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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 231764c14cdf3e6cb73ec9c9f35261f7a9e0a6dd..54c03361a700b13140350270a4ec107a2455e0f7 100644
(file)
--- a/
src/daComposant/daAlgorithms/NonLinearLeastSquares.py
+++ b/
src/daComposant/daAlgorithms/NonLinearLeastSquares.py
@@
-1,6
+1,6
@@
# -*- coding: utf-8 -*-
#
# -*- coding: utf-8 -*-
#
-# Copyright (C) 2008-202
0
EDF R&D
+# Copyright (C) 2008-202
1
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
#
# 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",
"CostFunctionJbAtCurrentOptimum",
"CostFunctionJo",
"CostFunctionJoAtCurrentOptimum",
+ "CurrentIterationNumber",
"CurrentOptimum",
"CurrentState",
"IndexOfOptimum",
"CurrentOptimum",
"CurrentState",
"IndexOfOptimum",
@@
-110,7
+111,7
@@
class ElementaryAlgorithm(BasicObjects.Algorithm):
))
def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
))
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":
#
# Correction pour pallier a un bug de TNC sur le retour du Minimum
if "Minimizer" in self._parameters and self._parameters["Minimizer"] == "TNC":
@@
-160,6
+161,7
@@
class ElementaryAlgorithm(BasicObjects.Algorithm):
Jo = float( 0.5 * _Innovation.T * RI * _Innovation )
J = Jb + Jo
#
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 )
self.StoredVariables["CostFunctionJb"].store( Jb )
self.StoredVariables["CostFunctionJo"].store( Jo )
self.StoredVariables["CostFunctionJ" ].store( J )