-#-*-coding:iso-8859-1-*-
+# -*- coding: utf-8 -*-
#
-# Copyright (C) 2008-2015 EDF R&D
+# Copyright (C) 2008-2021 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
-import numpy, scipy.optimize
+import numpy, scipy.optimize, scipy.version
# ==============================================================================
class ElementaryAlgorithm(BasicObjects.Algorithm):
name = "Minimizer",
default = "LBFGSB",
typecast = str,
- message = "Minimiseur utilisé",
+ message = "Minimiseur utilisé",
listval = ["LBFGSB","TNC", "CG", "NCG", "BFGS", "LM"],
)
self.defineRequiredParameter(
name = "CostDecrementTolerance",
default = 1.e-7,
typecast = float,
- message = "Diminution relative minimale du cout lors de l'arrêt",
+ message = "Diminution relative minimale du coût lors de l'arrêt",
+ minval = 0.,
)
self.defineRequiredParameter(
name = "ProjectedGradientTolerance",
default = -1,
typecast = float,
- message = "Maximum des composantes du gradient projeté lors de l'arrêt",
+ message = "Maximum des composantes du gradient projeté lors de l'arrêt",
minval = -1,
)
self.defineRequiredParameter(
name = "GradientNormTolerance",
default = 1.e-05,
typecast = float,
- message = "Maximum des composantes du gradient lors de l'arrêt",
+ message = "Maximum des composantes du gradient lors de l'arrêt",
+ minval = 0.,
)
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", "Innovation", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum"]
+ message = "Liste de calculs supplémentaires à stocker et/ou effectuer",
+ listval = [
+ "Analysis",
+ "BMA",
+ "CostFunctionJ",
+ "CostFunctionJAtCurrentOptimum",
+ "CostFunctionJb",
+ "CostFunctionJbAtCurrentOptimum",
+ "CostFunctionJo",
+ "CostFunctionJoAtCurrentOptimum",
+ "CurrentIterationNumber",
+ "CurrentOptimum",
+ "CurrentState",
+ "IndexOfOptimum",
+ "Innovation",
+ "InnovationAtCurrentState",
+ "OMA",
+ "OMB",
+ "SimulatedObservationAtBackground",
+ "SimulatedObservationAtCurrentOptimum",
+ "SimulatedObservationAtCurrentState",
+ "SimulatedObservationAtOptimum",
+ ]
)
+ self.defineRequiredParameter( # Pas de type
+ name = "Bounds",
+ message = "Liste des valeurs de bornes",
+ )
+ self.defineRequiredParameter(
+ name = "InitializationPoint",
+ typecast = numpy.ravel,
+ message = "État initial imposé (par défaut, c'est l'ébauche si None)",
+ )
+ self.requireInputArguments(
+ mandatory= ("Xb", "Y", "HO", "R"),
+ )
+ self.setAttributes(tags=(
+ "Optimization",
+ "NonLinear",
+ "Variational",
+ ))
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()
- if logging.getLogger().level < logging.WARNING:
- self.__iprint, self.__disp = 1, 1
- self.__message = scipy.optimize.tnc.MSG_ALL
- else:
- self.__iprint, self.__disp = -1, 0
- self.__message = scipy.optimize.tnc.MSG_NONE
- #
- # Paramètres de pilotage
- # ----------------------
- self.setParameters(Parameters)
- #
- 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
- #
- # Correction pour pallier a un bug de TNC sur le retour du Minimum
- if self._parameters.has_key("Minimizer") == "TNC":
- self.setParameterValue("StoreInternalVariables",True)
+ self._pre_run(Parameters, Xb, Y, U, HO, EM, CM, R, B, Q)
#
- # Opérateurs
+ # Opérateurs
# ----------
Hm = HO["Direct"].appliedTo
Ha = HO["Adjoint"].appliedInXTo
#
- # 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 = 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
- #
- # Calcul de l'innovation
- # ----------------------
if Y.size != HXb.size:
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))
if max(Y.shape) != max(HXb.shape):
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
#
- # Précalcul des inversions de B et R
+ # Précalcul des inversions de B et R
# ----------------------------------
RI = R.getI()
if self._parameters["Minimizer"] == "LM":
RdemiI = R.choleskyI()
#
- # 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 \
+ self._toStore("CurrentState") or \
+ self._toStore("CurrentOptimum"):
+ self.StoredVariables["CurrentState"].store( _X )
_HX = Hm( _X )
_HX = numpy.asmatrix(numpy.ravel( _HX )).T
- Jb = 0.
