--- /dev/null
+#-*-coding:iso-8859-1-*-
+#
+# Copyright (C) 2008-2015 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
+#
+# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
+
+import logging
+from daCore import BasicObjects
+import numpy, scipy.optimize
+
+# ==============================================================================
+class ElementaryAlgorithm(BasicObjects.Algorithm):
+ def __init__(self):
+ BasicObjects.Algorithm.__init__(self, "4DVAR")
+ self.defineRequiredParameter(
+ name = "ConstrainedBy",
+ default = "EstimateProjection",
+ typecast = str,
+ message = "Prise en compte des contraintes",
+ listval = ["EstimateProjection"],
+ )
+ self.defineRequiredParameter(
+ name = "EstimationOf",
+ default = "State",
+ typecast = str,
+ message = "Estimation d'etat ou de parametres",
+ listval = ["State", "Parameters"],
+ )
+ self.defineRequiredParameter(
+ name = "Minimizer",
+ default = "LBFGSB",
+ typecast = str,
+ message = "Minimiseur utilisé",
+ listval = ["LBFGSB","TNC", "CG", "NCG", "BFGS"],
+ )
+ self.defineRequiredParameter(
+ name = "MaximumNumberOfSteps",
+ default = 15000,
+ typecast = int,
+ message = "Nombre maximal de pas d'optimisation",
+ minval = -1,
+ )
+ self.defineRequiredParameter(
+ name = "CostDecrementTolerance",
+ default = 1.e-7,
+ typecast = float,
+ message = "Diminution relative minimale du cout lors de l'arrêt",
+ )
+ self.defineRequiredParameter(
+ name = "ProjectedGradientTolerance",
+ default = -1,
+ typecast = float,
+ 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",
+ )
+ 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", "CurrentState", "CostFunctionJ"]
+ )
+ self.defineRequiredParameter( # Pas de type
+ name = "Bounds",
+ message = "Liste des valeurs de bornes",
+ )
+
+ 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
+ #
+ # Correction pour pallier a un bug de TNC sur le retour du Minimum
+ if self._parameters.has_key("Minimizer") == "TNC":
+ self.setParameterValue("StoreInternalVariables",True)
+ #
+ # Opérateurs
+ # ----------
+ Hm = HO["Direct"].appliedControledFormTo
+ #
+ Mm = EM["Direct"].appliedControledFormTo
+ #
+ if CM is not None and CM.has_key("Tangent") and U is not None:
+ Cm = CM["Tangent"].asMatrix(Xb)
+ else:
+ Cm = None
+ #
+ def Un(_step):
+ if U is not None:
+ if hasattr(U,"store") and 1<=_step<len(U) :
+ _Un = numpy.asmatrix(numpy.ravel( U[_step] )).T
+ elif hasattr(U,"store") and len(U)==1:
+ _Un = numpy.asmatrix(numpy.ravel( U[0] )).T
+ else:
+ _Un = numpy.asmatrix(numpy.ravel( U )).T
+ else:
+ _Un = None
+ return _Un
+ def CmUn(_xn,_un):
+ if Cm is not None and _un is not None: # Attention : si Cm est aussi dans M, doublon !
+ _Cm = Cm.reshape(_xn.size,_un.size) # ADAO & check shape
+ _CmUn = _Cm * _un
+ else:
+ _CmUn = 0.
+ return _CmUn
+ #
+ # Remarque : les observations sont exploitées à partir du pas de temps
+ # numéro 1, et sont utilisées dans Yo comme rangées selon ces indices.
+ # Donc le pas 0 n'est pas utilisé puisque la première étape commence
+ # avec l'observation du pas 1.
+ #
+ # Nombre de pas identique au nombre de pas d'observations
+ # -------------------------------------------------------
+ if hasattr(Y,"stepnumber"):
+ duration = Y.stepnumber()
+ else:
+ duration = 2
+ #
+ # Précalcul des inversions de B et R
+ # ----------------------------------
+ BI = B.getI()
+ RI = R.getI()
+ #
+ # Définition de la fonction-coût
+ # ------------------------------
+ self.DirectCalculation = [None,] # Le pas 0 n'est pas observé
+ self.DirectInnovation = [None,] # Le pas 0 n'est pas observé
+ def CostFunction(x):
+ _X = numpy.asmatrix(numpy.ravel( x )).T
+ Jb = 0.5 * (_X - Xb).T * BI * (_X - Xb)
+ self.DirectCalculation = [None,]
+ self.DirectInnovation = [None,]
+ Jo = 0.
