regaleo605 commented on code in PR #1888:
URL: https://github.com/apache/systemds/pull/1888#discussion_r1306372930
##########
scripts/builtin/imputeByKNN.dml:
##########
@@ -76,30 +79,32 @@ m_imputeByKNN = function(Matrix[Double] X, String
method="dist", Int seed=-1)
#Get the minimum distance row-wise computation
minimum_index = rowIndexMin(distance_matrix)
- #Position of missing values in per row in which column
- position = rowSums(is.nan(X))
- position = position * minimum_index
+ #Loop through each column
+ parfor(i in 1:nrow(missing_col_index), check = 0){
+ #Position of missing values in per row in which column
+ position = masked[,as.scalar(missing_col_index[i,1])]
+ position = position * minimum_index
Review Comment:
I think I can manage to do the vectorization, but it seems the table() can
only be done using value not 0. If I try use (index,seq(1, nrow(X)) with index
containing 0 it will return an error, but I think provided with my previous
code of locating the missing rows and multiplying with A to get the new mask is
possible.
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
