X-Git-Url: http://git.salome-platform.org/gitweb/?a=blobdiff_plain;f=src%2FdaComposant%2FdaAlgorithms%2FUnscentedKalmanFilter.py;h=496f5d32399ba54e2ab11932c8c4ba51ffe53b66;hb=bd9eda86ec988924fa98879466a94787c1bd80fe;hp=27a18c5e0dc367125404559c6605411c889f8c46;hpb=43b3c71771b09633c003d2e3155858c4be16b992;p=modules%2Fadao.git diff --git a/src/daComposant/daAlgorithms/UnscentedKalmanFilter.py b/src/daComposant/daAlgorithms/UnscentedKalmanFilter.py index 27a18c5..496f5d3 100644 --- a/src/daComposant/daAlgorithms/UnscentedKalmanFilter.py +++ b/src/daComposant/daAlgorithms/UnscentedKalmanFilter.py @@ -1,28 +1,27 @@ -#-*-coding:iso-8859-1-*- +# -*- coding: utf-8 -*- # -# Copyright (C) 2008-2014 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, PlatformInfo -m = PlatformInfo.SystemUsage() +from daCore import BasicObjects import numpy, math # ============================================================================== @@ -76,29 +75,46 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): 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 = ["APosterioriCovariance", "BMA", "Innovation"] + message = "Liste de calculs supplémentaires à stocker et/ou effectuer", + listval = [ + "Analysis", + "APosterioriCorrelations", + "APosterioriCovariance", + "APosterioriStandardDeviations", + "APosterioriVariances", + "BMA", + "CostFunctionJ", + "CostFunctionJb", + "CostFunctionJo", + "CurrentState", + "InnovationAtCurrentState", + ] ) + self.defineRequiredParameter( # Pas de type + name = "Bounds", + message = "Liste des valeurs de bornes", + ) + self.requireInputArguments( + mandatory= ("Xb", "Y", "HO", "R", "B" ), + optional = ("U", "EM", "CM", "Q"), + ) + self.setAttributes(tags=( + "DataAssimilation", + "NonLinear", + "Filter", + "Ensemble", + "Dynamic", + )) def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None): - logging.debug("%s Lancement"%self._name) - logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M"))) - # - # Paramètres de pilotage - # ---------------------- - self.setParameters(Parameters) + self._pre_run(Parameters, Xb, Y, U, HO, EM, CM, 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 if self._parameters["EstimationOf"] == "Parameters": self._parameters["StoreInternalVariables"] = True # @@ -125,49 +141,49 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): Wc = numpy.array( Ww ) Wc[0] = Lambda / (L + Lambda) + (1. - Alpha**2 + Beta) # - # Opérateurs + # Opérateurs # ---------- - if B is None: - raise ValueError("Background error covariance matrix has to be properly defined!") - if R is None: - raise ValueError("Observation error covariance matrix has to be properly defined!") - # - H = HO["Direct"].appliedControledFormTo + Hm = HO["Direct"].appliedControledFormTo # if self._parameters["EstimationOf"] == "State": - M = EM["Direct"].appliedControledFormTo + Mm = EM["Direct"].appliedControledFormTo # - if CM is not None and CM.has_key("Tangent") and U is not None: + if CM is not None and "Tangent" in CM and U is not None: Cm = CM["Tangent"].asMatrix(Xb) else: Cm = None # - # Nombre de pas du Kalman identique au nombre de pas d'observations - # ----------------------------------------------------------------- + # Nombre de pas identique au nombre de pas d'observations + # ------------------------------------------------------- if hasattr(Y,"stepnumber"): duration = Y.stepnumber() else: duration = 2 # - # Précalcul des inversions de B et R + # Précalcul des inversions de B et R # ---------------------------------- - if self._parameters["StoreInternalVariables"]: + if self._parameters["StoreInternalVariables"] \ + or self._toStore("CostFunctionJ") \ + or self._toStore("CostFunctionJb") \ + or self._toStore("CostFunctionJo"): BI = B.getI() RI = R.getI() # # Initialisation # -------------- + __n = Xb.size Xn = Xb - if hasattr(B,"asfullmatrix"): - Pn = B.asfullmatrix(Xn.size) - else: - Pn = B + if hasattr(B,"asfullmatrix"): Pn = B.asfullmatrix(__n) + else: Pn = B # - self.StoredVariables["Analysis"].store( Xn.A1 ) - if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"]: - self.StoredVariables["APosterioriCovariance"].store( Pn ) - covarianceXa = Pn - Xa = Xn + if len(self.StoredVariables["Analysis"])==0 or not self._parameters["nextStep"]: + self.StoredVariables["Analysis"].store( numpy.ravel(Xb) ) + if self._toStore("APosterioriCovariance"): + self.StoredVariables["APosterioriCovariance"].store( Pn ) + covarianceXa = Pn + # + Xa = Xb + XaMin = Xb previousJMinimum = numpy.finfo(float).max # for step in range(duration-1): @@ -190,21 +206,21 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): Xnp = numpy.hstack([Xn, Xn+Gamma*Pndemi, Xn-Gamma*Pndemi]) nbSpts = 2*Xn.size+1 # - if Bounds is not None and self._parameters["ConstrainedBy"] == "EstimateProjection": + if self._parameters["Bounds"] is not None and self._parameters["ConstrainedBy"] == "EstimateProjection": for point in range(nbSpts): - Xnp[:,point] = numpy.max(numpy.hstack((Xnp[:,point],numpy.asmatrix(Bounds)[:,0])),axis=1) - Xnp[:,point] = numpy.min(numpy.hstack((Xnp[:,point],numpy.asmatrix(Bounds)[:,1])),axis=1) + Xnp[:,point] = numpy.max(numpy.hstack((Xnp[:,point],numpy.asmatrix(self._parameters["Bounds"])[:,0])),axis=1) + Xnp[:,point] = numpy.min(numpy.hstack((Xnp[:,point],numpy.asmatrix(self._parameters["Bounds"])[:,1])),axis=1) # XEtnnp = [] for point in range(nbSpts): if self._parameters["EstimationOf"] == "State": - XEtnnpi = numpy.asmatrix(numpy.ravel( M( (Xnp[:,point], Un) ) )).T + XEtnnpi = numpy.asmatrix(numpy.ravel( Mm( (Xnp[:,point], Un) ) )).T if Cm is not None and Un is not None: # Attention : si Cm est aussi dans M, doublon ! Cm = Cm.reshape(Xn.size,Un.size) # ADAO & check shape XEtnnpi = XEtnnpi + Cm * Un - if Bounds is not None and self._parameters["ConstrainedBy"] == "EstimateProjection": - XEtnnpi = numpy.max(numpy.hstack((XEtnnpi,numpy.asmatrix(Bounds)[:,0])),axis=1) - XEtnnpi = numpy.min(numpy.hstack((XEtnnpi,numpy.asmatrix(Bounds)[:,1])),axis=1) + if self._parameters["Bounds"] is not None and self._parameters["ConstrainedBy"] == "EstimateProjection": + XEtnnpi = numpy.max(numpy.hstack((XEtnnpi,numpy.asmatrix(self._parameters["Bounds"])[:,0])),axis=1) + XEtnnpi = numpy.min(numpy.hstack((XEtnnpi,numpy.asmatrix(self._parameters["Bounds"])[:,1])),axis=1) elif self._parameters["EstimationOf"] == "Parameters": # --- > Par principe, M = Id, Q = 0 XEtnnpi = Xnp[:,point] @@ -213,33 +229,33 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): # Xncm = numpy.matrix( XEtnnp.getA()*numpy.array(Wm) ).sum(axis=1) # - if Bounds is not None and self._parameters["ConstrainedBy"] == "EstimateProjection": - Xncm = numpy.max(numpy.hstack((Xncm,numpy.asmatrix(Bounds)[:,0])),axis=1) - Xncm = numpy.min(numpy.hstack((Xncm,numpy.asmatrix(Bounds)[:,1])),axis=1) + if self._parameters["Bounds"] is not None and self._parameters["ConstrainedBy"] == "EstimateProjection": + Xncm = numpy.max(numpy.hstack((Xncm,numpy.asmatrix(self._parameters["Bounds"])[:,0])),axis=1) + Xncm = numpy.min(numpy.hstack((Xncm,numpy.asmatrix(self._parameters["Bounds"])[:,1])),axis=1) # if self._parameters["EstimationOf"] == "State": Pnm = Q elif self._parameters["EstimationOf"] == "Parameters": Pnm = 0. for point in range(nbSpts): Pnm += Wc[i] * (XEtnnp[:,point]-Xncm) * (XEtnnp[:,point]-Xncm).T # - if self._parameters["EstimationOf"] == "Parameters" and Bounds is not None: + if self._parameters["EstimationOf"] == "Parameters" and self._parameters["Bounds"] is not None: Pnmdemi = self._parameters["Reconditioner"] * numpy.linalg.cholesky(Pnm) else: Pnmdemi = numpy.linalg.cholesky(Pnm) # Xnnp = numpy.hstack([Xncm, Xncm+Gamma*Pnmdemi, Xncm-Gamma*Pnmdemi]) # - if Bounds is not None and self._parameters["ConstrainedBy"] == "EstimateProjection": + if self._parameters["Bounds"] is not None and self._parameters["ConstrainedBy"] == "EstimateProjection": for point in range(nbSpts): - Xnnp[:,point] = numpy.max(numpy.hstack((Xnnp[:,point],numpy.asmatrix(Bounds)[:,0])),axis=1) - Xnnp[:,point] = numpy.min(numpy.hstack((Xnnp[:,point],numpy.asmatrix(Bounds)[:,1])),axis=1) + Xnnp[:,point] = numpy.max(numpy.hstack((Xnnp[:,point],numpy.asmatrix(self._parameters["Bounds"])[:,0])),axis=1) + Xnnp[:,point] = numpy.min(numpy.hstack((Xnnp[:,point],numpy.asmatrix(self._parameters["Bounds"])[:,1])),axis=1) # Ynnp = [] for point in