-#-*-coding:iso-8859-1-*-
+# -*- coding: utf-8 -*-
#
-# Copyright (C) 2008-2017 EDF R&D
+# Copyright (C) 2008-2018 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
name = "Minimizer",
default = "BOBYQA",
typecast = str,
- message = "Minimiseur utilisé",
+ message = "Minimiseur utilisé",
listval = ["BOBYQA", "COBYLA", "NEWUOA", "POWELL", "SIMPLEX", "SUBPLEX"],
)
self.defineRequiredParameter(
name = "MaximumNumberOfFunctionEvaluations",
default = 15000,
typecast = int,
- message = "Nombre maximal d'évaluations de la fonction",
+ message = "Nombre maximal d'évaluations de la fonction",
minval = -1,
)
self.defineRequiredParameter(
name = "StateVariationTolerance",
default = 1.e-4,
typecast = float,
- message = "Variation relative maximale de l'état lors de l'arrêt",
+ message = "Variation relative maximale de l'état lors de l'arrêt",
)
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 cout lors de l'arrêt",
)
self.defineRequiredParameter(
name = "QualityCriterion",
default = "AugmentedWeightedLeastSquares",
typecast = str,
- message = "Critère de qualité utilisé",
+ message = "Critère de qualité utilisé",
listval = ["AugmentedWeightedLeastSquares","AWLS","DA",
"WeightedLeastSquares","WLS",
"LeastSquares","LS","L2",
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 = ["CurrentState", "CostFunctionJ", "CostFunctionJb", "CostFunctionJo", "CostFunctionJAtCurrentOptimum", "CurrentOptimum", "IndexOfOptimum", "InnovationAtCurrentState", "BMA", "OMA", "OMB", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentOptimum", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum"]
+ message = "Liste de calculs supplémentaires à stocker et/ou effectuer",
+ listval = [
+ "CurrentState",
+ "CostFunctionJ",
+ "CostFunctionJb",
+ "CostFunctionJo",
+ "CostFunctionJAtCurrentOptimum",
+ "CostFunctionJbAtCurrentOptimum",
+ "CostFunctionJoAtCurrentOptimum",
+ "CurrentOptimum",
+ "IndexOfOptimum",
+ "InnovationAtCurrentState",
+ "BMA",
+ "OMA",
+ "OMB",
+ "SimulatedObservationAtBackground",
+ "SimulatedObservationAtCurrentOptimum",
+ "SimulatedObservationAtCurrentState",
+ "SimulatedObservationAtOptimum",
+ ]
)
self.defineRequiredParameter( # Pas de type
name = "Bounds",
message = "Liste des valeurs de bornes",
)
+ self.requireInputArguments(
+ mandatory= ("Xb", "Y", "HO", "R", "B" ),
+ )
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.__disp = 1
- else:
- self.__disp = 0
- #
- # Paramètres de pilotage
- # ----------------------
- self.setParameters(Parameters)
+ self._pre_run(Parameters, Xb, Y, 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"]))
self._parameters["Minimizer"] = "SIMPLEX"
- 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
#
- # Opérateurs
+ # Opérateurs
# ----------
Hm = HO["Direct"].appliedTo
#
- # Précalcul des inversions de B et R
+ # Précalcul des inversions de B et R
# ----------------------------------
BI = B.getI()
RI = R.getI()
#
- # Définition de la fonction-coût
+ # Définition de la fonction-coût
# ------------------------------
def CostFunction(x, QualityMeasure="AugmentedWeightedLeastSquares"):
_X = numpy.asmatrix(numpy.ravel( x )).T
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"]:
IndexMin = numpy.argmin( self.StoredVariables["CostFunctionJ"][nbPreviousSteps:] ) + nbPreviousSteps
if "IndexOfOptimum" in self._parameters["StoreSupplementaryCalculations"]:
if "SimulatedObservationAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
self.StoredVariables["SimulatedObservationAtCurrentOptimum"].store( self.StoredVariables["SimulatedObservationAtCurrentState"][IndexMin] )
if "CostFunctionJAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( self.StoredVariables["CostFunctionJ" ][IndexMin] )
+ if "CostFunctionJbAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
self.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJb"][IndexMin] )
+ if "CostFunctionJoAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
self.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJo"][IndexMin] )
- self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( self.StoredVariables["CostFunctionJ" ][IndexMin] )
return J
#
- # Point de démarrage de l'optimisation : Xini = Xb
+ # Point de démarrage de l'optimisation : Xini = Xb
# ------------------------------------
Xini = numpy.ravel(Xb)
if len(Xini) < 2 and self._parameters["Minimizer"] == "NEWUOA":
xtol = self._parameters["StateVariationTolerance"],
ftol = self._parameters["CostDecrementTolerance"],
full_output = True,
- disp = self.__disp,
+ disp = self._parameters["optdisp"],
)
elif self._parameters["Minimizer"] == "COBYLA" and not PlatformInfo.has_nlopt:
def make_constraints(bounds):
upper = lambda x: b - x[i]
constraints = constraints + [lower] + [upper]
return constraints
- if Bounds is None:
+ if self._parameters["Bounds"] is None:
raise ValueError("Bounds have to be given for all axes as a list of lower/upper pairs!")
