1 #-*-coding:iso-8859-1-*-
3 # Copyright (C) 2008-2011 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
23 from daCore import BasicObjects, PlatformInfo
24 m = PlatformInfo.SystemUsage()
29 if logging.getLogger().level < 30:
31 message = scipy.optimize.tnc.MSG_ALL
35 message = scipy.optimize.tnc.MSG_NONE
38 # ==============================================================================
39 class ElementaryAlgorithm(BasicObjects.Algorithm):
41 BasicObjects.Algorithm.__init__(self)
43 logging.debug("%s Initialisation"%self._name)
45 def run(self, Xb=None, Y=None, H=None, M=None, R=None, B=None, Q=None, Parameters=None):
47 Calcul de l'estimateur 3D-VAR
49 logging.debug("%s Lancement"%self._name)
50 logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("Mo")))
52 # Opérateur d'observation
53 # -----------------------
54 Hm = H["Direct"].appliedTo
55 Ht = H["Adjoint"].appliedInXTo
57 # Utilisation éventuelle d'un vecteur H(Xb) précalculé
58 # ----------------------------------------------------
59 if H["AppliedToX"] is not None and H["AppliedToX"].has_key("HXb"):
60 logging.debug("%s Utilisation de HXb"%self._name)
61 HXb = H["AppliedToX"]["HXb"]
63 logging.debug("%s Calcul de Hm(Xb)"%self._name)
65 HXb = numpy.asmatrix(HXb).flatten().T
67 # Calcul du préconditionnement
68 # ----------------------------
69 # Bdemi = numpy.linalg.cholesky(B)
71 # Calcul de l'innovation
72 # ----------------------
74 logging.debug("%s Innovation d = %s"%(self._name, d))
76 # Précalcul des inversion appellée dans les fonction-coût et gradient
77 # -------------------------------------------------------------------
81 # Définition de la fonction-coût
82 # ------------------------------
84 _X = numpy.asmatrix(x).flatten().T
85 logging.info("%s CostFunction X = %s"%(self._name, numpy.asmatrix( _X ).flatten()))
87 _HX = numpy.asmatrix(_HX).flatten().T
88 Jb = 0.5 * (_X - Xb).T * BI * (_X - Xb)
89 Jo = 0.5 * (Y - _HX).T * RI * (Y - _HX)
90 J = float( Jb ) + float( Jo )
91 logging.info("%s CostFunction Jb = %s"%(self._name, Jb))
92 logging.info("%s CostFunction Jo = %s"%(self._name, Jo))
93 logging.info("%s CostFunction J = %s"%(self._name, J))
94 self.StoredVariables["CurrentState"].store( _X.A1 )
95 self.StoredVariables["CostFunctionJb"].store( Jb )
96 self.StoredVariables["CostFunctionJo"].store( Jo )
97 self.StoredVariables["CostFunctionJ" ].store( J )
100 def GradientOfCostFunction(x):
101 _X = numpy.asmatrix(x).flatten().T
102 logging.info("%s GradientOfCostFunction X = %s"%(self._name, numpy.asmatrix( _X ).flatten()))
104 _HX = numpy.asmatrix(_HX).flatten().T
105 GradJb = BI * (_X - Xb)
106 GradJo = - Ht( (_X, RI * (Y - _HX)) )
107 GradJ = numpy.asmatrix( GradJb ).flatten().T + numpy.asmatrix( GradJo ).flatten().T
108 logging.debug("%s GradientOfCostFunction GradJb = %s"%(self._name, numpy.asmatrix( GradJb ).flatten()))
109 logging.debug("%s GradientOfCostFunction GradJo = %s"%(self._name, numpy.asmatrix( GradJo ).flatten()))
110 logging.debug("%s GradientOfCostFunction GradJ = %s"%(self._name, numpy.asmatrix( GradJ ).flatten()))
113 # Point de démarrage de l'optimisation : Xini = Xb
