On 21 September 2011 09:50, Gael Varoquaux <[email protected]>wrote:
> On Tue, Sep 20, 2011 at 04:25:58PM -0700, Jacob VanderPlas wrote:
> > I recently was contacted by someone interested in using manifold
> > learning methods on abstract metric spaces: that is, the training data
> > is a matrix of pairwise distances rather than a set of points. It would
> > be fairly straightforward to implement this for basic LLE and Isomap,
> > and could probably be done for the other manifold methods as well. Two
> > questions:
>
> Just a quick answer from someone who does too many things:
>
> - It is a general pattern that can be found with many other algorithms,
> therefore I think that it should be in the scikit
>
> - I don't know what interface is the right, but the problem pops up at
> many different places in the scikit, and we should give it some
> thoughts.
>
> my 2 cents,
>
> G
>
>
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I do really like the metric='precomputed' concept, which allows both
implementing actual metrics (euclidean, manhattan) as well as passing a
precomputed array in. If the algorithm doesn't allow it for whatever reason*
throw an error. The same interface works with kernels as well.
* k-means springs to mind - its only 'proven' for Euclidean distance, which
means that it should error if anything else is passed to it. I have an
implementation that works solely using a distance matrix, but I don't know
if it retains the qualities that the base algorithm does.
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definitive record of customers, application performance, security
threats, fraudulent activity and more. Splunk takes this data and makes
sense of it. Business sense. IT sense. Common sense.
http://p.sf.net/sfu/splunk-d2dcopy1
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