#-*-coding:iso-8859-1-*-
#
-# Copyright (C) 2008-2014 EDF R&D
+# Copyright (C) 2008-2017 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
import numpy, math
# ==============================================================================
class ElementaryAlgorithm(BasicObjects.Algorithm):
def __init__(self):
- BasicObjects.Algorithm.__init__(self, "FUNCTIONTEST")
+ BasicObjects.Algorithm.__init__(self, "LINEARITYTEST")
self.defineRequiredParameter(
name = "ResiduFormula",
default = "CenteredDL",
typecast = float,
message = "Amplitude de la direction initiale de la dérivée directionnelle autour du point nominal",
)
+ self.defineRequiredParameter(
+ name = "AmplitudeOfTangentPerturbation",
+ default = 1.e-2,
+ typecast = float,
+ message = "Amplitude de la perturbation pour le calcul de la forme tangente",
+ minval = 1.e-10,
+ maxval = 1.,
+ )
self.defineRequiredParameter(
name = "SetSeed",
typecast = numpy.random.seed,
typecast = str,
message = "Titre du tableau et de la figure",
)
+ self.defineRequiredParameter(
+ name = "StoreSupplementaryCalculations",
+ default = [],
+ typecast = tuple,
+ message = "Liste de calculs supplémentaires à stocker et/ou effectuer",
+ listval = ["CurrentState", "Residu", "SimulatedObservationAtCurrentState"]
+ )
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")))
+ self._pre_run()
#
# Paramètres de pilotage
# ----------------------
FX = numpy.asmatrix(numpy.ravel( Hm( Xn ) )).T
NormeX = numpy.linalg.norm( Xn )
NormeFX = numpy.linalg.norm( FX )
+ if "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["CurrentState"].store( numpy.ravel(Xn) )
+ if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(FX) )
#
# Fabrication de la direction de l'incrément dX
# ----------------------------------------------
# Calcul du gradient au point courant X pour l'incrément dX
# ---------------------------------------------------------
if self._parameters["ResiduFormula"] in ["Taylor", "NominalTaylor", "NominalTaylorRMS"]:
- GradFxdX = Ht( (Xn, dX0) )
+ dX1 = float(self._parameters["AmplitudeOfTangentPerturbation"]) * dX0
+ GradFxdX = Ht( (Xn, dX1) )
GradFxdX = numpy.asmatrix(numpy.ravel( GradFxdX )).T
+ GradFxdX = float(1./self._parameters["AmplitudeOfTangentPerturbation"]) * GradFxdX
#
# Entete des resultats
# --------------------
__marge = 12*" "
if self._parameters["ResiduFormula"] == "CenteredDL":
- __entete = " i Alpha ||X|| ||F(X)|| | R(Alpha) log( R ) "
+ __entete = " i Alpha ||X|| ||F(X)|| | R(Alpha) log10( R ) "
__msgdoc = """
- On observe le residu provenant de la différence centrée des valeurs de F
+ On observe le résidu provenant de la différence centrée des valeurs de F
au point nominal et aux points perturbés, normalisée par la valeur au
point nominal :
de F n'est pas vérifiée.
Si le résidu décroit et que la décroissance se fait en Alpha**2 selon Alpha,
- cela signifie que le gradient est bien calculé jusqu'à la précision d'arrêt
+ cela signifie que le gradient est calculable jusqu'à la précision d'arrêt
de la décroissance quadratique.
On prend dX0 = Normal(0,X) et dX = Alpha*dX0. F est le code de calcul.
"""
if self._parameters["ResiduFormula"] == "Taylor":
- __entete = " i Alpha ||X|| ||F(X)|| | R(Alpha) log( R ) "
+ __entete = " i Alpha ||X|| ||F(X)|| | R(Alpha) log10( R ) "
__msgdoc = """
- On observe le residu issu du développement de Taylor de la fonction F,
+ On observe le résidu issu du développement de Taylor de la fonction F,
normalisée par la valeur au point nominal :
|| F(X+Alpha*dX) - F(X) - Alpha * GradientF_X(dX) ||
Si le résidu décroit et que la décroissance se fait en Alpha**2 selon Alpha,
cela signifie que le gradient est bien calculé jusqu'à la précision d'arrêt
- de la décroissance.
+ de la décroissance quadratique.
On prend dX0 = Normal(0,X) et dX = Alpha*dX0. F est le code de calcul.
