X-Git-Url: http://git.salome-platform.org/gitweb/?a=blobdiff_plain;f=src%2FdaComposant%2FdaCore%2FBasicObjects.py;h=7b8953ac6cb8a0c3d3d811d2e512f379403717f0;hb=fce485f3b022e74f42a857c893021b90bc3cf838;hp=27d7695df5fc1db37dc7c090abbbca365ac63c06;hpb=57b58e12ace5867c972ad18986a72e71b25e6b9e;p=modules%2Fadao.git diff --git a/src/daComposant/daCore/BasicObjects.py b/src/daComposant/daCore/BasicObjects.py index 27d7695..7b8953a 100644 --- a/src/daComposant/daCore/BasicObjects.py +++ b/src/daComposant/daCore/BasicObjects.py @@ -624,6 +624,7 @@ class Algorithm(object): - OMB : Observation moins Background : Y - Xb - ForecastState : état prédit courant lors d'itérations - Residu : dans le cas des algorithmes de vérification + - SampledStateForQuantiles : échantillons d'états pour l'estimation des quantiles - SigmaBck2 : indicateur de correction optimale des erreurs d'ébauche - SigmaObs2 : indicateur de correction optimale des erreurs d'observation - SimulatedObservationAtBackground : l'état observé H(Xb) à l'ébauche @@ -661,7 +662,8 @@ class Algorithm(object): self.StoredVariables["CostFunctionJbAtCurrentOptimum"] = Persistence.OneScalar(name = "CostFunctionJbAtCurrentOptimum") self.StoredVariables["CostFunctionJo"] = Persistence.OneScalar(name = "CostFunctionJo") self.StoredVariables["CostFunctionJoAtCurrentOptimum"] = Persistence.OneScalar(name = "CostFunctionJoAtCurrentOptimum") - self.StoredVariables["CurrentIterationNumber"] = Persistence.OneIndex(name = "CurrentIterationNumber") + self.StoredVariables["CurrentEnsembleState"] = Persistence.OneMatrix(name = "CurrentEnsembleState") + self.StoredVariables["CurrentIterationNumber"] = Persistence.OneIndex(name = "CurrentIterationNumber") self.StoredVariables["CurrentOptimum"] = Persistence.OneVector(name = "CurrentOptimum") self.StoredVariables["CurrentState"] = Persistence.OneVector(name = "CurrentState") self.StoredVariables["ForecastState"] = Persistence.OneVector(name = "ForecastState") @@ -680,6 +682,7 @@ class Algorithm(object): self.StoredVariables["OMA"] = Persistence.OneVector(name = "OMA") self.StoredVariables["OMB"] = Persistence.OneVector(name = "OMB") self.StoredVariables["Residu"] = Persistence.OneScalar(name = "Residu") + self.StoredVariables["SampledStateForQuantiles"] = Persistence.OneMatrix(name = "SampledStateForQuantiles") self.StoredVariables["SigmaBck2"] = Persistence.OneScalar(name = "SigmaBck2") self.StoredVariables["SigmaObs2"] = Persistence.OneScalar(name = "SigmaObs2") self.StoredVariables["SimulatedObservationAtBackground"] = Persistence.OneVector(name = "SimulatedObservationAtBackground") @@ -1757,15 +1760,20 @@ class Covariance(object): __Matrix, __Scalar, __Vector, __Object = asCovariance, asEyeByScalar, asEyeByVector, asCovObject # if __Scalar is not None: - if numpy.matrix(__Scalar).size != 1: - raise ValueError(' The diagonal multiplier given to define a sparse matrix is not a unique scalar value.\n Its actual measured size is %i. Please check your scalar input.'%numpy.matrix(__Scalar).size) + if isinstance(__Scalar, str): + __Scalar = __Scalar.replace(";"," ").replace(","," ").split() + if len(__Scalar) > 0: __Scalar = __Scalar[0] + if numpy.array(__Scalar).size != 1: + raise ValueError(' The diagonal multiplier given to define a sparse matrix is not a unique scalar value.\n Its actual measured size is %i. Please check your scalar input.'%numpy.array(__Scalar).size) self.__is_scalar = True self.__C = numpy.abs( float(__Scalar) ) self.shape = (0,0) self.size = 0 elif __Vector is not None: + if isinstance(__Vector, str): + __Vector = __Vector.replace(";"," ").replace(","," ").split() self.__is_vector = True - self.__C = numpy.abs( numpy.array( numpy.ravel( numpy.matrix(__Vector, float ) ) ) ) + self.__C = numpy.abs( numpy.array( numpy.ravel( __Vector ), dtype=float ) ) self.shape = (self.__C.size,self.__C.size) self.size = self.__C.size**2 elif __Matrix is not None: @@ -1835,12 +1843,12 @@ class Covariance(object): def getI(self): "Inversion" if self.ismatrix(): - return Covariance(self.__name+"I", asCovariance = self.__C.I ) + return Covariance(self.__name+"I", asCovariance = numpy.linalg.inv(self.__C) ) elif self.isvector(): return Covariance(self.__name+"I", asEyeByVector = 1. / self.__C ) elif self.isscalar(): return Covariance(self.__name+"I", asEyeByScalar = 1. / self.__C ) - elif self.isobject(): + elif self.isobject() and hasattr(self.__C,"getI"): return Covariance(self.__name+"I", asCovObject = self.__C.getI() ) else: return None # Indispensable @@ -1853,8 +1861,10 @@ class Covariance(object): return Covariance(self.__name+"T", asEyeByVector = self.__C ) elif self.isscalar(): return Covariance(self.__name+"T", asEyeByScalar = self.__C ) - elif self.isobject(): + elif self.isobject() and hasattr(self.__C,"getT"): return Covariance(self.