range(nbSpts): if self._parameters["EstimationOf"] == "State": - Ynnpi = numpy.asmatrix(numpy.ravel( H( (Xnnp[:,point], None) ) )).T + Ynnpi = numpy.asmatrix(numpy.ravel( Hm( (Xnnp[:,point], None) ) )).T elif self._parameters["EstimationOf"] == "Parameters": - Ynnpi = numpy.asmatrix(numpy.ravel( H( (Xnnp[:,point], Un) ) )).T + Ynnpi = numpy.asmatrix(numpy.ravel( Hm( (Xnnp[:,point], Un) ) )).T Ynnp.append( Ynnpi ) Ynnp = numpy.hstack( Ynnp ) # @@ -260,48 +276,50 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): Xn = Xncm + Kn * d Pn = Pnm - Kn * Pyyn * Kn.T # - if Bounds is not None and self._parameters["ConstrainedBy"] == "EstimateProjection": - Xn = numpy.max(numpy.hstack((Xn,numpy.asmatrix(Bounds)[:,0])),axis=1) - Xn = numpy.min(numpy.hstack((Xn,numpy.asmatrix(Bounds)[:,1])),axis=1) + if self._parameters["Bounds"] is not None and self._parameters["ConstrainedBy"] == "EstimateProjection": + Xn = numpy.max(numpy.hstack((Xn,numpy.asmatrix(self._parameters["Bounds"])[:,0])),axis=1) + Xn = numpy.min(numpy.hstack((Xn,numpy.asmatrix(self._parameters["Bounds"])[:,1])),axis=1) + Xa = Xn # Pointeurs # - self.StoredVariables["Analysis"].store( Xn.A1 ) - if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"]: + # ---> avec analysis + self.StoredVariables["Analysis"].store( Xa ) + if self._toStore("APosterioriCovariance"): self.StoredVariables["APosterioriCovariance"].store( Pn ) - if "Innovation" in self._parameters["StoreSupplementaryCalculations"]: - self.StoredVariables["Innovation"].store( numpy.ravel( d.A1 ) ) - if self._parameters["StoreInternalVariables"]: - Jb = 0.5 * (Xn - Xb).T * BI * (Xn - Xb) - Jo = 0.5 * d.T * RI * d - J = float( Jb ) + float( Jo ) + # ---> avec current state + if self._toStore("InnovationAtCurrentState"): + self.StoredVariables["InnovationAtCurrentState"].store( d ) + if self._parameters["StoreInternalVariables"] \ + or self._toStore("CurrentState"): self.StoredVariables["CurrentState"].store( Xn ) + if self._parameters["StoreInternalVariables"] \ + or self._toStore("CostFunctionJ") \ + or self._toStore("CostFunctionJb") \ + or self._toStore("CostFunctionJo"): + Jb = float( 0.5 * (Xa - Xb).T * BI * (Xa - Xb) ) + Jo = float( 0.5 * d.T * RI * d ) + J = Jb + Jo self.StoredVariables["CostFunctionJb"].store( Jb ) self.StoredVariables["CostFunctionJo"].store( Jo ) self.StoredVariables["CostFunctionJ" ].store( J ) - if J < previousJMinimum: - previousJMinimum = J - Xa = Xn - if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"]: - covarianceXa = Pn - else: - Xa = Xn - # + if self._parameters["EstimationOf"] == "Parameters" \ + and J < previousJMinimum: + previousJMinimum = J + XaMin = Xa + if self._toStore("APosterioriCovariance"): + covarianceXaMin = Pn # - # Stockage supplementaire de l'optimum en estimation de parametres - # ---------------------------------------------------------------- + # Stockage final supplémentaire de l'optimum en estimation de paramètres + # ---------------------------------------------------------------------- if self._parameters["EstimationOf"] == "Parameters": - self.StoredVariables["Analysis"].store( Xa.A1 ) - if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"]: - self.StoredVariables["APosterioriCovariance"].store( covarianceXa ) - # - if "BMA" in self._parameters["StoreSupplementaryCalculations"]: - self.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(Xa) ) - # - logging.debug("%s Nombre d'évaluation(s) de l'opérateur d'observation direct/tangent/adjoint : %i/%i/%i"%(self._name, HO["Direct"].nbcalls(0),HO["Tangent"].nbcalls(0),HO["Adjoint"].nbcalls(0))) - logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M"))) - logging.debug("%s Terminé"%self._name) + self.StoredVariables["Analysis"].store( XaMin ) + if self._toStore("APosterioriCovariance"): + self.StoredVariables["APosterioriCovariance"].store( covarianceXaMin ) + if self._toStore("BMA"): + self.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(XaMin) ) # + self._post_run(HO) return 0 # ============================================================================== if __name__ == "__main__": - print '\n AUTODIAGNOSTIC \n' + print('\n AUTODIAGNOSTIC\n')