114 # ------------------------------------
115 if type(Xb) is type(numpy.matrix([])):
116 Xini = Xb.A1.tolist()
119 logging.debug("%s Point de démarrage Xini = %s"%(self._name, Xini))
121 # Paramètres de pilotage
122 # ----------------------
123 # Potentiels : "Bounds", "Minimizer", "MaximumNumberOfSteps"
124 if Parameters.has_key("Bounds") and (type(Parameters["Bounds"]) is type([]) or type(Parameters["Bounds"]) is type(())) and (len(Parameters["Bounds"]) > 0):
125 Bounds = Parameters["Bounds"]
128 MinimizerList = ["LBFGSB","TNC", "CG", "BFGS"]
129 if Parameters.has_key("Minimizer") and (Parameters["Minimizer"] in MinimizerList):
130 Minimizer = str( Parameters["Minimizer"] )
133 logging.debug("%s Minimiseur utilisé = %s"%(self._name, Minimizer))
134 if Parameters.has_key("MaximumNumberOfSteps") and (Parameters["MaximumNumberOfSteps"] > -1):
135 maxiter = int( Parameters["MaximumNumberOfSteps"] )
138 logging.debug("%s Nombre maximal de pas d'optimisation = %s"%(self._name, str(maxiter)))
140 # Minimisation de la fonctionnelle
141 # --------------------------------
142 if Minimizer == "LBFGSB":
143 Minimum, J_optimal, Informations = scipy.optimize.fmin_l_bfgs_b(
146 fprime = GradientOfCostFunction,
153 logging.debug("%s %s Minimum = %s"%(self._name, Minimizer, Minimum))
154 logging.debug("%s %s Nb of F = %s"%(self._name, Minimizer, Informations['funcalls']))
155 logging.debug("%s %s RetCode = %s"%(self._name, Minimizer, Informations['warnflag']))
156 elif Minimizer == "TNC":
157 Minimum, nfeval, rc = scipy.optimize.fmin_tnc(
160 fprime = GradientOfCostFunction,
166 logging.debug("%s %s Minimum = %s"%(self._name, Minimizer, Minimum))
167 logging.debug("%s %s Nb of F = %s"%(self._name, Minimizer, nfeval))
168 logging.debug("%s %s RetCode = %s"%(self._name, Minimizer, rc))
169 elif Minimizer == "CG":
170 Minimum, fopt, nfeval, grad_calls, rc = scipy.optimize.fmin_cg(
173 fprime = GradientOfCostFunction,
179 logging.debug("%s %s Minimum = %s"%(self._name, Minimizer, Minimum))
180 logging.debug("%s %s Nb of F = %s"%(self._name, Minimizer, nfeval))
181 logging.debug("%s %s RetCode = %s"%(self._name, Minimizer, rc))
182 elif Minimizer == "BFGS":
183 Minimum, fopt, gopt, Hopt, nfeval, grad_calls, rc = scipy.optimize.fmin_bfgs(
186 fprime = GradientOfCostFunction,
192 logging.debug("%s %s Minimum = %s"%(self._name, Minimizer, Minimum))
193 logging.debug("%s %s Nb of F = %s"%(self._name, Minimizer, nfeval))
194 logging.debug("%s %s RetCode = %s"%(self._name, Minimizer, rc))
196 raise ValueError("Error in Minimizer name: %s"%Minimizer)
198 # Calcul de l'analyse
199 # --------------------
200 Xa = numpy.asmatrix(Minimum).T
201 logging.debug("%s Analyse Xa = %s"%(self._name, Xa))
203 self.StoredVariables["Analysis"].store( Xa.A1 )
204 self.StoredVariables["Innovation"].store( d.A1 )
206 logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("MB")))
207 logging.debug("%s Terminé"%self._name)
211 # ==============================================================================
212 if __name__ == "__main__":
213 print '\n AUTODIAGNOSTIC \n'