__name+"T", asCovObject = self.__C.getT() ) + else: + raise AttributeError("the %s covariance matrix has no getT attribute."%(self.__name,)) def cholesky(self): "Décomposition de Cholesky" @@ -1866,41 +1876,49 @@ class Covariance(object): return Covariance(self.__name+"C", asEyeByScalar = numpy.sqrt( self.__C ) ) elif self.isobject() and hasattr(self.__C,"cholesky"): return Covariance(self.__name+"C", asCovObject = self.__C.cholesky() ) + else: + raise AttributeError("the %s covariance matrix has no cholesky attribute."%(self.__name,)) def choleskyI(self): "Inversion de la décomposition de Cholesky" if self.ismatrix(): - return Covariance(self.__name+"H", asCovariance = numpy.linalg.cholesky(self.__C).I ) + return Covariance(self.__name+"H", asCovariance = numpy.linalg.inv(numpy.linalg.cholesky(self.__C)) ) elif self.isvector(): return Covariance(self.__name+"H", asEyeByVector = 1.0 / numpy.sqrt( self.__C ) ) elif self.isscalar(): return Covariance(self.__name+"H", asEyeByScalar = 1.0 / numpy.sqrt( self.__C ) ) elif self.isobject() and hasattr(self.__C,"choleskyI"): return Covariance(self.__name+"H", asCovObject = self.__C.choleskyI() ) + else: + raise AttributeError("the %s covariance matrix has no choleskyI attribute."%(self.__name,)) def sqrtm(self): "Racine carrée matricielle" if self.ismatrix(): import scipy - return Covariance(self.__name+"C", asCovariance = scipy.linalg.sqrtm(self.__C) ) + return Covariance(self.__name+"C", asCovariance = numpy.real(scipy.linalg.sqrtm(self.__C)) ) elif self.isvector(): return Covariance(self.__name+"C", asEyeByVector = numpy.sqrt( self.__C ) ) elif self.isscalar(): return Covariance(self.__name+"C", asEyeByScalar = numpy.sqrt( self.__C ) ) - elif self.isobject() and hasattr(self.__C,"sqrt"): - return Covariance(self.__name+"C", asCovObject = self.__C.sqrt() ) + elif self.isobject() and hasattr(self.__C,"sqrtm"): + return Covariance(self.__name+"C", asCovObject = self.__C.sqrtm() ) + else: + raise AttributeError("the %s covariance matrix has no sqrtm attribute."%(self.__name,)) def sqrtmI(self): "Inversion de la racine carrée matricielle" if self.ismatrix(): import scipy - return Covariance(self.__name+"H", asCovariance = scipy.linalg.sqrtm(self.__C).I ) + return Covariance(self.__name+"H", asCovariance = numpy.linalg.inv(numpy.real(scipy.linalg.sqrtm(self.__C))) ) elif self.isvector(): return Covariance(self.__name+"H", asEyeByVector = 1.0 / numpy.sqrt( self.__C ) ) elif self.isscalar(): return Covariance(self.__name+"H", asEyeByScalar = 1.0 / numpy.sqrt( self.__C ) ) - elif self.isobject() and hasattr(self.__C,"sqrtI"): - return Covariance(self.__name+"H", asCovObject = self.__C.sqrtI() ) + elif self.isobject() and hasattr(self.__C,"sqrtmI"): + return Covariance(self.__name+"H", asCovObject = self.__C.sqrtmI() ) + else: + raise AttributeError("the %s covariance matrix has no sqrtmI attribute."%(self.__name,)) def diag(self, msize=None): "Diagonale de la matrice" @@ -1913,8 +1931,26 @@ class Covariance(object): raise ValueError("the size of the %s covariance matrix has to be given in case of definition as a scalar over the diagonal."%(self.__name,)) else: return self.__C * numpy.ones(int(msize)) - elif self.isobject(): + elif self.isobject() and hasattr(self.__C,"diag"): return self.__C.diag() + else: + raise AttributeError("the %s covariance matrix has no diag attribute."%(self.__name,)) + + def trace(self, msize=None): + "Trace de la matrice" + if self.ismatrix(): + return numpy.trace(self.__C) + elif self.isvector(): + return float(numpy.sum(self.__C)) + elif self.isscalar(): + if msize is None: + raise ValueError("the size of the %s covariance matrix has to be given in case of definition as a scalar over the diagonal."%(self.__name,)) + else: + return self.__C * int(msize) + elif self.isobject(): + return self.__C.trace() + else: + raise AttributeError("the %s covariance matrix has no trace attribute."%(self.__name,)) def asfullmatrix(self, msize=None): "Matrice pleine" @@ -1929,20 +1965,12 @@ class Covariance(object): return numpy.asarray( self.__C * numpy.eye(int(msize)), float ) elif self.isobject() and hasattr(self.__C,"asfullmatrix"): return self.__C.asfullmatrix() + else: + raise AttributeError("the %s covariance matrix has no asfullmatrix attribute."%(self.__name,)) - def trace(self, msize=None): - "Trace de la matrice" - if self.ismatrix(): - return numpy.trace(self.__C) - elif self.isvector(): - return float(numpy.sum(self.__C)) - elif self.isscalar(): - if msize is None: - raise ValueError("the size of the %s covariance matrix has to be given in case of definition as a scalar over the diagonal."%(self.__name,)) - else: - return self.__C * int(msize) - elif self.isobject(): - return self.__C.trace() + def assparsematrix(self): + "Valeur sparse" + return self.__C def getO(self): return self