SubsetTuple

class hybrid_learning.datasets.transforms.tuple_transforms.SubsetTuple(indices)[source]

Bases: _IndexedTupleTrafo

Return a tuple only containing the elements at given indices of input tuple. Indices may be given as positive or negative index. Elements are not

Public Data Attributes:

Inherited from : py: class:_IndexedTupleTrafo

settings

Settings to reproduce the instance.
Inherited from : py: class:Transform

IDENTITY_CLASS

The identity class or classes for composition / addition.

settings

Settings to reproduce the instance.

Public Methods:

apply_to(*inputs)

Apply the transformation to the tuple (inp, target); the output again is a tuple.
Inherited from : py: class:_IndexedTupleTrafo

unique_pos_indices_for(inputs)

For an input tuple of certain length return the unique positive indices to work on.
Inherited from : py: class:TupleTransforms

apply_to(*inputs)

Apply the transformation to the tuple (inp, target); the output again is a tuple.
Inherited from : py: class:Transform

apply_to(*inputs)

Apply the transformation to the tuple (inp, target); the output again is a tuple.

Special Methods:

Inherited from : py: class:_IndexedTupleTrafo

__init__(indices)

Init.
Inherited from : py: class:TupleTransforms

__init__(indices)

Init.

__call__(*inputs)

Apply the transformation to the tuple (inp, target); the output again is a tuple.
Inherited from : py: class:Transform

__repr__()

Return repr(self).

__eq__(other)

Return self==value.

__copy__()

Return a shallow copy of self using settings.

__add__(other)

Return a flat composition of self with other.

__radd__(other)

Return a flat composition of other and self.

__call__(*inputs)

Apply the transformation to the tuple (inp, target); the output again is a tuple.
Parameters

indices (Union[int, Sequence[int]]) –

apply_to(*inputs)[source]

Apply the transformation to the tuple (inp, target); the output again is a tuple.

Return type

Tuple