On Thu, Jun 25, 2020 at 11:10 AM Eda Oktay wrote:
> Dear Richard,
>
> From now on, I am actually trying to write a parallel k-means
> algorithm by using petsc routines (I don't have to use petsc but I
> believe it will be easier) and I used the algorithm you mentioned
> before about finding
Dear Richard,
>From now on, I am actually trying to write a parallel k-means
algorithm by using petsc routines (I don't have to use petsc but I
believe it will be easier) and I used the algorithm you mentioned
before about finding cluster centroids. However, something is
bothering me:
You said
Dear Richard,
I believe I don't need centroids. I just need cluster indices which
corresponds to idx.
What I am trying to do is this:
Step 6: Cluster the points (y_i) i=1,...,n in R^k with the k-means
algorithm into clusters C_1,...,C_k.
Output: Clusters A_1,,A_k with A_i = {j | y_j in C_i}
Hi Eda,
If you are using the MATLAB k-means function, calling it like
idx = kmeans(X,k)
will give you the index set, but if you do
[idx,C] = kmeans(X,k)
then you will also get a matrix C which contains the cluster centroids. Is this
not what you need?
--Richard
On 5/22/20 10:38 AM, Eda
I am sorry, I used VecDuplictaeVecs not MatDuplicateVecs
Eda Oktay , 22 May 2020 Cum, 20:31 tarihinde şunu
yazdı:
> Dear Richard,
>
> Thank you for your email. From MATLAB's kmeans() function I believe I got
> the final clustering index set, not centroids. What I am trying to do is to
> cluster
Dear Richard,
Thank you for your email. From MATLAB's kmeans() function I believe I got
the final clustering index set, not centroids. What I am trying to do is to
cluster vectors created by MatDuplicateVecs() according to the index set
(whose type is not IS since I took it from MATLAB) that I
Hi Eda,
Thanks for your reply. I'm still trying to understand why you say you need to
duplicate the row vectors across all processes. When I have implemented
parallel k-means, I don't duplicate the row vectors. (This would be very
unscalable and largely defeat the point of doing this with MPI
Eda,
You are trying to duplicate a group of MPI vectors to every process. Am I
correct?
--Junchao Zhang
On Wed, Apr 29, 2020 at 5:48 AM Eda Oktay wrote:
> Dear Richard,
>
> I am trying to use spectral clustering algorithm by using k-means
> clustering algorithm at some point. I am doing this
Dear Richard,
I am trying to use spectral clustering algorithm by using k-means
clustering algorithm at some point. I am doing this by producing a matrix
consisting of eigenvectors (of the adjacency matrix of the graph that I
want to partition), then forming row vectors of this matrix. This is
Hi Eda,
I think that you probably want to use VecScatter routines, as Junchao
has suggested, instead of the lower level star forest for this. I
believe that VecScatterCreateToZero() is what you want for the broadcast
problem you describe, in the second part of your question. I'm not sure
what
Check
https://www.mcs.anl.gov/petsc/petsc-current/docs/manualpages/Vec/VecScatterCreateToAll.html
to
see if it meets your needs.
--Junchao Zhang
On Sat, Apr 4, 2020 at 3:39 AM Eda Oktay wrote:
> Hi all,
>
> I created a parallel vector UV, by using VecDuplicateVecs since I need row
> vectors of
Hi all,
I created a parallel vector UV, by using VecDuplicateVecs since I need row
vectors of a matrix. However, I need the whole vector be in all processors,
which means I need to gather all and broadcast them to all processors. To
gather, I tried to use VecStrideGatherAll:
Vec UVG;
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