From: Jean-Philippe ARGAUD Date: Wed, 19 Apr 2023 18:27:27 +0000 (+0200) Subject: Documentation and code update for PSO X-Git-Tag: V9_11_0b1~7 X-Git-Url: http://git.salome-platform.org/gitweb/?a=commitdiff_plain;h=c64c13c5520813e6b027545b71166a150abbcbcd;p=modules%2Fadao.git Documentation and code update for PSO --- diff --git a/doc/en/bibliography.rst b/doc/en/bibliography.rst index 8d887ae..836a900 100644 --- a/doc/en/bibliography.rst +++ b/doc/en/bibliography.rst @@ -171,6 +171,8 @@ exhaustive bibliography. .. [WikipediaUKF] Wikipedia, *Unscented Kalman Filter*, https://en.wikipedia.org/wiki/Unscented_Kalman_filter +.. [ZambranoBigiarini13] Zambrano-Bigiarini M., Clerc M., Rojas R., *Standard Particle Swarm Optimisation 2011 at CEC-2013: A baseline for future PSO improvements*, 2013 IEEE Congress on Evolutionary Computation, pp.2337-2344, 2013 + .. [Zhu97] Zhu C., Byrd R. H., Nocedal J., *L-BFGS-B: Algorithm 778: L-BFGS-B, FORTRAN routines for large scale bound constrained optimization*, ACM Transactions on Mathematical Software, 23(4), pp.550-560, 1997 .. [Zupanski05] Zupanski M., *Maximum likelihood ensemble filter: Theoretical aspects*, Monthly Weather Review, 133(6), pp.1710–1726, 2005 diff --git a/doc/en/ref_algorithm_ParticleSwarmOptimization.rst b/doc/en/ref_algorithm_ParticleSwarmOptimization.rst index 61f58f5..11ed087 100644 --- a/doc/en/ref_algorithm_ParticleSwarmOptimization.rst +++ b/doc/en/ref_algorithm_ParticleSwarmOptimization.rst @@ -35,7 +35,8 @@ This algorithm realizes an estimation of the state of a system by minimization of a cost function :math:`J` by using an evolutionary strategy of particle swarm. It is a method that does not use the derivatives of the cost function. It is based on the evolution of a population (called a "swarm") of states (each -state is called a "particle"). It falls in the same category than the +state is called a "particle" or an "insect"). It falls in the same category +than the :ref:`section_ref_algorithm_DerivativeFreeOptimization`, the :ref:`section_ref_algorithm_DifferentialEvolution` or the :ref:`section_ref_algorithm_TabuSearch`. @@ -46,7 +47,19 @@ error function :math:`J` of type :math:`L^1`, :math:`L^2` or :ref:`section_theory_optimization`. The default error function is the augmented weighted least squares function, classically used in data assimilation. -.. ------------------------------------ .. +.There exists various variants of this algorithm. The following stable and +robust formulations are proposed here: + +.. index:: +.. index:: + pair: Variant ; PSO + pair: Variant ; CanonicalPSO + pair: Variant ; OGCR + +- "PSO" (Canonical PSO, see [ZambranoBigiarini13]_), canonical algorithm of particle swarm, robust and defining the reference for particle swarm algorithms, +- "OGCR" (Simple PSO with no bounds on insects or velocities), simplified algorithm of particle swarm, not recommanded because less robust, but sometimes a lot more efficient. + +. ------------------------------------ .. .. include:: snippets/Header2Algo02.rst .. include:: snippets/Background.rst @@ -62,22 +75,28 @@ weighted least squares function, classically used in data assimilation. .. ------------------------------------ .. .. include:: snippets/Header2Algo03AdOp.rst -.. include:: snippets/MaximumNumberOfIterations_50.rst +.. include:: snippets/BoundsWithNone.rst -.. include:: snippets/MaximumNumberOfFunctionEvaluations.rst +.. include:: snippets/BoxBounds.rst -.. include:: snippets/QualityCriterion.rst +.. include:: snippets/CognitiveAcceleration.rst -.. include:: snippets/NumberOfInsects.rst +.. include:: snippets/InertiaWeight.rst -.. include:: snippets/SwarmVelocity.rst +.. include:: snippets/InitializationPoint.rst -.. include:: snippets/GroupRecallRate.rst +.. include:: snippets/MaximumNumberOfFunctionEvaluations.rst -.. include:: snippets/BoxBounds.rst +.. include:: snippets/MaximumNumberOfIterations_50.rst + +.. include:: snippets/NumberOfInsects.rst + +.. include:: snippets/QualityCriterion.rst .. include:: snippets/SetSeed.rst +.. include:: snippets/SocialAcceleration.rst + StoreSupplementaryCalculations .. index:: single: StoreSupplementaryCalculations @@ -109,6 +128,8 @@ StoreSupplementaryCalculations Example : ``{"StoreSupplementaryCalculations":["CurrentState", "Residu"]}`` +.. include:: snippets/VelocityClampingFactor.rst + .. ------------------------------------ .. .. include:: snippets/Header2Algo04.rst @@ -162,3 +183,4 @@ StoreSupplementaryCalculations .. include:: snippets/Header2Algo07.rst - [WikipediaPSO]_ +- [ZambranoBigiarini13]_ diff --git a/doc/en/snippets/CognitiveAcceleration.rst b/doc/en/snippets/CognitiveAcceleration.rst new file mode 100644 index 0000000..082b52d --- /dev/null +++ b/doc/en/snippets/CognitiveAcceleration.rst @@ -0,0 +1,9 @@ +.. index:: single: CognitiveAcceleration + +CognitiveAcceleration + *Real value*. This key indicates the recall rate at the best previously known + value of the current insect. It is a floating point value between 0 and 1. + The default value is 0.5. + + Example : + ``{"CognitiveAcceleration":0.5}`` diff --git a/doc/en/snippets/GroupRecallRate.rst b/doc/en/snippets/GroupRecallRate.rst deleted file mode 100644 index ebde3e7..0000000 --- a/doc/en/snippets/GroupRecallRate.rst +++ /dev/null @@ -1,8 +0,0 @@ -.. index:: single: GroupRecallRate - -GroupRecallRate - *Real value*. This key indicates the recall rate at the best swarm insect. It - is a floating point value between 0 and 1. The default value is 0.5. - - Example : - ``{"GroupRecallRate":0.5}`` diff --git a/doc/en/snippets/InertiaWeight.rst b/doc/en/snippets/InertiaWeight.rst new file mode 100644 index 0000000..eb9bc99 --- /dev/null +++ b/doc/en/snippets/InertiaWeight.rst @@ -0,0 +1,9 @@ +.. index:: single: InertiaWeight + +InertiaWeight + *Real value*. This key indicates the part of the insect velocity which is + imposed by the swarm, named "inertia weight". It is a positive floating point + value. It is a floating point value between 0 and 1. The default value is 1. + + Example : + ``{"InertiaWeight":1.}`` diff --git a/doc/en/snippets/SocialAcceleration.rst b/doc/en/snippets/SocialAcceleration.rst new file mode 100644 index 0000000..034908c --- /dev/null +++ b/doc/en/snippets/SocialAcceleration.rst @@ -0,0 +1,9 @@ +.. index:: single: SocialAcceleration + +SocialAcceleration + *Real value*. This key indicates the recall rate at the best swarm insect in + the neighbourhood of the current insect, which is by default the whole swarm. + It is a floating point value between 0 and 1. The default value is 0.5. + + Example : + ``{"SocialAcceleration":0.5}`` diff --git a/doc/en/snippets/SwarmVelocity.rst b/doc/en/snippets/SwarmVelocity.rst deleted file mode 100644 index 57f4e07..0000000 --- a/doc/en/snippets/SwarmVelocity.rst +++ /dev/null @@ -1,9 +0,0 @@ -.. index:: single: SwarmVelocity - -SwarmVelocity - *Real value*. This key indicates the part of the insect velocity which is - imposed by the swarm, named "group velocity". It is a positive floating point - value. The default value is 1. - - Example : - ``{"SwarmVelocity":1.}`` diff --git a/doc/en/snippets/VelocityClampingFactor.rst b/doc/en/snippets/VelocityClampingFactor.rst new file mode 100644 index 0000000..cac44d9 --- /dev/null +++ b/doc/en/snippets/VelocityClampingFactor.rst @@ -0,0 +1,10 @@ +.. index:: single: VelocityClampingFactor + +VelocityClampingFactor + *Real value*. This key indicates the rate of group velocity attenuation in + the update for each insect, useful to avoid swarm explosion, i.e. + uncontrolled growth of insect velocity. It is a floating point value between + 0+ and 1. The default value is 1.0. + + Example : + ``{"VelocityClampingFactor":1.0}`` diff --git a/doc/fr/bibliography.rst b/doc/fr/bibliography.rst index 9479d74..e4305e2 100644 --- a/doc/fr/bibliography.rst +++ b/doc/fr/bibliography.rst @@ -171,6 +171,8 @@ néanmoins d'intention de constituer une bibliographie exhaustive. .. [WikipediaUKF] Wikipedia, *Unscented Kalman Filter*, https://en.wikipedia.org/wiki/Unscented_Kalman_filter +.. [ZambranoBigiarini13] Zambrano-Bigiarini M., Clerc M., Rojas R., *Standard Particle Swarm Optimisation 2011 at CEC-2013: A baseline for future PSO improvements*, 2013 IEEE Congress on Evolutionary Computation, pp.2337-2344, 2013 + .. [Zhu97] Zhu C., Byrd R. H., Nocedal J., *L-BFGS-B: Algorithm 778: L-BFGS-B, FORTRAN routines for large scale bound constrained optimization*, ACM Transactions on Mathematical Software, 23(4), pp.550-560, 1997 .. [Zupanski05] Zupanski M., *Maximum likelihood ensemble filter: Theoretical aspects*, Monthly Weather Review, 133(6), pp.1710–1726, 2005 diff --git a/doc/fr/ref_algorithm_ParticleSwarmOptimization.rst b/doc/fr/ref_algorithm_ParticleSwarmOptimization.rst index 3c194d7..973e242 100644 --- a/doc/fr/ref_algorithm_ParticleSwarmOptimization.rst +++ b/doc/fr/ref_algorithm_ParticleSwarmOptimization.rst @@ -36,8 +36,8 @@ Cet algorithme réalise une estimation de l'état d'un système par minimisation d'une fonctionnelle d'écart :math:`J` en utilisant une méthode évolutionnaire d'essaim particulaire. C'est une méthode qui n'utilise pas les dérivées de la fonctionnelle d'écart. Elle est basée sur l'évolution d'une population (appelée -"essaim") d'états (chaque individu étant appelé une "particule"). Elle entre -dans la même catégorie que les +"essaim") d'états (chaque individu étant appelé une "particule" ou un insecte). +Elle entre dans la même catégorie que les :ref:`section_ref_algorithm_DerivativeFreeOptimization`, :ref:`section_ref_algorithm_DifferentialEvolution` ou :ref:`section_ref_algorithm_TabuSearch`. @@ -49,6 +49,17 @@ la section pour :ref:`section_theory_optimization`. La fonctionnelle d'erreur par défaut est celle de moindres carrés pondérés augmentés, classiquement utilisée en assimilation de données. +Il existe diverses variantes de cet algorithme. On propose ici les formulations +stables et robustes suivantes : + +.. index:: + pair: Variant ; PSO + pair: Variant ; CanonicalPSO + pair: Variant ; OGCR + +- "PSO" (Canonical PSO, voir [ZambranoBigiarini13]_), algorithme canonique d'essaim particulaire, robuste et défini comme la référence des algorithmes d'essaims particulaires, +- "OGCR" (Simple PSO sans bornes sur les insectes ou les vitesses), algorithme simplifié d'essaim particulaire, déconseillé car peu robuste, mais parfois beaucoup plus rapide. + .. ------------------------------------ .. .. include:: snippets/Header2Algo02.rst @@ -65,22 +76,28 @@ utilisée en assimilation de données. .. ------------------------------------ .. .. include:: snippets/Header2Algo03AdOp.rst -.. include:: snippets/MaximumNumberOfIterations_50.rst +.. include:: snippets/BoundsWithNone.rst -.. include:: snippets/MaximumNumberOfFunctionEvaluations.rst +.. include:: snippets/BoxBounds.rst -.. include:: snippets/QualityCriterion.rst +.. include:: snippets/CognitiveAcceleration.rst -.. include:: snippets/NumberOfInsects.rst +.. include:: snippets/InertiaWeight.rst -.. include:: snippets/SwarmVelocity.rst +.. include:: snippets/InitializationPoint.rst -.. include:: snippets/GroupRecallRate.rst +.. include:: snippets/MaximumNumberOfFunctionEvaluations.rst -.. include:: snippets/BoxBounds.rst +.. include:: snippets/MaximumNumberOfIterations_50.rst + +.. include:: snippets/NumberOfInsects.rst + +.. include:: snippets/QualityCriterion.rst .. include:: snippets/SetSeed.rst +.. include:: snippets/SocialAcceleration.rst + StoreSupplementaryCalculations .. index:: single: StoreSupplementaryCalculations @@ -112,6 +129,8 @@ StoreSupplementaryCalculations Exemple : ``{"StoreSupplementaryCalculations":["CurrentState", "Residu"]}`` +.. include:: snippets/VelocityClampingFactor.rst + .. ------------------------------------ .. .. include:: snippets/Header2Algo04.rst @@ -165,3 +184,4 @@ StoreSupplementaryCalculations .. include:: snippets/Header2Algo07.rst - [WikipediaPSO]_ +- [ZambranoBigiarini13]_ diff --git a/doc/fr/snippets/CognitiveAcceleration.rst b/doc/fr/snippets/CognitiveAcceleration.rst new file mode 100644 index 0000000..3a97886 --- /dev/null +++ b/doc/fr/snippets/CognitiveAcceleration.rst @@ -0,0 +1,10 @@ +.. index:: single: CognitiveAcceleration + +CognitiveAcceleration + *Valeur réelle*. Cette clé indique le taux de rappel vers la meilleure valeur + connue précédemment de l'insecte courant. C'est une valeur réelle comprise + entre 0 et 1. Le défaut est de 0.5. + + Exemple : + ``{"CognitiveAcceleration":0.5}`` + diff --git a/doc/fr/snippets/GroupRecallRate.rst b/doc/fr/snippets/GroupRecallRate.rst deleted file mode 100644 index 3372e7f..0000000 --- a/doc/fr/snippets/GroupRecallRate.rst +++ /dev/null @@ -1,9 +0,0 @@ -.. index:: single: GroupRecallRate - -GroupRecallRate - *Valeur réelle*. Cette clé indique le taux de rappel vers le meilleur insecte - de l'essaim. C'est une valeur réelle comprise entre 0 et 1. Le défaut est de - 0.5. - - Exemple : - ``{"GroupRecallRate":0.5}`` diff --git a/doc/fr/snippets/InertiaWeight.rst b/doc/fr/snippets/InertiaWeight.rst new file mode 100644 index 0000000..8b5291e --- /dev/null +++ b/doc/fr/snippets/InertiaWeight.rst @@ -0,0 +1,9 @@ +.. index:: single: InertiaWeight + +InertiaWeight + *Valeur réelle*. Cette clé indique la part de la vitesse de l'essaim qui est + imposée à l'insecte, dite "poids de l'inertie". C'est une valeur réelle + comprise entre 0 et 1. Le défaut est de 1. + + Exemple : + ``{"InertiaWeight":1.}`` diff --git a/doc/fr/snippets/SocialAcceleration.rst b/doc/fr/snippets/SocialAcceleration.rst new file mode 100644 index 0000000..6e6e041 --- /dev/null +++ b/doc/fr/snippets/SocialAcceleration.rst @@ -0,0 +1,9 @@ +.. index:: single: SocialAcceleration + +SocialAcceleration + *Valeur réelle*. Cette clé indique le taux de rappel vers le meilleur insecte + du voisinage de l'insecte courant, qui est par défaut l'essaim complet. C'est + une valeur réelle comprise entre 0 et 1. Le défaut est de 0.5. + + Exemple : + ``{"SocialAcceleration":0.5}`` diff --git a/doc/fr/snippets/SwarmVelocity.rst b/doc/fr/snippets/SwarmVelocity.rst deleted file mode 100644 index b1e52ec..0000000 --- a/doc/fr/snippets/SwarmVelocity.rst +++ /dev/null @@ -1,9 +0,0 @@ -.. index:: single: SwarmVelocity - -SwarmVelocity - *Valeur réelle*. Cette clé indique la part de la vitesse d'insecte qui est - imposée par l'essaim, dite "vitesse de groupe". C'est une valeur réelle - positive. Le défaut est de 1. - - Exemple : - ``{"SwarmVelocity":1.}`` diff --git a/doc/fr/snippets/VelocityClampingFactor.rst b/doc/fr/snippets/VelocityClampingFactor.rst new file mode 100644 index 0000000..e2ea625 --- /dev/null +++ b/doc/fr/snippets/VelocityClampingFactor.rst @@ -0,0 +1,12 @@ +.. index:: single: VelocityClampingFactor + +VelocityClampingFactor + *Valeur réelle*. Cette clé indique le taux d'atténuation de la vitesse de + groupe dans la mise à jour pour chaque insecte, utile pour éviter l'explosion + de l'essaim, c'est-à-dire une croissance incontrôlée de la vitesse des + insectes. C'est une valeur réelle comprise entre 0+ et 1. Le défaut est de + 1.0. + + Exemple : + ``{"VelocityClampingFactor":1.0}`` + diff --git a/src/daComposant/daAlgorithms/Atoms/ecwnpso.py b/src/daComposant/daAlgorithms/Atoms/ecwnpso.py new file mode 100644 index 0000000..ef7c9d3 --- /dev/null +++ b/src/daComposant/daAlgorithms/Atoms/ecwnpso.py @@ -0,0 +1,211 @@ +# -*- coding: utf-8 -*- +# +# Copyright (C) 2008-2023 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 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 +# +# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com +# +# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D + +__doc__ = """ + Standard Particle Swarm Optimization +""" +__author__ = "Jean-Philippe ARGAUD" + +import numpy, logging, copy +from daCore.NumericObjects import ApplyBounds, ForceNumericBounds +from numpy.random import uniform as rand + +# ============================================================================== +def ecwnpso(selfA, Xb, Y, HO, R, B): + # + if ("BoxBounds" in selfA._parameters) and isinstance(selfA._parameters["BoxBounds"], (list, tuple)) and (len(selfA._parameters["BoxBounds"]) > 0): + BoxBounds = selfA._parameters["BoxBounds"] + logging.debug("%s Prise en compte des bornes d'incréments de paramètres effectuée"%(selfA._name,)) + else: + raise ValueError("Particle Swarm Optimization requires bounds on all variables increments to be truly given (BoxBounds).") + BoxBounds = numpy.array(BoxBounds) + if numpy.isnan(BoxBounds).any(): + raise ValueError("Particle Swarm Optimization requires bounds on all variables increments to be truly given (BoxBounds), \"None\" is not allowed. The actual increments bounds are:\n%s"%BoxBounds) + # + selfA._parameters["Bounds"] = ForceNumericBounds( selfA._parameters["Bounds"] ) + # + Hm = HO["Direct"].appliedTo + # + BI = B.getI() + RI = R.getI() + # + Xini = selfA._parameters["InitializationPoint"] + # + def CostFunction(x, QualityMeasure="AugmentedWeightedLeastSquares"): + _X = numpy.asarray( x ).reshape((-1,1)) + _HX = numpy.asarray( Hm( _X ) ).reshape((-1,1)) + _Innovation = Y - _HX + # + if QualityMeasure in ["AugmentedWeightedLeastSquares","AWLS","DA"]: + if BI is None or RI is None: + raise ValueError("Background and Observation error covariance matrices 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"]: + 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"]: + Jb = 0. + Jo = 0.5 * _Innovation.T @ _Innovation + elif QualityMeasure in ["AbsoluteValue","L1"]: + Jb = 0. + Jo = numpy.sum( numpy.abs(_Innovation) ) + elif QualityMeasure in ["MaximumError","ME", "Linf"]: + Jb = 0. + Jo = numpy.max( numpy.abs(_Innovation) ) + # + J = float( Jb ) + float( Jo ) + # + return J, float( Jb ), float( Jo ) + # + def KeepRunningCondition(__step, __nbfct): + if __step >= selfA._parameters["MaximumNumberOfIterations"]: + logging.debug("%s Stopping search because the number %i of evolving iterations is exceeding the maximum %i."%(selfA._name, __step, selfA._parameters["MaximumNumberOfIterations"])) + return False + elif __nbfct >= selfA._parameters["MaximumNumberOfFunctionEvaluations"]: + logging.debug("%s Stopping search because the number %i of function evaluations is exceeding the maximum %i."%(selfA._name, __nbfct, selfA._parameters["MaximumNumberOfFunctionEvaluations"])) + return False + else: + return True + # + # Paramètres internes + # ------------------- + __nbI = selfA._parameters["NumberOfInsects"] + __nbP = len(Xini) # Dimension ou nombre de paramètres + # + __iw = float( selfA._parameters["InertiaWeight"] ) + __sa = float( selfA._parameters["SocialAcceleration"] ) + __ca = float( selfA._parameters["CognitiveAcceleration"] ) + __vc = float( selfA._parameters["VelocityClampingFactor"] ) + logging.debug("%s Cognitive acceleration (recall to the best previously known value of the insect) = %s"%(selfA._name, str(__ca))) + logging.debug("%s Social acceleration (recall to the best insect value of the group) = %s"%(selfA._name, str(__sa))) + logging.debug("%s Velocity clamping factor = %s"%(selfA._name, str(__vc))) + # + # Initialisation de l'essaim + # -------------------------- + LimitStep = Xini.reshape((-1,1)) + BoxBounds + __dx = __vc * numpy.abs(BoxBounds[:,1]-BoxBounds[:,0]) + LimitSpeed = numpy.vstack((-__dx,__dx)).T + # + NumberOfFunctionEvaluations = 1 + JXini, JbXini, JoXini = CostFunction(Xini,selfA._parameters["QualityCriterion"]) + # + Swarm = numpy.zeros((__nbI,3,__nbP)) # 3 car (x,v,xbest) + for __p in range(__nbP) : + Swarm[:,0,__p] = rand( low=LimitStep[__p,0], high=LimitStep[__p,1], size=__nbI) # Position + Swarm[:,1,__p] = rand( low=LimitSpeed[__p,0], high=LimitSpeed[__p,1], size=__nbI) # Velocity + logging.debug("%s Initialisation of the swarm with %i insects of size %i "%(selfA._name,Swarm.shape[0],Swarm.shape[2])) + # + qSwarm = JXini * numpy.ones((__nbI,3)) # Qualité (J, Jb, Jo) par insecte + for __i in range(__nbI): + NumberOfFunctionEvaluations += 1 + JTest, JbTest, JoTest = CostFunction(Swarm[__i,0,:],selfA._parameters["QualityCriterion"]) + if JTest < JXini: + Swarm[__i,2,:] = Swarm[__i,0,:] # xBest + qSwarm[__i,:] = (JTest, JbTest, JoTest) + else: + Swarm[__i,2,:] = Xini # xBest + qSwarm[__i,:] = (JXini, JbXini, JoXini) + logging.debug("%s Initialisation of the best previous insects"%selfA._name) + # + iBest = numpy.argmin(qSwarm[:,0]) + xBest = Swarm[iBest,2,:] + if selfA._parameters["StoreInternalVariables"] or selfA._toStore("CurrentState"): + selfA.StoredVariables["CurrentState"].store( xBest ) + selfA.StoredVariables["CostFunctionJ" ].store( qSwarm[iBest,0] ) + selfA.StoredVariables["CostFunctionJb"].store( qSwarm[iBest,1] ) + selfA.StoredVariables["CostFunctionJo"].store( qSwarm[iBest,2] ) + # + selfA.StoredVariables["CurrentIterationNumber"].store( len(selfA.StoredVariables["CostFunctionJ"]) ) + # + # Minimisation de la fonctionnelle + # -------------------------------- + step = 0 + while KeepRunningCondition(step, NumberOfFunctionEvaluations): + step += 1 + for __i in range(__nbI): + rct = rand(size=__nbP) + rst = rand(size=__nbP) + # Vitesse + __velins = __iw * Swarm[__i,1,:] \ + + __ca * rct * (Swarm[__i,2,:] - Swarm[__i,0,:]) \ + + __sa * rst * (Swarm[iBest,2,:] - Swarm[__i,0,:]) + Swarm[__i,1,:] = ApplyBounds( __velins, LimitSpeed ) + # Position + __velins = Swarm[__i,0,:] + Swarm[__i,1,:] + Swarm[__i,0,:] = ApplyBounds( __velins, selfA._parameters["Bounds"] ) + # + NumberOfFunctionEvaluations += 1 + JTest, JbTest, JoTest = CostFunction(Swarm[__i,0,:],selfA._parameters["QualityCriterion"]) + if JTest < qSwarm[__i,0]: + Swarm[__i,2,:] = Swarm[__i,0,:] # xBest + qSwarm[__i,:] = (JTest, JbTest, JoTest) + # + iBest = numpy.argmin(qSwarm[:,0]) + xBest = Swarm[iBest,2,:] + selfA.StoredVariables["CurrentIterationNumber"].store( len(selfA.StoredVariables["CostFunctionJ"]) ) + if selfA._parameters["StoreInternalVariables"] or selfA._toStore("CurrentState"): + selfA.StoredVariables["CurrentState"].store( xBest ) + if selfA._toStore("SimulatedObservationAtCurrentState"): + selfA.StoredVariables["SimulatedObservationAtCurrentState"].store( Hm( xBest ) ) + selfA.StoredVariables["CostFunctionJ" ].store( qSwarm[iBest,0] ) + selfA.StoredVariables["CostFunctionJb"].store( qSwarm[iBest,1] ) + selfA.StoredVariables["CostFunctionJo"].store( qSwarm[iBest,2] ) + logging.debug("%s Step %i: insect %i is the better one with J =%.7f"%(selfA._name,step,iBest,qSwarm[iBest,0])) + # + # Obtention de l'analyse + # ---------------------- + Xa = xBest + # + selfA.StoredVariables["Analysis"].store( Xa ) + # + # Calculs et/ou stockages supplémentaires + # --------------------------------------- + if selfA._toStore("OMA") or \ + selfA._toStore("SimulatedObservationAtOptimum"): + HXa = Hm(Xa) + if selfA._toStore("Innovation") or \ + selfA._toStore("OMB") or \ + selfA._toStore("SimulatedObservationAtBackground"): + HXb = Hm(Xb) + Innovation = Y - HXb + if selfA._toStore("Innovation"): + selfA.StoredVariables["Innovation"].store( Innovation ) + if selfA._toStore("OMB"): + selfA.StoredVariables["OMB"].store( Innovation ) + if selfA._toStore("BMA"): + selfA.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(Xa) ) + if selfA._toStore("OMA"): + selfA.StoredVariables["OMA"].store( numpy.ravel(Y) - numpy.ravel(HXa) ) + if selfA._toStore("SimulatedObservationAtBackground"): + selfA.StoredVariables["SimulatedObservationAtBackground"].store( HXb ) + if selfA._toStore("SimulatedObservationAtOptimum"): + selfA.StoredVariables["SimulatedObservationAtOptimum"].store( HXa ) + # + selfA._post_run(HO) + return 0 + +# ============================================================================== +if __name__ == "__main__": + print('\n AUTODIAGNOSTIC\n') diff --git a/src/daComposant/daAlgorithms/Atoms/ecwopso.py b/src/daComposant/daAlgorithms/Atoms/ecwopso.py new file mode 100644 index 0000000..00e155f --- /dev/null +++ b/src/daComposant/daAlgorithms/Atoms/ecwopso.py @@ -0,0 +1,205 @@ +# -*- coding: utf-8 -*- +# +# Copyright (C) 2008-2023 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 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 +# +# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com +# +# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D + +__doc__ = """ + Standard Particle Swarm Optimization +""" +__author__ = "Jean-Philippe ARGAUD" + +import numpy, logging, copy +from numpy.random import uniform as rand + +# ============================================================================== +def ecwopso(selfA, Xb, Y, HO, R, B): + # + if ("BoxBounds" in selfA._parameters) and isinstance(selfA._parameters["BoxBounds"], (list, tuple)) and (len(selfA._parameters["BoxBounds"]) > 0): + BoxBounds = selfA._parameters["BoxBounds"] + logging.debug("%s Prise en compte des bornes d'incréments de paramètres effectuée"%(selfA._name,)) + else: + raise ValueError("Particle Swarm Optimization requires bounds on all variables increments to be truly given (BoxBounds).") + BoxBounds = numpy.array(BoxBounds) + if numpy.isnan(BoxBounds).any(): + raise ValueError("Particle Swarm Optimization requires bounds on all variables increments to be truly given (BoxBounds), \"None\" is not allowed. The actual increments bounds are:\n%s"%BoxBounds) + # + Hm = HO["Direct"].appliedTo + # + BI = B.getI() + RI = R.getI() + # + Xini = selfA._parameters["InitializationPoint"] + # + def CostFunction(x, QualityMeasure="AugmentedWeightedLeastSquares"): + _X = numpy.asarray( x ).reshape((-1,1)) + _HX = numpy.asarray( Hm( _X ) ).reshape((-1,1)) + _Innovation = Y - _HX + # + if QualityMeasure in ["AugmentedWeightedLeastSquares","AWLS","DA"]: + if BI is None or RI is None: + raise ValueError("Background and Observation error covariance matrices 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"]: + 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"]: + Jb = 0. + Jo = 0.5 * _Innovation.T @ _Innovation + elif QualityMeasure in ["AbsoluteValue","L1"]: + Jb = 0. + Jo = numpy.sum( numpy.abs(_Innovation) ) + elif QualityMeasure in ["MaximumError","ME", "Linf"]: + Jb = 0. + Jo = numpy.max( numpy.abs(_Innovation) ) + # + J = float( Jb ) + float( Jo ) + # + return J, float( Jb ), float( Jo ) + # + def KeepRunningCondition(__step, __nbfct): + if __step >= selfA._parameters["MaximumNumberOfIterations"]: + logging.debug("%s Stopping search because the number %i of evolving iterations is exceeding the maximum %i."