-#-*-coding:iso-8859-1-*-
+# -*- coding: utf-8 -*-
#
-# Copyright (C) 2008-2014 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):
self.defineRequiredParameter(
name = "SampleAsnUplet",
default = [],
- typecast = list,
- message = "Points de calcul définis par une liste de n-uplet",
+ typecast = tuple,
+ message = "Points de calcul définis par une liste de n-uplet",
)
self.defineRequiredParameter(
name = "SampleAsExplicitHyperCube",
default = [],
- typecast = list,
- message = "Points de calcul définis par un hyper-cube dont on donne la liste des échantillonages de chaque variable comme une liste",
+ typecast = tuple,
+ 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 = list,
- 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]",
+ 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]",
+ )
+ 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]",
)
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",
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","ObservedState"]
+ 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",
+ )
+ 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:
coordinatesList = []
for i,dim in enumerate(self._parameters["SampleAsMinMaxStepHyperCube"]):
if len(dim) != 3:
- raise ValueError("For dimension %i, the variable definition %s is incorrect, it should be [min,max,step]."%(i,dim))
+ 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[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 = []
+ for i,dim in enumerate(self._parameters["SampleAsIndependantRandomVariables"]):
+ if len(dim) != 3:
+ 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))
+ elif not( str(dim[0]) in ['normal','lognormal','uniform','weibull'] and hasattr(numpy.random,dim[0]) ):
+ 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]))
else:
- coordinatesList.append(numpy.linspace(*dim))
+ distribution = getattr(numpy.random,str(dim[0]),'normal')
+ parameters = dim[1]
+ 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 "ObservedState" in self._parameters["StoreSupplementaryCalculations"]:
- self.StoredVariables["ObservedState"].store( _HX )
+ 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 )
self.StoredVariables["CostFunctionJ" ].store( J )
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"]:
# ==============================================================================
if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
+ print('\n AUTODIAGNOSTIC\n')
