-#-*-coding:iso-8859-1-*-
+# -*- coding: utf-8 -*-
#
# Copyright (C) 2008-2017 EDF R&D
#
name = "Minimizer",
default = "LBFGSB",
typecast = str,
- message = "Minimiseur utilisé",
+ message = "Minimiseur utilisé",
listval = ["LBFGSB","TNC", "CG", "NCG", "BFGS", "LM"],
)
self.defineRequiredParameter(
name = "CostDecrementTolerance",
default = 1.e-7,
typecast = float,
- message = "Diminution relative minimale du cout lors de l'arrêt",
+ 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",
+ 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",
+ 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",
+ 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", "CurrentState", "CostFunctionJ", "CostFunctionJb", "CostFunctionJo", "Innovation", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum"]
+ message = "Liste de calculs supplémentaires à stocker et/ou effectuer",
+ listval = ["BMA", "OMA", "OMB", "CostFunctionJ", "CostFunctionJb", "CostFunctionJo", "CurrentState", "CurrentOptimum", "IndexOfOptimum", "Innovation", "InnovationAtCurrentState", "CostFunctionJAtCurrentOptimum", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum", "SimulatedObservationAtCurrentOptimum"]
)
self.defineRequiredParameter( # Pas de type
name = "Bounds",
message = "Liste des valeurs de bornes",
)
+ self.requireInputArguments(
+ mandatory= ("Xb", "Y", "HO", "R"),
+ )
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(Parameters)
+ self._pre_run(Parameters, R, B, Q)
#
# Correction pour pallier a un bug de TNC sur le retour du Minimum
- if self._parameters.has_key("Minimizer") == "TNC":
+ if "Minimizer" in self._parameters and self._parameters["Minimizer"] == "TNC":
self.setParameterValue("StoreInternalVariables",True)
#
- # Opérateurs
+ # 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
# ----------------------------------------------------
- if HO["AppliedToX"] is not None and HO["AppliedToX"].has_key("HXb"):
- HXb = Hm( Xb, 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
- #
- # 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
+ # Précalcul des inversions de B et R
# ----------------------------------
RI = R.getI()
if self._parameters["Minimizer"] == "LM":
RdemiI = R.choleskyI()
#
- # 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 "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ 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
- if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ _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 = 0.
- Jo = 0.5 * (Y - _HX).T * RI * (Y - _HX)
- J = float( Jb ) + float( Jo )
+ 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):
_X = numpy.asmatrix(numpy.ravel( x )).T
_HX = Hm( _X )
_HX = numpy.asmatrix(numpy.ravel( _HX )).T
+ _Innovation = Y - _HX
Jb = 0.
- Jo = 0.5 * (Y - _HX).T * RI * (Y - _HX)
- J = float( Jb ) + float( Jo )
- if self._parameters["StoreInternalVariables"] or "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ Jo = float( 0.5 * _Innovation.T * RI * _Innovation )
+ J = Jb + 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 numpy.ravel( RdemiI*(Y - _HX) )
+ return numpy.ravel( RdemiI*_Innovation )
#
def GradientOfCostFunctionLM(x):
_X = numpy.asmatrix(numpy.ravel( x )).T
GradJ = numpy.asmatrix( numpy.ravel( GradJb ) + numpy.ravel( GradJo ) ).T
return - RdemiI*HO["Tangent"].asMatrix( _X )
#
- # Point de démarrage de l'optimisation : Xini = Xb
+ # 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
# --------------------------------
#
if "OMA" in self._parameters["StoreSupplementaryCalculations"] or \
"SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"]:
- HXa = Hm(Xa)
+ 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)
#
#
- # Calculs et/ou stockages supplémentaires
+ # Calculs et/ou stockages supplémentaires
# ---------------------------------------
+ if "Innovation" in self._parameters["StoreSupplementaryCalculations"] or \
+ "OMB" 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"]:
self.StoredVariables["OMA"].store( numpy.ravel(Y) - numpy.ravel(HXa) )
if "OMB" in self._parameters["StoreSupplementaryCalculations"]:
self.StoredVariables["OMB"].store( numpy.ravel(d) )
+ 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) )
#
# ==============================================================================
if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
+ print('\n AUTODIAGNOSTIC \n')
