#-*-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, scipy.optimize
# ==============================================================================
default = [],
typecast = tuple,
message = "Liste de calculs supplémentaires à stocker et/ou effectuer",
- listval = ["APosterioriCovariance", "BMA", "OMA", "OMB", "Innovation", "SigmaObs2", "MahalanobisConsistency", "SimulationQuantiles"]
+ listval = ["APosterioriCorrelations", "APosterioriCovariance", "APosterioriStandardDeviations", "APosterioriVariances", "BMA", "OMA", "OMB", "CostFunctionJ", "CostFunctionJb", "CostFunctionJo", "CurrentState", "CurrentOptimum", "IndexOfOptimum", "Innovation", "InnovationAtCurrentState", "CostFunctionJAtCurrentOptimum", "SigmaObs2", "MahalanobisConsistency", "SimulationQuantiles", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum", "SimulatedObservationAtCurrentOptimum"]
)
self.defineRequiredParameter(
name = "Quantiles",
default = [],
typecast = tuple,
message = "Liste des valeurs de quantiles",
+ minval = 0.,
+ maxval = 1.,
)
self.defineRequiredParameter(
name = "SetSeed",
message = "Type de simulation pour l'estimation des quantiles",
listval = ["Linear", "NonLinear"]
)
+ 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):
- 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
- #
- 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)
#
# 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
- # -----------------------
+ # Opérateurs
+ # ----------
Hm = HO["Direct"].appliedTo
Ha = HO["Adjoint"].appliedInXTo
#
- # Utilisation éventuelle d'un vecteur H(Xb) précalculé
- # ----------------------------------------------------
+ # Utilisation éventuelle d'un vecteur H(Xb) précalculé (sans cout)
+ # ----------------------------------------------------------------
if HO["AppliedToX"] is not None and HO["AppliedToX"].has_key("HXb"):
- HXb = HO["AppliedToX"]["HXb"]
+ HXb = Hm( Xb, HO["AppliedToX"]["HXb"])
else:
HXb = Hm( Xb )
HXb = numpy.asmatrix(numpy.ravel( HXb )).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
#
# Précalcul des inversions de B et R
# ----------------------------------
# ------------------------------
def CostFunction(x):
_X = numpy.asmatrix(numpy.ravel( x )).T
+ if self._parameters["StoreInternalVariables"] or \
+ "CurrentState" in self._parameters["StoreSupplementaryCalculations"] or \
+ "CurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["CurrentState"].store( _X )
_HX = Hm( _X )
_HX = numpy.asmatrix(numpy.ravel( _HX )).T
- Jb = 0.5 * (_X - Xb).T * BI * (_X - Xb)
- Jo = 0.5 * (Y - _HX).T * RI * (Y - _HX)
- J = float( Jb ) + float( Jo )
- if self._parameters["StoreInternalVariables"]:
- self.StoredVariables["CurrentState"].store( _X.A1 )
+ _Innovation = Y - _HX
+ if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"] or \
+ "SimulatedObservationAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["SimulatedObservationAtCurrentState"].store( _HX )
+ if "InnovationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["InnovationAtCurrentState"].store( _Innovation )
+ #
+ Jb = float( 0.5 * (_X - Xb).T * BI * (_X - Xb) )
+ Jo = float( 0.5 * _Innovation.T * RI * _Innovation )
+ J = Jb + Jo
+ #
self.StoredVariables["CostFunctionJb"].store( Jb )
self.StoredVariables["CostFunctionJo"].store( Jo )
self.StoredVariables["CostFunctionJ" ].store( J )
+ if "IndexOfOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
+ "CurrentOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
+ "CostFunctionJAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
+ "SimulatedObservationAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ IndexMin = numpy.argmin( self.StoredVariables["CostFunctionJ"][nbPreviousSteps:] ) + nbPreviousSteps
+ if "IndexOfOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["IndexOfOptimum"].store( IndexMin )
+ if "CurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["CurrentOptimum"].store( self.StoredVariables["CurrentState"][IndexMin] )
+ if "SimulatedObservationAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["SimulatedObservationAtCurrentOptimum"].store( self.StoredVariables["SimulatedObservationAtCurrentState"][IndexMin] )
+ if "CostFunctionJAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJb"][IndexMin] )
+ self.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJo"][IndexMin] )
+ self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( self.StoredVariables["CostFunctionJ" ][IndexMin] )
return J
#
def GradientOfCostFunction(x):
#
# Point de démarrage de l'optimisation : Xini = Xb
# ------------------------------------
- if type(Xb) is type(numpy.matrix([])):
