-#-*-coding:iso-8859-1-*-
+# -*- coding: utf-8 -*-
#
-# Copyright (C) 2008-2010 EDF R&D
+# Copyright (C) 2008-2020 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
+# This library is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 2.1 of the License.
#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
+# This library is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
+# Lesser General Public License for more details.
#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
+# You should have received a copy of the GNU Lesser General Public
+# License along with this library; if not, write to the Free Software
+# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
+# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
#
-__doc__ = """
- Algorithme de moindre carres pondérés (analyse sans ebauche)
-"""
-__author__ = "Sophie RICCI, Jean-Philippe ARGAUD - Septembre 2008"
+# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
import logging
-from daCore import BasicObjects, PlatformInfo
-m = PlatformInfo.SystemUsage()
+from daCore import BasicObjects
+import numpy
# ==============================================================================
class ElementaryAlgorithm(BasicObjects.Algorithm):
def __init__(self):
- BasicObjects.Algorithm.__init__(self)
- self._name = "LINEARLEASTSQUARES"
+ BasicObjects.Algorithm.__init__(self, "LINEARLEASTSQUARES")
+ self.defineRequiredParameter(
+ name = "StoreInternalVariables",
+ default = False,
+ typecast = bool,
+ 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 = [
+ "Analysis",
+ "CostFunctionJ",
+ "CostFunctionJAtCurrentOptimum",
+ "CostFunctionJb",
+ "CostFunctionJbAtCurrentOptimum",
+ "CostFunctionJo",
+ "CostFunctionJoAtCurrentOptimum",
+ "CurrentOptimum",
+ "CurrentState",
+ "OMA",
+ "SimulatedObservationAtCurrentOptimum",
+ "SimulatedObservationAtCurrentState",
+ "SimulatedObservationAtOptimum",
+ ]
+ )
+ self.requireInputArguments(
+ mandatory= ("Y", "HO", "R"),
+ )
- def run(self, Xb=None, Y=None, H=None, M=None, R=None, B=None, Q=None, Parameters=None):
- """
- Calcul de l'estimateur au sens des moindres carres sans ebauche
- """
- logging.debug("%s Lancement"%self._name)
- logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("Mo")))
+ 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)
#
- Hm = H["Direct"].asMatrix()
- Ht = H["Adjoint"].asMatrix()
+ Hm = HO["Tangent"].asMatrix(None)
+ Hm = Hm.reshape(Y.size,-1) # ADAO & check shape
+ Ha = HO["Adjoint"].asMatrix(None)
+ Ha = Ha.reshape(-1,Y.size) # ADAO & check shape
#
- K = (Ht * R.I * Hm ).I * Ht * R.I
- Xa = K * Y
+ RI = R.getI()
#
+ # Calcul de la matrice de gain et de l'analyse
+ # --------------------------------------------
+ K = (Ha * RI * Hm).I * Ha * RI
+ Xa = K * Y
self.StoredVariables["Analysis"].store( Xa.A1 )
#
- logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("Mo")))
- logging.debug("%s Terminé"%self._name)
+ # Calcul de la fonction coût
+ # --------------------------
+ if self._parameters["StoreInternalVariables"] or \
+ self._toStore("CostFunctionJ") or self._toStore("CostFunctionJAtCurrentOptimum") or \
+ self._toStore("CostFunctionJb") or self._toStore("CostFunctionJbAtCurrentOptimum") or \
+ self._toStore("CostFunctionJo") or self._toStore("CostFunctionJoAtCurrentOptimum") or \
+ self._toStore("OMA") or \
+ self._toStore("SimulatedObservationAtCurrentOptimum") or \
+ self._toStore("SimulatedObservationAtCurrentState") or \
+ self._toStore("SimulatedObservationAtOptimum"):
+ HXa = Hm * Xa
+ oma = Y - HXa
+ if self._parameters["StoreInternalVariables"] or \
+ self._toStore("CostFunctionJ") or self._toStore("CostFunctionJAtCurrentOptimum") or \
+ self._toStore("CostFunctionJb") or self._toStore("CostFunctionJbAtCurrentOptimum") or \
+ self._toStore("CostFunctionJo") or self._toStore("CostFunctionJoAtCurrentOptimum"):
+ Jb = 0.
+ Jo = float( 0.5 * oma.T * RI * oma )
+ J = Jb + Jo
+ self.StoredVariables["CostFunctionJb"].store( Jb )
+ self.StoredVariables["CostFunctionJo"].store( Jo )
+ self.StoredVariables["CostFunctionJ" ].store( J )
+ self.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( Jb )
+ self.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( Jo )
+ self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( J )
+ #
+ # Calculs et/ou stockages supplémentaires
+ # ---------------------------------------
+ if self._parameters["StoreInternalVariables"] or self._toStore("CurrentState"):
+ self.StoredVariables["CurrentState"].store( numpy.ravel(Xa) )
+ if self._toStore("CurrentOptimum"):
+ self.StoredVariables["CurrentOptimum"].store( numpy.ravel(Xa) )
+ if self._toStore("OMA"):
+ self.StoredVariables["OMA"].store( numpy.ravel(oma) )
+ if self._toStore("SimulatedObservationAtBackground"):
+ self.StoredVariables["SimulatedObservationAtBackground"].store( numpy.ravel(HXb) )
+ if self._toStore("SimulatedObservationAtCurrentState"):
+ self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(HXa) )
+ if self._toStore("SimulatedObservationAtCurrentOptimum"):
+ self.StoredVariables["SimulatedObservationAtCurrentOptimum"].store( numpy.ravel(HXa) )
+ if self._toStore("SimulatedObservationAtOptimum"):
+ self.StoredVariables["SimulatedObservationAtOptimum"].store( numpy.ravel(HXa) )
#
+ self._post_run(HO)
return 0
# ==============================================================================
if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
-
-
+ print('\n AUTODIAGNOSTIC\n')
