def __init__(self, bases):
'''
Constructor for the class FullTensorProductFunctionalBasis.
Parameters
----------
bases : list or tensap.FunctionalBases
The bases associated with the object.
Returns
-------
None.
'''
tensap.FunctionalBasis.__init__(self)
if isinstance(bases, list):
bases = tensap.FunctionalBases(bases)
assert isinstance(bases, tensap.FunctionalBases), \
'The first argument must be a FunctionalBases.'
self.bases = bases
self.measure = tensap.ProductMeasure([x.measure for x in bases.bases])
self.is_orthonormal = np.all([x.is_orthonormal for x in bases.bases])
def __init__(self, bases, indices):
'''
Constructor for the class SparseTensorProductFunctionalBasis.
Parameters
----------
bases : list or tensap.FunctionalBases
The bases associated with the object.
indices : tensap.MultiIndices
The indices of the basis functions (the indices start at 0).
Returns
-------
None.
'''
tensap.FunctionalBasis.__init__(self)
assert isinstance(bases, tensap.FunctionalBases), \
'The first argument must be a FunctionalBases.'
assert isinstance(indices, tensap.MultiIndices), \
'The second argument must be a MultiIndices.'
self.bases = bases
self.indices = indices
self.measure = tensap.ProductMeasure([x.measure for x in bases.bases])
self.is_orthonormal = np.all([x.is_orthonormal for x in bases.bases])
def __init__(self, bases):
tensap.FunctionalBasis.__init__(self)
if isinstance(bases, list):
bases = tensap.FunctionalBases(bases)
assert isinstance(bases, tensap.FunctionalBases), \
'The first argument must be a FunctionalBases.'
self.bases = bases
self.measure = tensap.ProductMeasure([x.measure for x in bases.bases])
self.is_orthonormal = np.all([x.is_orthonormal for x in bases.bases])
def __init__(self, bases, indices):
tensap.FunctionalBasis.__init__(self)
assert isinstance(bases, tensap.FunctionalBases), \
'The first argument must be a FunctionalBases.'
assert isinstance(indices, tensap.MultiIndices), \
'The second argument must be a MultiIndices.'
self.bases = bases
self.indices = indices
self.measure = tensap.ProductMeasure([x.measure for x in bases.bases])
self.is_orthonormal = np.all([x.is_orthonormal for x in bases.bases])
def __init__(self, bases=None):
'''
Constructor for the class FunctionalBases.
To create a FunctionalBases by replication of FunctionalBasis,
see FunctionalBases.duplicate.
Parameters
----------
bases : list or numpy.ndarray, or tensap.FunctionalBases, optional
List or numpy.ndarray containing objects of type FunctionalBasis.
The default is None.
Raises
------
ValueError
If the provided bases are not of type FunctionalBasis.
Returns
-------
None.
'''
if bases is not None:
if isinstance(bases, FunctionalBases):
measure = bases.measure
bases = bases.bases
elif np.all([isinstance(x, tensap.FunctionalBasis)
for x in bases]):
measure = tensap.ProductMeasure([x.measure for x in bases])
else:
raise ValueError('Wrong argument.')
self.bases = np.atleast_1d(bases)
self.measure = measure
if not np.all(
[isinstance(x, tensap.FunctionalBasis) for x in self.bases]):
raise ValueError(
'Bases must contain objects of type FunctionalBasis')