Minor source coherency corrections

diff --git a/src/daComposant/daAlgorithms/3DVAR.py b/src/daComposant/daAlgorithms/3DVAR.py
index 852a2a0e7e4d89da665ace3cc0d14ce1e12cdd7b..e968200d88b7f68df6d9941807a261f04c2c4009 100644 (file)
--- a/src/daComposant/daAlgorithms/3DVAR.py
+++ b/src/daComposant/daAlgorithms/3DVAR.py
@@ -72,7 +72,33 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             default  = [],
             typecast = tuple,
             message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
-            listval  = ["APosterioriCorrelations", "APosterioriCovariance", "APosterioriStandardDeviations", "APosterioriVariances", "BMA", "OMA", "OMB", "CostFunctionJ", "CostFunctionJb", "CostFunctionJo", "CurrentState", "CurrentOptimum", "IndexOfOptimum", "Innovation", "InnovationAtCurrentState", "CostFunctionJAtCurrentOptimum", "SigmaObs2", "MahalanobisConsistency", "SimulationQuantiles", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum", "SimulatedObservationAtCurrentOptimum"]
+            listval  = [
+                "APosterioriCorrelations",
+                "APosterioriCovariance",
+                "APosterioriStandardDeviations",
+                "APosterioriVariances",
+                "BMA",
+                "OMA",
+                "OMB",
+                "CostFunctionJ",
+                "CostFunctionJb",
+                "CostFunctionJo",
+                "CurrentState",
+                "CurrentOptimum",
+                "IndexOfOptimum",
+                "Innovation",
+                "InnovationAtCurrentState",
+                "CostFunctionJAtCurrentOptimum",
+                "CostFunctionJbAtCurrentOptimum",
+                "CostFunctionJoAtCurrentOptimum",
+                "SigmaObs2",
+                "MahalanobisConsistency",
+                "SimulationQuantiles",
+                "SimulatedObservationAtBackground",
+                "SimulatedObservationAtCurrentState",
+                "SimulatedObservationAtOptimum",
+                "SimulatedObservationAtCurrentOptimum",
+                ]
             )
         self.defineRequiredParameter(
             name     = "Quantiles",
@@ -165,6 +191,8 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             if "IndexOfOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
                "CurrentOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
                "CostFunctionJAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
+               "CostFunctionJbAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
+               "CostFunctionJoAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
                "SimulatedObservationAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
                 IndexMin = numpy.argmin( self.StoredVariables["CostFunctionJ"][nbPreviousSteps:] ) + nbPreviousSteps
             if "IndexOfOptimum" in self._parameters["StoreSupplementaryCalculations"]:
@@ -174,9 +202,11 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             if "SimulatedObservationAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
                 self.StoredVariables["SimulatedObservationAtCurrentOptimum"].store( self.StoredVariables["SimulatedObservationAtCurrentState"][IndexMin] )
             if "CostFunctionJAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+                self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( self.StoredVariables["CostFunctionJ" ][IndexMin] )
+            if "CostFunctionJbAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
                 self.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJb"][IndexMin] )
+            if "CostFunctionJoAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
                 self.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJo"][IndexMin] )
-                self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( self.StoredVariables["CostFunctionJ" ][IndexMin] )
             return J
         #
         def GradientOfCostFunction(x):

diff --git a/src/daComposant/daAlgorithms/4DVAR.py b/src/daComposant/daAlgorithms/4DVAR.py
index ca35410027e97bbc2f2a744ff6663626de548ca0..48de48dd1c5404136eb398201c26cc7c81f254fa 100644 (file)
--- a/src/daComposant/daAlgorithms/4DVAR.py
+++ b/src/daComposant/daAlgorithms/4DVAR.py
@@ -86,7 +86,18 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             default  = [],
             typecast = tuple,
             message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
-            listval  = ["BMA", "CurrentState", "CostFunctionJ", "CostFunctionJb", "CostFunctionJo", "IndexOfOptimum", "CurrentOptimum", "CostFunctionJAtCurrentOptimum"]
+            listval  = [
+                "BMA",
+                "CurrentState",
+                "CostFunctionJ",
+                "CostFunctionJb",
+                "CostFunctionJo",
+                "IndexOfOptimum",
+                "CurrentOptimum",
+                "CostFunctionJAtCurrentOptimum",
+                "CostFunctionJbAtCurrentOptimum",
+                "CostFunctionJoAtCurrentOptimum",
+                ]
             )
         self.defineRequiredParameter( # Pas de type
             name     = "Bounds",
@@ -199,16 +210,20 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             self.StoredVariables["CostFunctionJ" ].store( J )
             if "IndexOfOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
                "CurrentOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
-               "CostFunctionJAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+               "CostFunctionJAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
+               "CostFunctionJbAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
+               "CostFunctionJoAtCurrentOptimum" 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 "CostFunctionJAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+                self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( self.StoredVariables["CostFunctionJ" ][IndexMin] )
+            if "CostFunctionJbAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
                 self.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJb"][IndexMin] )
+            if "CostFunctionJoAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
                 self.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJo"][IndexMin] )
-                self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( self.StoredVariables["CostFunctionJ" ][IndexMin] )
             return J
         #
         def GradientOfCostFunction(x):

