dinarior
dinarior
Using Python 3.9.9, Julia 1.6.2, PyCall 1.93.0, PyJulia 0.5.7 the bug is fixed for me. The same setting with PyCall 1.92.3 was broken, while a similar setting with Python 3.8...
@Immich conda in all 3 cases. Used pip for the installations.
Thanks, I intend to rewrite it, maybe extract common functionalities from the ARI metric, hopefully, will get to it soon enough.
Great!, I will wait for it to get pushed and update this commit accordingly.
Hi, Could you please provide information on your OS, scitkit-learn version and numpy version? Also, can you try with non-sparse matrix?
Thanks, env shouldn't be an issue then. Could you try the toy example: ``` from sklearn.datasets import make_blobs from pdc_dp_means import DPMeans # Generate sample data X, y_true = make_blobs(n_samples=300,...
@JuliaRegistrator register Release notes: predict now returns '(labels,probability_matrix)' where the matrix is of NxK. Added smart splits for Gaussians (added keyword to fit 'smart_splits=true'). Bugs:
This deadlock can easily be caught by modifying the `usertests` to repeat `linkunlink` tests, with `CPUS > 1`. ``` for(i=1; i
I was using 2.2.2 (injected to the pipx installation), this is the pip list - ``` aiohttp 3.9.3 aiosignal 1.3.1 altair 5.2.0 annotated-types 0.6.0 anyio 4.3.0 async-timeout 4.0.3 attrs 23.2.0...
Hi, Which Python version, and which OS are you using?