%(selfA._name, __step, selfA._parameters["MaximumNumberOfIterations"])) + return False + elif __nbfct >= selfA._parameters["MaximumNumberOfFunctionEvaluations"]: + logging.debug("%s Stopping search because the number %i of function evaluations is exceeding the maximum %i."%(selfA._name, __nbfct, selfA._parameters["MaximumNumberOfFunctionEvaluations"])) + return False + else: + return True + # + # Paramètres internes + # ------------------- + __nbI = selfA._parameters["NumberOfInsects"] + __nbP = len(Xini) # Dimension ou nombre de paramètres + # + __iw = float( selfA._parameters["InertiaWeight"] ) + __sa = float( selfA._parameters["SocialAcceleration"] ) + __ca = float( selfA._parameters["CognitiveAcceleration"] ) + __vc = float( selfA._parameters["VelocityClampingFactor"] ) + logging.debug("%s Cognitive acceleration (recall to the best previously known value of the insect) = %s"%(selfA._name, str(__ca))) + logging.debug("%s Social acceleration (recall to the best insect value of the group) = %s"%(selfA._name, str(__sa))) + logging.debug("%s Velocity clamping factor = %s"%(selfA._name, str(__vc))) + # + # Initialisation de l'essaim + # -------------------------- + LimitStep = Xini.reshape((-1,1)) + BoxBounds + __dx = __vc * numpy.abs(BoxBounds[:,1]-BoxBounds[:,0]) + LimitSpeed = numpy.vstack((-__dx,__dx)).T + # + NumberOfFunctionEvaluations = 1 + JXini, JbXini, JoXini = CostFunction(Xini,selfA._parameters["QualityCriterion"]) + # + Swarm = numpy.zeros((__nbI,3,__nbP)) # 3 car (x,v,xbest) + for __p in range(__nbP) : + Swarm[:,0,__p] = rand( low=LimitStep[__p,0], high=LimitStep[__p,1], size=__nbI) # Position + Swarm[:,1,__p] = rand( low=LimitSpeed[__p,0], high=LimitSpeed[__p,1], size=__nbI) # Velocity + logging.debug("%s Initialisation of the swarm with %i insects of size %i "%(selfA._name,Swarm.shape[0],Swarm.shape[2])) + # + qSwarm = JXini * numpy.ones((__nbI,3)) # Qualité (J, Jb, Jo) par insecte + for __i in range(__nbI): + NumberOfFunctionEvaluations += 1 + JTest, JbTest, JoTest = CostFunction(Swarm[__i,0,:],selfA._parameters["QualityCriterion"]) + if JTest < JXini: + Swarm[__i,2,:] = Swarm[__i,0,:] # xBest + qSwarm[__i,:] = (JTest, JbTest, JoTest) + else: + Swarm[__i,2,:] = Xini # xBest + qSwarm[__i,:] = (JXini, JbXini, JoXini) + logging.debug("%s Initialisation of the best previous insects"%selfA._name) + # + iBest = numpy.argmin(qSwarm[:,0]) + xBest = Swarm[iBest,2,:] + if selfA._parameters["StoreInternalVariables"] or selfA._toStore("CurrentState"): + selfA.StoredVariables["CurrentState"].store( xBest ) + selfA.StoredVariables["CostFunctionJ" ].store( qSwarm[iBest,0] ) + selfA.StoredVariables["CostFunctionJb"].store( qSwarm[iBest,1] ) + selfA.StoredVariables["CostFunctionJo"].store( qSwarm[iBest,2] ) + # + selfA.StoredVariables["CurrentIterationNumber"].store( len(selfA.StoredVariables["CostFunctionJ"]) ) + # + # Minimisation de la fonctionnelle + # -------------------------------- + step = 0 + while KeepRunningCondition(step, NumberOfFunctionEvaluations): + step += 1 + for __i in range(__nbI): + for __p in range(__nbP): + # Vitesse + Swarm[__i,1,__p] = __iw * Swarm[__i,1,__p] \ + + __ca * rand() * (Swarm[__i,2,__p] - Swarm[__i,0,__p]) \ + + __sa * rand() * (Swarm[iBest,2,__p] - Swarm[__i,0,__p]) + # Position + Swarm[__i,0,__p] = Swarm[__i,0,__p] + Swarm[__i,1,__p] + # + NumberOfFunctionEvaluations += 1 + JTest, JbTest, JoTest = CostFunction(Swarm[__i,0,:],selfA._parameters["QualityCriterion"]) + if JTest < qSwarm[__i,0]: + Swarm[__i,2,:] = Swarm[__i,0,:] # xBest + qSwarm[__i,:] = (JTest, JbTest, JoTest) + # + iBest = numpy.argmin(qSwarm[:,0]) + xBest = Swarm[iBest,2,:] + selfA.StoredVariables["CurrentIterationNumber"].store( len(selfA.StoredVariables["CostFunctionJ"]) ) + if selfA._parameters["StoreInternalVariables"] or selfA._toStore("CurrentState"): + selfA.StoredVariables["CurrentState"].store( xBest ) + if selfA._toStore("SimulatedObservationAtCurrentState"): + selfA.StoredVariables["SimulatedObservationAtCurrentState"].store( Hm( xBest ) ) + selfA.StoredVariables["CostFunctionJ" ].store( qSwarm[iBest,0] ) + selfA.StoredVariables["CostFunctionJb"].store( qSwarm[iBest,1] ) + selfA.StoredVariables["CostFunctionJo"].store( qSwarm[iBest,2] ) + logging.debug("%s Step %i: insect %i is the better one with J =%.7f"%(selfA._name,step,iBest,qSwarm[iBest,0])) + # + # Obtention de l'analyse + # ---------------------- + Xa = xBest + # + selfA.StoredVariables["Analysis"].store( Xa ) + # + # Calculs et/ou stockages supplémentaires + # --------------------------------------- + if selfA._toStore("OMA") or \ + selfA._toStore("SimulatedObservationAtOptimum"): + HXa = Hm(Xa) + if selfA._toStore("Innovation") or \ + selfA._toStore("OMB") or \ + selfA._toStore("SimulatedObservationAtBackground"): + HXb = Hm(Xb) + Innovation = Y - HXb + if selfA._toStore("Innovation"): + selfA.StoredVariables["Innovation"].store( Innovation ) + if selfA._toStore("OMB"): + selfA.StoredVariables["OMB"].store( Innovation ) + if selfA._toStore("BMA"): + selfA.