X-Git-Url: http://git.salome-platform.org/gitweb/?a=blobdiff_plain;f=src%2FdaComposant%2FdaAlgorithms%2FNonLinearLeastSquares.py;h=aad66fc737b92a4dac264863d8522024f8a2e278;hb=cf85bc4e4ff25a695443edbffb1800a97ba6afd8;hp=a77b09fc3091ff9029eeebb31f4c6fd84dff1ace;hpb=3213a7ad0e568555ac362f55281eabf64a896006;p=modules%2Fadao.git

diff --git a/src/daComposant/daAlgorithms/NonLinearLeastSquares.py b/src/daComposant/daAlgorithms/NonLinearLeastSquares.py
index a77b09f..aad66fc 100644
--- a/src/daComposant/daAlgorithms/NonLinearLeastSquares.py
+++ b/src/daComposant/daAlgorithms/NonLinearLeastSquares.py
@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 #
-# Copyright (C) 2008-2019 EDF R&D
+# Copyright (C) 2008-2021 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
@@ -83,6 +83,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 "CostFunctionJbAtCurrentOptimum",
                 "CostFunctionJo",
                 "CostFunctionJoAtCurrentOptimum",
+                "CurrentIterationNumber",
                 "CurrentOptimum",
                 "CurrentState",
                 "IndexOfOptimum",
@@ -100,16 +101,22 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             name     = "Bounds",
             message  = "Liste des valeurs de bornes",
             )
+        self.defineRequiredParameter(
+            name     = "InitializationPoint",
+            typecast = numpy.ravel,
+            message  = "État initial imposé (par défaut, c'est l'ébauche si None)",
+            )
         self.requireInputArguments(
             mandatory= ("Xb", "Y", "HO", "R"),
             )
+        self.setAttributes(tags=(
+            "Optimization",
+            "NonLinear",
+            "Variational",
+            ))
 
     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, 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)
+        self._pre_run(Parameters, Xb, Y, U, HO, EM, CM, R, B, Q)
         #
         # Opérateurs
         # ----------
@@ -155,6 +162,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             Jo  = float( 0.5 * _Innovation.T * RI * _Innovation )
             J   = Jb + Jo
             #
+            self.StoredVariables["CurrentIterationNumber"].store( len(self.StoredVariables["CostFunctionJ"]) )
             self.StoredVariables["CostFunctionJb"].store( Jb )
             self.StoredVariables["CostFunctionJo"].store( Jo )
             self.StoredVariables["CostFunctionJ" ].store( J )
@@ -216,7 +224,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         #
         # Point de démarrage de l'optimisation : Xini = Xb
         # ------------------------------------
-        Xini = numpy.ravel(Xb)
+        Xini = self._parameters["InitializationPoint"]
         #
         # Minimisation de la fonctionnelle
         # --------------------------------