--- /dev/null
+# -*- coding: utf-8 -*-
+#
+# Copyright (C) 2008-2018 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, "DIFFERENTIALEVOLUTION")
+ self.defineRequiredParameter(
+ name = "Minimizer",
+ default = "BEST1BIN",
+ typecast = str,
+ message = "Stratégie de minimisation utilisée",
+ listval = [
+ "BEST1BIN",
+ "BEST1EXP",
+ "RAND1EXP",
+ "RANDTOBEST1EXP",
+ "CURRENTTOBEST1EXP",
+ "BEST2EXP",
+ "RAND2EXP",
+ "RANDTOBEST1BIN",
+ "CURRENTTOBEST1BIN",
+ "BEST2BIN",
+ "RAND2BIN",
+ "RAND1BIN",
+ ],
+ )
+ self.defineRequiredParameter(
+ name = "MaximumNumberOfSteps",
+ default = 15000,
+ typecast = int,
+ message = "Nombre maximal de générations",
+ minval = 0,
+ )
+ self.defineRequiredParameter(
+ name = "MaximumNumberOfFunctionEvaluations",
+ default = 15000,
+ typecast = int,
+ message = "Nombre maximal d'évaluations de la fonction",
+ minval = -1,
+ )
+ self.defineRequiredParameter(
+ name = "PopulationSize",
+ default = 100,
+ typecast = int,
+ message = "Taille approximative de la population à chaque génération",
+ minval = 1,
+ )
+ self.defineRequiredParameter(
+ name = "MutationDifferentialWeight_F",
+ default = (0.5, 1),
+ typecast = tuple,
+ message = "Poids différentiel de mutation, constant ou aléatoire dans l'intervalle, noté F",
+ minval = 0.,
+ maxval = 2.,
+ )
+ self.defineRequiredParameter(
+ name = "CrossOverProbability_CR",
+ default = 0.7,
+ typecast = float,
+ message = "Probabilité de recombinaison ou de croisement, notée CR",
+ minval = 0.,
+ maxval = 1.,
+ )
+ 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",
+ "CostFunctionJb",
+ "CostFunctionJo",
+ "CostFunctionJAtCurrentOptimum",
+ "CostFunctionJbAtCurrentOptimum",
+ "CostFunctionJoAtCurrentOptimum",
+ "CurrentOptimum",
+ "IndexOfOptimum",
+ "InnovationAtCurrentState",
+ "BMA",
+ "OMA",
+ "OMB",
+ "SimulatedObservationAtBackground",
+ "SimulatedObservationAtCurrentOptimum",
+ "SimulatedObservationAtCurrentState",
+ "SimulatedObservationAtOptimum",
+ ]
+ )
+ self.defineRequiredParameter(
+ name = "SetSeed",
+ typecast = numpy.random.seed,
+ message = "Graine fixée pour le générateur aléatoire",
+ )
+ self.defineRequiredParameter( # Pas de type
+ name = "Bounds",
+ message = "Liste des valeurs de bornes",
+ )
+ self.requireInputArguments(
+ mandatory= ("Xb", "Y", "HO", "R", "B" ),
+ )
+
+ 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)
+ #
+ len_X = numpy.asarray(Xb).size
+ popsize = round(self._parameters["PopulationSize"]/len_X)
+ maxiter = min(self._parameters["MaximumNumberOfSteps"],round(self._parameters["MaximumNumberOfFunctionEvaluations"]/(popsize*len_X) - 1))
+ logging.debug("%s Nombre maximal de générations = %i, taille de la population à chaque génération = %i"%(self._name, maxiter, popsize*len_X))
+ #
+ # 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
+ _Innovation = Y - _HX
+ if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"] or \
+ "SimulatedObservationAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["SimulatedObservationAtCurrentState"].store( _HX )
+ if "InnovationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["InnovationAtCurrentState"].store( _Innovation )
+ #
+ 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 * (_Innovation).T * RI * (_Innovation)
+ 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 * (_Innovation).T * RI * (_Innovation)
+ elif QualityMeasure in ["LeastSquares","LS","L2"]:
+ Jb = 0.
