nemos.basis.BSplineEval.split_by_feature#

BSplineEval.split_by_feature(x, axis=1)[source]#

Decompose an array along a specified axis into sub-arrays based on the number of expected inputs.

This function takes an array (e.g., a design matrix or model coefficients) and splits it along a designated axis.

How it works:

  • If the basis expects an input shape (n_samples, n_inputs), then the feature axis length will be total_n_features = n_inputs * n_basis_funcs. This axis is reshaped into dimensions (n_inputs, n_basis_funcs).

  • If the basis expects an input of shape (n_samples,), then the feature axis length will be total_n_features = n_basis_funcs. This axis is reshaped into (1, n_basis_funcs).

For example, if the input array x has shape (1, 2, total_n_features, 4, 5), then after applying this method, it will be reshaped into (1, 2, n_inputs, n_basis_funcs, 4, 5).

The specified axis (axis) determines where the split occurs, and all other dimensions remain unchanged. See the example section below for the most common use cases.

Parameters:

  • x (NDArray) –

    The input array to be split, representing concatenated features, coefficients, or other data. The shape of x along the specified axis must match the total number of features generated by the basis, i.e., self.n_output_features.

    Examples:

    • For a design matrix: (n_samples, total_n_features)

    • For model coefficients: (total_n_features,) or (total_n_features, n_neurons).

  • axis (int, optional) – The axis along which to split the features. Defaults to 1. Use axis=1 for design matrices (features along columns) and axis=0 for coefficient arrays (features along rows). All other dimensions are preserved.

Raises:

ValueError – If the shape of x along the specified axis does not match self.n_output_features.

Returns:

A dictionary where:

  • Key: Label of the basis.

  • Value: the array reshaped to: (..., n_inputs, n_basis_funcs, ...)

Return type:

dict

Examples

>>> import numpy as np
>>> from nemos.basis import BSplineEval
>>> from nemos.glm import GLM
>>> basis = BSplineEval(n_basis_funcs=6, label="one_input")
>>> X = basis.compute_features(np.random.randn(20,))
>>> split_features_multi = basis.split_by_feature(X, axis=1)
>>> for feature, sub_dict in split_features_multi.items():
...        print(f"{feature}, shape {sub_dict.shape}")
one_input, shape (20, 6)