# -*- coding: utf-8 -*-
#
-# Copyright (C) 2008-2021 EDF R&D
+# Copyright (C) 2008-2022 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
Arguments :
- name : nom d'opérateur
- fromMethod : argument de type fonction Python
- - fromMatrix : argument adapté au constructeur numpy.matrix
+ - fromMatrix : argument adapté au constructeur numpy.array/matrix
- avoidingRedundancy : booléen évitant (ou pas) les calculs redondants
- reducingMemoryUse : booléen forçant (ou pas) des calculs moins
gourmands en mémoire
self.__Type = "Method"
elif fromMatrix is not None:
self.__Method = None
- self.__Matrix = numpy.matrix( fromMatrix, numpy.float )
+ if isinstance(fromMatrix, str):
+ fromMatrix = PlatformInfo.strmatrix2liststr( fromMatrix )
+ self.__Matrix = numpy.asarray( fromMatrix, dtype=float )
self.__Type = "Matrix"
else:
self.__Method = None
assert len(_xValue) == len(_HValue), "Incompatible number of elements in xValue and HValue"
_HxValue = []
for i in range(len(_HValue)):
- _HxValue.append( numpy.asmatrix( numpy.ravel( _HValue[i] ) ).T )
+ _HxValue.append( _HValue[i] )
if self.__avoidRC:
Operator.CM.storeValueInX(_xValue[i],_HxValue[-1],self.__name)
else:
else:
if self.__Matrix is not None:
self.__addOneMatrixCall()
- _xv = numpy.ravel(xv).reshape((-1,1))
- _hv = self.__Matrix * _xv
+ _hv = self.__Matrix @ numpy.ravel(xv)
else:
self.__addOneMethodCall()
_xserie.append( xv )
_HxValue = []
for paire in _xuValue:
_xValue, _uValue = paire
- _xValue = numpy.matrix(numpy.ravel(_xValue)).T
self.__addOneMatrixCall()
- _HxValue.append( self.__Matrix * _xValue )
+ _HxValue.append( self.__Matrix @ numpy.ravel(_xValue) )
else:
_xuArgs = []
for paire in _xuValue:
_HxValue = []
for paire in _nxValue:
_xNominal, _xValue = paire
- _xValue = numpy.matrix(numpy.ravel(_xValue)).T
self.__addOneMatrixCall()
- _HxValue.append( self.__Matrix * _xValue )
+ _HxValue.append( self.__Matrix @ numpy.ravel(_xValue) )
else:
self.__addOneMethodCall( len(_nxValue) )
if self.__extraArgs is None:
if argsAsSerie:
self.__addOneMethodCall( len(ValueForMethodForm) )
for _vfmf in ValueForMethodForm:
- mValue.append( numpy.matrix( self.__Method(((_vfmf, None),)) ) )
+ mValue.append( self.__Method(((_vfmf, None),)) )
else:
self.__addOneMethodCall()
mValue = self.__Method(((ValueForMethodForm, None),))
self.__FO["Tangent"] = Operator( name = self.__name+"Tangent", fromMethod = __Function["Tangent"], reducingMemoryUse = __reduceM, avoidingRedundancy = __avoidRC, inputAsMultiFunction = inputAsMF, extraArguments = self.__extraArgs )
self.__FO["Adjoint"] = Operator( name = self.__name+"Adjoint", fromMethod = __Function["Adjoint"], reducingMemoryUse = __reduceM, avoidingRedundancy = __avoidRC, inputAsMultiFunction = inputAsMF, extraArguments = self.__extraArgs )
elif asMatrix is not None:
- __matrice = numpy.matrix( __Matrix, numpy.float )
+ if isinstance(__Matrix, str):
+ __Matrix = PlatformInfo.strmatrix2liststr( __Matrix )
+ __matrice = numpy.asarray( __Matrix, dtype=float )
self.__FO["Direct"] = Operator( name = self.__name, fromMatrix = __matrice, reducingMemoryUse = __reduceM, avoidingRedundancy = __avoidRC, inputAsMultiFunction = inputAsMF, enableMultiProcess = __Parameters["EnableMultiProcessingInEvaluation"] )
self.__FO["Tangent"] = Operator( name = self.__name+"Tangent", fromMatrix = __matrice, reducingMemoryUse = __reduceM, avoidingRedundancy = __avoidRC, inputAsMultiFunction = inputAsMF )
self.__FO["Adjoint"] = Operator( name = self.__name+"Adjoint", fromMatrix = __matrice.T, reducingMemoryUse = __reduceM, avoidingRedundancy = __avoidRC, inputAsMultiFunction = inputAsMF )
else:
return __SC
+# ==============================================================================
+class PartialAlgorithm(object):
+ """
+ Classe pour mimer "Algorithm" du point de vue stockage, mais sans aucune
+ action avancée comme la vérification . Pour les méthodes reprises ici,
+ le fonctionnement est identique à celles de la classe "Algorithm".
