Add a post-processing step to "smooth" U by replacing the U-hat for clustered elements with the mean U-hat for elements in that cluster. This won't change the implied clusterings, but it will improve some minor problems with the path graphics, where the centroids do not exactly coincide.
(Note that this isn't a real CARP / CBASS issue but rather an deeper issue with running the ADMM for a finite amount of time on these problems: similar to how interior point methods will never get exact zeros even when run to essentially numerical convergence on the lasso and need a final thresholding step.)
Once this is done, we can speed up plotting by only showing the distinct path elements instead of every path element. The plot_frame elements passed to ggplot can go through a dplyr::distinct first to speed things up / avoid massive over-plotting. This should alleviate some of the slowness issues at the heart of #56.
TODO:
[x] Add CARPU smoothing
[ ] Add CBASSU smoothing
[ ] Update get_cluster_centroids and get_clustered_data
Add a post-processing step to "smooth"
U
by replacing theU-hat
for clustered elements with the meanU-hat
for elements in that cluster. This won't change the implied clusterings, but it will improve some minor problems with the path graphics, where the centroids do not exactly coincide.(Note that this isn't a real
CARP
/CBASS
issue but rather an deeper issue with running the ADMM for a finite amount of time on these problems: similar to how interior point methods will never get exact zeros even when run to essentially numerical convergence on the lasso and need a final thresholding step.)Once this is done, we can speed up plotting by only showing the distinct path elements instead of every path element. The
plot_frame
elements passed toggplot
can go through adplyr::distinct
first to speed things up / avoid massive over-plotting. This should alleviate some of the slowness issues at the heart of #56.TODO:
CARP
U
smoothingCBASS
U
smoothingget_cluster_centroids
andget_clustered_data