[Numpy-discussion] Large symmetrical matrix

Simon Palmer simon.palmer@gmail....
Wed Jun 11 10:33:26 CDT 2008


Pretty simple.  I don't do any transformations.  It is a euclidean distance
matrix between n vectors in my data space, so I use it for lookup of minima
and I recalculate portions of it during the processing of my algorithm.

It is the single largest limitation of my code, both in terms of performance
and scalability.  A fast and efficient solution to this issue would make a
huge difference to me.

On Wed, Jun 11, 2008 at 1:05 AM, Robert Kern <robert.kern@gmail.com> wrote:

> On Tue, Jun 10, 2008 at 18:53, Simon Palmer <simon.palmer@gmail.com>
> wrote:
> > Hi I have a problem which involves the creation of a large square matrix
> > which is zero across its diagonal and symmetrical about the diagonal i.e.
> > m[i,j] = m[j,i] and m[i,i] = 0.  So, in fact, it is a large triangular
> > matrix.  I was wondering whether there is any way of easily handling a
> > matrix of this shape without either incurring a memory penalty or a whole
> > whack of proprietary code?
> >
> > To get through this I have implemented a 1D array which has ((n-1)^2)/2
> > elements inside a wrapper class which manpulates the arguments of array
> > accessors with some arithmetic to return the approriate value.  To be
> honest
> > I'd love to throw this away, but I haven't yet come across a feasible
> > alternative.
> >
> > Any ideas?
>
> What operations do you want to perform on this array?
>
> --
> Robert Kern
>
> "I have come to believe that the whole world is an enigma, a harmless
> enigma that is made terrible by our own mad attempt to interpret it as
> though it had an underlying truth."
>  -- Umberto Eco
> _______________________________________________
> Numpy-discussion mailing list
> Numpy-discussion@scipy.org
> http://projects.scipy.org/mailman/listinfo/numpy-discussion
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: http://projects.scipy.org/pipermail/numpy-discussion/attachments/20080611/88611d4a/attachment.html 


More information about the Numpy-discussion mailing list