+ """
+ def __init__(self, name):
+ self._name = str( name )
+ self._parameters = {"StoreSupplementaryCalculations":[]}
+ #
+ self.StoredVariables = {}
+ self.StoredVariables["Analysis"] = Persistence.OneVector(name = "Analysis")
+ self.StoredVariables["CostFunctionJ"] = Persistence.OneScalar(name = "CostFunctionJ")
+ self.StoredVariables["CostFunctionJb"] = Persistence.OneScalar(name = "CostFunctionJb")
+ self.StoredVariables["CostFunctionJo"] = Persistence.OneScalar(name = "CostFunctionJo")
+ self.StoredVariables["CurrentIterationNumber"] = Persistence.OneIndex(name = "CurrentIterationNumber")
+ #
+ self.__canonical_stored_name = {}
+ for k in self.StoredVariables:
+ self.__canonical_stored_name[k.lower()] = k
+
+ def _toStore(self, key):
+ "True if in StoreSupplementaryCalculations, else False"
+ return key in self._parameters["StoreSupplementaryCalculations"]
+
+ def get(self, key=None):
+ """
+ Renvoie l'une des variables stockées identifiée par la clé, ou le
+ dictionnaire de l'ensemble des variables disponibles en l'absence de
+ clé. Ce sont directement les variables sous forme objet qui sont
+ renvoyées, donc les méthodes d'accès à l'objet individuel sont celles
+ des classes de persistance.
+ """
+ if key is not None:
+ return self.StoredVariables[self.__canonical_stored_name[key.lower()]]
+ else:
+ return self.StoredVariables
+
# ==============================================================================
class AlgorithmAndParameters(object):
"""
if self.__B is not None and len(self.__B) > 0 and not( __B_shape[1] == max(__Xb_shape) ):
if self.__algorithmName in ["EnsembleBlue",]:
asPersistentVector = self.__Xb.reshape((-1,min(__B_shape)))
- self.__Xb = Persistence.OneVector("Background", basetype=numpy.matrix)
+ self.__Xb = Persistence.OneVector("Background")
for member in asPersistentVector:
- self.__Xb.store( numpy.matrix( numpy.ravel(member), numpy.float ).T )
+ self.__Xb.store( numpy.asarray(member, dtype=float) )
__Xb_shape = min(__B_shape)
else:
raise ValueError("Shape characteristic of a priori errors covariance matrix (B) \"%s\" and background (Xb) \"%s\" are incompatible."%(__B_shape,__Xb_shape))
#
if __Vector is not None:
self.__is_vector = True
- self.__V = numpy.matrix( numpy.asmatrix(__Vector).A1, numpy.float ).T
+ if isinstance(__Vector, str):
+ __Vector = PlatformInfo.strvect2liststr( __Vector )
+ self.__V = numpy.ravel(numpy.asarray( __Vector, dtype=float )).reshape((-1,1))
self.shape = self.__V.shape
self.size = self.__V.size
elif __Series is not None:
self.__is_series = True
if isinstance(__Series, (tuple, list, numpy.ndarray, numpy.matrix, str)):
- self.__V = Persistence.OneVector(self.__name, basetype=numpy.matrix)
- if isinstance(__Series, str): __Series = eval(__Series)
+ #~ self.__V = Persistence.OneVector(self.__name, basetype=numpy.matrix)
+ self.__V = Persistence.OneVector(self.__name)
+ if isinstance(__Series, str):
+ __Series = PlatformInfo.strmatrix2liststr(__Series)
for member in __Series:
- self.__V.store( numpy.matrix( numpy.asmatrix(member).A1, numpy.float ).T )
+ if isinstance(member, str):
+ member = PlatformInfo.strvect2liststr( member )
+ self.__V.store(numpy.asarray( member, dtype=float ))
else:
self.__V = __Series
if isinstance(self.__V.shape, (tuple, list)):
#
if __Scalar is not None:
if isinstance(__Scalar, str):
- __Scalar = __Scalar.replace(";"," ").replace(","," ").split()
+ __Scalar = PlatformInfo.strvect2liststr( __Scalar )
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.size = 0
elif __Vector is not None:
if isinstance(__Vector, str):
- __Vector = __Vector.replace(";"," ").replace(","," ").split()
+ __Vector = PlatformInfo.strvect2liststr( __Vector )
self.__is_vector = True
- self.__C = numpy.abs( numpy.array( numpy.ravel( __Vector ), dtype=float ) )
+ self.__C = numpy.abs( numpy.ravel(numpy.asarray( __Vector, dtype=float )) )
self.shape = (self.__C.size,self.__C.size)
self.size = self.__C.size**2
elif __Matrix is not None:
def asfullmatrix(self, msize=None):
"Matrice pleine"
if self.ismatrix():
- return numpy.asarray(self.__C)
+ return numpy.asarray(self.__C, dtype=float)
elif self.isvector():
- return numpy.asarray( numpy.diag(self.__C), float )
+ return numpy.asarray( numpy.diag(self.__C), dtype=float )
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 numpy.asarray( self.__C * numpy.eye(int(msize)), float )
+ return numpy.asarray( self.__C * numpy.eye(int(msize)), dtype=float )
elif self.isobject() and hasattr(self.__C,"asfullmatrix"):
return self.__C.asfullmatrix()
else:
raise ValueError("operands could not be broadcast together with shapes %s %s in %s matrix"%(numpy.ravel(other).shape,self.shape,self.__name))
elif self.isscalar() and isinstance(other,numpy.matrix):
return other * self.__C
+ elif self.isscalar() and isinstance(other,float):
+ return other * self.__C
elif self.isobject():
return self.__C.__rmul__(other)
else:
