-#-*-coding:iso-8859-1-*-
+# -*- coding: utf-8 -*-
#
-# Copyright (C) 2008-2017 EDF R&D
+# Copyright (C) 2008-2024 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
#
# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
-import logging
+import numpy, logging, scipy.optimize
from daCore import BasicObjects, PlatformInfo
-import numpy, scipy.optimize
+from daCore.NumericObjects import ApplyBounds, ForceNumericBounds
+from daCore.PlatformInfo import vfloat
+lpi = PlatformInfo.PlatformInfo()
# ==============================================================================
class ElementaryAlgorithm(BasicObjects.Algorithm):
name = "Minimizer",
default = "BOBYQA",
typecast = str,
- message = "Minimiseur utilisé",
- listval = ["BOBYQA", "COBYLA", "NEWUOA", "POWELL", "SIMPLEX", "SUBPLEX"],
- )
+ message = "Minimiseur utilisé",
+ listval = [
+ "BOBYQA",
+ "COBYLA",
+ "NEWUOA",
+ "POWELL",
+ "SIMPLEX",
+ "SUBPLEX",
+ ],
+ )
self.defineRequiredParameter(
- name = "MaximumNumberOfSteps",
+ name = "MaximumNumberOfIterations",
default = 15000,
typecast = int,
message = "Nombre maximal de pas d'optimisation",
minval = -1,
- )
+ oldname = "MaximumNumberOfSteps",
+ )
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",
- listval = ["AugmentedWeightedLeastSquares","AWLS","DA",
- "WeightedLeastSquares","WLS",
- "LeastSquares","LS","L2",
- "AbsoluteValue","L1",
- "MaximumError","ME"],
- )
+ message = "Critère de qualité utilisé",
+ listval = [
+ "AugmentedWeightedLeastSquares", "AWLS", "DA",
+ "WeightedLeastSquares", "WLS",
+ "LeastSquares", "LS", "L2",
+ "AbsoluteValue", "L1",
+ "MaximumError", "ME", "Linf",
+ ],
+ )
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 = ["CurrentState", "CostFunctionJ", "CostFunctionJb", "CostFunctionJo", "CostFunctionJAtCurrentOptimum", "CurrentOptimum", "IndexOfOptimum", "InnovationAtCurrentState", "BMA", "OMA", "OMB", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentOptimum", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum"]
- )
- self.defineRequiredParameter( # Pas de type
+ message = "Liste de calculs supplémentaires à stocker et/ou effectuer",
+ listval = [
+ "Analysis",
+ "BMA",
+ "CostFunctionJ",
+ "CostFunctionJb",
+ "CostFunctionJo",
+ "CostFunctionJAtCurrentOptimum",
+ "CostFunctionJbAtCurrentOptimum",
+ "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.requireInputArguments(
+ mandatory= ("Xb", "Y", "HO", "R", "B"),
+ )
+ self.setAttributes(
+ tags=(
+ "Optimization",
+ "NonLinear",
+ "MetaHeuristic",
+ ),
+ features=(
+ "NonLocalOptimization",
+ "DerivativeFree",
+ "ParallelFree",
+ "ConvergenceOnBoth",
+ ),
+ )
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)
+ 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"]:
+ if not lpi.has_nlopt and not self._parameters["Minimizer"] in ["COBYLA", "POWELL", "SIMPLEX"]:
+ logging.warning(
+ "%s Minimization by SIMPLEX is forced because %s "%(self._name, self._parameters["Minimizer"]) + \
+ "is unavailable (COBYLA, POWELL are also available)")
self._parameters["Minimizer"] = "SIMPLEX"
#
- # Opérateurs
- # ----------
Hm = HO["Direct"].appliedTo
#
- # Précalcul des inversions de B et R
- # ----------------------------------
BI = B.getI()
RI = R.getI()
- #
- # Définition de la fonction-coût
- # ------------------------------
+
def CostFunction(x, QualityMeasure="AugmentedWeightedLeastSquares"):
- _X = numpy.asmatrix(numpy.ravel( x )).T
- self.StoredVariables["CurrentState"].store( _X )
- _HX = Hm( _X )
- _HX = numpy.asmatrix(numpy.ravel( _HX )).T
+ _X = numpy.ravel( x ).reshape((-1, 1))
+ _HX = numpy.ravel( Hm( _X ) ).reshape((-1, 1))
_Innovation = Y - _HX
- if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"] or \
- "SimulatedObservationAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ self.StoredVariables["CurrentState"].store( _X )
+ 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"]:
+ if QualityMeasure in ["AugmentedWeightedLeastSquares", "AWLS", "DA"]:
if BI is None or RI is None:
- raise ValueError("Background and Observation error covariance matrix has to be properly defined!")
