[Numpy-discussion] Proposal: scipy.spatial

[David Bolme]

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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