You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Input: sets of models to be clustered
Output: clusters with more than number of models
Method: K-means clustering using mpi
Assessment: the output gives more than the total number of models in input.
What it looks like is that each process runs its own clustering and somehow merges the files together.
Ideally, mpi clustering should be used to have each process taking care of one of the K-clusters.
The text was updated successfully, but these errors were encountered:
I have the same problem but not able to reproduce it on an example: it could be stochastic. If we start clustering with 1000 models for example, after kmeans clustering for some k, the total number of models in the output cluster.0,cluster.1... directories is more than 1000. Some models get copied. The same problem was not seen while running on a single core. So it appears to be MPI related.
Input: sets of models to be clustered
Output: clusters with more than number of models
Method: K-means clustering using mpi
Assessment: the output gives more than the total number of models in input.
What it looks like is that each process runs its own clustering and somehow merges the files together.
Ideally, mpi clustering should be used to have each process taking care of one of the K-clusters.
The text was updated successfully, but these errors were encountered: