frites.core.gccmi_model_nd_cdnd#
- frites.core.gccmi_model_nd_cdnd(x, y, *z, mvaxis=None, traxis=- 1, gcrn=True, shape_checking=True)[source]#
Conditional GCMI between a continuous and a discret variable.
This function performs a GC-CMI between a continuous and a discret variable conditioned with multiple discrete variables.
- Parameters
- xnumpy:array_like
Continuous variable
- ynumpy:array_like
Discret variable
- z
python:list| numpy:array_like Array that describes the conditions across the trial axis. Should be a list of arrays of shape (n_trials,) of integers (e.g. [0, 0, …, 1, 1, 2, 2])
- 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
- gcrnbool |
python:True Apply a Gaussian Copula rank normalization. This operation is relatively slow for big arrays.
- shape_checkingbool |
python:True Perform a reshape and check that x and y shapes are consistents. For high performances and to avoid extensive memory usage, it’s better to already have x and y with a shape of (…, mvaxis, traxis) and to set this parameter to False
- Returns
- cminumpy:array_like
Conditional mutual-information with the same shape as x and y without the mvaxis and traxis