frites.core.gcmi_model_nd_cd#
- frites.core.gcmi_model_nd_cd(x, y, mvaxis=None, traxis=- 1, shape_checking=True, gcrn=True)[source]#
GCMI between a continuous and discret variables.
The only difference with mi_gg is that a normalization is performed for each continuous variable.
- Parameters
- xnumpy:array_like
Continuous variable
- ynumpy:array_like
Discret variable of shape (n_trials,)
- mvaxis
python:int|python:None Spatial location of the axis to consider if multi-variate analysis is needed
- traxis
python:int| -1 Spatial location of the trial axis. By default the last axis is considered
- shape_checkingbool |
python:True Perform a reshape and check that x is consistents. For high performances and to avoid extensive memory usage, it’s better to already have x with a shape of (…, mvaxis, traxis) and to set this parameter to False
- gcrnbool |
python:True Apply a Gaussian Copula rank normalization. This operation is relatively slow for big arrays.
- Returns
- minumpy:array_like
The mutual information with the same shape as x, without the mvaxis and traxis