nemos.basis._transformer_basis.TransformerBasis.fit#

TransformerBasis.fit(X, y=None)[source]#

Check the input structure and, if necessary, compute the convolutional kernels.

Parameters:

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

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

Returns:

The transformer object.

Return type:

self

Raises:

  • RuntimeError – If self.n_basis_input is None. Call self.set_input_shape before calling fit to avoid this.

  • ValueError: – If the number of columns in X do not self.n_basis_input_.

Examples

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

>>> # Example input
>>> X = np.random.normal(size=(100, 2))

>>> # Define and fit tranformation basis
>>> basis = MSplineEval(10).set_input_shape(2)
>>> transformer = TransformerBasis(basis)
>>> transformer_fitted = transformer.fit(X)
On this page

This Page