[SciPy-dev] Implementing a distance matrix between two sets of vectors concept

[Peter Skomoroch]

I've rolled my own in the past.  If the vectors are really large and you are
holding a collection of them, you probably want to use a sparse matrix data
structure in either numpy or C.

On 7/3/07, Bill Baxter <wbaxter at gmail.com> wrote:
>
> I would use it.
>
> I only need Euclidean distance,
> Python API only is ok.
> Data-types: float and double would do it for me.  Double only if it's too
> much effort to do both.
> Order -- all combos of F and C both would be nice, but not critical
> Strides -- with strides better than without, but not critical
> nan -- don't need it.
>
> --bb
>
> On 7/3/07, David Cournapeau < david at ar.media.kyoto-u.ac.jp> wrote:
> >
> > Hi,
> >
> >     for my machine learning toolbox, I need the concept of distance
> > matrix, that is for two sets of vectors v and u (N u and M v), of
> > dimension d, I want to compute the matrix D such as d(i,j) =
> > distance(v_i, u_j). This is easy to do in numpy, but for big datasets,
> > this becomes difficult without a significance loss of efficiency or big
> > memory consumption.
> >     So I am thinking about implementing it in C. I think the overall
> > concept is useful for other people, so before implementing something, I
> > was wondering if other people would need/use it, and what would they
> > need:
> >     - several distance (Euclidian, Mahalanobis, etc...), which would be
> > a separate object to handle different sets of parameters.
> >     - C Api ?
> >     - datatypes ? Layout ? Contiguity ?
> >     - handling Nan ?
> >
> >      cheers,
> >
> >     David
> > _______________________________________________
> > Scipy-dev mailing list
> > Scipy-dev at scipy.org
> > http://projects.scipy.org/mailman/listinfo/scipy-dev
> >
>
>
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>


-- 
Peter N. Skomoroch
peter.skomoroch at gmail.com
http://www.datawrangling.com
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