nemos.basis._transformer_basis.TransformerBasis.transform

TransformerBasis.transform(X, y=None)

Transform the data using the fitted basis functions.

Parameters:

• X (TsdFrame | ndarray[tuple[int, ...], dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]) – The data to transform using the basis functions, shape (num_samples, num_input).

• y – Not used, present for API consistency by convention.

Return type:

TsdFrame | ndarray[tuple[int, ...], dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]

Returns:

The data transformed by the basis functions.

Examples

>>> import numpy as np
>>> from nemos.basis import MSplineConv, TransformerBasis

>>> # Example input
>>> X = np.random.normal(size=(10000, 2))

>>> basis = MSplineConv(10, window_size=200).set_input_shape(2)
>>> transformer = TransformerBasis(basis)
>>> # Before calling `fit` the convolution kernel is not set
>>> transformer.kernel_

>>> transformer_fitted = transformer.fit(X)
>>> # Now the convolution kernel is initialized and has shape (window_size, n_basis_funcs)
>>> transformer_fitted.kernel_.shape
(200, 10)

>>> # Transform basis
>>> feature_transformed = transformer.transform(X)