Tries are interesting, but it appears that while they use less memory
that dicts/maps they are generally slower than dicts for a large number
of elements. See e.g.
https://github.com/pytries/marisa-trie/blob/master/docs/benchmarks.rst.
This is also consistent with the results in the below linke
Hello devs,
I'm a final year computer engineering student, currently doing my masters
and engineering degree in recommender systems.
Last summer, after an optimization course, I found a quite interesting
recognition algorithm called : Artificial immune recognition system
(described in the paper b
I think tries might be an interesting datastructure, but it really
depends on where the bottleneck is.
I'm really surprised they are not used more, but maybe that's just
because implementations are missing?
On 11/26/18 8:39 AM, Roman Yurchak via scikit-learn wrote:
Hi Matthieu,
if you are int
Hi Matthieu,
if you are interested in general questions regarding improving
scikit-learn performance, you might be want to have a look at the draft
roadmap
https://github.com/scikit-learn/scikit-learn/wiki/Draft-Roadmap-2018 --
there is a lot topics where suggestions / PRs on improving performa
