1 #-*-coding:iso-8859-1-*-
3 # Copyright (C) 2008-2012 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, PlatformInfo
25 m = PlatformInfo.SystemUsage()
29 # ==============================================================================
30 class ElementaryAlgorithm(BasicObjects.Algorithm):
32 BasicObjects.Algorithm.__init__(self, "QUANTILEREGRESSION")
33 self.defineRequiredParameter(
37 message = "Quantile pour la regression de quantile",
41 self.defineRequiredParameter(
45 message = "Minimiseur utilisé",
48 self.defineRequiredParameter(
49 name = "MaximumNumberOfSteps",
52 message = "Nombre maximal de pas d'optimisation",
55 self.defineRequiredParameter(
56 name = "CostDecrementTolerance",
59 message = "Maximum de variation de la fonction d'estimation lors de l'arrêt",
61 self.defineRequiredParameter(
62 name = "StoreInternalVariables",
65 message = "Stockage des variables internes ou intermédiaires du calcul",
68 def run(self, Xb=None, Y=None, H=None, M=None, R=None, B=None, Q=None, Parameters=None):
70 Calcul des parametres definissant le quantile
72 logging.debug("%s Lancement"%self._name)
73 logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
75 # Paramètres de pilotage
76 # ----------------------
77 self.setParameters(Parameters)
79 # Opérateur d'observation
80 # -----------------------
81 Hm = H["Direct"].appliedTo
83 # Utilisation éventuelle d'un vecteur H(Xb) précalculé
84 # ----------------------------------------------------
85 if H["AppliedToX"] is not None and H["AppliedToX"].has_key("HXb"):
86 logging.debug("%s Utilisation de HXb"%self._name)
87 HXb = H["AppliedToX"]["HXb"]
89 logging.debug("%s Calcul de Hm(Xb)"%self._name)
91 HXb = numpy.asmatrix(HXb).flatten().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))
100 logging.debug("%s Innovation d = %s"%(self._name, d))
102 # Définition de la fonction-coût
103 # ------------------------------
105 _X = numpy.asmatrix(x).flatten().T
106 logging.debug("%s CostFunction X = %s"%(self._name, numpy.asmatrix( _X ).flatten()))
108 _HX = numpy.asmatrix(_HX).flatten().T
112 logging.debug("%s CostFunction Jb = %s"%(self._name, Jb))
113 logging.debug("%s CostFunction Jo = %s"%(self._name, Jo))
114 logging.debug("%s CostFunction J = %s"%(self._name, J))
115 if self._parameters["StoreInternalVariables"]:
116 self.StoredVariables["CurrentState"].store( _X.A1 )
117 self.StoredVariables["CostFunctionJb"].store( Jb )
118 self.StoredVariables["CostFunctionJo"].store( Jo )
119 self.StoredVariables["CostFunctionJ" ].store( J )
122 def GradientOfCostFunction(x):
123 _X = numpy.asmatrix(x).flatten().T
124 logging.debug("%s GradientOfCostFunction X = %s"%(self._name, _X.A1))
125 Hg = H["Tangent"].asMatrix( _X )
128 # Point de démarrage de l'optimisation : Xini = Xb
129 # ------------------------------------
130 if type(Xb) is type(numpy.matrix([])):
131 Xini = Xb.A1.tolist()
134 logging.debug("%s Point de démarrage Xini = %s"%(self._name, Xini))
136 # Minimisation de la fonctionnelle
137 # --------------------------------
138 if self._parameters["Minimizer"] == "MMQR":
140 Minimum, J_optimal, Informations = mmqr.mmqr(
143 fprime = GradientOfCostFunction,
144 quantile = self._parameters["Quantile"],
145 maxfun = self._parameters["MaximumNumberOfSteps"],
146 toler = self._parameters["CostDecrementTolerance"],
149 nfeval = Informations[2]
152 raise ValueError("Error in Minimizer name: %s"%self._parameters["Minimizer"])
154 logging.debug("%s %s Step of min cost = %s"%(self._name, self._parameters["Minimizer"], nfeval))
155 logging.debug("%s %s Minimum cost = %s"%(self._name, self._parameters["Minimizer"], J_optimal))
156 logging.debug("%s %s Minimum state = %s"%(self._name, self._parameters["Minimizer"], Minimum))
157 logging.debug("%s %s Nb of F = %s"%(self._name, self._parameters["Minimizer"], nfeval))
158 logging.debug("%s %s RetCode = %s"%(self._name, self._parameters["Minimizer"], rc))
160 # Obtention de l'analyse
161 # ----------------------
162 Xa = numpy.asmatrix(Minimum).flatten().T
163 logging.debug("%s Analyse Xa = %s"%(self._name, Xa))
165 self.StoredVariables["Analysis"].store( Xa.A1 )
166 self.StoredVariables["Innovation"].store( d.A1 )
168 logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
169 logging.debug("%s Terminé"%self._name)
173 # ==============================================================================
174 if __name__ == "__main__":
175 print '\n AUTODIAGNOSTIC \n'