NaN after trained rbig transform

[gzhu06]

First of all, thanks for the paper and code, it's very inspiring.

Specifically, first of all, I tried to generate 100 classes of vectors (each class contains 300 samples). These vectors follow multivariate student t distribution of 100 class-dependent means and variances. Then I split these 100 classes into 80 and 20 for training and inferencing respectively. Finally, I try to use rbig following:

#rBIG
n_layers = 1000
rotation_type = 'pca'
random_state = 123
zero_tolerance = 10 # I also tried 60

#Initialize RBIG class
rbig_model = RBIG(n_layers=n_layers, rotation_type=rotation_type, random_state=random_state, zero_tolerance=zero_tolerance)

train_dataset_rbig = rbig_model.fit_transform(train_dataset)
test_dataset_rbig = rbig_model.transform(test_dataset)

then train_dataset_rbig is Okay, but NaN appears in test_dataset_rbig.

Am I wrongly using rbig or something else?

Thanks!

[jejjohnson]

Hi thanks for you interest! 

So in order to debug, would you be able to provide a minimal working code example? Then maybe I could see if there is an issue. My first thought might be a type issue but I'm not sure. Let me know!

[gzhu06]

You can download this toy.py.zip to see the working code. (just updated)
toy.py.zip

thank you so much!