X-Git-Url: http://git.salome-platform.org/gitweb/?a=blobdiff_plain;f=src%2FdaComposant%2FdaAlgorithms%2FExtendedBlue.py;h=ea93f62c7465c34a16a7d8cec7d3ee5bdf9dd396;hb=e0a87290afe3094c8daa93597c8bec55df0ba5b0;hp=7ccf7c409679517a9c38cdb392c8374c0168b2a5;hpb=b02fec5bed967ec37e545b67dc841d15dd9c3e78;p=modules%2Fadao.git

diff --git a/src/daComposant/daAlgorithms/ExtendedBlue.py b/src/daComposant/daAlgorithms/ExtendedBlue.py
index 7ccf7c4..ea93f62 100644
--- a/src/daComposant/daAlgorithms/ExtendedBlue.py
+++ b/src/daComposant/daAlgorithms/ExtendedBlue.py
@@ -1,6 +1,6 @@
 #-*-coding:iso-8859-1-*-
 #
-#  Copyright (C) 2008-2014 EDF R&D
+#  Copyright (C) 2008-2015 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
@@ -39,13 +39,15 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             default  = [],
             typecast = tuple,
             message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
-            listval  = ["APosterioriCovariance", "BMA", "OMA", "OMB", "Innovation", "SigmaBck2", "SigmaObs2", "MahalanobisConsistency", "SimulationQuantiles"]
+            listval  = ["APosterioriCovariance", "BMA", "OMA", "OMB", "CostFunctionJ", "Innovation", "SigmaBck2", "SigmaObs2", "MahalanobisConsistency", "SimulationQuantiles", "SimulatedObservationAtBackground", "SimulatedObservationAtOptimum"]
             )
         self.defineRequiredParameter(
             name     = "Quantiles",
             default  = [],
             typecast = tuple,
             message  = "Liste des valeurs de quantiles",
+            minval   = 0.,
+            maxval   = 1.,
             )
         self.defineRequiredParameter(
             name     = "SetSeed",
@@ -126,14 +128,17 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         # Calcul de la fonction coût
         # --------------------------
         if self._parameters["StoreInternalVariables"] or \
-           "OMA" in self._parameters["StoreSupplementaryCalculations"] or \
-           "SigmaObs2" in self._parameters["StoreSupplementaryCalculations"] or \
-           "MahalanobisConsistency" in self._parameters["StoreSupplementaryCalculations"] or \
-           "SimulationQuantiles" in self._parameters["StoreSupplementaryCalculations"]:
+           "CostFunctionJ"                 in self._parameters["StoreSupplementaryCalculations"] or \
+           "OMA"                           in self._parameters["StoreSupplementaryCalculations"] or \
+           "SigmaObs2"                     in self._parameters["StoreSupplementaryCalculations"] or \
+           "MahalanobisConsistency"        in self._parameters["StoreSupplementaryCalculations"] or \
+           "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
+           "SimulationQuantiles"           in self._parameters["StoreSupplementaryCalculations"]:
             HXa  = numpy.matrix(numpy.ravel( H( Xa ) )).T
             oma = Y - HXa
         if self._parameters["StoreInternalVariables"] or \
-           "MahalanobisConsistency" in self._parameters["StoreSupplementaryCalculations"]:
+           "CostFunctionJ"                 in self._parameters["StoreSupplementaryCalculations"] or \
+           "MahalanobisConsistency"        in self._parameters["StoreSupplementaryCalculations"]:
             Jb  = 0.5 * (Xa - Xb).T * BI * (Xa - Xb)
             Jo  = 0.5 * oma.T * RI * oma
             J   = float( Jb ) + float( Jo )
@@ -203,6 +208,10 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 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) )
         #
         self._post_run(HO)
         return 0