diff --git a/src/daComposant/daAlgorithms/Blue.py b/src/daComposant/daAlgorithms/Blue.py
index 8ffcdfbc2df5e84c5a50cbeb7f44c39edbf415d7..5c2e7618194953d8c2aaee0aba1e618d5cf1e990 100644 (file)
--- a/src/daComposant/daAlgorithms/Blue.py
+++ b/src/daComposant/daAlgorithms/Blue.py
@@ -39,7 +39,27 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             default  = [],
             typecast = tuple,
             message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
-            listval  = ["APosterioriCorrelations", "APosterioriCovariance", "APosterioriStandardDeviations", "APosterioriVariances", "BMA", "OMA", "OMB", "CurrentState", "CostFunctionJ", "CostFunctionJb", "CostFunctionJo", "Innovation", "SigmaBck2", "SigmaObs2", "MahalanobisConsistency", "SimulationQuantiles", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum"]
+            listval  = [
+                "APosterioriCorrelations",
+                "APosterioriCovariance",
+                "APosterioriStandardDeviations",
+                "APosterioriVariances",
+                "BMA",
+                "OMA",
+                "OMB",
+                "CurrentState",
+                "CostFunctionJ",
+                "CostFunctionJb",
+                "CostFunctionJo",
+                "Innovation",
+                "SigmaBck2",
+                "SigmaObs2",
+                "MahalanobisConsistency",
+                "SimulationQuantiles",
+                "SimulatedObservationAtBackground",
+                "SimulatedObservationAtCurrentState",
+                "SimulatedObservationAtOptimum",
+                ]
             )
         self.defineRequiredParameter(
             name     = "Quantiles",
@@ -80,8 +100,8 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         Ha = HO["Adjoint"].asMatrix(Xb)
         Ha = Ha.reshape(Xb.size,Y.size) # ADAO & check shape
         #
-        # Utilisation éventuelle d'un vecteur H(Xb) précalculé (sans cout)
-        # ----------------------------------------------------------------
+        # Utilisation éventuelle d'un vecteur H(Xb) précalculé
+        # ----------------------------------------------------
         if HO["AppliedInX"] is not None and "HXb" in HO["AppliedInX"]:
             HXb = HO["AppliedInX"]["HXb"]
         else:
@@ -128,11 +148,9 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         if self._parameters["StoreInternalVariables"] or \
            "CostFunctionJ"                 in self._parameters["StoreSupplementaryCalculations"] or \
            "MahalanobisConsistency"        in self._parameters["StoreSupplementaryCalculations"]:
-            #
             Jb  = float( 0.5 * (Xa - Xb).T * BI * (Xa - Xb) )
             Jo  = float( 0.5 * oma.T * RI * oma )
             J   = Jb + Jo
-            #
             self.StoredVariables["CostFunctionJb"].store( Jb )
             self.StoredVariables["CostFunctionJo"].store( Jo )
             self.StoredVariables["CostFunctionJ" ].store( J )