Minimum = scipy.optimize.fmin_cobyla(
func = CostFunction,
x0 = Xini,
- cons = make_constraints( Bounds ),
+ cons = make_constraints( self._parameters["Bounds"] ),
args = (self._parameters["QualityCriterion"],),
consargs = (), # To avoid extra-args
maxfun = self._parameters["MaximumNumberOfFunctionEvaluations"],
rhobeg = 1.0,
rhoend = self._parameters["StateVariationTolerance"],
catol = 2.*self._parameters["StateVariationTolerance"],
- disp = self.__disp,
+ disp = self._parameters["optdisp"],
)
elif self._parameters["Minimizer"] == "COBYLA" and PlatformInfo.has_nlopt:
import nlopt
# DFO, so no gradient
return CostFunction(_Xx, self._parameters["QualityCriterion"])
opt.set_min_objective(_f)
- if Bounds is not None:
- lub = numpy.array(Bounds).reshape((Xini.size,2))
- lb = lub[:,0]
- ub = lub[:,1]
- if self.__disp:
- print "%s: upper bounds %s"%(opt.get_algorithm_name(),ub)
- print "%s: lower bounds %s"%(opt.get_algorithm_name(),lb)
+ if self._parameters["Bounds"] is not None:
+ lub = numpy.array(self._parameters["Bounds"],dtype=float).reshape((Xini.size,2))
+ lb = lub[:,0] ; lb[numpy.isnan(lb)] = -float('inf')
+ ub = lub[:,1] ; ub[numpy.isnan(ub)] = +float('inf')
+ if self._parameters["optdisp"]:
+ print("%s: upper bounds %s"%(opt.get_algorithm_name(),ub))
+ print("%s: lower bounds %s"%(opt.get_algorithm_name(),lb))
opt.set_upper_bounds(ub)
opt.set_lower_bounds(lb)
opt.set_ftol_rel(self._parameters["CostDecrementTolerance"])
opt.set_xtol_rel(2.*self._parameters["StateVariationTolerance"])
opt.set_maxeval(self._parameters["MaximumNumberOfFunctionEvaluations"])
Minimum = opt.optimize( Xini )
- if self.__disp:
- print "%s: optimal state: %s"%(opt.get_algorithm_name(),Minimum)
- print "%s: minimum of J: %s"%(opt.get_algorithm_name(),opt.last_optimum_value())
- print "%s: return code: %i"%(opt.get_algorithm_name(),opt.last_optimize_result())
+ if self._parameters["optdisp"]:
+ print("%s: optimal state: %s"%(opt.get_algorithm_name(),Minimum))
+ print("%s: minimum of J: %s"%(opt.get_algorithm_name(),opt.last_optimum_value()))
+ print("%s: return code: %i"%(opt.get_algorithm_name(),opt.last_optimize_result()))
elif self._parameters["Minimizer"] == "SIMPLEX" and not PlatformInfo.has_nlopt:
Minimum, J_optimal, niter, nfeval, rc = scipy.optimize.fmin(
func = CostFunction,
xtol = self._parameters["StateVariationTolerance"],
ftol = self._parameters["CostDecrementTolerance"],
full_output = True,
- disp = self.__disp,
+ disp = self._parameters["optdisp"],
)
elif self._parameters["Minimizer"] == "SIMPLEX" and PlatformInfo.has_nlopt:
import nlopt
# DFO, so no gradient
return CostFunction(_Xx, self._parameters["QualityCriterion"])
opt.set_min_objective(_f)
- if Bounds is not None:
- lub = numpy.array(Bounds).reshape((Xini.size,2))
- lb = lub[:,0]
- ub = lub[:,1]
- if self.__disp:
- print "%s: upper bounds %s"%(opt.get_algorithm_name(),ub)
- print "%s: lower bounds %s"%(opt.get_algorithm_name(),lb)
+ if self._parameters["Bounds"] is not None:
+ lub = numpy.array(self._parameters["Bounds"],dtype=float).reshape((Xini.size,2))
+ lb = lub[:,0] ; lb[numpy.isnan(lb)] = -float('inf')
+ ub = lub[:,1] ; ub[numpy.isnan(ub)] = +float('inf')
