#-*-coding:iso-8859-1-*-
#
-# Copyright (C) 2008-2016 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
default = [],
typecast = tuple,
message = "Liste de calculs supplémentaires à stocker et/ou effectuer",
- listval = ["BMA", "OMA", "OMB", "CurrentState", "CostFunctionJ", "Innovation", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum"]
+ listval = ["BMA", "OMA", "OMB", "CurrentState", "CostFunctionJ", "CostFunctionJb", "CostFunctionJo", "Innovation", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum"]
)
self.defineRequiredParameter( # Pas de type
name = "Bounds",
)
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()
+ self._pre_run(Parameters)
#
- # 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
- #
- # Opérateur d'observation
- # -----------------------
Hm = HO["Direct"].appliedTo
#
# Utilisation éventuelle d'un vecteur H(Xb) précalculé
# ----------------------------------------------------
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
func = CostFunction,
x0 = Xini,
fprime = GradientOfCostFunction,
- bounds = Bounds,
+ bounds = self._parameters["Bounds"],
quantile = self._parameters["Quantile"],
maxfun = self._parameters["MaximumNumberOfSteps"],
toler = self._parameters["CostDecrementTolerance"],