frites.core.mi_model_1d_gd#
- frites.core.mi_model_1d_gd(x, y, biascorrect=True, demeaned=False)[source]#
Mutual information between a Gaussian and a discrete variable in bits.
This method is based on ANOVA style model comparison. I = mi_model_gd(x,y) returns the MI between the (possibly multidimensional) Gaussian variable x and the discrete variable y.
- Parameters
- x, ynumpy:array_like
Gaussian arrays of shape (n_epochs,) or (n_dimensions, n_epochs). y must be an array of integers
- biascorrectbool |
python:True Specifies whether bias correction should be applied to the estimated MI
- demeanedbool |
python:False Specifies whether the input data already has zero mean (true if it has been copula-normalized)
- Returns
- i
python:float Information shared by x and y (in bits)
- i