On 09/08/2013 06:51 PM, Olivier Grisel wrote:
>
> I just had a look at the results section and it looks very
> interesting, in particular in its ability to bring noise robustness to
> single linkage. Have you tried to compare it with ward?
Yeah. I think the "experiments.py" had ward in it:
https://
On 09/08/2013 07:10 PM, Olivier Grisel wrote:
> BTW it might make sense to keep `SingleLinkageClustering` as a special case
> as:
>
> - the MST algorithm can benefit from extracting the nearest neighbors
> graph only using the ball tree as done in Andreas implementation:
> https://github.com/amuel
BTW it might make sense to keep `SingleLinkageClustering` as a special case as:
- the MST algorithm can benefit from extracting the nearest neighbors
graph only using the ball tree as done in Andreas implementation:
https://github.com/amueller/information-theoretic-mst/blob/master/itm.py#L76
and t
2013/9/8 Gael Varoquaux :
> On Sun, Sep 08, 2013 at 05:14:35PM +0200, Alexandre Gramfort wrote:
>> I would be in favor of a HierarchicalClustering object that supports
>> various linkage
>> criteria.
>
>> something like:
>
>> hc = HierarchicalClustering(linkage='single')
>
>> linkage='ward' would b
2013/9/7 Andreas Mueller :
> On 09/07/2013 12:35 PM, Lars Buitinck wrote:
>> 2013/9/7 Robert Layton :
>>> This algorithm finds a minimum spanning tree, then cuts any edge higher than
>>> a given threshold.
>>>
>>> This is equivalent to the single linkage clustering. Olivier and I are
>>> talking ab
On Sun, Sep 08, 2013 at 05:14:35PM +0200, Alexandre Gramfort wrote:
> I would be in favor of a HierarchicalClustering object that supports
> various linkage
> criteria.
> something like:
> hc = HierarchicalClustering(linkage='single')
> linkage='ward' would be another option.
Yes, indeed. This
I would be in favor of a HierarchicalClustering object that supports
various linkage
criteria.
something like:
hc = HierarchicalClustering(linkage='single')
linkage='ward' would be another option.
Alex
On Sat, Sep 7, 2013 at 4:25 PM, Jacob Vanderplas
wrote:
> On Sat, Sep 7, 2013 at 5:21 AM,
Hi Safi,
On Sun, Sep 8, 2013 at 1:58 PM, Safi Ullah Marwat
mailto:[email protected]>> wrote:
Thank you Gael,
I actually want to know the distance between the entries of existing mixing
matrix and the newly calculated ones (the questioned thing)
As Gael you compute your `A` only once.
Thank you Gael,
I actually want to know the distance between the entries of existing mixing
matrix and the newly calculated ones (the questioned thing)
Thanks
On Sun, Sep 8, 2013 at 2:18 PM, Gael Varoquaux <
[email protected]> wrote:
> On Sun, Sep 08, 2013 at 01:40:49AM +0500, Safi
On Sun, Sep 08, 2013 at 01:40:49AM +0500, Safi Ullah Marwat wrote:
> My question, Is there any way to find mixing matrix for the new data using
> existing estimated sources.
The mixing matrix is computed once and for all, you are not recomuting a
mixing matrix given new data but existing estimated
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