GCMI in Python gives unexpected results about gcmi HOT 3 CLOSED

nk.mutual_information(np.abs(x), y + noise, method="gc")

would I think be sensitive to y=x**2 effect, but requires some sort of prior knowledge (and would lose linear effects). In general if you want to have sensitivity to a symmetric effect you can create a 2d variable np.stack([x, np.abs(x-np.median(x))]) and put that as a 2d input for GCMI. There is a cost in terms of sensitivity and sampling variance for going to 2d and it's not very elegant, but just a suggestion :)

nk.mutual_information(np.stack([x, np.abs(x-np.median(x))]), y + noise, method="gc")

Sorry about the incompatible license. I have always preferred the GPL for publically funded academic work, but perhaps that view is a bit out of date now!

from gcmi.

I think your signal is y=x**2 + noise. GCMI won't detect this sort of symmetric effect, it can detect only monotonic effects (it is like a rank correlation). You could try adding in abs(x) as a second dimension of x.

Also I apprecaite your interest but GCMI is licensed under the GPL so code can't be reused and released under MIT.

from gcmi.

Thanks for your reply

You could try adding in abs(x) as a second dimension of x.

I am not sure what you mean here?

GCMI is licensed under the GPL

Ah sorry I missed that. Shame, I'll close the PR then!

from gcmi.