1 # -*- coding: utf-8 -*-
3 # Copyright (C) 2008-2019 EDF R&D
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.
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.
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
19 # See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
21 # Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
24 from daCore import BasicObjects
27 # ==============================================================================
28 class ElementaryAlgorithm(BasicObjects.Algorithm):
30 BasicObjects.Algorithm.__init__(self, "QUANTILEREGRESSION")
31 self.defineRequiredParameter(
35 message = "Quantile pour la regression de quantile",
39 self.defineRequiredParameter(
43 message = "Minimiseur utilisé",
46 self.defineRequiredParameter(
47 name = "MaximumNumberOfSteps",
50 message = "Nombre maximal de pas d'optimisation",
53 self.defineRequiredParameter(
54 name = "CostDecrementTolerance",
57 message = "Maximum de variation de la fonction d'estimation lors de l'arrêt",
59 self.defineRequiredParameter(
60 name = "StoreInternalVariables",
63 message = "Stockage des variables internes ou intermédiaires du calcul",
65 self.defineRequiredParameter(
66 name = "StoreSupplementaryCalculations",
69 message = "Liste de calculs supplémentaires à stocker et/ou effectuer",
70 listval = ["BMA", "OMA", "OMB", "CurrentState", "CostFunctionJ", "CostFunctionJb", "CostFunctionJo", "Innovation", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum"]
72 self.defineRequiredParameter( # Pas de type
74 message = "Liste des valeurs de bornes",
76 self.requireInputArguments(
77 mandatory= ("Xb", "Y", "HO" ),
80 def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
81 self._pre_run(Parameters, Xb, Y, R, B, Q)
83 Hm = HO["Direct"].appliedTo
85 # Utilisation éventuelle d'un vecteur H(Xb) précalculé
86 # ----------------------------------------------------
87 if HO["AppliedInX"] is not None and "HXb" in HO["AppliedInX"]:
88 HXb = Hm( Xb, HO["AppliedInX"]["HXb"])
91 HXb = numpy.asmatrix(numpy.ravel( HXb )).T
93 # Calcul de l'innovation
94 # ----------------------
95 if Y.size != HXb.size:
96 raise ValueError("The size %i of observations Y and %i of observed calculation H(X) are different, they have to be identical."%(Y.size,HXb.size))
97 if max(Y.shape) != max(HXb.shape):
98 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))
101 # Définition de la fonction-coût
102 # ------------------------------
104 _X = numpy.asmatrix(numpy.ravel( x )).T
105 if self._parameters["StoreInternalVariables"] or self._toStore("CurrentState"):
106 self.StoredVariables["CurrentState"].store( _X )
108 _HX = numpy.asmatrix(numpy.ravel( _HX )).T
109 if self._toStore("SimulatedObservationAtCurrentState"):
110 self.StoredVariables["SimulatedObservationAtCurrentState"].store( _HX )
114 self.StoredVariables["CostFunctionJb"].store( Jb )
115 self.StoredVariables["CostFunctionJo"].store( Jo )
116 self.StoredVariables["CostFunctionJ" ].store( J )
119 def GradientOfCostFunction(x):
120 _X = numpy.asmatrix(numpy.ravel( x )).T
121 Hg = HO["Tangent"].asMatrix( _X )
124 # Point de démarrage de l'optimisation : Xini = Xb
125 # ------------------------------------
126 if isinstance(Xb, type(numpy.matrix([]))):
127 Xini = Xb.A1.tolist()
131 # Minimisation de la fonctionnelle
132 # --------------------------------
133 if self._parameters["Minimizer"] == "MMQR":
135 Minimum, J_optimal, Informations = mmqr.mmqr(
138 fprime = GradientOfCostFunction,
139 bounds = self._parameters["Bounds"],
140 quantile = self._parameters["Quantile"],
141 maxfun = self._parameters["MaximumNumberOfSteps"],
142 toler = self._parameters["CostDecrementTolerance"],
145 nfeval = Informations[2]
148 raise ValueError("Error in Minimizer name: %s"%self._parameters["Minimizer"])
150 # Obtention de l'analyse
151 # ----------------------
152 Xa = numpy.asmatrix(numpy.ravel( Minimum )).T
154 self.StoredVariables["Analysis"].store( Xa.A1 )
156 if self._toStore("OMA") or \
157 self._toStore("SimulatedObservationAtOptimum"):
160 # Calculs et/ou stockages supplémentaires
161 # ---------------------------------------
162 if self._toStore("Innovation"):
163 self.StoredVariables["Innovation"].store( numpy.ravel(d) )
164 if self._toStore("BMA"):
165 self.StoredVariables["BMA"].store( numpy.ravel(Xb - Xa) )
166 if self._toStore("OMA"):
167 self.StoredVariables["OMA"].store( numpy.ravel(Y - HXa) )
168 if self._toStore("OMB"):
169 self.StoredVariables["OMB"].store( numpy.ravel(d) )
170 if self._toStore("SimulatedObservationAtBackground"):
171 self.StoredVariables["SimulatedObservationAtBackground"].store( numpy.ravel(HXb) )
172 if self._toStore("SimulatedObservationAtOptimum"):
173 self.StoredVariables["SimulatedObservationAtOptimum"].store( numpy.ravel(HXa) )
178 # ==============================================================================
179 if __name__ == "__main__":
180 print('\n AUTODIAGNOSTIC \n')