1 # -*- coding: utf-8 -*-
3 # Copyright (C) 2008-2017 EDF R&D
5 # This library is free software; you can redistribute it and/or
6 # modify it under the terms of the GNU Lesser General Public
7 # License as published by the Free Software Foundation; either
8 # version 2.1 of the License.
10 # This library is distributed in the hope that it will be useful,
11 # but WITHOUT ANY WARRANTY; without even the implied warranty of
12 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
13 # Lesser General Public License for more details.
15 # You should have received a copy of the GNU Lesser General Public
16 # License along with this library; if not, write to the Free Software
17 # Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
19 # See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
21 # Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
24 from daCore import BasicObjects
25 import numpy, copy, itertools
27 # ==============================================================================
28 class ElementaryAlgorithm(BasicObjects.Algorithm):
30 BasicObjects.Algorithm.__init__(self, "SAMPLINGTEST")
31 self.defineRequiredParameter(
32 name = "SampleAsnUplet",
35 message = "Points de calcul définis par une liste de n-uplet",
37 self.defineRequiredParameter(
38 name = "SampleAsExplicitHyperCube",
41 message = "Points de calcul définis par un hyper-cube dont on donne la liste des échantillonages de chaque variable comme une liste",
43 self.defineRequiredParameter(
44 name = "SampleAsMinMaxStepHyperCube",
47 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]",
49 self.defineRequiredParameter(
50 name = "SampleAsIndependantRandomVariables",
53 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]",
55 self.defineRequiredParameter(
56 name = "QualityCriterion",
57 default = "AugmentedWeightedLeastSquares",
59 message = "Critère de qualité utilisé",
60 listval = ["AugmentedWeightedLeastSquares","AWLS","AugmentedPonderatedLeastSquares","APLS","DA",
61 "WeightedLeastSquares","WLS","PonderatedLeastSquares","PLS",
62 "LeastSquares","LS","L2",
66 self.defineRequiredParameter(
70 message = "Activation du mode debug lors de l'exécution",
72 self.defineRequiredParameter(
73 name = "StoreSupplementaryCalculations",
76 message = "Liste de calculs supplémentaires à stocker et/ou effectuer",
77 listval = ["CostFunctionJ", "CostFunctionJb", "CostFunctionJo","CurrentState","InnovationAtCurrentState","SimulatedObservationAtCurrentState"]
79 self.defineRequiredParameter(
81 typecast = numpy.random.seed,
82 message = "Graine fixée pour le générateur aléatoire",
85 def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
86 self._pre_run(Parameters)
88 Hm = HO["Direct"].appliedTo
92 # ---------------------------
93 if len(self._parameters["SampleAsnUplet"]) > 0:
94 sampleList = self._parameters["SampleAsnUplet"]
95 for i,Xx in enumerate(sampleList):
96 if numpy.ravel(Xx).size != Xn.size:
97 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))
98 elif len(self._parameters["SampleAsExplicitHyperCube"]) > 0:
99 sampleList = itertools.product(*list(self._parameters["SampleAsExplicitHyperCube"]))
100 elif len(self._parameters["SampleAsMinMaxStepHyperCube"]) > 0:
102 for i,dim in enumerate(self._parameters["SampleAsMinMaxStepHyperCube"]):
104 raise ValueError("For dimension %i, the variable definition \"%s\" is incorrect, it should be [min,max,step]."%(i,dim))
106 coordinatesList.append(numpy.linspace(dim[0],dim[1],1+int((float(dim[1])-float(dim[0]))/float(dim[2]))))
107 sampleList = itertools.product(*coordinatesList)
108 elif len(self._parameters["SampleAsIndependantRandomVariables"]) > 0:
110 for i,dim in enumerate(self._parameters["SampleAsIndependantRandomVariables"]):
112 raise ValueError("For dimension %i, the variable definition \"%s\" is incorrect, it should be ('distribution',(parameters),length) with distribution in ['normal'(mean,std),'lognormal'(mean,sigma),'uniform'(low,high),'weibull'(shape)]."%(i,dim))
113 elif not( str(dim[0]) in ['normal','lognormal','uniform','weibull'] and hasattr(numpy.random,dim[0]) ):
114 raise ValueError("For dimension %i, the distribution name \"%s\" is not allowed, please choose in ['normal'(mean,std),'lognormal'(mean,sigma),'uniform'(low,high),'weibull'(shape)]"%(i,dim[0]))
116 distribution = getattr(numpy.random,str(dim[0]),'normal')
118 coordinatesList.append(distribution(*dim[1], size=max(1,int(dim[2]))))
119 sampleList = itertools.product(*coordinatesList)
121 sampleList = iter([Xn,])
125 def CostFunction(x, HmX, QualityMeasure="AugmentedWeightedLeastSquares"):
126 if numpy.any(numpy.isnan(HmX)):
129 Jb, Jo, J = numpy.nan, numpy.nan, numpy.nan
131 _X = numpy.asmatrix(numpy.ravel( x )).T
132 _HX = numpy.asmatrix(numpy.ravel( HmX )).T
133 if QualityMeasure in ["AugmentedWeightedLeastSquares","AWLS","AugmentedPonderatedLeastSquares","APLS","DA"]:
134 if BI is None or RI is None:
135 raise ValueError("Background and Observation error covariance matrix has to be properly defined!")
136 Jb = 0.5 * (_X - Xb).T * BI * (_X - Xb)
137 Jo = 0.5 * (Y - _HX).T * RI * (Y - _HX)
138 elif QualityMeasure in ["WeightedLeastSquares","WLS","PonderatedLeastSquares","PLS"]:
140 raise ValueError("Observation error covariance matrix has to be properly defined!")
142 Jo = 0.5 * (Y - _HX).T * RI * (Y - _HX)
143 elif QualityMeasure in ["LeastSquares","LS","L2"]:
145 Jo = 0.5 * (Y - _HX).T * (Y - _HX)
146 elif QualityMeasure in ["AbsoluteValue","L1"]:
148 Jo = numpy.sum( numpy.abs(Y - _HX) )
149 elif QualityMeasure in ["MaximumError","ME"]:
151 Jo = numpy.max( numpy.abs(Y - _HX) )
153 J = float( Jb ) + float( Jo )
154 if "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
155 self.StoredVariables["CurrentState"].store( _X )
156 if "InnovationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
157 self.StoredVariables["InnovationAtCurrentState"].store( Y - _HX )
158 if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
159 self.StoredVariables["SimulatedObservationAtCurrentState"].store( _HX )
160 self.StoredVariables["CostFunctionJb"].store( Jb )
161 self.StoredVariables["CostFunctionJo"].store( Jo )
162 self.StoredVariables["CostFunctionJ" ].store( J )
165 if self._parameters["SetDebug"]:
166 CUR_LEVEL = logging.getLogger().getEffectiveLevel()
167 logging.getLogger().setLevel(logging.DEBUG)
168 print("===> Beginning of evaluation, activating debug\n")
169 print(" %s\n"%("-"*75,))
172 for i,Xx in enumerate(sampleList):
173 if self._parameters["SetDebug"]:
174 print("===> Launching evaluation for state %i"%i)
175 __Xn = numpy.asmatrix(numpy.ravel( Xx )).T
181 J, Jb, Jo = CostFunction(__Xn,Yn,self._parameters["QualityCriterion"])
184 if self._parameters["SetDebug"]:
185 print("\n %s\n"%("-"*75,))
186 print("===> End evaluation, deactivating debug if necessary\n")
187 logging.getLogger().setLevel(CUR_LEVEL)
192 # ==============================================================================
193 if __name__ == "__main__":
194 print('\n AUTODIAGNOSTIC \n')