X-Git-Url: http://git.salome-platform.org/gitweb/?a=blobdiff_plain;f=src%2FdaComposant%2FdaAlgorithms%2FSamplingTest.py;h=279f789af1c489a77464a8b052a0926d8cd4cafa;hb=cf85bc4e4ff25a695443edbffb1800a97ba6afd8;hp=7856b236df20a796e1a2e57518b49e1ceec80026;hpb=fec1983f6538d1e421147346eefe5283fbb9aa33;p=modules%2Fadao.git diff --git a/src/daComposant/daAlgorithms/SamplingTest.py b/src/daComposant/daAlgorithms/SamplingTest.py index 7856b23..279f789 100644 --- a/src/daComposant/daAlgorithms/SamplingTest.py +++ b/src/daComposant/daAlgorithms/SamplingTest.py @@ -1,28 +1,30 @@ -#-*-coding:iso-8859-1-*- +# -*- coding: utf-8 -*- # -# Copyright (C) 2008-2015 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 -# License as published by the Free Software Foundation; either -# version 2.1 of the License. +# 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. +# 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 +# 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 +# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com # -# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D +# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D -import logging +import copy, logging, itertools +import numpy from daCore import BasicObjects -import numpy, copy, itertools +from daCore.PlatformInfo import PlatformInfo +mfp = PlatformInfo().MaximumPrecision() # ============================================================================== class ElementaryAlgorithm(BasicObjects.Algorithm): @@ -32,31 +34,31 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): name = "SampleAsnUplet", default = [], typecast = tuple, - message = "Points de calcul définis par une liste de n-uplet", + message = "Points de calcul définis par une liste de n-uplet", ) self.defineRequiredParameter( name = "SampleAsExplicitHyperCube", default = [], typecast = tuple, - message = "Points de calcul définis par un hyper-cube dont on donne la liste des échantillonages de chaque variable comme une liste", + message = "Points de calcul définis par un hyper-cube dont on donne la liste des échantillonages de chaque variable comme une liste", ) self.defineRequiredParameter( name = "SampleAsMinMaxStepHyperCube", default = [], typecast = tuple, - message = "Points de calcul définis par un hyper-cube dont on donne la liste des échantillonages de chaque variable par un triplet [min,max,step]", + message = "Points de calcul définis par un hyper-cube dont on donne la liste des échantillonages de chaque variable par un triplet [min,max,step]", ) self.defineRequiredParameter( name = "SampleAsIndependantRandomVariables", default = [], typecast = tuple, - message = "Points de calcul définis par un hyper-cube dont les points sur chaque axe proviennent de l'échantillonage indépendant de la variable selon la spécification ['distribution',[parametres],nombre]", + message = "Points de calcul définis par un hyper-cube dont les points sur chaque axe proviennent de l'échantillonage indépendant de la variable selon la spécification ['distribution',[parametres],nombre]", ) self.defineRequiredParameter( name = "QualityCriterion", default = "AugmentedWeightedLeastSquares", typecast = str, - message = "Critère de qualité utilisé", + message = "Critère de qualité utilisé", listval = ["AugmentedWeightedLeastSquares","AWLS","AugmentedPonderatedLeastSquares","APLS","DA", "WeightedLeastSquares","WLS","PonderatedLeastSquares","PLS", "LeastSquares","LS","L2", @@ -67,36 +69,48 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): name = "SetDebug", default = False, typecast = bool, - message = "Activation du mode debug lors de l'exécution", + message = "Activation du mode debug lors de l'exécution", ) self.defineRequiredParameter( name = "StoreSupplementaryCalculations", default = [], typecast = tuple, - message = "Liste de calculs supplémentaires à stocker et/ou effectuer", - listval = ["CostFunctionJ","CurrentState","Innovation","SimulatedObservationAtCurrentState"] + message = "Liste de calculs supplémentaires à stocker et/ou effectuer", + listval = [ + "CostFunctionJ", + "CostFunctionJb", + "CostFunctionJo", + "CurrentState", + "InnovationAtCurrentState", + "SimulatedObservationAtCurrentState", + ] ) self.defineRequiredParameter( name = "SetSeed", typecast = numpy.random.seed, - message = "Graine fixée pour le générateur aléatoire", + message = "Graine fixée pour le générateur aléatoire", ) + self.requireInputArguments( + mandatory= ("Xb", "HO"), + ) + self.setAttributes(tags=( + "Checking", + )) 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() - # - self.setParameters(Parameters) + self._pre_run(Parameters, Xb, Y, U, HO, EM, CM, R, B, Q) # Hm = HO["Direct"].appliedTo # - Xn = copy.copy( Xb ) + X0 = numpy.ravel( Xb ) + Y0 = numpy.ravel( Y ) # # --------------------------- if len(self._parameters["SampleAsnUplet"]) > 0: sampleList = self._parameters["SampleAsnUplet"] for i,Xx in enumerate(sampleList): - if numpy.ravel(Xx).size != Xn.size: - raise ValueError("The size %i of the %ith state X in the sample and %i of the checking point Xb are different, they have to be identical."