-# ==============================================================================
-def CostFunction3D(_x,
- _Hm = None, # Pour simuler Hm(x) : HO["Direct"].appliedTo
- _HmX = None, # Simulation déjà faite de Hm(x)
- _arg = None, # Arguments supplementaires pour Hm, sous la forme d'un tuple
- _BI = None,
- _RI = None,
- _Xb = None,
- _Y = None,
- _SIV = False, # A résorber pour la 8.0
- _SSC = [], # self._parameters["StoreSupplementaryCalculations"]
- _nPS = 0, # nbPreviousSteps
- _QM = "DA", # QualityMeasure
- _SSV = {}, # Entrée et/ou sortie : self.StoredVariables
- _fRt = False, # Restitue ou pas la sortie étendue
- _sSc = True, # Stocke ou pas les SSC
- ):
- """
- Fonction-coût générale utile pour les algorithmes statiques/3D : 3DVAR, BLUE
- et dérivés, Kalman et dérivés, LeastSquares, SamplingTest, PSO, SA, Tabu,
- DFO, QuantileRegression
- """
- if not _sSc:
- _SIV = False
- _SSC = {}
- else:
- for k in ["CostFunctionJ",
- "CostFunctionJb",
- "CostFunctionJo",
- "CurrentOptimum",
- "CurrentState",
- "IndexOfOptimum",
- "SimulatedObservationAtCurrentOptimum",
- "SimulatedObservationAtCurrentState",
- ]:
- if k not in _SSV:
- _SSV[k] = []
- if hasattr(_SSV[k],"store"):
- _SSV[k].append = _SSV[k].store # Pour utiliser "append" au lieu de "store"
- #
- _X = numpy.asmatrix(numpy.ravel( _x )).T
- if _SIV or "CurrentState" in _SSC or "CurrentOptimum" in _SSC:
- _SSV["CurrentState"].append( _X )
- #
- if _HmX is not None:
- _HX = _HmX
- else:
- if _Hm is None:
- raise ValueError("COSTFUNCTION3D Operator has to be defined.")
- if _arg is None:
- _HX = _Hm( _X )
- else:
- _HX = _Hm( _X, *_arg )
- _HX = numpy.asmatrix(numpy.ravel( _HX )).T
- #
- if "SimulatedObservationAtCurrentState" in _SSC or \
- "SimulatedObservationAtCurrentOptimum" in _SSC:
- _SSV["SimulatedObservationAtCurrentState"].append( _HX )
- #
- if numpy.any(numpy.isnan(_HX)):
- Jb, Jo, J = numpy.nan, numpy.nan, numpy.nan
- else:
- _Y = numpy.asmatrix(numpy.ravel( _Y )).T
- if _QM 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!")
- _Xb = numpy.asmatrix(numpy.ravel( _Xb )).T
- Jb = 0.5 * (_X - _Xb).T * _BI * (_X - _Xb)
- Jo = 0.5 * (_Y - _HX).T * _RI * (_Y - _HX)
- elif _QM 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)
- elif _QM in ["LeastSquares", "LS", "L2"]:
- Jb = 0.
- Jo = 0.5 * (_Y - _HX).T * (_Y - _HX)
- elif _QM in ["AbsoluteValue", "L1"]:
- Jb = 0.
- Jo = numpy.sum( numpy.abs(_Y - _HX) )
- elif _QM in ["MaximumError", "ME"]:
- Jb = 0.
- Jo = numpy.max( numpy.abs(_Y - _HX) )
- elif _QM in ["QR", "Null"]:
- Jb = 0.
- Jo = 0.
- else:
- raise ValueError("Unknown asked quality measure!")
- #
- J = float( Jb ) + float( Jo )
- #
- if _sSc:
- _SSV["CostFunctionJb"].append( Jb )
- _SSV["CostFunctionJo"].append( Jo )
- _SSV["CostFunctionJ" ].append( J )
- #
- if "IndexOfOptimum" in _SSC or \
- "CurrentOptimum" in _SSC or \
- "SimulatedObservationAtCurrentOptimum" in _SSC:
- IndexMin = numpy.argmin( _SSV["CostFunctionJ"][_nPS:] ) + _nPS
- if "IndexOfOptimum" in _SSC:
- _SSV["IndexOfOptimum"].append( IndexMin )
- if "CurrentOptimum" in _SSC:
- _SSV["CurrentOptimum"].append( _SSV["CurrentState"][IndexMin] )
- if "SimulatedObservationAtCurrentOptimum" in _SSC:
- _SSV["SimulatedObservationAtCurrentOptimum"].append( _SSV["SimulatedObservationAtCurrentState"][IndexMin] )
- #
- if _fRt:
- return _SSV
- else:
- if _QM in ["QR"]: # Pour le QuantileRegression
- return _HX
- else:
- return J
-