# -*- coding: utf-8 -*-
#
-# Copyright (C) 2008-2017 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
import logging
from daCore import BasicObjects
-import numpy, scipy.optimize
+import numpy, scipy.optimize, scipy.version
# ==============================================================================
class ElementaryAlgorithm(BasicObjects.Algorithm):
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.e-05,
typecast = float,
message = "Maximum des composantes du gradient lors de l'arrêt",
+ minval = 0.,
)
self.defineRequiredParameter(
name = "StoreInternalVariables",
default = [],
typecast = tuple,
message = "Liste de calculs supplémentaires à stocker et/ou effectuer",
- listval = ["BMA", "OMA", "OMB", "CostFunctionJ", "CostFunctionJb", "CostFunctionJo", "CurrentState", "CurrentOptimum", "IndexOfOptimum", "Innovation", "InnovationAtCurrentState", "CostFunctionJAtCurrentOptimum", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum", "SimulatedObservationAtCurrentOptimum"]
+ 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(Parameters)
- #
- # Correction pour pallier a un bug de TNC sur le retour du Minimum
- if "Minimizer" in self._parameters and self._parameters["Minimizer"] == "TNC":
- self.setParameterValue("StoreInternalVariables",True)
+ self._pre_run(Parameters, Xb, Y, U, HO, EM, CM, R, B, Q)
#
# Opérateurs
# ----------
# Utilisation éventuelle d'un vecteur H(Xb) précalculé
# ----------------------------------------------------
if HO["AppliedInX"] is not None and "HXb" in HO["AppliedInX"]:
- HXb = Hm( Xb, HO["AppliedInX"]["HXb"])
+ HXb = Hm( Xb, HO["AppliedInX"]["HXb"] )
else:
HXb = Hm( Xb )
HXb = numpy.asmatrix(numpy.ravel( HXb )).T
def CostFunction(x):
_X = numpy.asmatrix(numpy.ravel( x )).T
if self._parameters["StoreInternalVariables"] or \
- "CurrentState" in self._parameters["StoreSupplementaryCalculations"] or \
- "CurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ self._toStore("CurrentState") or \
+ self._toStore("CurrentOptimum"):
self.StoredVariables["CurrentState"].store( _X )
_HX = Hm( _X )
_HX = numpy.asmatrix(numpy.ravel( _HX )).T
_Innovation = Y - _HX
- if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"] or \
- "SimulatedObservationAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("SimulatedObservationAtCurrentState") or \
+ self._toStore("SimulatedObservationAtCurrentOptimum"):
self.StoredVariables["SimulatedObservationAtCurrentState"].store( _HX )
- if "InnovationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ 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 "IndexOfOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
- "CurrentOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
- "CostFunctionJAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
- "SimulatedObservationAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ 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 "IndexOfOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("IndexOfOptimum"):
self.StoredVariables["IndexOfOptimum"].store( IndexMin )
- if "CurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("CurrentOptimum"):
self.StoredVariables["CurrentOptimum"].store( self.StoredVariables["CurrentState"][IndexMin] )
- if "SimulatedObservationAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("SimulatedObservationAtCurrentOptimum"):
self.StoredVariables["SimulatedObservationAtCurrentOptimum"].store( self.StoredVariables["SimulatedObservationAtCurrentState"][IndexMin] )
- if "CostFunctionJAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ 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
#
Jo = float( 0.5 * _Innovation.T * RI * _Innovation )
J = Jb + Jo
if self._parameters["StoreInternalVariables"] or \
- "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ self._toStore("CurrentState"):
self.StoredVariables["CurrentState"].store( _X )
self.StoredVariables["CostFunctionJb"].store( Jb )
self.StoredVariables["CostFunctionJo"].store( Jo )
#
# Point de démarrage de l'optimisation : Xini = Xb
# ------------------------------------
- Xini = numpy.ravel(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,
#
# 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"]:
- if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("OMA") or \
+ self._toStore("SimulatedObservationAtOptimum"):
+ if self._toStore("SimulatedObservationAtCurrentState"):
HXa = self.StoredVariables["SimulatedObservationAtCurrentState"][IndexMin]
- elif "SimulatedObservationAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ elif self._toStore("SimulatedObservationAtCurrentOptimum"):
HXa = self.StoredVariables["SimulatedObservationAtCurrentOptimum"][-1]
else:
- HXa = Hm(Xa)
+ HXa = Hm( Xa )
#
#
# Calculs et/ou stockages supplémentaires
# ---------------------------------------
- if "Innovation" in self._parameters["StoreSupplementaryCalculations"] or \
- "OMB" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("Innovation") or \
+ self._toStore("OMB"):
d = Y - HXb
- if "Innovation" in self._parameters["StoreSupplementaryCalculations"]:
+ 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 "SimulatedObservationAtBackground" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("SimulatedObservationAtBackground"):
self.StoredVariables["SimulatedObservationAtBackground"].store( numpy.ravel(HXb) )
- if "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ 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')