- Jb = 0.5 * (_X - Xb).T * BI * (_X - Xb)
- Jo = 0.5 * (_Innovation).T * RI * (_Innovation)
- elif QualityMeasure in ["WeightedLeastSquares","WLS"]:
+ raise ValueError("Background and Observation error covariance matrices has to be properly defined!")
+ Jb = vfloat(0.5 * (_X - Xb).T @ (BI @ (_X - Xb)))
+ Jo = vfloat(0.5 * _Innovation.T @ (RI @ _Innovation))
+ elif QualityMeasure in ["WeightedLeastSquares", "WLS"]:
if RI is None:
raise ValueError("Observation error covariance matrix has to be properly defined!")
Jb = 0.
- Jo = 0.5 * (_Innovation).T * RI * (_Innovation)
- elif QualityMeasure in ["LeastSquares","LS","L2"]:
+ Jo = vfloat(0.5 * _Innovation.T @ (RI @ _Innovation))
+ elif QualityMeasure in ["LeastSquares", "LS", "L2"]:
Jb = 0.
- Jo = 0.5 * (_Innovation).T * (_Innovation)
- elif QualityMeasure in ["AbsoluteValue","L1"]:
+ Jo = vfloat(0.5 * _Innovation.T @ _Innovation)
+ elif QualityMeasure in ["AbsoluteValue", "L1"]:
Jb = 0.
- Jo = numpy.sum( numpy.abs(_Innovation) )
- elif QualityMeasure in ["MaximumError","ME"]:
+ Jo = vfloat(numpy.sum( numpy.abs(_Innovation) ))
+ elif QualityMeasure in ["MaximumError", "ME", "Linf"]:
Jb = 0.
- Jo = numpy.max( numpy.abs(_Innovation) )
+ Jo = vfloat(numpy.max( numpy.abs(_Innovation) ))
#
- J = float( Jb ) + float( Jo )
+ 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"]:
- self.StoredVariables["CurrentOptimum"].store( self.StoredVariables["CurrentState"][IndexMin] )
- if "SimulatedObservationAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
- self.StoredVariables["SimulatedObservationAtCurrentOptimum"].store( self.StoredVariables["SimulatedObservationAtCurrentState"][IndexMin] )
- if "CostFunctionJAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
- self.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJb"][IndexMin] )
- self.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJo"][IndexMin] )
- self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( self.StoredVariables["CostFunctionJ" ][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("CostFunctionJAtCurrentOptimum"):
+ self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store(
+ self.StoredVariables["CostFunctionJ" ][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] )
return J
#
- # Point de démarrage de l'optimisation : Xini = Xb
- # ------------------------------------
Xini = numpy.ravel(Xb)
if len(Xini) < 2 and self._parameters["Minimizer"] == "NEWUOA":
- raise ValueError("The minimizer %s can not be used when the optimisation state dimension is 1. Please choose another minimizer."%self._parameters["Minimizer"])
+ raise ValueError(
+ "The minimizer %s "%self._parameters["Minimizer"] + \
+ "can not be used when the optimisation state dimension " + \
+ "is 1. Please choose another minimizer.")
