Passing kNN distances or graph as input?

[davisidarta]

HI! Thank you for this fast and powerful package. Its concepts towards optimization are novel to DR and I really enjoyed your paper.

I have a question: is it possible to pass pre-computed kNN distances (or the affinity or adjecency graphs) as input to NCVis?

For now I'm testing it with a small dataset (it's indeed blazing fast) but will soon advance to one of around 1.3M samples x 5k observations for which I already have precomputed affinities. While I believe it will have no trouble computing distances rather rapidly, I can also foresee several situations where users may want to embed distance matrices, such as in chemistry, NLP, and bioinformatics, so the ability to obtain visualizations from these would be really great.

Edit: I'm aware this is a completely different question, but feel like should not open an entirely new issue just for it: I just noticed the package seems to not support user-provided initializations, and instead always employs some optimization from a random projection. Would that power iteration approach work on user-provided initializations?