`polychan`#

hypercoil.functional.tsconv.polychan(X: Tensor, degree: int = 2, include_const: bool = False) → Tensor[source]#

Create a polynomial channel basis for the data.

Single-channel data are mapped across K channels, and raised to the ith power at the ith channel.

Dimension:

Input : \((N, *, C, obs)\): N denotes batch size, * denotes any number of intervening dimensions, C denotes number of data channels or variables, obs denotes number of observations per channel.
Output : \((N, K, *, C, obs)\): K denotes maximum polynomial degree to include

Parameters:

XTensor: Dataset to expand as a polynomial basis. A new channel will be created containing the same dataset raised to each power up to the specified degree.
degreeint >= 2 (default 2): Maximum polynomial degree to be included in the output basis.
include_constbool (default False): Indicates that a constant or intercept term corresponding to the zeroth power should be included.

Returns:

polychan#