#
# Minimisation de la fonctionnelle
# --------------------------------
func = CostFunction,
x0 = Xini,
args = (self._parameters["QualityCriterion"],),
- maxiter = self._parameters["MaximumNumberOfSteps"]-1,
+ maxiter = self._parameters["MaximumNumberOfIterations"] - 1,
maxfun = self._parameters["MaximumNumberOfFunctionEvaluations"],
xtol = self._parameters["StateVariationTolerance"],
ftol = self._parameters["CostDecrementTolerance"],
full_output = True,
disp = self._parameters["optdisp"],
- )
- elif self._parameters["Minimizer"] == "COBYLA" and not PlatformInfo.has_nlopt:
+ )
+ elif self._parameters["Minimizer"] == "COBYLA" and not lpi.has_nlopt:
def make_constraints(bounds):
constraints = []
- for (i,(a,b)) in enumerate(bounds):
- lower = lambda x: x[i] - a
- upper = lambda x: b - x[i]
+ for (i, (a, b)) in enumerate(bounds):
+ lower = lambda x: x[i] - a # noqa: E731
+ upper = lambda x: b - x[i] # noqa: E731
constraints = constraints + [lower] + [upper]
return constraints
if self._parameters["Bounds"] is None:
raise ValueError("Bounds have to be given for all axes as a list of lower/upper pairs!")
+ self._parameters["Bounds"] = ForceNumericBounds( self._parameters["Bounds"] )
+ Xini = ApplyBounds( Xini, self._parameters["Bounds"] )
Minimum = scipy.optimize.fmin_cobyla(
func = CostFunction,
x0 = Xini,
cons = make_constraints( self._parameters["Bounds"] ),
args = (self._parameters["QualityCriterion"],),
- consargs = (), # To avoid extra-args
+ consargs = (), # To avoid extra-args
maxfun = self._parameters["MaximumNumberOfFunctionEvaluations"],
rhobeg = 1.0,
rhoend = self._parameters["StateVariationTolerance"],
- catol = 2.*self._parameters["StateVariationTolerance"],
+ catol = 2. * self._parameters["StateVariationTolerance"],
disp = self._parameters["optdisp"],
- )
- elif self._parameters["Minimizer"] == "COBYLA" and PlatformInfo.has_nlopt:
+ )
+ elif self._parameters["Minimizer"] == "COBYLA" and lpi.has_nlopt:
import nlopt
opt = nlopt.opt(nlopt.LN_COBYLA, Xini.size)
+
def _f(_Xx, Grad):
# DFO, so no gradient
return CostFunction(_Xx, self._parameters["QualityCriterion"])
opt.set_min_objective(_f)
+ self._parameters["Bounds"] = ForceNumericBounds( self._parameters["Bounds"] )
+ Xini = ApplyBounds( Xini, self._parameters["Bounds"] )
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')
+ lub = numpy.array(self._parameters["Bounds"], dtype=float).reshape((Xini.size, 2))
+ lb = lub[:, 0]; lb[numpy.isnan(lb)] = -float('inf') # noqa: E702
+ ub = lub[:, 1]; ub[numpy.isnan(ub)] = +float('inf') # noqa: E702
if self._parameters["optdisp"]:
- print("%s: upper bounds %s"%(opt.get_algorithm_name(),ub))
- print("%s: lower bounds %s"%(opt.get_algorithm_name(),lb))
+ 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_xtol_rel(2. * self._parameters["StateVariationTolerance"])
opt.set_maxeval(self._parameters["MaximumNumberOfFunctionEvaluations"])
Minimum = opt.optimize( Xini )
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:
+ 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 lpi.has_nlopt:
Minimum, J_optimal, niter, nfeval, rc = scipy.optimize.fmin(
func = CostFunction,
x0 = Xini,
args = (self._parameters["QualityCriterion"],),
- maxiter = self._parameters["MaximumNumberOfSteps"]-1,
+ maxiter = self._parameters["MaximumNumberOfIterations"] - 1,
maxfun = self._parameters["MaximumNumberOfFunctionEvaluations"],
xtol = self._parameters["StateVariationTolerance"],
ftol = self._parameters["CostDecrementTolerance"],
full_output = True,
disp = self._parameters["optdisp"],
- )