+ _Xn = _X
+ for step in range(0,duration-1):
+ self.DirectCalculation.append( _Xn )
+ if hasattr(Y,"store"):
+ _Ynpu = numpy.asmatrix(numpy.ravel( Y[step+1] )).T
+ else:
+ _Ynpu = numpy.asmatrix(numpy.ravel( Y )).T
+ _Un = Un(step)
+ #
+ # Etape d'évolution
+ if self._parameters["EstimationOf"] == "State":
+ _Xn = Mm( (_Xn, _Un) ) + CmUn(_Xn, _Un)
+ elif self._parameters["EstimationOf"] == "Parameters":
+ pass
+ #
+ if Bounds is not None and self._parameters["ConstrainedBy"] == "EstimateProjection":
+ _Xn = numpy.max(numpy.hstack((_Xn,numpy.asmatrix(Bounds)[:,0])),axis=1)
+ _Xn = numpy.min(numpy.hstack((_Xn,numpy.asmatrix(Bounds)[:,1])),axis=1)
+ #
+ # Etape de différence aux observations
+ if self._parameters["EstimationOf"] == "State":
+ _YmHMX = _Ynpu - numpy.asmatrix(numpy.ravel( Hm( (_Xn, None) ) )).T
+ elif self._parameters["EstimationOf"] == "Parameters":
+ _YmHMX = _Ynpu - numpy.asmatrix(numpy.ravel( Hm( (_Xn, _Un) ) )).T - CmUn(_Xn, _Un)
+ self.DirectInnovation.append( _YmHMX )
+ # Ajout dans la fonctionnelle d'observation
+ Jo = Jo + _YmHMX.T * RI * _YmHMX
+ Jo = 0.5 * Jo
+ J = float( Jb ) + float( Jo )
+ if self._parameters["StoreInternalVariables"] or "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["CurrentState"].store( _X )
+ self.StoredVariables["CostFunctionJb"].store( Jb )
+ self.StoredVariables["CostFunctionJo"].store( Jo )
+ self.StoredVariables["CostFunctionJ" ].store( J )
+ return J
+ #
+ def GradientOfCostFunction(x):
+ _X = numpy.asmatrix(numpy.ravel( x )).T
+ GradJb = BI * (_X - Xb)
+ GradJo = 0.
+ for step in range(duration-1,0,-1):
+ # Etape de récupération du dernier stockage de l'évolution
+ _Xn = self.DirectCalculation.pop()
+ # Etape de récupération du dernier stockage de l'innovation
+ _YmHMX = self.DirectInnovation.pop()
+ # Calcul des adjoints
+ Ha = HO["Adjoint"].asMatrix(ValueForMethodForm = _Xn)
+ Ha = Ha.reshape(_Xn.size,_YmHMX.size) # ADAO & check shape
+ Ma = EM["Adjoint"].asMatrix(ValueForMethodForm = _Xn)
+ Ma = Ma.reshape(_Xn.size,_Xn.size) # ADAO & check shape
+ # Calcul du gradient par etat adjoint
+ GradJo = GradJo + Ha * RI * _YmHMX # Equivaut pour Ha lineaire à : Ha( (_Xn, RI * _YmHMX) )
+ GradJo = Ma * GradJo # Equivaut pour Ma lineaire à : Ma( (_Xn, GradJo) )
+ GradJ = numpy.asmatrix( numpy.ravel( GradJb ) - numpy.ravel( GradJo ) ).T
+ 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)
+ #
+ # 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,
+ x0 = Xini,
+ fprime = GradientOfCostFunction,
+ args = (),
+ bounds = Bounds,
+ maxfun = self._parameters["MaximumNumberOfSteps"]-1,
+ factr = self._parameters["CostDecrementTolerance"]*1.e14,
+ pgtol = self._parameters["ProjectedGradientTolerance"],
+ iprint = self.__iprint,
+ )
+ nfeval = Informations['funcalls']
+ rc = Informations['warnflag']
+ elif self._parameters["Minimizer"] == "TNC":
+ Minimum, nfeval, rc = scipy.optimize.fmin_tnc(
+ func = CostFunction,
+ x0 = Xini,
+ fprime = GradientOfCostFunction,
+ args = (),
+ bounds = Bounds,
+ maxfun = self._parameters["MaximumNumberOfSteps"],
+ pgtol = self._parameters["ProjectedGradientTolerance"],
+ ftol = self._parameters["CostDecrementTolerance"],
+ messages = self.__message,
+ )
+ elif self._parameters["Minimizer"] == "CG":
+ Minimum, fopt, nfeval, grad_calls, rc = scipy.optimize.fmin_cg(
+ f = CostFunction,
+ x0 = Xini,
+ fprime = GradientOfCostFunction,
+ args = (),
+ maxiter = self._parameters["MaximumNumberOfSteps"],
+ gtol = self._parameters["GradientNormTolerance"],
+ disp = self.__disp,
+ full_output = True,
+ )
+ elif self._parameters["Minimizer"] == "NCG":
+ Minimum, fopt, nfeval, grad_calls, hcalls, rc = scipy.optimize.fmin_ncg(
+ f = CostFunction,
+ x0 = Xini,
+ fprime = GradientOfCostFunction,
+ args = (),
+ maxiter = self._parameters["MaximumNumberOfSteps"],
+ avextol = self._parameters["CostDecrementTolerance"],
+ disp = self.__disp,
+ full_output = True,
+ )
+ elif self._parameters["Minimizer"] == "BFGS":
+ Minimum, fopt, gopt, Hopt, nfeval, grad_calls, rc = scipy.optimize.fmin_bfgs(
+ f = CostFunction,
+ x0 = Xini,
+ fprime = GradientOfCostFunction,
+ args = (),
+ maxiter = self._parameters["MaximumNumberOfSteps"],
+ gtol = self._parameters["GradientNormTolerance"],
+ disp = self.__disp,
+ full_output = True,
+ )
+ 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
+ # ----------------------------------------------------------------
+ if self._parameters["StoreInternalVariables"] or "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ Minimum = self.StoredVariables["CurrentState"][IndexMin]
+ #
+ # Obtention de l'analyse
+ # ----------------------
+ Xa = numpy.asmatrix(numpy.ravel( Minimum )).T
+ #
+ self.StoredVariables["Analysis"].store( Xa.A1 )
+ #
+ # Calculs et/ou stockages supplémentaires
+ # ---------------------------------------
+ if "BMA" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(Xa) )
+ #
+ self._post_run(HO)
+ return 0
+
+# ==============================================================================
+if __name__ == "__main__":
+ print '\n AUTODIAGNOSTIC \n'