+ if self._parameters["optdisp"]:
+ print("%s: upper bounds %s"%(opt.get_algorithm_name(),ub))
+ print("%s: lower bounds %s"%(opt.get_algorithm_name(),lb))
opt.set_upper_bounds(ub)
opt.set_lower_bounds(lb)
opt.set_ftol_rel(self._parameters["CostDecrementTolerance"])
opt.set_xtol_rel(2.*self._parameters["StateVariationTolerance"])
opt.set_maxeval(self._parameters["MaximumNumberOfFunctionEvaluations"])
Minimum = opt.optimize( Xini )
- if self.__disp:
- print "%s: optimal state: %s"%(opt.get_algorithm_name(),Minimum)
- print "%s: minimum of J: %s"%(opt.get_algorithm_name(),opt.last_optimum_value())
- print "%s: return code: %i"%(opt.get_algorithm_name(),opt.last_optimize_result())
+ if self._parameters["optdisp"]:
+ print("%s: optimal state: %s"%(opt.get_algorithm_name(),Minimum))
+ print("%s: minimum of J: %s"%(opt.get_algorithm_name(),opt.last_optimum_value()))
+ print("%s: return code: %i"%(opt.get_algorithm_name(),opt.last_optimize_result()))
elif self._parameters["Minimizer"] == "BOBYQA" and PlatformInfo.has_nlopt:
import nlopt
opt = nlopt.opt(nlopt.LN_BOBYQA, Xini.size)
# DFO, so no gradient
return CostFunction(_Xx, self._parameters["QualityCriterion"])
opt.set_min_objective(_f)
- if Bounds is not None:
- lub = numpy.array(Bounds).reshape((Xini.size,2))
- lb = lub[:,0]
- ub = lub[:,1]
- if self.__disp:
- print "%s: upper bounds %s"%(opt.get_algorithm_name(),ub)
- print "%s: lower bounds %s"%(opt.get_algorithm_name(),lb)
+ if self._parameters["Bounds"] is not None:
+ lub = numpy.array(self._parameters["Bounds"],dtype=float).reshape((Xini.size,2))
+ lb = lub[:,0] ; lb[numpy.isnan(lb)] = -float('inf')
+ ub = lub[:,1] ; ub[numpy.isnan(ub)] = +float('inf')
+ if self._parameters["optdisp"]:
+ print("%s: upper bounds %s"%(opt.get_algorithm_name(),ub))
+ print("%s: lower bounds %s"%(opt.get_algorithm_name(),lb))
opt.set_upper_bounds(ub)
opt.set_lower_bounds(lb)
opt.set_ftol_rel(self._parameters["CostDecrementTolerance"])
opt.set_xtol_rel(2.*self._parameters["StateVariationTolerance"])
opt.set_maxeval(self._parameters["MaximumNumberOfFunctionEvaluations"])
Minimum = opt.optimize( Xini )
- if self.__disp:
- print "%s: optimal state: %s"%(opt.get_algorithm_name(),Minimum)
- print "%s: minimum of J: %s"%(opt.get_algorithm_name(),opt.last_optimum_value())
- print "%s: return code: %i"%(opt.get_algorithm_name(),opt.last_optimize_result())
+ if self._parameters["optdisp"]:
+ print("%s: optimal state: %s"%(opt.get_algorithm_name(),Minimum))
+ print("%s: minimum of J: %s"%(opt.get_algorithm_name(),opt.last_optimum_value()))
+ print("%s: return code: %i"%(opt.get_algorithm_name(),opt.last_optimize_result()))
elif self._parameters["Minimizer"] == "NEWUOA" and PlatformInfo.has_nlopt:
import nlopt
opt = nlopt.opt(nlopt.LN_NEWUOA, Xini.size)
# DFO, so no gradient
return CostFunction(_Xx, self._parameters["QualityCriterion"])
opt.set_min_objective(_f)
- if Bounds is not None:
- lub = numpy.array(Bounds).reshape((Xini.size,2))
- lb = lub[:,0]
- ub = lub[:,1]
- if self.__disp:
- print "%s: upper bounds %s"%(opt.get_algorithm_name(),ub)
- print "%s: lower bounds %s"%(opt.get_algorithm_name(),lb)
