Hi I would like to propose a better implementation for 'test_indices':

We can remove the unneeded np.array casting:

Cleaner/New:
test_indices = list(set(range(len(texts))) - set(train_indices))

Old:
test_indices = np.array(list(set(range(len(texts))) - set(train_indices)))

What is the desired addition or change?

The algorithm for finding the centroids of clusters should be customizable similar to the way metric is.

What is the motivation for this feature?

This will allow implementation of weighted k-means algorithm as well algorithms

Unlike N-Gram, the current Skip-Gram tokenizer does not allow variable length tokens. This ticket is to implement a min max scheme similar to the N-Gram tokenizer such that each token is a k-skip-n-gram. In other words, instead of fi

Hi @JavierAntoran @stratisMarkou,

First of all, thanks for making all of this code available - it's been great to look through!

Im currently spending some time trying to work through the Weight Uncertainty in Neural Networks in order to implement Bayes-by-Backprop. I was struggling to understand the difference between your implementation of `Bayes-by-Bac