-#-*-coding:iso-8859-1-*-
+# -*- coding: utf-8 -*-
#
-# Copyright (C) 2008-2014 EDF R&D
+# Copyright (C) 2008-2019 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, PlatformInfo
-m = PlatformInfo.SystemUsage()
+from daCore import BasicObjects, NumericObjects
import numpy
# ==============================================================================
name = "Minimizer",
default = "MMQR",
typecast = str,
- message = "Minimiseur utilisé",
+ message = "Minimiseur utilisé",
listval = ["MMQR"],
)
self.defineRequiredParameter(
name = "CostDecrementTolerance",
default = 1.e-6,
typecast = float,
- message = "Maximum de variation de la fonction d'estimation lors de l'arrêt",
+ message = "Maximum de variation de la fonction d'estimation lors de l'arrêt",
)
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",
+ "CostFunctionJb",
+ "CostFunctionJo",
+ "CurrentState",
+ "Innovation",
+ "OMA",
+ "OMB",
+ "SimulatedObservationAtBackground",
+ "SimulatedObservationAtCurrentState",
+ "SimulatedObservationAtOptimum",
+ ]
+ )
+ self.defineRequiredParameter( # Pas de type
+ name = "Bounds",
+ message = "Liste des valeurs de bornes",
+ )
+ self.requireInputArguments(
+ mandatory= ("Xb", "Y", "HO" ),
)
def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
- logging.debug("%s Lancement"%self._name)
- logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
- #
- # 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, R, B, Q)
#
- # Opérateur d'observation
- # -----------------------
Hm = HO["Direct"].appliedTo
#
- # 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
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
#
- # 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"):
+ self.StoredVariables["CurrentState"].store( _X )
_HX = Hm( _X )
_HX = numpy.asmatrix(numpy.ravel( _HX )).T
+ if self._toStore("SimulatedObservationAtCurrentState"):
+ self.StoredVariables["SimulatedObservationAtCurrentState"].store( _HX )
Jb = 0.
Jo = 0.
J = Jb + 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 )
Hg = HO["Tangent"].asMatrix( _X )
return Hg
#
- # Point de démarrage de l'optimisation : Xini = Xb
+ # Point de démarrage de l'optimisation : Xini = Xb
# ------------------------------------
- if type(Xb) is type(numpy.matrix([])):
+ if isinstance(Xb, type(numpy.matrix([]))):
Xini = Xb.A1.tolist()
else:
Xini = list(Xb)
# Minimisation de la fonctionnelle
# --------------------------------
if self._parameters["Minimizer"] == "MMQR":
- import mmqr
- Minimum, J_optimal, Informations = mmqr.mmqr(
+ Minimum, J_optimal, Informations = NumericObjects.mmqr(
func = CostFunction,
x0 = Xini,
fprime = GradientOfCostFunction,
- bounds = Bounds,
+ bounds = self._parameters["Bounds"],
quantile = self._parameters["Quantile"],
maxfun = self._parameters["MaximumNumberOfSteps"],
toler = self._parameters["CostDecrementTolerance"],
#
self.StoredVariables["Analysis"].store( Xa.A1 )
#
- # Calculs et/ou stockages supplémentaires
+ if self._toStore("OMA") or \
+ self._toStore("SimulatedObservationAtOptimum"):
+ HXa = Hm(Xa)
+ #
+ # Calculs et/ou stockages supplémentaires
# ---------------------------------------
- if "Innovation" in self._parameters["StoreSupplementaryCalculations"]:
+ 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 - Xa) )
- if "OMA" in self._parameters["StoreSupplementaryCalculations"]:
- self.StoredVariables["OMA"].store( numpy.ravel(Y - Hm(Xa)) )
- if "OMB" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("OMA"):
+ self.StoredVariables["OMA"].store( numpy.ravel(Y - 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) )
#
- logging.debug("%s Nombre d'évaluation(s) de l'opérateur d'observation direct/tangent/adjoint : %i/%i/%i"%(self._name, HO["Direct"].nbcalls()[0],HO["Tangent"].nbcalls()[0],HO["Adjoint"].nbcalls()[0]))
- logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
- logging.debug("%s Terminé"%self._name)
- #
+ self._post_run(HO)
return 0
# ==============================================================================
if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
+ print('\n AUTODIAGNOSTIC\n')