nemos.basis.IdentityEval.compute_features#

IdentityEval.compute_features(xi)[source]#

Evaluate basis at sample points.

The basis is evaluated at the locations specified in the inputs. For example, compute_features(np.array([0, .5])) would return the array:

b_1(0) ... b_n(0)
b_1(.5) ... b_n(.5)

where b_i is the i-th basis.

Parameters:

*xi (ArrayLike) – The input samples over which to apply the basis transformation. The samples can be passed as multiple arguments, each representing a different dimension for multivariate inputs.

Return type:

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

Returns:

A matrix with the transformed features.

Examples

>>> import numpy as np
>>> from nemos.basis import IdentityEval

>>> # Generate data
>>> num_samples = 1000
>>> X = np.random.normal(size=(num_samples, ))  # raw time series
>>> basis = IdentityEval()
>>> features = basis.compute_features(X)  # basis transformed time series
>>> features.shape
(1000, 1)