# -*- 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
"APosterioriStandardDeviations",
"APosterioriVariances",
"BMA",
- "OMA",
- "OMB",
- "CurrentState",
"CostFunctionJ",
+ "CostFunctionJAtCurrentOptimum",
"CostFunctionJb",
+ "CostFunctionJbAtCurrentOptimum",
"CostFunctionJo",
+ "CostFunctionJoAtCurrentOptimum",
+ "CurrentOptimum",
+ "CurrentState",
"Innovation",
+ "MahalanobisConsistency",
+ "OMA",
+ "OMB",
"SigmaBck2",
"SigmaObs2",
- "MahalanobisConsistency",
- "SimulationQuantiles",
"SimulatedObservationAtBackground",
+ "SimulatedObservationAtCurrentOptimum",
"SimulatedObservationAtCurrentState",
"SimulatedObservationAtOptimum",
+ "SimulationQuantiles",
]
)
self.defineRequiredParameter(
# Calcul de la matrice de gain et de l'analyse
# --------------------------------------------
if Y.size <= Xb.size:
- _A = R + Hm * B * Ha
+ _A = R + numpy.dot(Hm, B * Ha)
_u = numpy.linalg.solve( _A , d )
Xa = Xb + B * Ha * _u
else:
- _A = BI + Ha * RI * Hm
- _u = numpy.linalg.solve( _A , Ha * RI * d )
+ _A = BI + numpy.dot(Ha, RI * Hm)
+ _u = numpy.linalg.solve( _A , numpy.dot(Ha, RI * d) )
Xa = Xb + _u
self.StoredVariables["Analysis"].store( Xa.A1 )
#
# Calcul de la fonction coût
# --------------------------
if self._parameters["StoreInternalVariables"] or \
- "CostFunctionJ" in self._parameters["StoreSupplementaryCalculations"] or \
- "OMA" in self._parameters["StoreSupplementaryCalculations"] or \
- "SigmaObs2" in self._parameters["StoreSupplementaryCalculations"] or \
- "MahalanobisConsistency" in self._parameters["StoreSupplementaryCalculations"] or \
- "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"] or \
- "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
- "SimulationQuantiles" in self._parameters["StoreSupplementaryCalculations"]:
+ self._toStore("CostFunctionJ") or self._toStore("CostFunctionJAtCurrentOptimum") or \
+ self._toStore("CostFunctionJb") or self._toStore("CostFunctionJbAtCurrentOptimum") or \
+ self._toStore("CostFunctionJo") or self._toStore("CostFunctionJoAtCurrentOptimum") or \
+ self._toStore("OMA") or \
+ self._toStore("SigmaObs2") or \
+ self._toStore("MahalanobisConsistency") or \
+ self._toStore("SimulatedObservationAtCurrentOptimum") or \
+ self._toStore("SimulatedObservationAtCurrentState") or \
+ self._toStore("SimulatedObservationAtOptimum") or \
+ self._toStore("SimulationQuantiles"):
HXa = Hm * Xa
oma = Y - HXa
if self._parameters["StoreInternalVariables"] or \
- "CostFunctionJ" in self._parameters["StoreSupplementaryCalculations"] or \
- "MahalanobisConsistency" in self._parameters["StoreSupplementaryCalculations"]:
+ self._toStore("CostFunctionJ") or self._toStore("CostFunctionJAtCurrentOptimum") or \
+ self._toStore("CostFunctionJb") or self._toStore("CostFunctionJbAtCurrentOptimum") or \
+ self._toStore("CostFunctionJo") or self._toStore("CostFunctionJoAtCurrentOptimum") or \
+ self._toStore("MahalanobisConsistency"):
Jb = float( 0.5 * (Xa - Xb).T * BI * (Xa - Xb) )
Jo = float( 0.5 * oma.T * RI * oma )
J = Jb + Jo
self.StoredVariables["CostFunctionJb"].store( Jb )
self.StoredVariables["CostFunctionJo"].store( Jo )
self.StoredVariables["CostFunctionJ" ].store( J )
+ self.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( Jb )
+ self.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( Jo )
+ self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( J )
#
# Calcul de la covariance d'analyse
# ---------------------------------
- if "APosterioriCovariance" in self._parameters["StoreSupplementaryCalculations"] or \
- "SimulationQuantiles" in self._parameters["StoreSupplementaryCalculations"]:
- if (Y.size <= Xb.size): K = B * Ha * (R + Hm * B * Ha).I
- elif (Y.size > Xb.size): K = (BI + Ha * RI * Hm).I * Ha * RI
+ if self._toStore("APosterioriCovariance") or \
+ self._toStore("SimulationQuantiles"):
+ if (Y.size <= Xb.size): K = B * Ha * (R + numpy.dot(Hm, B * Ha)).I
+ elif (Y.size > Xb.size): K = (BI + numpy.dot(Ha, RI * Hm)).I * Ha * RI
A = B - K * Hm * B
if min(A.shape) != max(A.shape):
raise ValueError("The %s a posteriori covariance matrix A is of shape %s, despites it has to be a squared matrix. There is an error in the observation operator, please check it."%(self._name,str(A.shape)))
#
# Calculs et/ou stockages supplémentaires
# ---------------------------------------
- if self._parameters["StoreInternalVariables"] or "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._parameters["StoreInternalVariables"] or self._toStore("CurrentState"):
self.StoredVariables["CurrentState"].store( numpy.ravel(Xa) )
- if "Innovation" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("CurrentOptimum"):
+ self.StoredVariables["CurrentOptimum"].store( numpy.ravel(Xa) )
+ if self._toStore("Innovation"):
self.StoredVariables["Innovation"].store( numpy.ravel(d) )
- if "BMA" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("BMA"):
self.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(Xa) )
- if "OMA" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("OMA"):
self.StoredVariables["OMA"].store( numpy.ravel(oma) )
- if "OMB" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("OMB"):
self.StoredVariables["OMB"].store( numpy.ravel(d) )
- if "SigmaObs2" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("SigmaObs2"):
TraceR = R.trace(Y.size)
self.StoredVariables["SigmaObs2"].store( float( (d.T * (numpy.asmatrix(numpy.ravel(oma)).T)) ) / TraceR )
- if "SigmaBck2" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("SigmaBck2"):
self.StoredVariables["SigmaBck2"].store( float( (d.T * Hm * (Xa - Xb))/(Hm * B * Hm.T).trace() ) )
- if "MahalanobisConsistency" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("MahalanobisConsistency"):
self.StoredVariables["MahalanobisConsistency"].store( float( 2.*J/d.size ) )
- if "SimulationQuantiles" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("SimulationQuantiles"):
nech = self._parameters["NumberOfSamplesForQuantiles"]
YfQ = None
for i in range(nech):
if YQ is None: YQ = YfQ[:,indice]
else: YQ = numpy.hstack((YQ,YfQ[:,indice]))
self.StoredVariables["SimulationQuantiles"].store( YQ )
- if "SimulatedObservationAtBackground" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("SimulatedObservationAtBackground"):
self.StoredVariables["SimulatedObservationAtBackground"].store( numpy.ravel(HXb) )
- if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("SimulatedObservationAtCurrentState"):
self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(HXa) )
- if "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+ if self._toStore("SimulatedObservationAtCurrentOptimum"):
+ self.StoredVariables["SimulatedObservationAtCurrentOptimum"].store( numpy.ravel(HXa) )
+ if self._toStore("SimulatedObservationAtOptimum"):
self.StoredVariables["SimulatedObservationAtOptimum"].store( numpy.ravel(HXa) )
#
self._post_run(HO)