- elif self._parameters["Minimizer"] == "SIMPLEX" and PlatformInfo.has_nlopt:
+ )
+ elif self._parameters["Minimizer"] == "SIMPLEX" and lpi.has_nlopt:
import nlopt
opt = nlopt.opt(nlopt.LN_NELDERMEAD, Xini.size)
+
def _f(_Xx, Grad):
# DFO, so no gradient
return CostFunction(_Xx, self._parameters["QualityCriterion"])
opt.set_min_objective(_f)
+ self._parameters["Bounds"] = ForceNumericBounds( self._parameters["Bounds"] )
+ Xini = ApplyBounds( Xini, self._parameters["Bounds"] )
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')
+ lub = numpy.array(self._parameters["Bounds"], dtype=float).reshape((Xini.size, 2))
+ lb = lub[:, 0]; lb[numpy.isnan(lb)] = -float('inf') # noqa: E702
+ ub = lub[:, 1]; ub[numpy.isnan(ub)] = +float('inf') # noqa: E702
if self._parameters["optdisp"]:
- print("%s: upper bounds %s"%(opt.get_algorithm_name(),ub))
- print("%s: lower bounds %s"%(opt.get_algorithm_name(),lb))
+ 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_xtol_rel(2. * self._parameters["StateVariationTolerance"])
opt.set_maxeval(self._parameters["MaximumNumberOfFunctionEvaluations"])
Minimum = opt.optimize( Xini )
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:
+ 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 lpi.has_nlopt:
import nlopt
opt = nlopt.opt(nlopt.LN_BOBYQA, Xini.size)
+
def _f(_Xx, Grad):
# DFO, so no gradient
return CostFunction(_Xx, self._parameters["QualityCriterion"])
opt.set_min_objective(_f)
+ self._parameters["Bounds"] = ForceNumericBounds( self._parameters["Bounds"] )
+ Xini = ApplyBounds( Xini, self._parameters["Bounds"] )
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')
+ lub = numpy.array(self._parameters["Bounds"], dtype=float).reshape((Xini.size, 2))
+ lb = lub[:, 0]; lb[numpy.isnan(lb)] = -float('inf') # noqa: E702
+ ub = lub[:, 1]; ub[numpy.isnan(ub)] = +float('inf') # noqa: E702
if self._parameters["optdisp"]:
- print("%s: upper bounds %s"%(opt.get_algorithm_name(),ub))
- print("%s: lower bounds %s"%(opt.get_algorithm_name(),lb))
+ 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_xtol_rel(2. * self._parameters["StateVariationTolerance"])
opt.set_maxeval(self._parameters["MaximumNumberOfFunctionEvaluations"])
Minimum = opt.optimize( Xini )
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:
+ 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 lpi.has_nlopt:
import nlopt
opt = nlopt.opt(nlopt.LN_NEWUOA, Xini.size)
+
def _f(_Xx, Grad):
# DFO, so no gradient
return CostFunction(_Xx, self._parameters["QualityCriterion"])
opt.set_min_objective(_f)
+ self._parameters["Bounds"] = ForceNumericBounds( self._parameters["Bounds"] )
+ Xini = ApplyBounds( Xini, self._parameters["Bounds"] )
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')
+ lub = numpy.array(self._parameters["Bounds"], dtype=float).reshape((Xini.size, 2))
+ lb = lub[:, 0]; lb[numpy.isnan(lb)] = -float('inf') # noqa: E702
+ ub = lub[:, 1]; ub[numpy.isnan(ub)] = +float('inf') # noqa: E702
if self._parameters["optdisp"]:
- print("%s: upper bounds %s"%(opt.get_algorithm_name(),ub))
- print("%s: lower bounds %s"%(opt.get_algorithm_name(),lb))
+ 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_xtol_rel(2. * self._parameters["StateVariationTolerance"])
opt.set_maxeval(self._parameters["MaximumNumberOfFunctionEvaluations"])
Minimum = opt.optimize( Xini )
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:
+ 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 lpi.has_nlopt:
import nlopt
opt = nlopt.opt(nlopt.LN_SBPLX, Xini.size)
+
def _f(_Xx, Grad):
# DFO, so no gradient
return CostFunction(_Xx, self._parameters["QualityCriterion"])
