[Numpy-discussion] Proposal: scipy.spatial
David Bolme
bolme1234 at comcast.net
Thu Oct 2 17:17:13 EDT 2008
It may be useful to have an interface that handles both cases:
similarity and dissimilarity. Often I have seen "Nearest Neighbor"
algorithms that look for maximum similarity instead of minimum
distance. In my field (biometrics) we often deal with very
specialized distance or similarity measures. I would like to see
support for user defined distance and similarity functions. It should
be easy to implement by passing a function object to the KNN class. I
am not sure if kd-trees or other fast algorithms are compatible with
similarities or non-euclidian norms, however I would be willing to
implement an exhaustive search KNN that would support user defined
functions.
On Oct 2, 2008, at 2:01 PM, Matthieu Brucher wrote:
> 2008/10/2 David Bolme <bolme1234 at comcast.net>:
>> I also like the idea of a scipy.spatial library. For the research I
>> do in machine learning and computer vision we are often interested in
>> specifying different distance measures. It would be nice to have a
>> way to specify the distance measure. I would like to see a standard
>> set included: City Block, Euclidean, Correlation, etc as well as a
>> capability for a user defined distance or similarity function.
>
> You mean similarity or dissimilarity ? Distance is a dissimilarity but
> correlation is a similarity measure.
>
> Matthieu
> --
> French PhD student
> Information System Engineer
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