X-Git-Url: http://git.salome-platform.org/gitweb/?a=blobdiff_plain;f=src%2FdaComposant%2FdaAlgorithms%2FQuantileRegression.py;h=3893d5c22de275004b8248c1a54d6b8e5069d0c1;hb=087028cb881b07298c2b68bf8b2c080dff09a042;hp=ce516313c218e52c09d7255817103fbfa66641da;hpb=2fecd26f25728d31f058af117534c17ab94d46d0;p=modules%2Fadao.git diff --git a/src/daComposant/daAlgorithms/QuantileRegression.py b/src/daComposant/daAlgorithms/QuantileRegression.py index ce51631..3893d5c 100644 --- a/src/daComposant/daAlgorithms/QuantileRegression.py +++ b/src/daComposant/daAlgorithms/QuantileRegression.py @@ -1,27 +1,27 @@ -#-*-coding:iso-8859-1-*- +# -*- coding: utf-8 -*- # -# Copyright (C) 2008-2015 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 # -# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D +# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D import logging -from daCore import BasicObjects +from daCore import BasicObjects, NumericObjects import numpy # ============================================================================== @@ -40,7 +40,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): name = "Minimizer", default = "MMQR", typecast = str, - message = "Minimiseur utilisé", + message = "Minimiseur utilisé", listval = ["MMQR"], ) self.defineRequiredParameter( @@ -54,43 +54,51 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): name = "CostDecrementTolerance", default = 1.e-6, typecast = float, - message = "Maximum de variation de la fonction d'estimation lors de l'arrêt", + message = "Maximum de variation de la fonction d'estimation lors de l'arrêt", ) self.defineRequiredParameter( name = "StoreInternalVariables", default = False, typecast = bool, - message = "Stockage des variables internes ou intermédiaires du calcul", + 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 = ["BMA", "OMA", "OMB", "Innovation", "SimulatedObservationAtBackground", "SimulatedObservationAtOptimum"] + message = "Liste de calculs supplémentaires à stocker et/ou effectuer", + listval = [ + "Analysis", + "BMA", + "CostFunctionJ", + "CostFunctionJb", + "CostFunctionJo", + "CurrentState", + "Innovation", + "OMA", + "OMB", + "SimulatedObservationAtBackground", + "SimulatedObservationAtCurrentState", + "SimulatedObservationAtOptimum", + ] + ) + self.defineRequiredParameter( # Pas de type + name = "Bounds", + message = "Liste des valeurs de bornes", + ) + self.requireInputArguments( + mandatory= ("Xb", "Y", "HO" ), ) 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() - # - # Paramètres de pilotage - # ---------------------- - self.setParameters(Parameters) + self._pre_run(Parameters, Xb, Y, R, B, Q) # - if self._parameters.has_key("Bounds") and (type(self._parameters["Bounds"]) is type([]) or type(self._parameters["Bounds"]) is type(())) and (len(self._parameters["Bounds"]) > 0): - Bounds = self._parameters["Bounds"] - logging.debug("%s Prise en compte des bornes effectuee"%(self._name,)) - else: - Bounds = None - # - # Opérateur d'observation - # ----------------------- Hm = HO["Direct"].appliedTo # - # Utilisation éventuelle d'un vecteur H(Xb) précalculé + # Utilisation éventuelle d'un vecteur H(Xb) précalculé # ---------------------------------------------------- - if HO["AppliedToX"] is not None and HO["AppliedToX"].has_key("HXb"): - HXb = HO["AppliedToX"]["HXb"] + if HO["AppliedInX"] is not None and "HXb" in HO["AppliedInX"]: + HXb = Hm( Xb, HO["AppliedInX"]["HXb"]) else: HXb = Hm( Xb ) HXb = numpy.asmatrix(numpy.ravel( HXb )).T @@ -103,17 +111,19 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): raise ValueError("The shapes %s of observations Y and %s of observed calculation H(X) are different, they have to be identical."