Damjan Kužnar
Damjan Kužnar
Thanks for the pull request, but I think this problem is resolved with the latest commit c6144fe67961fbf38fe2931219ff895cb7f7a5d3 that references issue #7. Could you please verify?
This implementation of LOF is very straightforward and without any performance considerations, which is the reason you are seeing the difference - the paper mentions the use of materialization database...
I made an improved implementation of LOF using Numpy which works much faster which is currently in branch `numpy` (see a66218f42fc652c6059c09cd15e6071342ed07c6). This branch also has an updated [README.md](https://github.com/damjankuznar/pylof/blob/numpy/README.md#performance) with added...
Because this is not actually euclidean distance, but rather root **mean** square error (RMSE). See here https://en.wikipedia.org/wiki/Root-mean-square_deviation Does that answer your question?
I second this proposal as it's really forcing devs to do hacky or suboptimal things to get serializable dict from Pydantic model and it really should not be this hard.
@elprans using `statement_cache_size=0` in `connect` doesn't change anything. It still hangs indefinitely.
Yes, `await conn.execute('SELECT 1')` works!
@elprans Will you be fixing the `fetch` methods or do we need to use `execute`?
I've been testing a bit with `execute` and it also hangs indefinitely if arguments are used, e.g.: ```python conn.execute('''SELECT 1 WHERE 2 > $1''', 1) ``` Full example: ```python import...
@romgar could you please check this feature