- Xini = Xb.A1.tolist()
- else:
- Xini = list(Xb)
+ Xini = numpy.ravel(Xb)
#
# Minimisation de la fonctionnelle
# --------------------------------
x0 = Xini,
fprime = GradientOfCostFunction,
args = (),
- bounds = Bounds,
+ bounds = self._parameters["Bounds"],
maxfun = self._parameters["MaximumNumberOfSteps"]-1,
factr = self._parameters["CostDecrementTolerance"]*1.e14,
pgtol = self._parameters["ProjectedGradientTolerance"],
- iprint = self.__iprint,
+ iprint = self._parameters["optiprint"],
)
nfeval = Informations['funcalls']
rc = Informations['warnflag']
x0 = Xini,
fprime = GradientOfCostFunction,
args = (),
- bounds = Bounds,
+ bounds = self._parameters["Bounds"],
maxfun = self._parameters["MaximumNumberOfSteps"],
pgtol = self._parameters["ProjectedGradientTolerance"],
ftol = self._parameters["CostDecrementTolerance"],
- messages = self.__message,
+ messages = self._parameters["optmessages"],
)
elif self._parameters["Minimizer"] == "CG":
Minimum, fopt, nfeval, grad_calls, rc = scipy.optimize.fmin_cg(
args = (),
maxiter = self._parameters["MaximumNumberOfSteps"],
gtol = self._parameters["GradientNormTolerance"],
- disp = self.__disp,
+ disp = self._parameters["optdisp"],
full_output = True,
)
elif self._parameters["Minimizer"] == "NCG":
args = (),
maxiter = self._parameters["MaximumNumberOfSteps"],
avextol = self._parameters["CostDecrementTolerance"],
- disp = self.__disp,
+ disp = self._parameters["optdisp"],
full_output = True,
)
elif self._parameters["Minimizer"] == "BFGS":
args = (),
maxiter = self._parameters["MaximumNumberOfSteps"],
gtol = self._parameters["GradientNormTolerance"],
- disp = self.__disp,
+ disp = self._parameters["optdisp"],
full_output = True,
)
else:
#
# Correction pour pallier a un bug de TNC sur le retour du Minimum
# ----------------------------------------------------------------
- if self._parameters["StoreInternalVariables"]:
+ if self._parameters["StoreInternalVariables"] or "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
Minimum = self.StoredVariables["CurrentState"][IndexMin]
#
# Obtention de l'analyse
#
self.StoredVariables["Analysis"].store( Xa.A1 )
#
- if "OMA" in self._parameters["StoreSupplementaryCalculations"] or \
- "SigmaObs2" in self._parameters["StoreSupplementaryCalculations"] or \
- "SimulationQuantiles" in self._parameters["StoreSupplementaryCalculations"]:
- HXa = Hm(Xa)
+ if "OMA" in self._parameters["StoreSupplementaryCalculations"] or \
+ "SigmaObs2" in self._parameters["StoreSupplementaryCalculations"] or \
+ "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
+ "SimulationQuantiles" in self._parameters["StoreSupplementaryCalculations"]:
+ if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ HXa = self.StoredVariables["SimulatedObservationAtCurrentState"][IndexMin]
+ elif "SimulatedObservationAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ HXa = self.StoredVariables["SimulatedObservationAtCurrentOptimum"][-1]
+ else:
+ HXa = Hm(Xa)
#
# Calcul de la covariance d'analyse
# ---------------------------------
#
# Calculs et/ou stockages supplémentaires
# ---------------------------------------
+ if "Innovation" in self._parameters["StoreSupplementaryCalculations"] or \
+ "OMB" in self._parameters["StoreSupplementaryCalculations"] or \
+ "SigmaObs2" in self._parameters["StoreSupplementaryCalculations"] or \
+ "MahalanobisConsistency" in self._parameters["StoreSupplementaryCalculations"]:
+ d = Y - HXb
if "Innovation" in self._parameters["StoreSupplementaryCalculations"]:
self.StoredVariables["Innovation"].store( numpy.ravel(d) )
if "BMA" in self._parameters["StoreSupplementaryCalculations"]:
if "MahalanobisConsistency" in self._parameters["StoreSupplementaryCalculations"]:
self.StoredVariables["MahalanobisConsistency"].store( float( 2.*MinJ/d.size ) )
if "SimulationQuantiles" in self._parameters["StoreSupplementaryCalculations"]:
- Qtls = self._parameters["Quantiles"]
+ Qtls = map(float, self._parameters["Quantiles"])
nech = self._parameters["NumberOfSamplesForQuantiles"]
HXa = numpy.matrix(numpy.ravel( HXa )).T
YfQ = None
if YQ is None: YQ = YfQ[:,indice]
else: YQ = numpy.hstack((YQ,YfQ[:,indice]))
self.StoredVariables["SimulationQuantiles"].store( YQ )
+ if "SimulatedObservationAtBackground" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["SimulatedObservationAtBackground"].store( numpy.ravel(HXb) )
+ if "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ 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
# ==============================================================================
