X-Git-Url: http://git.salome-platform.org/gitweb/?a=blobdiff_plain;f=src%2FdaComposant%2FdaAlgorithms%2FQuantileRegression.py;h=4b0dd223528f1ad1c12dab21206bc9de2e779e0c;hb=67a9e7898a4e0455f469b63898f42e965da0b33e;hp=671bfa5ee2e5f69de7a7b67f098e634706a7a787;hpb=ca04ebfa18e547eb61480179e25654e6c1c6a276;p=modules%2Fadao.git diff --git a/src/daComposant/daAlgorithms/QuantileRegression.py b/src/daComposant/daAlgorithms/QuantileRegression.py index 671bfa5..4b0dd22 100644 --- a/src/daComposant/daAlgorithms/QuantileRegression.py +++ b/src/daComposant/daAlgorithms/QuantileRegression.py @@ -1,6 +1,6 @@ -#-*-coding:iso-8859-1-*- +# -*- coding: utf-8 -*- # -# Copyright (C) 2008-2017 EDF R&D +# Copyright (C) 2008-2019 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 @@ -21,7 +21,7 @@ # 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( @@ -54,35 +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", "CurrentState", "CostFunctionJ", "CostFunctionJb", "CostFunctionJo", "Innovation", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentState", "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(Parameters) + self._pre_run(Parameters, Xb, Y, R, B, Q) # 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 = Hm( Xb, 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 @@ -95,15 +111,15 @@ 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 "CurrentState" in self._parameters["StoreSupplementaryCalculations"]: + if self._parameters["StoreInternalVariables"] or self._toStore("CurrentState"): self.StoredVariables["CurrentState"].store( _X ) _HX = Hm( _X ) _HX = numpy.asmatrix(numpy.ravel( _HX )).T - if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]: + if self._toStore("SimulatedObservationAtCurrentState"): self.StoredVariables["SimulatedObservationAtCurrentState"].store( _HX ) Jb = 0. Jo = 0. @@ -118,9 +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) @@ -128,8 +144,7 @@ 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, @@ -150,23 +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) @@ -174,4 +189,4 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): # ============================================================================== if __name__ == "__main__": - print '\n AUTODIAGNOSTIC \n' + print('\n AUTODIAGNOSTIC\n')