diff --git a/src/daComposant/daAlgorithms/DerivativeFreeOptimization.py b/src/daComposant/daAlgorithms/DerivativeFreeOptimization.py
index 548c25fea59c8c47b77bae88dd0a85fd2ed5bad6..e54e4c88c4b7f7ec03533c57ecc1c812f5a6b224 100644 (file)
--- a/src/daComposant/daAlgorithms/DerivativeFreeOptimization.py
+++ b/src/daComposant/daAlgorithms/DerivativeFreeOptimization.py
@@ -83,7 +83,25 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             default  = [],
             typecast = tuple,
             message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
-            listval  = ["CurrentState", "CostFunctionJ", "CostFunctionJb", "CostFunctionJo", "CostFunctionJAtCurrentOptimum", "CurrentOptimum", "IndexOfOptimum", "InnovationAtCurrentState", "BMA", "OMA", "OMB", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentOptimum", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum"]
+            listval  = [
+                "CurrentState",
+                "CostFunctionJ",
+                "CostFunctionJb",
+                "CostFunctionJo",
+                "CostFunctionJAtCurrentOptimum",
+                "CostFunctionJbAtCurrentOptimum",
+                "CostFunctionJoAtCurrentOptimum",
+                "CurrentOptimum",
+                "IndexOfOptimum",
+                "InnovationAtCurrentState",
+                "BMA",
+                "OMA",
+                "OMB",
+                "SimulatedObservationAtBackground",
+                "SimulatedObservationAtCurrentOptimum",
+                "SimulatedObservationAtCurrentState",
+                "SimulatedObservationAtOptimum",
+                ]
             )
         self.defineRequiredParameter( # Pas de type
             name     = "Bounds",
@@ -151,6 +169,8 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             if "IndexOfOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
                "CurrentOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
                "CostFunctionJAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
+               "CostFunctionJbAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
+               "CostFunctionJoAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
                "SimulatedObservationAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
                 IndexMin = numpy.argmin( self.StoredVariables["CostFunctionJ"][nbPreviousSteps:] ) + nbPreviousSteps
             if "IndexOfOptimum" in self._parameters["StoreSupplementaryCalculations"]:
@@ -160,9 +180,11 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             if "SimulatedObservationAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
                 self.StoredVariables["SimulatedObservationAtCurrentOptimum"].store( self.StoredVariables["SimulatedObservationAtCurrentState"][IndexMin] )
             if "CostFunctionJAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+                self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( self.StoredVariables["CostFunctionJ" ][IndexMin] )
+            if "CostFunctionJbAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
                 self.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJb"][IndexMin] )
+            if "CostFunctionJoAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
                 self.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJo"][IndexMin] )
-                self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( self.StoredVariables["CostFunctionJ" ][IndexMin] )
             return J
         #
         # Point de démarrage de l'optimisation : Xini = Xb

diff --git a/src/daComposant/daAlgorithms/EnsembleBlue.py b/src/daComposant/daAlgorithms/EnsembleBlue.py
index 1cc80cd485c614bbd9a92304dbdab2ac1635feef..e74223f0104091e2dacb9f99bbe9e5ab2ad2a00c 100644 (file)
--- a/src/daComposant/daAlgorithms/EnsembleBlue.py
+++ b/src/daComposant/daAlgorithms/EnsembleBlue.py
@@ -39,7 +39,13 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             default  = [],
             typecast = tuple,
             message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
-            listval  = ["CurrentState", "Innovation", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum"]
+            listval  = [
+                "CurrentState",
+                "Innovation",
+                "SimulatedObservationAtBackground",
+                "SimulatedObservationAtCurrentState",
+                "SimulatedObservationAtOptimum",
+                ]
             )
         self.defineRequiredParameter(
             name     = "SetSeed",
@@ -47,7 +53,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             message  = "Graine fixée pour le générateur aléatoire",
             )
         self.requireInputArguments(
-            mandatory= ("Xb", "Y", "HO", "R", "B" ),
+            mandatory= ("Xb", "Y", "HO", "R", "B"),
             )
 
     def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):

