[re-sending due to mail server problems]
On 01/04/2012 04:22 PM, Mathias Verbeke wrote:
> Dear all,
>
> I just started working with Scikit Learn and I'm currently using the Nearest
> Neighbors
> module. In the documentation is stated that it currently only supports the
> Euclidean
> distance metric, and I was wondering if it would be easy to extend it with
> other
> distance metrics? Since it uses the scipy.sparse matrices as input, I was
> thinking about
> the distance metrics in scipy.distance.spatial. Would that be possible, or
> were there
> certain considerations to only allow for Euclidean distance?
>
>
Hi,
Recently I started to investigate cover trees [0] which are another
data structure like the ball tree or kd-tree with some interesting
properties. I stumbled upon PyCoverTree which is a pure python
implementation of cover trees which I am tweaking to make
it more usable (e.g. initially it worked only on Python>=v2.7) and
eventually faster:
https://github.com/emanuele/PyCoverTree
This algorithm might be of interest to you because it is like the BallTree
class but it accepts a generic distance functionm thus non-vectorial input
too. Of course it is (much) slower than the BallTree during insertion.
But if that is not a big problem, then queries are not that much slower :-)
Time permitting I'd like to improve PyCoverTree and make it sklearn-compliant.
But do not expect too much in the short term ;-)
Note: the current LICENSE.txt of PyCoverTree says "all rights reserved"
but I am in the process of getting an appropriate open source license
from the original authors. If there is interest I'll post news here.
Best,
Emanuele
[0]: http://hunch.net/~jl/projects/cover_tree/cover_tree.html
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