"""
if self._parameters["ResiduFormula"] == "NominalTaylor":
__entete = " i Alpha ||X|| ||F(X)|| | R(Alpha) |R-1| en % "
__msgdoc = """
- On observe le residu obtenu à partir de deux approximations d'ordre 1 de F(X),
+ On observe le résidu obtenu à partir de deux approximations d'ordre 1 de F(X),
normalisées par la valeur au point nominal :
R(Alpha) = max(
if self._parameters["ResiduFormula"] == "NominalTaylorRMS":
__entete = " i Alpha ||X|| ||F(X)|| | R(Alpha) |R| en % "
__msgdoc = """
- On observe le residu obtenu à partir de deux approximations d'ordre 1 de F(X),
+ On observe le résidu obtenu à partir de deux approximations d'ordre 1 de F(X),
normalisées par la valeur au point nominal :
R(Alpha) = max(
dX = amplitude * dX0
#
if self._parameters["ResiduFormula"] == "CenteredDL":
+ if "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["CurrentState"].store( numpy.ravel(Xn + dX) )
+ self.StoredVariables["CurrentState"].store( numpy.ravel(Xn - dX) )
+ #
FX_plus_dX = numpy.asmatrix(numpy.ravel( Hm( Xn + dX ) )).T
FX_moins_dX = numpy.asmatrix(numpy.ravel( Hm( Xn - dX ) )).T
#
+ if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(FX_plus_dX) )
+ self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(FX_moins_dX) )
+ #
Residu = numpy.linalg.norm( FX_plus_dX + FX_moins_dX - 2 * FX ) / NormeFX
#
- self.StoredVariables["CostFunctionJ"].store( Residu )
+ self.StoredVariables["Residu"].store( Residu )
msg = " %2i %5.0e %9.3e %9.3e | %9.3e %4.0f"%(i,amplitude,NormeX,NormeFX,Residu,math.log10(max(1.e-99,Residu)))
msgs += "\n" + __marge + msg
#
if self._parameters["ResiduFormula"] == "Taylor":
+ if "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["CurrentState"].store( numpy.ravel(Xn + dX) )
+ #
FX_plus_dX = numpy.asmatrix(numpy.ravel( Hm( Xn + dX ) )).T
#
+ if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(FX_plus_dX) )
+ #
Residu = numpy.linalg.norm( FX_plus_dX - FX - amplitude * GradFxdX ) / NormeFX
#
- self.StoredVariables["CostFunctionJ"].store( Residu )
+ self.StoredVariables["Residu"].store( Residu )
msg = " %2i %5.0e %9.3e %9.3e | %9.3e %4.0f"%(i,amplitude,NormeX,NormeFX,Residu,math.log10(max(1.e-99,Residu)))
msgs += "\n" + __marge + msg
#
if self._parameters["ResiduFormula"] == "NominalTaylor":
+ if "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["CurrentState"].store( numpy.ravel(Xn + dX) )
+ self.StoredVariables["CurrentState"].store( numpy.ravel(Xn - dX) )
+ self.StoredVariables["CurrentState"].store( numpy.ravel(dX) )
+ #
FX_plus_dX = numpy.asmatrix(numpy.ravel( Hm( Xn + dX ) )).T
FX_moins_dX = numpy.asmatrix(numpy.ravel( Hm( Xn - dX ) )).T
FdX = numpy.asmatrix(numpy.ravel( Hm( dX ) )).T
#
+ if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(FX_plus_dX) )
+ self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(FX_moins_dX) )
+ self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(FdX) )
+ #
Residu = max(
numpy.linalg.norm( FX_plus_dX - amplitude * FdX ) / NormeFX,
numpy.linalg.norm( FX_moins_dX + amplitude * FdX ) / NormeFX,
)
#
- self.StoredVariables["CostFunctionJ"].store( Residu )
- msg = " %2i %5.0e %9.3e %9.3e | %9.3e %5i %s"%(i,amplitude,NormeX,NormeFX,Residu,100*abs(Residu-1),"%")
+ self.StoredVariables["Residu"].store( Residu )
+ msg = " %2i %5.0e %9.3e %9.3e | %9.3e %5i %s"%(i,amplitude,NormeX,NormeFX,Residu,100.*abs(Residu-1.),"%")
msgs += "\n" + __marge + msg
#
if self._parameters["ResiduFormula"] == "NominalTaylorRMS":
+ if "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["CurrentState"].store( numpy.ravel(Xn + dX) )
+ self.StoredVariables["CurrentState"].store( numpy.ravel(Xn - dX) )
+ self.StoredVariables["CurrentState"].store( numpy.ravel(dX) )
+ #
FX_plus_dX = numpy.asmatrix(numpy.ravel( Hm( Xn + dX ) )).T
FX_moins_dX = numpy.asmatrix(numpy.ravel( Hm( Xn - dX ) )).T
FdX = numpy.asmatrix(numpy.ravel( Hm( dX ) )).T
#
+ if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(FX_plus_dX) )
+ self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(FX_moins_dX) )
+ self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(FdX) )
+ #
Residu = max(
RMS( FX, FX_plus_dX - amplitude * FdX ) / NormeFX,
RMS( FX, FX_moins_dX + amplitude * FdX ) / NormeFX,
)
#
- self.StoredVariables["CostFunctionJ"].store( Residu )
- msg = " %2i %5.0e %9.3e %9.3e | %9.3e %5i %s"%(i,amplitude,NormeX,NormeFX,Residu,100*Residu,"%")
+ self.StoredVariables["Residu"].store( Residu )
+ msg = " %2i %5.0e %9.3e %9.3e | %9.3e %5i %s"%(i,amplitude,NormeX,NormeFX,Residu,100.*Residu,"%")
msgs += "\n" + __marge + msg
#
msgs += "\n" + __marge + "-"*__nbtirets
print "Results of linearity check by \"%s\" formula:"%self._parameters["ResiduFormula"]
print msgs
#
- 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
# ==============================================================================
