Custom Transformations (Custom
)
The Custom
class allows you to specify your own transformations
that operate on a single observation sequence. This allows your own transformations
to be seamlessly combined with others provided by Sequentia, by using the Compose
class.
API reference
- class sequentia.preprocessing.Custom(func, name=None, desc=None)[source]
Apply a custom transformation to the input observation sequence(s).
- Parameters
- func: callable
A lambda or function that specifies the transformation that should be applied to a single observation sequence.
- name: str
Name of the transformation.
- desc: str
Description of the transformation.
Examples
>>> # Create some sample data >>> X = [np.random.random((10 * i, 3)) for i in range(1, 4)] >>> # Apply a custom transformation >>> X = Custom(lambda x: x**2, name='Square', desc='Square observations element-wise')(X)
- transform(x)
Applies the transformation to a single observation sequence.
- Parameters
- X: numpy.ndarray (float)
An individual observation sequence.
- Returns
- transformed:class:numpy:numpy.ndarray (float)
The transformed input observation sequence.
- __call__(X, validate=True)
Applies the transformation to the observation sequence(s).
- Parameters
- X: numpy.ndarray (float) or list of numpy.ndarray (float)
An individual observation sequence or a list of multiple observation sequences.
- validate: bool
Whether or not to validate the input sequences.
- Returns
- transformed:class:numpy:numpy.ndarray (float) or list of
numpy.ndarray
(float) The transformed input observation sequence(s).
- transformed:class:numpy:numpy.ndarray (float) or list of