Improving warning message for available DFO minimizer choice

[modules/adao.git] / src / daComposant / daAlgorithms / NonLinearLeastSquares.py

diff --git a/src/daComposant/daAlgorithms/NonLinearLeastSquares.py b/src/daComposant/daAlgorithms/NonLinearLeastSquares.py
index a2e4433ea88b2ca8697ee5d7873e55cf2e15557f..5db18e40803430d366ef739ea64bffb69c906971 100644 (file)
--- a/src/daComposant/daAlgorithms/NonLinearLeastSquares.py
+++ b/src/daComposant/daAlgorithms/NonLinearLeastSquares.py
@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 #
-# Copyright (C) 2008-2017 EDF R&D
+# Copyright (C) 2008-2018 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
@@ -72,15 +72,37 @@ 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",
             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, Xb, Y, R, B, Q)
         #
         # Correction pour pallier a un bug de TNC sur le retour du Minimum
         if "Minimizer" in self._parameters and self._parameters["Minimizer"] == "TNC":
@@ -136,6 +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"]:
@@ -145,9 +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):