src/daComposant/daAlgorithms/LinearLeastSquares.py

   1 # -*- coding: utf-8 -*-
   2 #
   3 # Copyright (C) 2008-2024 EDF R&D
   4 #
   5 # This library is free software; you can redistribute it and/or
   6 # modify it under the terms of the GNU Lesser General Public
   7 # License as published by the Free Software Foundation; either
   8 # version 2.1 of the License.
   9 #
  10 # This library is distributed in the hope that it will be useful,
  11 # but WITHOUT ANY WARRANTY; without even the implied warranty of
  12 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
  13 # Lesser General Public License for more details.
  14 #
  15 # You should have received a copy of the GNU Lesser General Public
  16 # License along with this library; if not, write to the Free Software
  17 # Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307 USA
  18 #
  19 # See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
  20 #
  21 # Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
  22
  23 from daCore import BasicObjects, NumericObjects
  24 from daAlgorithms.Atoms import ecwlls
  25
  26 # ==============================================================================
  27 class ElementaryAlgorithm(BasicObjects.Algorithm):
  28     def __init__(self):
  29         BasicObjects.Algorithm.__init__(self, "LINEARLEASTSQUARES")
  30         self.defineRequiredParameter(
  31             name     = "Variant",
  32             default  = "LinearLeastSquares",
  33             typecast = str,
  34             message  = "Variant ou formulation de la méthode",
  35             listval  = [
  36                 "LinearLeastSquares",
  37             ],
  38             listadv  = [
  39                 "OneCorrection",
  40             ],
  41         )
  42         self.defineRequiredParameter(
  43             name     = "EstimationOf",
  44             default  = "Parameters",
  45             typecast = str,
  46             message  = "Estimation d'état ou de paramètres",
  47             listval  = ["State", "Parameters"],
  48         )
  49         self.defineRequiredParameter(
  50             name     = "StoreInternalVariables",
  51             default  = False,
  52             typecast = bool,
  53             message  = "Stockage des variables internes ou intermédiaires du calcul",
  54         )
  55         self.defineRequiredParameter(
  56             name     = "StoreSupplementaryCalculations",
  57             default  = [],
  58             typecast = tuple,
  59             message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
  60             listval  = [
  61                 "Analysis",
  62                 "CostFunctionJ",
  63                 "CostFunctionJAtCurrentOptimum",
  64                 "CostFunctionJb",
  65                 "CostFunctionJbAtCurrentOptimum",
  66                 "CostFunctionJo",
  67                 "CostFunctionJoAtCurrentOptimum",
  68                 "CurrentOptimum",
  69                 "CurrentState",
  70                 "CurrentStepNumber",
  71                 "ForecastState",
  72                 "InnovationAtCurrentAnalysis",
  73                 "OMA",
  74                 "SimulatedObservationAtCurrentOptimum",
  75                 "SimulatedObservationAtCurrentState",
  76                 "SimulatedObservationAtOptimum",
  77             ]
  78         )
  79         self.requireInputArguments(
  80             mandatory= ("Y", "HO"),
  81             optional = ("R"),
  82         )
  83         self.setAttributes(
  84             tags=(
  85                 "Optimization",
  86                 "Linear",
  87                 "Variational",
  88             ),
  89             features=(
  90                 "LocalOptimization",
  91                 "DerivativeNeeded",
  92                 "ParallelDerivativesOnly",
  93             ),
  94         )
  95
  96     def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
  97         self._pre_run(Parameters, Xb, Y, U, HO, EM, CM, R, B, Q)
  98         #
  99         # --------------------------
 100         if self._parameters["Variant"] == "LinearLeastSquares":
 101             NumericObjects.multiXOsteps(self, Xb, Y, U, HO, EM, CM, R, B, Q, ecwlls.ecwlls)
 102         #
 103         # --------------------------
 104         elif self._parameters["Variant"] == "OneCorrection":
 105             ecwlls.ecwlls(self, Xb, Y, U, HO, CM, R, B)
 106         #
 107         # --------------------------
 108         else:
 109             raise ValueError("Error in Variant name: %s"%self._parameters["Variant"])
 110         #
 111         self._post_run(HO, EM)
 112         return 0
 113
 114 # ==============================================================================
 115 if __name__ == "__main__":
 116     print("\n AUTODIAGNOSTIC\n")