On Tue, Apr 02, 2013 at 08:26:34PM +, Pieraut, Francis wrote:
> Andy, concerning the computation of the mean, the function has to be
> configurable too but the default function mean is also good for cosine &
> bregman divergence (http://www-users.cs.umn.edu/~kumar/dmbook/ch8.pdf see
> table 8.
On 04/02/2013 10:26 PM, Pieraut, Francis wrote:
Hi Andy & Ken,
Thanks Ken for the alternative but I am using a cosine distance.
Andy, concerning the computation of the mean, the function has to be
configurable too but the default function mean is also good for cosine
& bregman divergence
(h
rying to push for sklearn in my team, quite impress so far.
Thanks,
Francis
From: Kenneth C. Arnold [mailto:kcarn...@seas.harvard.edu]
Sent: April-02-13 3:32 PM
To: scikit-learn-general@lists.sourceforge.net
Subject: Re: [Scikit-learn-general] kmeans distance function not configurable
If yo
If you want a Mahalanobis distance, though, you can instead just transform
your data using the Cholesky decomposition of the distance matrix.
-Ken
On Tue, Apr 2, 2013 at 3:09 PM, Andreas Mueller wrote:
> Hi Francis.
> No. It is highly non-trivial for most distance functions to do k-means as
>
Hi Francis.
No. It is highly non-trivial for most distance functions to do k-means as
the computation of the mean has to be replaced by a different computation.
If you know how to do that, implementing k-means in pure numpy is not
all that hard.
This question comes up quite a lot. Maybe we sho
Hi guys,
Is there is simple way to change the distance function used in the kmeans
implementation?
Thanks,
Francis
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