar"] is not None: + RI = 1.0 / Parameters["R_scalar"] + # + # Définition de la fonction-coût + # ------------------------------ + def CostFunction(x): + _X = numpy.asmatrix(x).flatten().T + logging.info("%s CostFunction X = %s"%(self._name, numpy.asmatrix( _X ).flatten())) + _HX = Hm( _X ) + _HX = numpy.asmatrix(_HX).flatten().T + Jb = 0. + Jo = 0.5 * (Y - _HX).T * RI * (Y - _HX) + J = float( Jb ) + float( Jo ) + logging.info("%s CostFunction Jb = %s"%(self._name, Jb)) + logging.info("%s CostFunction Jo = %s"%(self._name, Jo)) + logging.info("%s CostFunction J = %s"%(self._name, J)) + self.StoredVariables["CurrentState"].store( _X.A1 ) + self.StoredVariables["CostFunctionJb"].store( Jb ) + self.StoredVariables["CostFunctionJo"].store( Jo ) + self.StoredVariables["CostFunctionJ" ].store( J ) + return float( J ) + # + def GradientOfCostFunction(x): + _X = numpy.asmatrix(x).flatten().T + logging.info("%s GradientOfCostFunction X = %s"%(self._name, numpy.asmatrix( _X ).flatten())) + _HX = Hm( _X ) + _HX = numpy.asmatrix(_HX).flatten().T + GradJb = 0. + GradJo = - Ht( (_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())) + return GradJ.A1 + # + # 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)) + # + # Paramètres de pilotage + # ---------------------- + # Potentiels : "Bounds", "Minimizer", "MaximumNumberOfSteps", "ProjectedGradientTolerance", "GradientNormTolerance", "InnerMinimizer" + if Parameters.has_key("Bounds") and (type(Parameters["Bounds"]) is type([]) or type(Parameters["Bounds"]) is type(())) and (len(Parameters["Bounds"]) > 0): + Bounds = Parameters["Bounds"] + else: + Bounds = None + MinimizerList = ["LBFGSB","TNC", "CG", "NCG", "BFGS"] + if Parameters.has_key("Minimizer") and (Parameters["Minimizer"] in MinimizerList): + Minimizer = str( Parameters["Minimizer"] ) + else: + Minimizer = "LBFGSB" + logging.warning("%s Unknown or undefined minimizer, replaced by the default one \"%s\""%(self._name,Minimizer)) + logging.debug("%s Minimiseur utilisé = %s"%(self._name, Minimizer)) + if Parameters.has_key("MaximumNumberOfSteps") and (Parameters["MaximumNumberOfSteps"] > -1): + maxiter = int( Parameters["MaximumNumberOfSteps"] ) + else: + maxiter = 15000 + logging.debug("%s Nombre maximal de pas d'optimisation = %s"%(self._name, str(maxiter))) + if Parameters.has_key("CostDecrementTolerance") and (Parameters["CostDecrementTolerance"] > 0): + ftol = float(Parameters["CostDecrementTolerance"]) + factr = ftol * 1.e14 + else: + ftol = 1.e-7 + factr = ftol * 1.e14 + logging.debug("%s Diminution relative minimale du cout lors de l'arret = %s"%(self._name, str(1./factr))) + if Parameters.has_key("ProjectedGradientTolerance") and (Parameters["ProjectedGradientTolerance"] > -1): + pgtol = float(Parameters["ProjectedGradientTolerance"]) + else: + pgtol = -1 + logging.debug("%s Maximum des composantes du gradient projete lors de l'arret = %s"%(self._name, str(pgtol))) + if Parameters.has_key("GradientNormTolerance") and (Parameters["GradientNormTolerance"] > -1): + gtol = float(Parameters["GradientNormTolerance"]) + else: + gtol = 1.e-05 + logging.debug("%s Maximum des composantes du gradient lors de l'arret = %s"%(self._name, str(gtol))) + InnerMinimizerList = ["CG", "NCG", "BFGS"] + if Parameters.has_key("InnerMinimizer") and (Parameters["InnerMinimizer"] in InnerMinimizerList): + InnerMinimizer = str( Parameters["InnerMinimizer"] ) + else: + InnerMinimizer = "BFGS" + logging.debug("%s Minimiseur interne utilisé = %s"%(self._name, InnerMinimizer)) + # + # Minimisation de la fonctionnelle + # -------------------------------- + if Minimizer == "LBFGSB": + Minimum, J_optimal, Informations = scipy.optimize.fmin_l_bfgs_b( + func = CostFunction, + x0 = Xini, + fprime = GradientOfCostFunction, + args = (), + bounds = Bounds, + maxfun = maxiter-1, + factr = factr, + pgtol = pgtol, + iprint = iprint, + ) + nfeval = Informations['funcalls'] + rc = Informations['warnflag'] + elif Minimizer == "TNC": + Minimum, nfeval, rc = scipy.optimize.fmin_tnc( + func = CostFunction, + x0 = Xini, + fprime = GradientOfCostFunction, + args = (), + bounds = Bounds, + maxfun = maxiter, + pgtol = pgtol, + ftol = ftol, + messages = message, + ) + elif Minimizer == "CG": + Minimum, fopt, nfeval, grad_calls, rc = scipy.optimize.fmin_cg( + f = CostFunction, + x0 = Xini, + fprime = GradientOfCostFunction, + args = (), + maxiter = maxiter, + gtol = gtol, + disp = disp, + full_output = True, + ) + elif Minimizer == "NCG": + Minimum, fopt, nfeval, grad_calls, hcalls, rc = scipy.optimize.fmin_ncg( + f = CostFunction, + x0 = Xini, + fprime = GradientOfCostFunction, + args = (), + maxiter = maxiter, + avextol = ftol, + disp = disp, + full_output = True, + ) + elif Minimizer == "BFGS": + Minimum, fopt, gopt, Hopt, nfeval, grad_calls, rc = scipy.optimize.fmin_bfgs( + f = CostFunction, + x0 = Xini, + fprime = GradientOfCostFunction, + args = (), + maxiter = maxiter, + gtol = gtol, + disp = disp, + full_output = True, + ) + else: + raise ValueError("Error in Minimizer name: %s"%Minimizer) + # + # 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, Minimizer, StepMin)) + logging.debug("%s %s Minimum cost = %s"%(self._name, Minimizer, MinJ)) + logging.debug("%s %s Minimum state = %s"%(self._name, Minimizer, Minimum)) + logging.debug("%s %s Nb of F = %s"%(self._name, Minimizer, nfeval)) + logging.debug("%s %s RetCode = %s"%(self._name, Minimizer, rc)) + # + # Calcul de l'analyse + # -------------------- + Xa = numpy.asmatrix(Minimum).T + logging.debug("%s Analyse Xa = %s"%(self._name, Xa)) + # + 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) + # + return 0 + +# ============================================================================== +if __name__ == "__main__": + print '\n AUTODIAGNOSTIC \n'