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 <[EMAIL PROTECTED]>: >> 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 > Website: http://matthieu-brucher.developpez.com/ > Blogs: http://matt.eifelle.com and http://blog.developpez.com/?blog=92 > LinkedIn: http://www.linkedin.com/in/matthieubrucher > _______________________________________________ > Numpy-discussion mailing list > Numpy-discussion@scipy.org > http://projects.scipy.org/mailman/listinfo/numpy-discussion _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion