1 #-*-coding:iso-8859-1-*-
3 # Copyright (C) 2008-2014 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(
53 message = "Activation du mode debug lors de l'exécution",
55 self.defineRequiredParameter(
56 name = "StoreSupplementaryCalculations",
59 message = "Liste de calculs supplémentaires à stocker et/ou effectuer",
60 listval = ["CostFunctionJ","CurrentState","Innovation","ObservedState"]
63 def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
66 self.setParameters(Parameters)
68 Hm = HO["Direct"].appliedTo
72 # ---------------------------
73 if len(self._parameters["SampleAsnUplet"]) > 0:
74 sampleList = self._parameters["SampleAsnUplet"]
75 for i,Xx in enumerate(sampleList):
76 if numpy.ravel(Xx).size != Xn.size:
77 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))
78 elif len(self._parameters["SampleAsExplicitHyperCube"]) > 0:
79 sampleList = itertools.product(*list(self._parameters["SampleAsExplicitHyperCube"]))
80 elif len(self._parameters["SampleAsMinMaxStepHyperCube"]) > 0:
82 for i,dim in enumerate(self._parameters["SampleAsMinMaxStepHyperCube"]):
84 raise ValueError("For dimension %i, the variable definition %s is incorrect, it should be [min,max,step]."%(i,dim))
86 coordinatesList.append(numpy.linspace(*dim))
87 sampleList = itertools.product(*coordinatesList)
89 sampleList = iter([Xn,])
93 def CostFunction(x,HmX):
94 if numpy.any(numpy.isnan(HmX)):
97 Jb, Jo, J = numpy.nan, numpy.nan, numpy.nan
99 _X = numpy.asmatrix(numpy.ravel( x )).T
100 _HX = numpy.asmatrix(numpy.ravel( HmX )).T
101 Jb = 0.5 * (_X - Xb).T * BI * (_X - Xb)
102 Jo = 0.5 * (Y - _HX).T * RI * (Y - _HX)
103 J = float( Jb ) + float( Jo )
104 if "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
105 self.StoredVariables["CurrentState"].store( _X )
106 if "Innovation" in self._parameters["StoreSupplementaryCalculations"]:
107 self.StoredVariables["Innovation"].store( Y - _HX )
108 if "ObservedState" in self._parameters["StoreSupplementaryCalculations"]:
109 self.StoredVariables["ObservedState"].store( _HX )
110 self.StoredVariables["CostFunctionJb"].store( Jb )
111 self.StoredVariables["CostFunctionJo"].store( Jo )
112 self.StoredVariables["CostFunctionJ" ].store( J )
115 if self._parameters["SetDebug"]:
116 CUR_LEVEL = logging.getLogger().getEffectiveLevel()
117 logging.getLogger().setLevel(logging.DEBUG)
118 print("===> Beginning of evaluation, activating debug\n")
119 print(" %s\n"%("-"*75,))
122 for i,Xx in enumerate(sampleList):
123 if self._parameters["SetDebug"]:
124 print("===> Launching evaluation for state %i"%i)
125 __Xn = numpy.asmatrix(numpy.ravel( Xx )).T
131 J, Jb, Jo = CostFunction(__Xn,Yn)
134 if self._parameters["SetDebug"]:
135 print("\n %s\n"%("-"*75,))
136 print("===> End evaluation, deactivating debug if necessary\n")
137 logging.getLogger().setLevel(CUR_LEVEL)
142 # ==============================================================================
143 if __name__ == "__main__":
144 print '\n AUTODIAGNOSTIC \n'