opt.set_min_objective(_f)
+ self._parameters["Bounds"] = ForceNumericBounds( self._parameters["Bounds"] )
+ Xini = ApplyBounds( Xini, self._parameters["Bounds"] )
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')
+ lub = numpy.array(self._parameters["Bounds"], dtype=float).reshape((Xini.size, 2))
+ lb = lub[:, 0]; lb[numpy.isnan(lb)] = -float('inf') # noqa: E702
+ ub = lub[:, 1]; ub[numpy.isnan(ub)] = +float('inf') # noqa: E702
if self._parameters["optdisp"]:
- print("%s: upper bounds %s"%(opt.get_algorithm_name(),ub))
- print("%s: lower bounds %s"%(opt.get_algorithm_name(),lb))
+ 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_xtol_rel(2. * self._parameters["StateVariationTolerance"])
opt.set_maxeval(self._parameters["MaximumNumberOfFunctionEvaluations"])
Minimum = opt.optimize( Xini )
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()))
+ 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"])
+ raise ValueError("Error in minimizer name: %s is unkown"%self._parameters["Minimizer"])
#
IndexMin = numpy.argmin( self.StoredVariables["CostFunctionJ"][nbPreviousSteps:] ) + nbPreviousSteps
- MinJ = self.StoredVariables["CostFunctionJ"][IndexMin]
Minimum = self.StoredVariables["CurrentState"][IndexMin]
#
# Obtention de l'analyse
# ----------------------
- Xa = numpy.asmatrix(numpy.ravel( Minimum )).T
+ Xa = Minimum
#
- self.StoredVariables["Analysis"].store( Xa.A1 )
+ self.StoredVariables["Analysis"].store( Xa )
#
- if "OMA" in self._parameters["StoreSupplementaryCalculations"] or \
- "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"]:
- if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ # Calculs et/ou stockages supplémentaires
+ # ---------------------------------------
+ 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"]:
- self.StoredVariables["Innovation"].store( numpy.ravel(d) )
- if "OMB" in self._parameters["StoreSupplementaryCalculations"]:
- self.StoredVariables["OMB"].store( numpy.ravel(d) )
- if "BMA" in self._parameters["StoreSupplementaryCalculations"]:
+ HXa = HXa.reshape((-1, 1))
+ if self._toStore("Innovation") or \
+ self._toStore("OMB") or \
+ self._toStore("SimulatedObservationAtBackground"):
+ HXb = Hm(Xb).reshape((-1, 1))
+ Innovation = Y - HXb
+ if self._toStore("Innovation"):
+ self.StoredVariables["Innovation"].store( Innovation )
+ if self._toStore("OMB"):
+ self.StoredVariables["OMB"].store( Innovation )
+ if self._toStore("BMA"):
self.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(Xa) )
- if "OMA" in self._parameters["StoreSupplementaryCalculations"]:
- self.StoredVariables["OMA"].store( numpy.ravel(Y) - numpy.ravel(HXa) )
- if "SimulatedObservationAtBackground" in self._parameters["StoreSupplementaryCalculations"]:
- self.StoredVariables["SimulatedObservationAtBackground"].store( numpy.ravel(Hm(Xb)) )
- if "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"]:
- self.StoredVariables["SimulatedObservationAtOptimum"].store( numpy.ravel(HXa) )
+ if self._toStore("OMA"):
+ self.StoredVariables["OMA"].store( Y - HXa )
+ if self._toStore("SimulatedObservationAtBackground"):
+ self.StoredVariables["SimulatedObservationAtBackground"].store( HXb )
+ if self._toStore("SimulatedObservationAtOptimum"):
+ self.StoredVariables["SimulatedObservationAtOptimum"].store( HXa )
#
- self._post_run()
+ self._post_run(HO, EM)
return 0
# ==============================================================================
if __name__ == "__main__":
- print('\n AUTODIAGNOSTIC \n')
+ print("\n AUTODIAGNOSTIC\n")