%(numpy.ravel(Xx).size,i+1,Xn.size)) + if numpy.ravel(Xx).size != X0.size: + raise ValueError("The size %i of the %ith state X in the sample and %i of the checking point Xb are different, they have to be identical."%(numpy.ravel(Xx).size,i+1,X0.size)) elif len(self._parameters["SampleAsExplicitHyperCube"]) > 0: sampleList = itertools.product(*list(self._parameters["SampleAsExplicitHyperCube"])) elif len(self._parameters["SampleAsMinMaxStepHyperCube"]) > 0: @@ -105,7 +119,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): if len(dim) != 3: raise ValueError("For dimension %i, the variable definition \"%s\" is incorrect, it should be [min,max,step]."%(i,dim)) else: - coordinatesList.append(numpy.linspace(*dim)) + coordinatesList.append(numpy.linspace(dim[0],dim[1],1+int((float(dim[1])-float(dim[0]))/float(dim[2])))) sampleList = itertools.product(*coordinatesList) elif len(self._parameters["SampleAsIndependantRandomVariables"]) > 0: coordinatesList = [] @@ -120,44 +134,44 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): coordinatesList.append(distribution(*dim[1], size=max(1,int(dim[2])))) sampleList = itertools.product(*coordinatesList) else: - sampleList = iter([Xn,]) + sampleList = iter([X0,]) # ---------- BI = B.getI() RI = R.getI() - def CostFunction(x,HmX, QualityMeasure="AugmentedWeightedLeastSquares"): + def CostFunction(x, HmX, QualityMeasure="AugmentedWeightedLeastSquares"): if numpy.any(numpy.isnan(HmX)): _X = numpy.nan _HX = numpy.nan Jb, Jo, J = numpy.nan, numpy.nan, numpy.nan else: - _X = numpy.asmatrix(numpy.ravel( x )).T - _HX = numpy.asmatrix(numpy.ravel( HmX )).T + _X = numpy.ravel( x ) + _HX = numpy.ravel( HmX ) if QualityMeasure in ["AugmentedWeightedLeastSquares","AWLS","AugmentedPonderatedLeastSquares","APLS","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 * (Y - _HX).T * RI * (Y - _HX) + Jb = float( 0.5 * (_X - X0).T * (BI * (_X - X0)) ) + Jo = float( 0.5 * (Y0 - _HX).T * (RI * (Y0 - _HX)) ) elif QualityMeasure in ["WeightedLeastSquares","WLS","PonderatedLeastSquares","PLS"]: if RI is None: raise ValueError("Observation error covariance matrix has to be properly defined!") Jb = 0. - Jo = 0.5 * (Y - _HX).T * RI * (Y - _HX) + Jo = float( 0.5 * (Y0 - _HX).T * (RI * (Y0 - _HX)) ) elif QualityMeasure in ["LeastSquares","LS","L2"]: Jb = 0. - Jo = 0.5 * (Y - _HX).T * (Y - _HX) + Jo = float( 0.5 * (Y0 - _HX).T @ (Y0 - _HX) ) elif QualityMeasure in ["AbsoluteValue","L1"]: Jb = 0. - Jo = numpy.sum( numpy.abs(Y - _HX) ) + Jo = float( numpy.sum( numpy.abs(Y0 - _HX), dtype=mfp ) ) elif QualityMeasure in ["MaximumError","ME"]: Jb = 0. - Jo = numpy.max( numpy.abs(Y - _HX) ) + Jo = numpy.max( numpy.abs(Y0 - _HX) ) # - J = float( Jb ) + float( Jo ) - if "CurrentState" in self._parameters["StoreSupplementaryCalculations"]: + J = Jb + Jo + if self._toStore("CurrentState"): self.StoredVariables["CurrentState"].store( _X ) - if "Innovation" in self._parameters["StoreSupplementaryCalculations"]: - self.StoredVariables["Innovation"].store( Y - _HX ) - if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]: + if self._toStore("InnovationAtCurrentState"): + self.StoredVariables["InnovationAtCurrentState"].store( Y0 - _HX ) + if self._toStore("SimulatedObservationAtCurrentState"): self.StoredVariables["SimulatedObservationAtCurrentState"].store( _HX ) self.StoredVariables["CostFunctionJb"].store( Jb ) self.StoredVariables["CostFunctionJo"].store( Jo ) @@ -174,13 +188,12 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): for i,Xx in enumerate(sampleList): if self._parameters["SetDebug"]: print("===> Launching evaluation for state %i"%i) - __Xn = numpy.asmatrix(numpy.ravel( Xx )).T try: - Yn = Hm( __Xn ) + Yy = Hm( numpy.ravel( Xx ) ) except: - Yn = numpy.nan + Yy = numpy.nan # - J, Jb, Jo = CostFunction(__Xn,Yn,self._parameters["QualityCriterion"]) + J, Jb, Jo = CostFunction( Xx, Yy, self._parameters["QualityCriterion"]) # ---------- # if self._parameters["SetDebug"]: @@ -193,4 +206,4 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): # ============================================================================== if __name__ == "__main__": - print '\n AUTODIAGNOSTIC \n' + print('\n AUTODIAGNOSTIC\n')