- Jo = 0.5 * (Y - _HX).T * RI * (Y - _HX)
- J = float( Jb ) + float( Jo )
- if self._parameters["StoreInternalVariables"] or "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
- self.StoredVariables["CurrentState"].store( _X )
- if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ _Innovation = Y - _HX
+ if self._toStore("SimulatedObservationAtCurrentState") or \
+ self._toStore("SimulatedObservationAtCurrentOptimum"):
self.StoredVariables["SimulatedObservationAtCurrentState"].store( _HX )
+ if self._toStore("InnovationAtCurrentState"):
+ self.StoredVariables["InnovationAtCurrentState"].store( _Innovation )
+ #
+ Jb = 0.
+ Jo = float( 0.5 * _Innovation.T * RI * _Innovation )
+ J = Jb + Jo
+ #
+ self.StoredVariables["CurrentIterationNumber"].store( len(self.StoredVariables["CostFunctionJ"]) )
self.StoredVariables["CostFunctionJb"].store( Jb )
self.StoredVariables["CostFunctionJo"].store( Jo )
self.StoredVariables["CostFunctionJ" ].store( J )
+ if self._toStore("IndexOfOptimum") or \
+ self._toStore("CurrentOptimum") or \
+ self._toStore("CostFunctionJAtCurrentOptimum") or \
+ self._toStore("CostFunctionJbAtCurrentOptimum") or \
+ self._toStore("CostFunctionJoAtCurrentOptimum") or \
+ self._toStore("SimulatedObservationAtCurrentOptimum"):
+ IndexMin = numpy.argmin( self.StoredVariables["CostFunctionJ"][nbPreviousSteps:] ) + nbPreviousSteps
+ if self._toStore("IndexOfOptimum"):
+ self.StoredVariables["IndexOfOptimum"].store( IndexMin )
+ if self._toStore("CurrentOptimum"):
+ self.StoredVariables["CurrentOptimum"].store( self.StoredVariables["CurrentState"][IndexMin] )
+ if self._toStore("SimulatedObservationAtCurrentOptimum"):
+ self.StoredVariables["SimulatedObservationAtCurrentOptimum"].store( self.StoredVariables["SimulatedObservationAtCurrentState"][IndexMin] )
+ if self._toStore("CostFunctionJbAtCurrentOptimum"):
+ self.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJb"][IndexMin] )
+ if self._toStore("CostFunctionJoAtCurrentOptimum"):
+ self.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJo"][IndexMin] )
+ if self._toStore("CostFunctionJAtCurrentOptimum"):
+ self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( self.StoredVariables["CostFunctionJ" ][IndexMin] )
return J
#
def GradientOfCostFunction(x):
_X = numpy.asmatrix(numpy.ravel( x )).T
_HX = Hm( _X )
_HX = numpy.asmatrix(numpy.ravel( _HX )).T
+ _Innovation = Y - _HX
Jb = 0.
- Jo = 0.5 * (Y - _HX).T * RI * (Y - _HX)
- J = float( Jb ) + float( Jo )
- if self._parameters["StoreInternalVariables"] or "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ Jo = float( 0.5 * _Innovation.T * RI * _Innovation )
+ J = Jb + Jo
+ if self._parameters["StoreInternalVariables"] or \
+ self._toStore("CurrentState"):
self.StoredVariables["CurrentState"].store( _X )
self.StoredVariables["CostFunctionJb"].store( Jb )
self.StoredVariables["CostFunctionJo"].store( Jo )
self.StoredVariables["CostFunctionJ" ].store( J )
#
- return numpy.ravel( RdemiI*(Y - _HX) )
+ return numpy.ravel( RdemiI*_Innovation )
#
def GradientOfCostFunctionLM(x):
_X = numpy.asmatrix(numpy.ravel( x )).T
GradJ = numpy.asmatrix( numpy.ravel( GradJb ) + numpy.ravel( GradJo ) ).T
return - RdemiI*HO["Tangent"].asMatrix( _X )
#
- # Point de démarrage de l'optimisation : Xini = Xb
+ # Point de démarrage de l'optimisation : Xini = Xb
# ------------------------------------
- if type(Xb) is type(numpy.matrix([])):
- Xini = Xb.A1.tolist()
- else:
- Xini = list(Xb)
+ Xini = self._parameters["InitializationPoint"]
#
# Minimisation de la fonctionnelle
# --------------------------------
nbPreviousSteps = self.StoredVariables["CostFunctionJ"].stepnumber()
#
if self._parameters["Minimizer"] == "LBFGSB":
- Minimum, J_optimal, Informations = scipy.optimize.fmin_l_bfgs_b(
+ # Minimum, J_optimal, Informations = scipy.optimize.fmin_l_bfgs_b(
+ if "0.19" <= scipy.version.version <= "1.1.0":
+ import lbfgsbhlt as optimiseur
+ else:
+ import scipy.optimize as optimiseur
+ Minimum, J_optimal, Informations = optimiseur.fmin_l_bfgs_b(
func = CostFunction,
x0 = Xini,
fprime = GradientOfCostFunction,
args = (),
- bounds = Bounds,
+ bounds = self._parameters["Bounds"],
maxfun = self._parameters["MaximumNumberOfSteps"]-1,
factr = self._parameters["CostDecrementTolerance"]*1.e14,
pgtol = self._parameters["ProjectedGradientTolerance"],
- iprint = self.__iprint,
+ iprint = self._parameters["optiprint"],
)
nfeval = Informations['funcalls']
rc = Informations['warnflag']
x0 = Xini,
fprime = GradientOfCostFunction,
args = (),
- bounds = Bounds,
+ bounds = self._parameters["Bounds"],
maxfun = self._parameters["MaximumNumberOfSteps"],
pgtol = self._parameters["ProjectedGradientTolerance"],
ftol = self._parameters["CostDecrementTolerance"],
- messages = self.__message,
+ messages = self._parameters["optmessages"],
)
elif self._parameters["Minimizer"] == "CG":
Minimum, fopt, nfeval, grad_calls, rc = scipy.optimize.fmin_cg(
args = (),
maxiter = self._parameters["MaximumNumberOfSteps"],
gtol = self._parameters["GradientNormTolerance"],
- disp = self.__disp,
+ disp = self._parameters["optdisp"],
full_output = True,
)
elif self._parameters["Minimizer"] == "NCG":
args = (),
maxiter = self._parameters["MaximumNumberOfSteps"],
avextol = self._parameters["CostDecrementTolerance"],
- disp = self.__disp,
+ disp = self._parameters["optdisp"],
full_output = True,
)
elif self._parameters["Minimizer"] == "BFGS":
args = (),
maxiter = self._parameters["MaximumNumberOfSteps"],
gtol = self._parameters["GradientNormTolerance"],
- disp = self.__disp,
+ disp = self._parameters["optdisp"],
full_output = True,
)
elif self._parameters["Minimizer"] == "LM":
#
# Correction pour pallier a un bug de TNC sur le retour du Minimum
# ----------------------------------------------------------------
- if self._parameters["StoreInternalVariables"] or "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._parameters["StoreInternalVariables"] or self._toStore("CurrentState"):
Minimum = self.StoredVariables["CurrentState"][IndexMin]
#
# Obtention de l'analyse
#
self.StoredVariables["Analysis"].store( Xa.A1 )
#
- if "OMA" in self._parameters["StoreSupplementaryCalculations"] or \
- "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"]:
- HXa = Hm(Xa)
+ if self._toStore("OMA") or \
+ self._toStore("SimulatedObservationAtOptimum"):
+ if self._toStore("SimulatedObservationAtCurrentState"):
+ HXa = self.StoredVariables["SimulatedObservationAtCurrentState"][IndexMin]
+ elif self._toStore("SimulatedObservationAtCurrentOptimum"):
+ HXa = self.StoredVariables["SimulatedObservationAtCurrentOptimum"][-1]
+ else:
+ HXa = Hm( Xa )
#
#
- # Calculs et/ou stockages supplémentaires
+ # Calculs et/ou stockages supplémentaires
# ---------------------------------------
- if "Innovation" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("Innovation") or \
+ self._toStore("OMB"):
+ d = Y - HXb
+ 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) - numpy.ravel(Xa) )
- if "OMA" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("OMA"):
self.StoredVariables["OMA"].store( numpy.ravel(Y) - numpy.ravel(HXa) )
- if "OMB" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("OMB"):
self.StoredVariables["OMB"].store( numpy.ravel(d) )
- if "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("SimulatedObservationAtBackground"):
+ self.StoredVariables["SimulatedObservationAtBackground"].store( numpy.ravel(HXb) )
+ if self._toStore("SimulatedObservationAtOptimum"):
self.StoredVariables["SimulatedObservationAtOptimum"].store( numpy.ravel(HXa) )
#
self._post_run(HO)
# ==============================================================================
if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
+ print('\n AUTODIAGNOSTIC\n')