+ if self._parameters["Bounds"] is not None:
+ lub = numpy.array(self._parameters["Bounds"],dtype=float).reshape((Xini.size,2))
+ lb = lub[:,0] ; lb[numpy.isnan(lb)] = -float('inf')
+ ub = lub[:,1] ; ub[numpy.isnan(ub)] = +float('inf')
+ if self._parameters["optdisp"]:
+ print("%s: upper bounds %s"%(opt.get_algorithm_name(),ub))
+ print("%s: lower bounds %s"%(opt.get_algorithm_name(),lb))
opt.set_upper_bounds(ub)
opt.set_lower_bounds(lb)
opt.set_ftol_rel(self._parameters["CostDecrementTolerance"])
opt.set_xtol_rel(2.*self._parameters["StateVariationTolerance"])
opt.set_maxeval(self._parameters["MaximumNumberOfFunctionEvaluations"])
Minimum = opt.optimize( Xini )
- if self.__disp:
- print "%s: optimal state: %s"%(opt.get_algorithm_name(),Minimum)
- print "%s: minimum of J: %s"%(opt.get_algorithm_name(),opt.last_optimum_value())
- print "%s: return code: %i"%(opt.get_algorithm_name(),opt.last_optimize_result())
+ if self._parameters["optdisp"]:
+ print("%s: optimal state: %s"%(opt.get_algorithm_name(),Minimum))
+ print("%s: minimum of J: %s"%(opt.get_algorithm_name(),opt.last_optimum_value()))
+ print("%s: return code: %i"%(opt.get_algorithm_name(),opt.last_optimize_result()))
elif self._parameters["Minimizer"] == "SUBPLEX" and PlatformInfo.has_nlopt:
import nlopt
opt = nlopt.opt(nlopt.LN_SBPLX, Xini.size)
# DFO, so no gradient
return CostFunction(_Xx, self._parameters["QualityCriterion"])
opt.set_min_objective(_f)
- if Bounds is not None:
- lub = numpy.array(Bounds).reshape((Xini.size,2))
- lb = lub[:,0]
- ub = lub[:,1]
- if self.__disp:
- print "%s: upper bounds %s"%(opt.get_algorithm_name(),ub)
- print "%s: lower bounds %s"%(opt.get_algorithm_name(),lb)
+ if self._parameters["Bounds"] is not None:
+ lub = numpy.array(self._parameters["Bounds"],dtype=float).reshape((Xini.size,2))
+ lb = lub[:,0] ; lb[numpy.isnan(lb)] = -float('inf')
+ ub = lub[:,1] ; ub[numpy.isnan(ub)] = +float('inf')
+ if self._parameters["optdisp"]:
+ print("%s: upper bounds %s"%(opt.get_algorithm_name(),ub))
+ print("%s: lower bounds %s"%(opt.get_algorithm_name(),lb))
opt.set_upper_bounds(ub)
opt.set_lower_bounds(lb)
opt.set_ftol_rel(self._parameters["CostDecrementTolerance"])
opt.set_xtol_rel(2.*self._parameters["StateVariationTolerance"])
opt.set_maxeval(self._parameters["MaximumNumberOfFunctionEvaluations"])
Minimum = opt.optimize( Xini )
- if self.__disp:
- print "%s: optimal state: %s"%(opt.get_algorithm_name(),Minimum)
- print "%s: minimum of J: %s"%(opt.get_algorithm_name(),opt.last_optimum_value())
- print "%s: return code: %i"%(opt.get_algorithm_name(),opt.last_optimize_result())
+ if self._parameters["optdisp"]:
+ print("%s: optimal state: %s"%(opt.get_algorithm_name(),Minimum))
+ print("%s: minimum of J: %s"%(opt.get_algorithm_name(),opt.last_optimum_value()))
+ print("%s: return code: %i"%(opt.get_algorithm_name(),opt.last_optimize_result()))
else:
raise ValueError("Error in Minimizer name: %s"%self._parameters["Minimizer"])
#
# ==============================================================================
if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
+ print('\n AUTODIAGNOSTIC \n')
