Updating version and copyright date information

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

diff --git a/src/daComposant/daAlgorithms/NonLinearLeastSquares.py b/src/daComposant/daAlgorithms/NonLinearLeastSquares.py
index 6060817cf99d25d6873880d3fecbd100ab83c74a..54c03361a700b13140350270a4ec107a2455e0f7 100644 (file)
--- a/src/daComposant/daAlgorithms/NonLinearLeastSquares.py
+++ b/src/daComposant/daAlgorithms/NonLinearLeastSquares.py
@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 #
-# Copyright (C) 2008-2018 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
@@ -22,7 +22,7 @@
 
 import logging
 from daCore import BasicObjects
-import numpy, scipy.optimize
+import numpy, scipy.optimize, scipy.version
 
 # ==============================================================================
 class ElementaryAlgorithm(BasicObjects.Algorithm):
@@ -47,6 +47,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             default  = 1.e-7,
             typecast = float,
             message  = "Diminution relative minimale du coût lors de l'arrêt",
+            minval   = 0.,
             )
         self.defineRequiredParameter(
             name     = "ProjectedGradientTolerance",
@@ -60,6 +61,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             default  = 1.e-05,
             typecast = float,
             message  = "Maximum des composantes du gradient lors de l'arrêt",
+            minval   = 0.,
             )
         self.defineRequiredParameter(
             name     = "StoreInternalVariables",
@@ -73,24 +75,26 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             typecast = tuple,
             message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
             listval  = [
+                "Analysis",
                 "BMA",
-                "OMA",
-                "OMB",
                 "CostFunctionJ",
+                "CostFunctionJAtCurrentOptimum",
                 "CostFunctionJb",
+                "CostFunctionJbAtCurrentOptimum",
                 "CostFunctionJo",
-                "CurrentState",
+                "CostFunctionJoAtCurrentOptimum",
+                "CurrentIterationNumber",
                 "CurrentOptimum",
+                "CurrentState",
                 "IndexOfOptimum",
                 "Innovation",
                 "InnovationAtCurrentState",
-                "CostFunctionJAtCurrentOptimum",
-                "CostFunctionJbAtCurrentOptimum",
-                "CostFunctionJoAtCurrentOptimum",
+                "OMA",
+                "OMB",
                 "SimulatedObservationAtBackground",
+                "SimulatedObservationAtCurrentOptimum",
                 "SimulatedObservationAtCurrentState",
                 "SimulatedObservationAtOptimum",
-                "SimulatedObservationAtCurrentOptimum",
                 ]
             )
         self.defineRequiredParameter( # Pas de type
@@ -100,9 +104,14 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         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)
+        self._pre_run(Parameters, Xb, Y, U, HO, EM, CM, 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":
@@ -116,7 +125,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         # Utilisation éventuelle d'un vecteur H(Xb) précalculé
         # ----------------------------------------------------
         if HO["AppliedInX"] is not None and "HXb" in HO["AppliedInX"]:
-            HXb = Hm( Xb, HO["AppliedInX"]["HXb"])
+            HXb = Hm( Xb, HO["AppliedInX"]["HXb"] )
         else:
             HXb = Hm( Xb )
         HXb = numpy.asmatrix(numpy.ravel( HXb )).T
@@ -152,6 +161,7 @@ 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 )
@@ -168,12 +178,12 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 self.StoredVariables["CurrentOptimum"].store( self.StoredVariables["CurrentState"][IndexMin] )
             if self._toStore("SimulatedObservationAtCurrentOptimum"):
                 self.StoredVariables["SimulatedObservationAtCurrentOptimum"].store( self.StoredVariables["SimulatedObservationAtCurrentState"][IndexMin] )
-            if self._toStore("CostFunctionJAtCurrentOptimum"):
-                self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( self.StoredVariables["CostFunctionJ" ][IndexMin] )
             if self._toStore("CostFunctionJbAtCurrentOptimum"):
                 self.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJb"][IndexMin] )
             if self._toStore("CostFunctionJoAtCurrentOptimum"):
                 self.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJo"][IndexMin] )
+            if self._toStore("CostFunctionJAtCurrentOptimum"):
+                self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( self.StoredVariables["CostFunctionJ" ][IndexMin] )
             return J
         #
         def GradientOfCostFunction(x):
@@ -221,8 +231,11 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         #
         if self._parameters["Minimizer"] == "LBFGSB":
             # Minimum, J_optimal, Informations = scipy.optimize.fmin_l_bfgs_b(
-            import lbfgsbhlt
-            Minimum, J_optimal, Informations = lbfgsbhlt.fmin_l_bfgs_b(
+            if "0.19" <= scipy.version.version <= "1.1.0":
+                import lbfgsbhlt as optimiseur
+            else:
+                import scipy.optimize as optimiseur
+            Minimum, J_optimal, Informations = optimiseur.fmin_l_bfgs_b(
                 func        = CostFunction,
                 x0          = Xini,
                 fprime      = GradientOfCostFunction,
@@ -316,12 +329,13 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             elif self._toStore("SimulatedObservationAtCurrentOptimum"):
                 HXa = self.StoredVariables["SimulatedObservationAtCurrentOptimum"][-1]
             else:
-                HXa = Hm(Xa)
+                HXa = Hm( Xa )
         #
         #
         # Calculs et/ou stockages supplémentaires
         # ---------------------------------------
-        if self._toStore("Innovation") or self._toStore("OMB"):
+        if self._toStore("Innovation") or \
+            self._toStore("OMB"):
             d  = Y - HXb
         if self._toStore("Innovation"):
             self.StoredVariables["Innovation"].store( numpy.ravel(d) )
@@ -341,4 +355,4 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
 
 # ==============================================================================
 if __name__ == "__main__":
-    print('\n AUTODIAGNOSTIC \n')
+    print('\n AUTODIAGNOSTIC\n')