- YfQ = None
- for i in range(nech):
- if self._parameters["SimulationForQuantiles"] == "Linear":
- dXr = numpy.matrix(numpy.random.multivariate_normal(Xa.A1,A) - Xa.A1).T
- dYr = numpy.matrix(numpy.ravel( HtM * dXr )).T
- Yr = HXa + dYr
- elif self._parameters["SimulationForQuantiles"] == "NonLinear":
- Xr = numpy.matrix(numpy.random.multivariate_normal(Xa.A1,A)).T
- Yr = numpy.matrix(numpy.ravel( H( Xr ) )).T
- if YfQ is None:
- YfQ = Yr
- else:
- YfQ = numpy.hstack((YfQ,Yr))
- YfQ.sort(axis=-1)
- YQ = None
- for quantile in Qtls:
- if not (0. <= quantile <= 1.): continue
- indice = int(nech * quantile - 1./nech)
- if YQ is None: YQ = YfQ[:,indice]
- else: YQ = numpy.hstack((YQ,YfQ[:,indice]))
- self.StoredVariables["SimulationQuantiles"].store( YQ )
- #
- logging.debug("%s Nombre d'évaluation(s) de l'opérateur d'observation direct/tangent/adjoint.: %i/%i/%i"%(self._name, HO["Direct"].nbcalls(0),HO["Tangent"].nbcalls(0),HO["Adjoint"].nbcalls(0)))
- logging.debug("%s Nombre d'appels au cache d'opérateur d'observation direct/tangent/adjoint..: %i/%i/%i"%(self._name, HO["Direct"].nbcalls(3),HO["Tangent"].nbcalls(3),HO["Adjoint"].nbcalls(3)))
- logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M")))
- logging.debug("%s Terminé"%self._name)
+ NumericObjects.QuantilesEstimations(self, A, Xa, HXa, H, HtM)
+ if self._toStore("SimulatedObservationAtBackground"):
+ self.StoredVariables["SimulatedObservationAtBackground"].store( numpy.ravel(HXb) )
+ if self._toStore("SimulatedObservationAtCurrentState"):
+ self.StoredVariables["SimulatedObservationAtCurrentState"].store( numpy.ravel(HXa) )
+ if self._toStore("SimulatedObservationAtCurrentOptimum"):
+ self.StoredVariables["SimulatedObservationAtCurrentOptimum"].store( numpy.ravel(HXa) )
+ if self._toStore("SimulatedObservationAtOptimum"):
+ self.StoredVariables["SimulatedObservationAtOptimum"].store( numpy.ravel(HXa) )