# -*- coding: utf-8 -*-
#
-# Copyright (C) 2008-2017 EDF R&D
+# Copyright (C) 2008-2019 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
default = [],
typecast = tuple,
message = "Liste de calculs supplémentaires à stocker et/ou effectuer",
- listval = ["APosterioriCorrelations", "APosterioriCovariance", "APosterioriStandardDeviations", "APosterioriVariances", "BMA", "CurrentState", "CostFunctionJ", "CostFunctionJb", "CostFunctionJo", "Innovation"]
+ listval = [
+ "Analysis",
+ "APosterioriCorrelations",
+ "APosterioriCovariance",
+ "APosterioriStandardDeviations",
+ "APosterioriVariances",
+ "BMA",
+ "CostFunctionJ",
+ "CostFunctionJb",
+ "CostFunctionJo",
+ "CurrentState",
+ "InnovationAtCurrentState",
+ ]
)
self.defineRequiredParameter( # Pas de type
name = "Bounds",
)
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, R, B, Q)
+ self._pre_run(Parameters, Xb, Y, R, B, Q)
#
if self._parameters["EstimationOf"] == "Parameters":
self._parameters["StoreInternalVariables"] = True
#
# Opérateurs
# ----------
- H = HO["Direct"].appliedControledFormTo
+ Hm = HO["Direct"].appliedControledFormTo
#
if self._parameters["EstimationOf"] == "State":
- M = EM["Direct"].appliedControledFormTo
+ Mm = EM["Direct"].appliedControledFormTo
#
if CM is not None and "Tangent" in CM and U is not None:
Cm = CM["Tangent"].asMatrix(Xb)
#
# Précalcul des inversions de B et R
# ----------------------------------
- if self._parameters["StoreInternalVariables"]:
+ if self._parameters["StoreInternalVariables"] \
+ or self._toStore("CostFunctionJ") \
+ or self._toStore("CostFunctionJb") \
+ or self._toStore("CostFunctionJo"):
BI = B.getI()
RI = R.getI()
#
# Initialisation
# --------------
Xn = Xb
- if hasattr(B,"asfullmatrix"):
- Pn = B.asfullmatrix(Xn.size)
- else:
- Pn = B
+ if hasattr(B,"asfullmatrix"): Pn = B.asfullmatrix(Xn.size)
+ else: Pn = B
#
self.StoredVariables["Analysis"].store( Xn.A1 )
- if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("APosterioriCovariance"):
self.StoredVariables["APosterioriCovariance"].store( Pn )
covarianceXa = Pn
- Xa = Xn
+ Xa = XaMin = Xb
previousJMinimum = numpy.finfo(float).max
#
for step in range(duration-1):
XEtnnp = []
for point in range(nbSpts):
if self._parameters["EstimationOf"] == "State":
- XEtnnpi = numpy.asmatrix(numpy.ravel( M( (Xnp[:,point], Un) ) )).T
+ XEtnnpi = numpy.asmatrix(numpy.ravel( Mm( (Xnp[:,point], Un) ) )).T
if Cm is not None and Un is not None: # Attention : si Cm est aussi dans M, doublon !
Cm = Cm.reshape(Xn.size,Un.size) # ADAO & check shape
XEtnnpi = XEtnnpi + Cm * Un
Ynnp = []
for point in range(nbSpts):
if self._parameters["EstimationOf"] == "State":
- Ynnpi = numpy.asmatrix(numpy.ravel( H( (Xnnp[:,point], None) ) )).T
+ Ynnpi = numpy.asmatrix(numpy.ravel( Hm( (Xnnp[:,point], None) ) )).T
elif self._parameters["EstimationOf"] == "Parameters":
- Ynnpi = numpy.asmatrix(numpy.ravel( H( (Xnnp[:,point], Un) ) )).T
+ Ynnpi = numpy.asmatrix(numpy.ravel( Hm( (Xnnp[:,point], Un) ) )).T
Ynnp.append( Ynnpi )
Ynnp = numpy.hstack( Ynnp )
#
if self._parameters["Bounds"] is not None and self._parameters["ConstrainedBy"] == "EstimateProjection":
Xn = numpy.max(numpy.hstack((Xn,numpy.asmatrix(self._parameters["Bounds"])[:,0])),axis=1)
Xn = numpy.min(numpy.hstack((Xn,numpy.asmatrix(self._parameters["Bounds"])[:,1])),axis=1)
+ Xa = Xn # Pointeurs
#
- self.StoredVariables["Analysis"].store( Xn.A1 )
- if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"]:
+ # ---> avec analysis
+ self.StoredVariables["Analysis"].store( Xa )
+ if self._toStore("APosterioriCovariance"):
self.StoredVariables["APosterioriCovariance"].store( Pn )
- if "Innovation" in self._parameters["StoreSupplementaryCalculations"]:
- self.StoredVariables["Innovation"].store( numpy.ravel( d.A1 ) )
- if self._parameters["StoreInternalVariables"]:
- Jb = 0.5 * (Xn - Xb).T * BI * (Xn - Xb)
- Jo = 0.5 * d.T * RI * d
- J = float( Jb ) + float( Jo )
- if self._parameters["StoreInternalVariables"] or "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
- self.StoredVariables["CurrentState"].store( Xn )
+ # ---> avec current state
+ if self._toStore("InnovationAtCurrentState"):
+ self.StoredVariables["InnovationAtCurrentState"].store( d )
+ if self._parameters["StoreInternalVariables"] \
+ or self._toStore("CurrentState"):
+ self.StoredVariables["CurrentState"].store( Xn )
+ if self._parameters["StoreInternalVariables"] \
+ or self._toStore("CostFunctionJ") \
+ or self._toStore("CostFunctionJb") \
+ or self._toStore("CostFunctionJo"):
+ Jb = float( 0.5 * (Xa - Xb).T * BI * (Xa - Xb) )
+ Jo = float( 0.5 * d.T * RI * d )
+ J = Jb + Jo
self.StoredVariables["CostFunctionJb"].store( Jb )
self.StoredVariables["CostFunctionJo"].store( Jo )
self.StoredVariables["CostFunctionJ" ].store( J )
- if J < previousJMinimum:
- previousJMinimum = J
- Xa = Xn
- if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"]:
- covarianceXa = Pn
- else:
- Xa = Xn
- #
+ if self._parameters["EstimationOf"] == "Parameters" \
+ and J < previousJMinimum:
+ previousJMinimum = J
+ XaMin = Xa
+ if self._toStore("APosterioriCovariance"):
+ covarianceXaMin = Pn
#
- # Stockage supplementaire de l'optimum en estimation de parametres
- # ----------------------------------------------------------------
+ # Stockage final supplémentaire de l'optimum en estimation de paramètres
+ # ----------------------------------------------------------------------
if self._parameters["EstimationOf"] == "Parameters":
- self.StoredVariables["Analysis"].store( Xa.A1 )
- if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"]:
- self.StoredVariables["APosterioriCovariance"].store( covarianceXa )
- #
- if "BMA" in self._parameters["StoreSupplementaryCalculations"]:
- self.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(Xa) )
+ self.StoredVariables["Analysis"].store( XaMin )
+ if self._toStore("APosterioriCovariance"):
+ self.StoredVariables["APosterioriCovariance"].store( covarianceXaMin )
+ if self._toStore("BMA"):
+ self.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(XaMin) )
#
self._post_run(HO)
return 0
# ==============================================================================
if __name__ == "__main__":
- print('\n AUTODIAGNOSTIC \n')
+ print('\n AUTODIAGNOSTIC\n')