%(Y.shape,HXb.shape)) d = Y - HXb # - # Définition de la fonction-coût + # Définition de la fonction-coût # ------------------------------ def CostFunction(x): _X = numpy.asmatrix(numpy.ravel( x )).T + if self._parameters["StoreInternalVariables"] or self._toStore("CurrentState"): + self.StoredVariables["CurrentState"].store( _X ) _HX = Hm( _X ) _HX = numpy.asmatrix(numpy.ravel( _HX )).T + if self._toStore("SimulatedObservationAtCurrentState"): + self.StoredVariables["SimulatedObservationAtCurrentState"].store( _HX ) Jb = 0. Jo = 0. J = Jb + Jo - if self._parameters["StoreInternalVariables"]: - self.StoredVariables["CurrentState"].store( _X ) self.StoredVariables["CostFunctionJb"].store( Jb ) self.StoredVariables["CostFunctionJo"].store( Jo ) self.StoredVariables["CostFunctionJ" ].store( J ) @@ -124,9 +134,9 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): Hg = HO["Tangent"].asMatrix( _X ) return Hg # - # Point de démarrage de l'optimisation : Xini = Xb + # Point de démarrage de l'optimisation : Xini = Xb # ------------------------------------ - if type(Xb) is type(numpy.matrix([])): + if isinstance(Xb, type(numpy.matrix([]))): Xini = Xb.A1.tolist() else: Xini = list(Xb) @@ -134,12 +144,11 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): # Minimisation de la fonctionnelle # -------------------------------- if self._parameters["Minimizer"] == "MMQR": - import mmqr - Minimum, J_optimal, Informations = mmqr.mmqr( + Minimum, J_optimal, Informations = NumericObjects.mmqr( func = CostFunction, x0 = Xini, fprime = GradientOfCostFunction, - bounds = Bounds, + bounds = self._parameters["Bounds"], quantile = self._parameters["Quantile"], maxfun = self._parameters["MaximumNumberOfSteps"], toler = self._parameters["CostDecrementTolerance"], @@ -156,23 +165,23 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): # self.StoredVariables["Analysis"].store( Xa.A1 ) # - if "OMA" in self._parameters["StoreSupplementaryCalculations"] or \ - "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"]: + if self._toStore("OMA") or \ + self._toStore("SimulatedObservationAtOptimum"): HXa = Hm(Xa) # - # Calculs et/ou stockages supplémentaires + # Calculs et/ou stockages supplémentaires # --------------------------------------- - if "Innovation" in self._parameters["StoreSupplementaryCalculations"]: + if self._toStore("Innovation"): self.StoredVariables["Innovation"].store( numpy.ravel(d) ) - if "BMA" in self._parameters["StoreSupplementaryCalculations"]: + if self._toStore("BMA"): self.StoredVariables["BMA"].store( numpy.ravel(Xb - Xa) ) - if "OMA" in self._parameters["StoreSupplementaryCalculations"]: + if self._toStore("OMA"): self.StoredVariables["OMA"].store( numpy.ravel(Y - HXa) ) - if "OMB" in self._parameters["StoreSupplementaryCalculations"]: + if self._toStore("OMB"): self.StoredVariables["OMB"].store( numpy.ravel(d) ) - if "SimulatedObservationAtBackground" in self._parameters["StoreSupplementaryCalculations"]: + if self._toStore("SimulatedObservationAtBackground"): self.StoredVariables["SimulatedObservationAtBackground"].store( numpy.ravel(HXb) ) - if "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"]: + if self._toStore("SimulatedObservationAtOptimum"): self.StoredVariables["SimulatedObservationAtOptimum"].store( numpy.ravel(HXa) ) # self._post_run(HO) @@ -180,4 +189,4 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): # ============================================================================== if __name__ == "__main__": - print '\n AUTODIAGNOSTIC \n' + print('\n AUTODIAGNOSTIC\n')