Is our ability to match components good or bad? We can test by making random cuts of the datset, then seeing our ability to match (and the average value of the match dissimilarity metric).
Initial design:
Create a subclass of MniMasker that selects a given # of MNI voxels randomly.
Use that mask to separate the voxels into two sets.
Run the rest of the matching procedure as-is, in a file called match-random.py (or in match.py with a --random flag.
Is our ability to match components good or bad? We can test by making random cuts of the datset, then seeing our ability to match (and the average value of the match dissimilarity metric).
Initial design:
MniMasker
that selects a given # of MNI voxels randomly.match-random.py
(or inmatch.py
with a--random
flag.