--- /dev/null
+#-*-coding:iso-8859-1-*-
+#
+# Copyright (C) 2008-2015 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 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
+#
+# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
+#
+# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
+
+import logging
+from daCore import BasicObjects
+import numpy, scipy.optimize
+
+# ==============================================================================
+class ElementaryAlgorithm(BasicObjects.Algorithm):
+ def __init__(self):
+ BasicObjects.Algorithm.__init__(self, "DERIVATIVESFREEOPTIMIZATION")
+ self.defineRequiredParameter(
+ name = "Minimizer",
+ default = "POWELL",
+ typecast = str,
+ message = "Minimiseur utilisé",
+ listval = ["POWELL", "SIMPLEX"],
+ )
+ self.defineRequiredParameter(
+ name = "MaximumNumberOfSteps",
+ default = 15000,
+ typecast = int,
+ message = "Nombre maximal de pas d'optimisation",
+ minval = -1,
+ )
+ self.defineRequiredParameter(
+ name = "MaximumNumberOfFunctionEvaluations",
+ default = 15000,
+ typecast = int,
+ message = "Nombre maximal de d'évaluations de la function",
+ minval = -1,
+ )
+ self.defineRequiredParameter(
+ name = "StateVariationTolerance",
+ default = 1.e-4,
+ typecast = float,
+ message = "Variation relative minimale de l'état lors de l'arrêt",
+ )
+ self.defineRequiredParameter(
+ name = "CostDecrementTolerance",
+ default = 1.e-7,
+ typecast = float,
+ message = "Diminution relative minimale du cout lors de l'arrêt",
+ )
+ self.defineRequiredParameter(
+ name = "QualityCriterion",
+ default = "AugmentedWeightedLeastSquares",
+ typecast = str,
+ message = "Critère de qualité utilisé",
+ listval = ["AugmentedWeightedLeastSquares","AWLS","DA",
+ "WeightedLeastSquares","WLS",
+ "LeastSquares","LS","L2",
+ "AbsoluteValue","L1",
+ "MaximumError","ME"],
+ )
+ 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 = ["CurrentState", "CostFunctionJ", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum"]
+ )
+
+ 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()
+ if logging.getLogger().level < logging.WARNING:
+ self.__disp = 1
+ else:
+ self.__disp = 0
+ #
+ # Paramètres de pilotage
+ # ----------------------
+ self.setParameters(Parameters)
+# self.setParameterValue("StoreInternalVariables",True)
+# print self._parameters["StoreInternalVariables"]
+ #
+ # Opérateurs
+ # ----------
+ Hm = HO["Direct"].appliedTo
+ #
+ # Précalcul des inversions de B et R
+ # ----------------------------------
+ BI = B.getI()
+ RI = R.getI()
+ #
+ # Définition de la fonction-coût
+ # ------------------------------
+ def CostFunction(x, QualityMeasure="AugmentedWeightedLeastSquares"):
+ _X = numpy.asmatrix(numpy.ravel( x )).T
+ self.StoredVariables["CurrentState"].store( _X )
+ _HX = Hm( _X )
+ _HX = numpy.asmatrix(numpy.ravel( _HX )).T
+ if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["SimulatedObservationAtCurrentState"].store( _HX )
+ #
+ if QualityMeasure in ["AugmentedWeightedLeastSquares","AWLS","DA"]:
+ if BI is None or RI is None:
+ raise ValueError("Background and Observation error covariance matrix has to be properly defined!")
+ Jb = 0.5 * (_X - Xb).T * BI * (_X - Xb)
+ Jo = 0.5 * (Y - _HX).T * RI * (Y - _HX)
+ elif QualityMeasure in ["WeightedLeastSquares","WLS"]:
+ if RI is None:
+ raise ValueError("Observation error covariance matrix has to be properly defined!")
+ Jb = 0.
+ Jo = 0.5 * (Y - _HX).T * RI * (Y - _HX)
+ elif QualityMeasure in ["LeastSquares","LS","L2"]:
+ Jb = 0.
+ Jo = 0.5 * (Y - _HX).T * (Y - _HX)
+ elif QualityMeasure in ["AbsoluteValue","L1"]:
+ Jb = 0.
+ Jo = numpy.sum( numpy.abs(Y - _HX) )
+ elif QualityMeasure in ["MaximumError","ME"]:
+ Jb = 0.
+ Jo = numpy.max( numpy.abs(Y - _HX) )
+ #
+ J = float( Jb ) + float( Jo )
+ #
+ self.StoredVariables["CostFunctionJb"].store( Jb )
+ self.StoredVariables["CostFunctionJo"].store( Jo )
+ self.StoredVariables["CostFunctionJ" ].store( J )
+ return J
+ #
+ # Point de démarrage de l'optimisation : Xini = Xb
+ # ------------------------------------
+ Xini = numpy.ravel(Xb)
+ #
+ # Minimisation de la fonctionnelle
+ # --------------------------------
+ nbPreviousSteps = self.StoredVariables["CostFunctionJ"].stepnumber()
+ #
+ if self._parameters["Minimizer"] == "POWELL":
+ Minimum, J_optimal, direc, niter, nfeval, rc = scipy.optimize.fmin_powell(
+ func = CostFunction,
+ x0 = Xini,
+ args = (self._parameters["QualityCriterion"],),
+ maxiter = self._parameters["MaximumNumberOfSteps"]-1,
+ maxfun = self._parameters["MaximumNumberOfFunctionEvaluations"]-1,
+ xtol = self._parameters["StateVariationTolerance"],
+ ftol = self._parameters["CostDecrementTolerance"],
+ full_output = True,
+ disp = self.__disp,
+ )
+ elif self._parameters["Minimizer"] == "SIMPLEX":
+ Minimum, J_optimal, niter, nfeval, rc = scipy.optimize.fmin(
+ func = CostFunction,
+ x0 = Xini,
+ args = (self._parameters["QualityCriterion"],),
+ maxiter = self._parameters["MaximumNumberOfSteps"]-1,
+ maxfun = self._parameters["MaximumNumberOfFunctionEvaluations"]-1,
+ xtol = self._parameters["StateVariationTolerance"],
+ ftol = self._parameters["CostDecrementTolerance"],
+ full_output = True,
+ disp = self.__disp,
+ )
+ else:
+ raise ValueError("Error in Minimizer name: %s"%self._parameters["Minimizer"])
+ #
+ IndexMin = numpy.argmin( self.StoredVariables["CostFunctionJ"][nbPreviousSteps:] ) + nbPreviousSteps
+ MinJ = self.StoredVariables["CostFunctionJ"][IndexMin]
+ Minimum = self.StoredVariables["CurrentState"][IndexMin]
+ #
+ # Obtention de l'analyse
+ # ----------------------
+ Xa = numpy.asmatrix(numpy.ravel( Minimum )).T
+ #
+ self.StoredVariables["Analysis"].store( Xa.A1 )
+ #
+ if "SimulatedObservationAtBackground" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["SimulatedObservationAtBackground"].store( numpy.ravel(Hm(Xb)) )
+ if "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["SimulatedObservationAtOptimum"].store( numpy.ravel(Hm(Xa)) )
+ #
+ self._post_run()
+ return 0
+
+# ==============================================================================
+if __name__ == "__main__":
+ print '\n AUTODIAGNOSTIC \n'