+ Jo = 0.5 * (_Innovation).T * (_Innovation)
+ elif QualityMeasure in ["AbsoluteValue","L1"]:
+ Jb = 0.
+ Jo = numpy.sum( numpy.abs(_Innovation) )
+ elif QualityMeasure in ["MaximumError","ME"]:
+ Jb = 0.
+ Jo = numpy.max( numpy.abs(_Innovation) )
+ #
+ J = float( Jb ) + float( Jo )
+ #
+ self.StoredVariables["CostFunctionJb"].store( Jb )
+ self.StoredVariables["CostFunctionJo"].store( Jo )
+ self.StoredVariables["CostFunctionJ" ].store( J )
+ if "IndexOfOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
+ "CurrentOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
+ "CostFunctionJAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
+ "CostFunctionJbAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
+ "CostFunctionJoAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
+ "SimulatedObservationAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ IndexMin = numpy.argmin( self.StoredVariables["CostFunctionJ"][nbPreviousSteps:] ) + nbPreviousSteps
+ if "IndexOfOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["IndexOfOptimum"].store( IndexMin )
+ if "CurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["CurrentOptimum"].store( self.StoredVariables["CurrentState"][IndexMin] )
+ if "SimulatedObservationAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["SimulatedObservationAtCurrentOptimum"].store( self.StoredVariables["SimulatedObservationAtCurrentState"][IndexMin] )
+ if "CostFunctionJAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( self.StoredVariables["CostFunctionJ" ][IndexMin] )
+ if "CostFunctionJbAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJb"][IndexMin] )
+ if "CostFunctionJoAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJo"][IndexMin] )
+ return J
+ #
+ # Point de démarrage de l'optimisation : Xini = Xb
+ # ------------------------------------
+ Xini = numpy.ravel(Xb)
+ #
+ # Minimisation de la fonctionnelle
+ # --------------------------------
+ nbPreviousSteps = self.StoredVariables["CostFunctionJ"].stepnumber()
+ #
+ optResults = scipy.optimize.differential_evolution(
+ CostFunction,
+ self._parameters["Bounds"],
+ strategy = str(self._parameters["Minimizer"]).lower(),
+ maxiter = maxiter,
+ popsize = popsize,
+ mutation = self._parameters["MutationDifferentialWeight_F"],
+ recombination = self._parameters["CrossOverProbability_CR"],
+ disp = self._parameters["optdisp"],
+ )
+ #
+ IndexMin = numpy.argmin( self.StoredVariables["CostFunctionJ"][nbPreviousSteps:] ) + nbPreviousSteps
+ MinJ = self.StoredVariables["CostFunctionJ"][IndexMin]
+ Minimum = self.StoredVariables["CurrentState"][IndexMin]
+ #
+ # Obtention de l'analyse
+ # ----------------------
+ Xa = numpy.ravel( Minimum )
+ #
+ self.StoredVariables["Analysis"].store( Xa )
+ #
+ # Calculs et/ou stockages supplémentaires
+ # ---------------------------------------
+ if "OMA" in self._parameters["StoreSupplementaryCalculations"] or \
+ "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ HXa = self.StoredVariables["SimulatedObservationAtCurrentState"][IndexMin]
+ elif "SimulatedObservationAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ HXa = self.StoredVariables["SimulatedObservationAtCurrentOptimum"][-1]
+ else:
+ HXa = Hm(Xa)
+ if "Innovation" in self._parameters["StoreSupplementaryCalculations"] or \
+ "OMB" in self._parameters["StoreSupplementaryCalculations"]:
+ d = Y - HXb
+ if "Innovation" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["Innovation"].store( numpy.ravel(d) )
+ if "OMB" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["OMB"].store( numpy.ravel(d) )
+ if "BMA" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(Xa) )
+ if "OMA" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["OMA"].store( numpy.ravel(Y) - numpy.ravel(HXa) )
+ 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(HXa) )
+ #
+ self._post_run()
+ return 0
+
+# ==============================================================================
+if __name__ == "__main__":
+ print('\n AUTODIAGNOSTIC \n')
