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 83e29630b054f9abf6e8fb76b5547144e1b392a8..5db18e40803430d366ef739ea64bffb69c906971 100644 (file)
--- a/src/daComposant/daAlgorithms/NonLinearLeastSquares.py
+++ b/src/daComposant/daAlgorithms/NonLinearLeastSquares.py
@@ -1,6 +1,6 @@
-#-*-coding:iso-8859-1-*-
+# -*- 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
@@ -32,7 +32,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             name     = "Minimizer",
             default  = "LBFGSB",
             typecast = str,
-            message  = "Minimiseur utilisé",
+            message  = "Minimiseur utilisé",
             listval  = ["LBFGSB","TNC", "CG", "NCG", "BFGS", "LM"],
             )
         self.defineRequiredParameter(
@@ -46,55 +46,77 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             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", "CostFunctionJ", "CostFunctionJb", "CostFunctionJo", "CurrentState", "CurrentOptimum", "IndexOfOptimum", "Innovation", "InnovationAtCurrentState", "CostFunctionJAtCurrentOptimum", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum", "SimulatedObservationAtCurrentOptimum"]
+            message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
+            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":
             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 "HXb" in HO["AppliedToX"]:
-            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
@@ -103,13 +125,13 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         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
+        # 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
@@ -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):
@@ -185,7 +211,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             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
         # ------------------------------------
         Xini = numpy.ravel(Xb)
         #
@@ -291,7 +317,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 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"]: