# -*- coding: utf-8 -*-
#
-# Copyright (C) 2008-2018 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
default = "BOBYQA",
typecast = str,
message = "Minimiseur utilisé",
- listval = ["BOBYQA", "COBYLA", "NEWUOA", "POWELL", "SIMPLEX", "SUBPLEX"],
+ listval = [
+ "BOBYQA",
+ "COBYLA",
+ "NEWUOA",
+ "POWELL",
+ "SIMPLEX",
+ "SUBPLEX",
+ ],
)
self.defineRequiredParameter(
name = "MaximumNumberOfSteps",
typecast = tuple,
message = "Liste de calculs supplémentaires à stocker et/ou effectuer",
listval = [
+ "Analysis",
"BMA",
"CostFunctionJ",
"CostFunctionJb",
"CostFunctionJAtCurrentOptimum",
"CostFunctionJbAtCurrentOptimum",
"CostFunctionJoAtCurrentOptimum",
+ "CurrentIterationNumber",
"CurrentOptimum",
"CurrentState",
"IndexOfOptimum",
self.requireInputArguments(
mandatory= ("Xb", "Y", "HO", "R", "B" ),
)
+ self.setAttributes(tags=(
+ "Optimization",
+ "NonLinear",
+ "MetaHeuristic",
+ ))
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, Xb, Y, R, B, Q)
+ self._pre_run(Parameters, Xb, Y, U, HO, EM, CM, R, B, Q)
#
if not PlatformInfo.has_nlopt and not self._parameters["Minimizer"] in ["COBYLA", "POWELL", "SIMPLEX"]:
logging.warning("%s Minimization by SIMPLEX is forced because %s is unavailable (COBYLA, POWELL are also available)"%(self._name,self._parameters["Minimizer"]))
_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 )
#
if QualityMeasure in ["AugmentedWeightedLeastSquares","AWLS","DA"]:
#
J = float( Jb ) + float( 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 \
- "CostFunctionJbAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
- "CostFunctionJoAtCurrentOptimum" 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("CostFunctionJAtCurrentOptimum"):
self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( self.StoredVariables["CostFunctionJ" ][IndexMin] )
- if "CostFunctionJbAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("CostFunctionJbAtCurrentOptimum"):
self.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJb"][IndexMin] )
- if "CostFunctionJoAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("CostFunctionJoAtCurrentOptimum"):
self.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJo"][IndexMin] )
return J
#
#
# Calculs et/ou stockages supplémentaires
# ---------------------------------------
- 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)
- 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 "OMB" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("OMB"):
self.StoredVariables["OMB"].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 "SimulatedObservationAtBackground" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("SimulatedObservationAtBackground"):
self.StoredVariables["SimulatedObservationAtBackground"].store( numpy.ravel(Hm(Xb)) )
- if "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("SimulatedObservationAtOptimum"):
self.StoredVariables["SimulatedObservationAtOptimum"].store( numpy.ravel(HXa) )
#
self._post_run()
# ==============================================================================
if __name__ == "__main__":
- print('\n AUTODIAGNOSTIC \n')
+ print('\n AUTODIAGNOSTIC\n')