regaleo605 commented on code in PR #1888:
URL: https://github.com/apache/systemds/pull/1888#discussion_r1306372930
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scripts/builtin/imputeByKNN.dml:
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@@ -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.or is there a workaround to ignore 0 or let 0 be a row 0?
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