diff --git a/src/daComposant/daAlgorithms/ExtendedBlue.py b/src/daComposant/daAlgorithms/ExtendedBlue.py
index 5dbfd804e731374a1a94c74f827f7cc4a2fbbc9b..0755e257ffa1faef23effc8e49aab189d2c62698 100644 (file)
--- a/src/daComposant/daAlgorithms/ExtendedBlue.py
+++ b/src/daComposant/daAlgorithms/ExtendedBlue.py
@@ -39,7 +39,27 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             default  = [],
             typecast = tuple,
             message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
-            listval  = ["APosterioriCorrelations", "APosterioriCovariance", "APosterioriStandardDeviations", "APosterioriVariances", "BMA", "OMA", "OMB", "CurrentState", "CostFunctionJ", "CostFunctionJb", "CostFunctionJo", "Innovation", "SigmaBck2", "SigmaObs2", "MahalanobisConsistency", "SimulationQuantiles", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum"]
+            listval  = [
+                "APosterioriCorrelations",
+                "APosterioriCovariance",
+                "APosterioriStandardDeviations",
+                "APosterioriVariances",
+                "BMA",
+                "OMA",
+                "OMB",
+                "CurrentState",
+                "CostFunctionJ",
+                "CostFunctionJb",
+                "CostFunctionJo",
+                "Innovation",
+                "SigmaBck2",
+                "SigmaObs2",
+                "MahalanobisConsistency",
+                "SimulationQuantiles",
+                "SimulatedObservationAtBackground",
+                "SimulatedObservationAtCurrentState",
+                "SimulatedObservationAtOptimum",
+                ]
             )
         self.defineRequiredParameter(
             name     = "Quantiles",
@@ -69,7 +89,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             listval  = ["Linear", "NonLinear"]
             )
         self.requireInputArguments(
-            mandatory= ("Xb", "Y", "HO", "R", "B" ),
+            mandatory= ("Xb", "Y", "HO", "R", "B"),
             )
 
     def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
@@ -88,6 +108,10 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         else:
             HXb = H( Xb )
         HXb = numpy.asmatrix(numpy.ravel( HXb )).T
+        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))
         #
         # Précalcul des inversions de B et R
         # ----------------------------------
@@ -96,10 +120,6 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         #
         # 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
         #
         # Calcul de la matrice de gain et de l'analyse

diff --git a/src/daComposant/daAlgorithms/NonLinearLeastSquares.py b/src/daComposant/daAlgorithms/NonLinearLeastSquares.py
index 7cd98ae4e9122728e2672308a28f2d7cf575151d..f246d5528e1814907303b84507243c102ae8044e 100644 (file)
--- a/src/daComposant/daAlgorithms/NonLinearLeastSquares.py
+++ b/src/daComposant/daAlgorithms/NonLinearLeastSquares.py
@@ -72,7 +72,26 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             default  = [],
             typecast = tuple,
             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"]
+            listval  = [
+                "BMA",
+                "OMA",
+                "OMB",
+                "CostFunctionJ",
+                "CostFunctionJb",
+                "CostFunctionJo",
+                "CurrentState",
+                "CurrentOptimum",
+                "IndexOfOptimum",
+                "Innovation",
+                "InnovationAtCurrentState",
+                "CostFunctionJAtCurrentOptimum",
+                "CostFunctionJbAtCurrentOptimum",
+                "CostFunctionJoAtCurrentOptimum",
+                "SimulatedObservationAtBackground",
+                "SimulatedObservationAtCurrentState",
+                "SimulatedObservationAtOptimum",
+                "SimulatedObservationAtCurrentOptimum",
+                ]
             )
         self.defineRequiredParameter( # Pas de type
             name     = "Bounds",
@@ -139,6 +158,8 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             if "IndexOfOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
                "CurrentOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
                "CostFunctionJAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
+               "CostFunctionJbAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
+               "CostFunctionJoAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
                "SimulatedObservationAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
                 IndexMin = numpy.argmin( self.StoredVariables["CostFunctionJ"][nbPreviousSteps:] ) + nbPreviousSteps
             if "IndexOfOptimum" in self._parameters["StoreSupplementaryCalculations"]:
@@ -148,9 +169,11 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             if "SimulatedObservationAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
                 self.StoredVariables["SimulatedObservationAtCurrentOptimum"].store( self.StoredVariables["SimulatedObservationAtCurrentState"][IndexMin] )
             if "CostFunctionJAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+                self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( self.StoredVariables["CostFunctionJ" ][IndexMin] )
+            if "CostFunctionJbAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
                 self.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJb"][IndexMin] )
+            if "CostFunctionJoAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
                 self.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJo"][IndexMin] )
-                self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( self.StoredVariables["CostFunctionJ" ][IndexMin] )
             return J
         #
         def GradientOfCostFunction(x):