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(Xa) ) + if selfA._toStore("OMA"): + selfA.StoredVariables["OMA"].store( numpy.ravel(Y) - numpy.ravel(HXa) ) + if selfA._toStore("SimulatedObservationAtBackground"): + selfA.StoredVariables["SimulatedObservationAtBackground"].store( HXb ) + if selfA._toStore("SimulatedObservationAtOptimum"): + selfA.StoredVariables["SimulatedObservationAtOptimum"].store( HXa ) + # + selfA._post_run(HO) + return 0 + +# ============================================================================== +if __name__ == "__main__": + print('\n AUTODIAGNOSTIC\n') diff --git a/src/daComposant/daAlgorithms/ParticleSwarmOptimization.py b/src/daComposant/daAlgorithms/ParticleSwarmOptimization.py index 6b857e5..3579096 100644 --- a/src/daComposant/daAlgorithms/ParticleSwarmOptimization.py +++ b/src/daComposant/daAlgorithms/ParticleSwarmOptimization.py @@ -22,11 +22,26 @@ import numpy, logging, copy from daCore import BasicObjects +from daAlgorithms.Atoms import ecwnpso, ecwopso # ============================================================================== class ElementaryAlgorithm(BasicObjects.Algorithm): def __init__(self): BasicObjects.Algorithm.__init__(self, "PARTICLESWARMOPTIMIZATION") + self.defineRequiredParameter( + name = "Variant", + default = "PSO", + typecast = str, + message = "Variant ou formulation de la méthode", + listval = [ + "PSO", + "OGCR", + ], + listadv = [ + "CanonicalPSO", + "SPSO-2011", + ], + ) self.defineRequiredParameter( name = "MaximumNumberOfIterations", default = 50, @@ -55,19 +70,38 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): minval = -1, ) self.defineRequiredParameter( - name = "SwarmVelocity", + name = "InertiaWeight", default = 1., typecast = float, - message = "Vitesse de groupe imposée par l'essaim", + message = "Part de la vitesse de l'essaim qui est imposée à l'insecte, ou poids de l'inertie (entre 0 et 1)", + minval = 0., + maxval = 1., + oldname = "SwarmVelocity", + ) + self.defineRequiredParameter( + name = "CognitiveAcceleration", + default = 0.5, + typecast = float, + message = "Taux de rappel à la meilleure position de l'insecte précédemment connue (entre 0 et 1)", minval = 0., + maxval = 1., ) self.defineRequiredParameter( - name = "GroupRecallRate", + name = "SocialAcceleration", default = 0.5, typecast = float, - message = "Taux de rappel au meilleur insecte du groupe (entre 0 et 1)", + message = "Taux de rappel au meilleur insecte du groupe local (entre 0 et 1)", minval = 0., maxval = 1., + oldname = "GroupRecallRate", + ) + self.defineRequiredParameter( + name = "VelocityClampingFactor", + default = 0.3, + typecast = float, + message = "Facteur de réduction de l'amplitude de variation des vitesses (entre 0 et 1)", + minval = 0.0001, + maxval = 1., ) self.defineRequiredParameter( name = "QualityCriterion", @@ -113,6 +147,15 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): name = "BoxBounds", message = "Liste des valeurs de bornes d'incréments de paramètres", ) + self.defineRequiredParameter( # Pas de type + name = "Bounds", + message = "Liste des paires 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", "B"), ) @@ -126,159 +169,16 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): 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, U, HO, EM, CM, R, B, Q) # - if ("BoxBounds" in self._parameters) and isinstance(self._parameters["BoxBounds"], (list, tuple)) and (len(self._parameters["BoxBounds"]) > 0): - BoxBounds = self._parameters["BoxBounds"] - logging.debug("%s Prise en compte des bornes d'incréments de paramètres effectuée"%(self._name,)) - else: - raise ValueError("Particle Swarm Optimization requires bounds on all variables increments to be truly given (BoxBounds).") - BoxBounds = numpy.array(BoxBounds) - if numpy.isnan(BoxBounds).any(): - raise ValueError("Particle Swarm Optimization requires bounds on all variables increments to be truly given (BoxBounds), \"None\" is not allowed. The actual increments bounds are:\n%s"%BoxBounds) - # - Phig = float( self._parameters["GroupRecallRate"] ) - Phip = 1. - Phig - logging.debug("%s Taux de rappel au meilleur insecte du groupe (entre 0 et 1) = %s et à la meilleure position précédente (son complémentaire à 1) = %s"%(self._name, str(Phig), str(Phip))) + #-------------------------- + if self._parameters["Variant"] in ["CanonicalPSO", "PSO"]: + ecwnpso.ecwnpso(self, Xb, Y, HO, R, B) # - Hm = HO["Direct"].appliedTo + elif self._parameters["Variant"] in ["OGCR"]: + ecwopso.ecwopso(self, Xb, Y, HO, R, B) # - BI = B.getI() - RI = R.getI() - # - def CostFunction(x, QualityMeasure="AugmentedWeightedLeastSquares"): - _X = numpy.ravel( x ).reshape((-1,1)) - _HX = numpy.ravel( Hm( _X ) ).reshape((-1,1)) - _Innovation = Y - _HX - # - if QualityMeasure in ["AugmentedWeightedLeastSquares","AWLS","DA"]: - if BI is None or RI is None: - raise ValueError("Background and Observation error covariance matrices 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"]: - 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"]: - Jb = 0. - Jo = 0.5 * _Innovation.T @ _Innovation - elif QualityMeasure in ["AbsoluteValue","L1"]: - Jb = 0. - Jo = numpy.sum( numpy.abs(_Innovation) ) - elif QualityMeasure in ["MaximumError","ME", "Linf"]: - Jb = 0. - Jo = numpy.max( numpy.abs(_Innovation) ) - # - J = float( Jb ) + float( Jo ) - # - return J - # - if Xb is not None: - Xini = numpy.ravel(Xb) + #-------------------------- else: - Xini = numpy.zeros(len(BoxBounds[:,0])) - # - SpaceUp = BoxBounds[:,1] + Xini - SpaceLow = BoxBounds[:,0] + Xini - nbparam = len(SpaceUp) - # - # Initialisation de l'essaim - # -------------------------- - NumberOfFunctionEvaluations = 0 - LimitVelocity = numpy.abs(SpaceUp-SpaceLow) - # - PosInsect = [] - VelocityInsect = [] - for i in range(nbparam) : - PosInsect.append(numpy.random.uniform(low=SpaceLow[i], high=SpaceUp[i], size=self._parameters["NumberOfInsects"])) - VelocityInsect.append(numpy.random.uniform(low=-LimitVelocity[i], high=LimitVelocity[i], size=self._parameters["NumberOfInsects"])) - VelocityInsect = numpy.array(VelocityInsect) - PosInsect = numpy.array(PosInsect) - # - BestPosInsect = numpy.array(PosInsect) - qBestPosInsect = [] - _Best = copy.copy(SpaceLow) - _qualityBest = CostFunction(_Best,self._parameters["QualityCriterion"]) - NumberOfFunctionEvaluations += 1 - # - for i in range(self._parameters["NumberOfInsects"]): - insect = numpy.ravel(PosInsect[:,i]) - quality = CostFunction(insect,self._parameters["QualityCriterion"]) - NumberOfFunctionEvaluations += 1 - qBestPosInsect.append(quality) - if quality < _qualityBest: - _Best = copy.copy( insect ) - _qualityBest = copy.copy( quality ) - logging.debug("%s Initialisation, Insecte = %s, Qualité = %s"%(self._name, str(_Best), str(_qualityBest))) - # - self.StoredVariables["CurrentIterationNumber"].store( len(self.StoredVariables["CostFunctionJ"]) ) - if self._parameters["StoreInternalVariables"] or self._toStore("CurrentState"): - self.StoredVariables["CurrentState"].store( _Best ) - self.StoredVariables["CostFunctionJb"].store( 0. ) - self.StoredVariables["CostFunctionJo"].store( 0. ) - self.StoredVariables["CostFunctionJ" ].store( _qualityBest ) - # - # Minimisation de la fonctionnelle - # -------------------------------- - for n in range(self._parameters["MaximumNumberOfIterations"]): - for i in range(self._parameters["NumberOfInsects"]) : - insect = numpy.ravel(PosInsect[:,i]) - rp = numpy.random.uniform(size=nbparam) - rg = numpy.random.uniform(size=nbparam) - for j in range(nbparam) : - VelocityInsect[j,i] = self._parameters["SwarmVelocity"]*VelocityInsect[j,i] + Phip*rp[j]*(BestPosInsect[j,i]-PosInsect[j,i]) + Phig*rg[j]*(_Best[j]-PosInsect[j,i]) - PosInsect[j,i] = PosInsect[j,i]+VelocityInsect[j,i] - quality = CostFunction(insect,self._parameters["QualityCriterion"]) - NumberOfFunctionEvaluations += 1 - if quality < qBestPosInsect[i]: - BestPosInsect[:,i] = copy.copy( insect ) - qBestPosInsect[i] = copy.copy( quality ) - if quality < _qualityBest : - _Best = copy.copy( insect ) - _qualityBest = copy.copy( quality ) - logging.debug("%s Etape %i, Insecte = %s, Qualité = %s"%(self._name, n, str(_Best), str(_qualityBest))) - # - self.StoredVariables["CurrentIterationNumber"].store( len(self.StoredVariables["CostFunctionJ"]) ) - if self._parameters["StoreInternalVariables"] or self._toStore("CurrentState"): - self.StoredVariables["CurrentState"].store( _Best ) - if self._toStore("SimulatedObservationAtCurrentState"): - _HmX = Hm( _Best ) - self.StoredVariables["SimulatedObservationAtCurrentState"].store( _HmX ) - self.StoredVariables["CostFunctionJb"].store( 0. ) - self.StoredVariables["CostFunctionJo"].store( 0. ) - self.StoredVariables["CostFunctionJ" ].store( _qualityBest ) - if NumberOfFunctionEvaluations > self._parameters["MaximumNumberOfFunctionEvaluations"]: - logging.debug("%s Stopping search because the number %i of function evaluations is exceeding the maximum %i."%(self._name, NumberOfFunctionEvaluations, self._parameters["MaximumNumberOfFunctionEvaluations"])) - break - # - # Obtention de l'analyse - # ---------------------- - Xa = _Best - # - self.StoredVariables["Analysis"].store( Xa ) - # - # Calculs et/ou stockages supplémentaires - # --------------------------------------- - if self._toStore("OMA") or \ - self._toStore("SimulatedObservationAtOptimum"): - HXa = Hm(Xa) - if self._toStore("Innovation") or \ - self._toStore("OMB") or \ - self._toStore("SimulatedObservationAtBackground"): - HXb = Hm(Xb) - 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 self._toStore("OMA"): - self.StoredVariables["OMA"].store( numpy.ravel(Y) - numpy.ravel(HXa) ) - if self._toStore("SimulatedObservationAtBackground"): - self.StoredVariables["SimulatedObservationAtBackground"].store( HXb ) - if self._toStore("SimulatedObservationAtOptimum"): - self.StoredVariables["SimulatedObservationAtOptimum"].store( HXa ) + raise ValueError("Error in Variant name: %s"%self._parameters["Variant"]) # self._post_run(HO) return 0 diff --git a/src/daSalome/__init__.py b/src/daSalome/__init__.py index 06b84f1..75c34a2 100644 --- a/src/daSalome/__init__.py +++ b/src/daSalome/__init__.py @@ -122,6 +122,8 @@ quote at least one of the references given below: The documentation of the module is also covered by the license and the requirement of quoting. """ +__author__ = "Jean-Philippe ARGAUD" +__all__ = ["adaoBuilder"] import os, sys, logging adao_py_dir = os.path.abspath(os.path.dirname(__file__))