regaleo605 commented on code in PR #1888:
URL: https://github.com/apache/systemds/pull/1888#discussion_r1306430830
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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:
By replacing the 0 value with (nrow(X) + 1) and then use table with
restricted odim1 and odim2. I can get directly the A with the same dimensions
as the mask and ignore the non-problematic rows and thus the imputed value. I
am not sure if using the replace() function will be bad in terms of efficiency.
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